System

Abstract

To provide a system which selects and displays characters and images at high speed and with accuracy by utilizing eye movement.SOLUTION: A system includes a detection part, a selection part, a determination part, and a display part. The detection part detects movement of a user's eyes. The selection part selects characters based on eye movements detected by the detection part. The determination part determines a character selected by the selection part. The display part displays a character, a picture or an image determined by the determination part.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 leaves room for improvement in the speed and accuracy of character input using eye movements.

[0005] The system according to the embodiment aims to use eye movements to quickly and accurately select and display characters and images. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a selection unit, a confirmation unit, and a display unit. The detection unit detects eye movement of a user. The selection unit selects a character based on the eye movement detected by the detection unit. The confirmation unit confirms the character selected by the selection unit. The display unit displays the character, picture, or image confirmed by the confirmation unit. Effect of the Invention

[0007] The system according to the embodiment can utilize eye movements to quickly and accurately select and display characters and images. [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 goggle-type screen system according to the embodiment of the present invention is a system that aims to use AI to speed up the mechanism of detecting the movement of the user's eyes, displaying characters, and determining the characters by the movement of the eyes, and put it into practical use. This system detects the movement of the user's eyes, selects characters based on the movement, and the AI ​​analyzes the user's eye movement to speed up the determination of the characters. Furthermore, by having the AI ​​learn the user's personal preferences and daily conversations, it is possible to select not only characters but also pictures and images, thereby speeding up user input. Finally, it is a mechanism for displaying the selected characters, pictures, and images on the attached speaker or the opposite side of the goggles, and for communication. For example, an infrared sensor or a camera built into the goggles monitors the user's eye movement in real time and collects the data. When the user fixes his / her gaze on a specific character, that character is selected. Next, the AI ​​analyzes the collected data on the eye movement using machine learning or deep learning to determine which character the user selected. This speeds up the determination of the characters. For example, the character is determined by analyzing the time the user fixed his / her gaze on a specific character and the pattern of the gaze movement. In addition, the AI ​​learns the user's past input data and conversation history, and prioritizes displaying characters, pictures, and images that the user frequently uses. This speeds up user input. For example, by prioritizing display of emojis and images that the user frequently uses, selection becomes easier. Finally, the selected characters, pictures, and images are displayed on the attached speaker or on the opposite side of the goggles. This allows the user to communicate the selection to others. For example, the selected characters are output as voice from the speaker, allowing the user to communicate by voice. In addition, visual communication is also possible by displaying the selected pictures and images on the opposite side of the goggles. This allows the goggle-type screen system to speed up user input by selecting, confirming, and displaying characters based on the user's eye movements.

[0029] The goggle-type screen system according to the embodiment includes a detection unit, a selection unit, a determination unit, and a display unit. The detection unit detects the user's eye movement. The user's eye movement includes, for example, the direction of gaze and the frequency of blinking, but is not limited to such examples. The detection unit monitors the user's eye movement in real time using, for example, an infrared sensor or a camera, and collects data. For example, the infrared sensor detects the eye movement with high accuracy, and the camera records the eye movement as an image. The selection unit uses machine learning or deep learning to analyze the eye movement data collected by the detection unit and determine which character the user selected. For example, a support vector machine or a random forest can be used as a machine learning algorithm. A convolutional neural network or a recurrent neural network can be used as a deep learning algorithm. The determination unit determines the character selected by the selection unit. The determination unit learns the user's personal preferences and daily conversations, and preferentially displays characters, pictures, and images that the user frequently uses. For example, frequently used characters, pictures, and images are preferentially displayed based on the user's past selection history and conversation history. The display unit displays the characters, pictures, and images confirmed by the confirmation unit on the attached speaker or the opposite side of the goggles. For example, the selected characters are output as sound from the speaker. Also, the selected pictures and images are displayed on the opposite side of the goggles. This allows the user to communicate the content of the selection to others. As a result, the goggle-type screen system according to the embodiment can speed up user input by selecting, confirming, and displaying characters based on the user's eye movements.

[0030] The detection unit detects the user's eye movement. The user's eye movement includes, for example, the direction of gaze and the frequency of blinking, but is not limited to such examples. The detection unit monitors the user's eye movement in real time using, for example, an infrared sensor or a camera, and collects data. Specifically, the infrared sensor detects the eye movement with high accuracy, and the camera records the eye movement as an image. The infrared sensor tracks the position and movement of the eye pupil with high accuracy, and can accurately grasp the direction of gaze. This makes it possible to detect in real time which direction the user is looking. In addition, the camera can analyze in detail the frequency of blinking and the open / closed state of the eyes by recording the eye movement as an image. This makes it possible to collect various data regarding the user's eye movement and improve the accuracy of the analysis. Furthermore, the detection unit can process the collected data in real time and provide feedback according to the user's eye movement. For example, the cursor on the screen can be moved according to the direction of gaze. This allows the user to intuitively operate objects on the screen with eye movements. The detection unit detects the user's eye movements with high accuracy and collects data in real time, thereby improving the performance of the entire system.

[0031] The selection unit uses machine learning or deep learning to analyze the eye movement data collected by the detection unit and to determine which character the user selected. Specifically, a support vector machine or a random forest can be used as a machine learning algorithm. The support vector machine can determine the character selected by the user with high accuracy based on the feature amount of the eye movement. The random forest can combine multiple decision trees to analyze the eye movement data and predict the user's selection with high accuracy. A convolutional neural network or a recurrent neural network can be used as a deep learning algorithm. The convolutional neural network can analyze the image data of the eye movement and recognize the direction of gaze and the blinking pattern with high accuracy. The recurrent neural network is suitable for analyzing temporal data and can analyze the continuous pattern of the eye movement to predict the user's selection. This allows the selection unit to analyze the eye movement data with high accuracy using machine learning or deep learning and quickly determine which character the user selected. Furthermore, the selection unit can learn the user's past selection history and patterns and continuously improve the accuracy of the selection. This allows the selection unit to quickly and accurately select characters based on the user's eye movements.

[0032] The confirmation unit confirms the characters selected by the selection unit. The confirmation unit learns the user's personal tastes and daily conversations, and preferentially displays characters, pictures, and images that the user frequently uses. Specifically, based on the user's past selection history and conversation history, the confirmation unit preferentially displays frequently used characters, pictures, and images. For example, by preferentially displaying words and phrases that the user frequently uses, the efficiency of input can be improved. The confirmation unit analyzes the user's selection history and learns characters, pictures, and images that the user frequently uses. As a result, it is possible to predict characters, pictures, and images that the user is likely to select next, and display them preferentially. Furthermore, the confirmation unit can learn the user's tastes and daily conversation patterns, and provide the optimal options for the user. For example, if the user is interested in a specific theme or topic, it is possible to preferentially display characters, pictures, and images related to that theme. As a result, the confirmation unit can provide the optimal options according to the individual needs of the user, and improve the efficiency and accuracy of input. The confirmation unit confirms the characters selected by the selection unit, and learns the user's tastes and daily conversations, and can provide the optimal options for the user.

[0033] The display unit displays the characters, pictures, and images confirmed by the confirmation unit on the attached speaker or on the opposite side of the goggles. Specifically, the selected characters are output as voice from the speaker. The selected pictures and images are also displayed on the opposite side of the goggles. For example, the selected characters are output as voice from the speaker, so that the user can communicate the selected content to others. Furthermore, the selected pictures and images are displayed on the opposite side of the goggles, so that the user can visually confirm the selected content. The display unit can display the characters, pictures, and images confirmed by the confirmation unit in real time. This allows the user to immediately confirm the selected content and correct it as necessary. Furthermore, the display unit can provide an optimal display method according to the user's visual and auditory senses. For example, it is possible to provide audio output preferentially to a visually impaired user, and visual display preferentially to a hearing impaired user. This allows the display unit to provide an optimal display method according to the individual needs of the user, thereby improving the user's convenience. The display unit displays the characters, pictures, and images confirmed by the confirmation unit in real time, and provides an optimal display method according to the user's visual and auditory senses, thereby improving user convenience.

[0034] The detection unit can monitor the user's eye movement in real time using an infrared sensor or a camera and collect data. The infrared sensor includes, for example, an arrangement and a detection range for detecting eye movement with high accuracy. For example, the infrared sensor is arranged inside the goggles and accurately detects the user's eye movement. The camera includes, for example, a resolution and a frame rate. For example, the camera records the eye movement as an image with high resolution and adjusts the frame rate to monitor the eye movement in real time. In this way, the eye movement can be accurately detected and data can be collected by using the infrared sensor or the camera. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input data acquired by the infrared sensor or the camera to the generation AI and cause the generation AI to detect the eye movement.

[0035] The selection unit can analyze the collected eye movement data using machine learning or deep learning to determine which character the user selected. Machine learning algorithms include, for example, support vector machines and random forests. For example, a support vector machine classifies the eye movement data and determines which character the user selected. A random forest analyzes the eye movement data with multiple decision trees to determine which character the user selected. Deep learning algorithms include, for example, convolutional neural networks and recurrent neural networks. For example, a convolutional neural network filters the eye movement data, extracts features, and analyzes them. A recurrent neural network analyzes temporal changes in the eye movement data and determines which character the user selected. In this way, by using machine learning or deep learning, the eye movement data can be accurately analyzed and the selection of the character can be determined. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the eye movement data to a generation AI and cause the generation AI to select the character.

[0036] The determination unit can learn the user's personal taste or daily conversation and preferentially display characters, pictures, and images that the user frequently uses. The taste includes, for example, past selection history and survey results. For example, the determination unit analyzes the user's past selection history and preferentially displays frequently used characters, pictures, and images. Based on the survey results, the user's taste is identified and the display content is adjusted. The daily conversation includes, for example, voice recognition and text analysis. For example, the determination unit analyzes the user's daily conversation using voice recognition technology and learns frequently used words and phrases. Using text analysis technology, the user's conversation history is analyzed and frequently used characters, pictures, and images are preferentially displayed. In this way, by learning the user's personal taste and daily conversation, frequently used characters, pictures, and images can be preferentially displayed. Part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the user's taste and daily conversation to the generation AI and cause the generation AI to execute the priority order of the display content.

[0037] The display unit can display the selected characters, pictures, or images on the other side of the attached speaker or goggles, enabling the user to communicate with others. The speaker includes, for example, volume and sound quality. For example, the speaker outputs the selected characters as sound, enabling the user to communicate with others. The volume is adjusted to output sound at an appropriate volume. The sound quality is adjusted to provide clear sound. The goggles include, for example, a viewing angle and resolution. For example, the goggles display the selected picture or image on the other side, enabling the user to communicate with others. The viewing angle is widened to make the display content easier to see. The resolution is increased to make the display content clearer. This allows the user to communicate information to others by displaying the selected characters, pictures, or images. Some 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 selected characters, pictures, or images to a generation AI, and cause the generation AI to generate the display content.

[0038] The detection unit can analyze the user's past eye movement data and select an appropriate detection algorithm. The detection unit selects an optimal detection algorithm based on, for example, eye movement patterns frequently performed by the user in the past. The detection unit can also cluster the user's past eye movement data and apply an optimal detection algorithm to each cluster. Furthermore, the user's past eye movement data can be analyzed by time period and an optimal detection algorithm according to the time period can be selected. In this way, the optimal detection algorithm can be selected by analyzing the past eye movement data. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the past eye movement data to the generation AI and cause the generation AI to select the optimal detection algorithm.

[0039] The detection unit can perform filtering based on the user's current visual state or ambient light when detecting eye movements. For example, when the user is in a dark environment, the detection unit detects eye movements using an infrared sensor. Also, when the user is in a bright environment, the detection unit can adjust the exposure of the camera to detect eye movements. Furthermore, when the user is visually tired, the detection accuracy of eye movements can be appropriately adjusted to prevent erroneous detection. As a result, the detection accuracy of eye movements is improved by filtering based on the visual state and ambient light. Part or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the visual state and ambient light to the generation AI and cause the generation AI to adjust the filtering.

[0040] The detection unit can detect highly relevant movements preferentially based on the geographical location information of the user when detecting eye movements. For example, when the user is in a specific location, the detection unit preferentially detects eye movements related to the location. Also, when the user is moving, the detection unit can preferentially detect eye movements related to the movement. Furthermore, when the user is at home, the detection unit can preferentially detect daily eye movements. In this way, by taking the geographical location information into consideration, highly relevant movements can be preferentially detected. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input data of geographical location information to the generation AI and cause the generation AI to execute detection of highly relevant movements.

[0041] When detecting eye movements, the detection unit can detect related movements based on the user's social media activities. For example, the detection unit preferentially detects eye movements related to emojis that the user frequently uses on social media. The detection unit can also preferentially detect eye movements related to images that the user frequently sees on social media. Furthermore, eye movements can be detected based on the time period during which the user is active on social media. In this way, related movements can be detected by analyzing social media activities. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data of social media activities to the generation AI and cause the generation AI to detect related movements.

[0042] The selection unit can select an appropriate selection method based on the user's past selection history when selecting characters. For example, the selection unit preferentially displays characters that the user has frequently selected in the past. The selection unit can also analyze the user's past selection history and suggest an optimal selection method. Furthermore, the selection unit can predict and suggest characters to be used in a specific time period from the user's past selection history. This makes it possible to select an optimal selection method by referring to the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can input data of the past selection history to the generation AI and cause the generation AI to select the optimal selection method.

[0043] The selection unit can improve the accuracy of selection based on the user's current visual state or ambient light when selecting characters. For example, when the user is in a dark environment, the selection unit can improve the accuracy of selection by increasing the contrast of the characters. Also, when the user is in a bright environment, the selection unit can improve the accuracy of selection by increasing the size of the characters. Furthermore, when the user is visually tired, the display time of the characters can be extended to improve the accuracy of selection. This improves the accuracy of selection by improving the accuracy of selection based on the visual state and ambient light. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can input data on the visual state and ambient light to the generation AI and cause the generation AI to improve the accuracy of selection.

[0044] The selection unit can, when selecting characters, preferentially select characters with high relevance based on the geographical location information of the user. For example, when the user is in a specific location, the selection unit preferentially selects characters related to the location. In addition, when the user is moving, the selection unit can preferentially select characters related to movement. Furthermore, when the user is at home, the selection unit can preferentially select everyday characters. In this way, by taking the geographical location information into consideration, characters with high relevance can be preferentially selected. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input data of geographical location information to the generation AI and cause the generation AI to select characters with high relevance.

[0045] The selection unit can select related characters based on the user's social media activity when selecting characters. For example, the selection unit preferentially selects emojis that the user frequently uses on social media. The selection unit can also preferentially select characters related to images that the user often sees on social media. Furthermore, the selection unit can select characters based on the time period during which the user is active on social media. In this way, related characters can be selected by analyzing social media activities. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input data on social media activities to the generation AI and cause the generation AI to select related characters.

[0046] The confirmation unit can select an appropriate confirmation method based on the user's past confirmation history when confirming characters. For example, the confirmation unit preferentially displays characters that the user has frequently confirmed in the past. The confirmation unit can also analyze the user's past confirmation history and suggest an optimal confirmation method. Furthermore, it can predict and suggest characters to be used in a specific time period from the user's past confirmation history. This makes it possible to select an optimal confirmation method by referring to the past confirmation history. Some or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit can input data of the past confirmation history to the generation AI and cause the generation AI to select an optimal confirmation method.

[0047] The confirmation unit can improve the accuracy of confirmation based on the user's current visual state or ambient light when confirming characters. For example, when the user is in a dark environment, the confirmation unit can improve the accuracy of confirmation by increasing the contrast of the characters. Also, when the user is in a bright environment, the confirmation unit can improve the accuracy of confirmation by increasing the size of the characters. Furthermore, when the user is visually tired, the display time of the characters can be extended to improve the accuracy of confirmation. This improves the accuracy of confirmation by improving the accuracy of confirmation based on the visual state and ambient light. Part or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit can input data on the visual state and ambient light to the generation AI and cause the generation AI to improve the accuracy of confirmation.

[0048] The confirmation unit can, when confirming characters, prioritize the confirmation of highly relevant characters based on the geographical location information of the user. For example, when the user is in a specific location, the confirmation unit prioritizes the confirmation of characters related to the location. In addition, when the user is moving, the confirmation unit can also prioritize the confirmation of characters related to the movement. Furthermore, when the user is at home, the confirmation unit can also prioritize the confirmation of everyday characters. In this way, by taking into account the geographical location information, highly relevant characters can be prioritized. Part or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the confirmation unit can input data of geographical location information to the generation AI and cause the generation AI to execute the confirmation of highly relevant characters.

[0049] The determination unit can determine related characters based on the user's social media activity when determining characters. For example, the determination unit preferentially determines emojis that the user frequently uses on social media. The determination unit can also preferentially determine characters related to images that the user often sees on social media. Furthermore, the determination unit can also determine characters based on the time period during which the user is active on social media. In this way, related characters can be determined by analyzing social media activities. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on social media activities to the generation AI and cause the generation AI to perform the determination of related characters.

[0050] The display unit can select an appropriate display method based on the user's past display history at the time of display. For example, the display unit preferentially displays information that the user has frequently displayed in the past. The display unit can also analyze the user's past display history and propose an optimal display method. Furthermore, the display unit can predict and propose information to be displayed in a specific time period from the user's past display history. This makes it possible to select an optimal display method by referring to the past display history. 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 data of the past display history to the generation AI and cause the generation AI to select an optimal display method.

[0051] The display unit can improve the accuracy of the display based on the user's current visual state or ambient light when displaying. For example, when the user is in a dark environment, the display unit can increase the contrast of the display to improve visibility. Also, when the user is in a bright environment, the display size can be increased to improve visibility. Furthermore, when the user is visually tired, the display time can be extended to improve visibility. This improves the display accuracy by improving the accuracy of the display based on the visual state or ambient light. 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 data on the visual state or ambient light to the generation AI and cause the generation AI to improve the accuracy of the display.

[0052] The display unit can display highly relevant content preferentially based on the geographical location information of the user at the time of display. For example, when the user is in a specific location, the display unit can display information related to the location preferentially. In addition, when the user is moving, the display unit can also display information related to the movement preferentially. Furthermore, when the user is at home, the display unit can display daily information preferentially. In this way, by taking the geographical location information into consideration, highly relevant content can be displayed preferentially. 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 data of the geographical location information to the generation AI and cause the generation AI to execute the display of highly relevant content.

[0053] The display unit can display related content based on the user's social media activity at the time of display. For example, the display unit can preferentially display emojis that the user frequently uses on social media. The display unit can also preferentially display information related to images that the user often sees on social media. Furthermore, the display unit can display information based on the time period during which the user is active on social media. This makes it possible to display related content by analyzing social media activity. A part or all of the above-mentioned processing in the display unit can be performed, for example, using AI or can be performed without using AI. For example, the display unit can input data on social media activity to a generation AI and cause the generation AI to display related content.

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

[0055] The goggle-type screen system can further include a voice recognition unit. The voice recognition unit can recognize a user's voice command and assist in character selection and confirmation. For example, when a user selects a specific character, the user can say "this character" by voice, and the selection unit can preferentially select that character. Also, the user can say "confirm" by voice, and the confirmation unit can confirm that character. Furthermore, the voice recognition unit can analyze the user's voice command and estimate the user's intention, thereby achieving smoother operation. As a result, the use of voice recognition makes user operation more intuitive and efficient.

[0056] The goggle-type screen system can further include a vibration feedback unit. The vibration feedback unit can generate minute vibrations when a user selects a specific character to confirm the selection. For example, when a user fixes his / her gaze on a specific character, the goggles vibrate lightly to inform the user that the selection is complete. In addition, vibration feedback can be provided when the confirmation unit confirms the character, to inform the user that the confirmation is complete. Furthermore, by adjusting the strength and pattern of the vibration, different feedback can be provided for different operations. As a result, the use of vibration feedback makes it possible to confirm an operation using a sense other than vision.

[0057] The goggle-type screen system may further include a temperature feedback unit. The temperature feedback unit may generate a temperature change when the user performs a specific operation, thereby allowing the user to confirm the operation. For example, when the user selects a character, a part of the goggles may become warm to indicate the completion of the selection. Also, when the confirmation unit confirms the character, a part of the goggles may become cold to indicate the completion of the confirmation. Furthermore, by adjusting the speed and range of the temperature change, different feedback may be provided for different operations. Thus, by using temperature feedback, it becomes possible to confirm an operation using a sense other than sight or touch.

[0058] The goggle-type screen system can further include a scent feedback unit. The scent feedback unit can emit a scent when the user performs a specific operation to confirm the operation. For example, when the user selects a character, a specific scent is emitted to notify the completion of the selection. Also, when the confirmation unit confirms the character, a different scent can be emitted to notify the completion of the confirmation. Furthermore, by adjusting the type and strength of the scent, different feedback can be provided for different operations. As a result, the scent feedback can be used to confirm an operation using the sense of smell.

[0059] The goggle-type screen system may further include a tactile feedback unit. The tactile feedback unit may generate a tactile stimulus when the user performs a specific operation, thereby allowing the user to confirm the operation. For example, when the user selects a character, the user may feel a part of the goggles being lightly pressed to indicate the completion of the selection. When the confirmation unit confirms the character, the user may feel a part of the goggles being lightly pulled to indicate the completion of the confirmation. Furthermore, by adjusting the strength and pattern of the tactile stimulus, different feedback may be provided for different operations. Thus, the use of tactile feedback allows the user to confirm an operation using senses other than sight and hearing.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The detection unit detects the user's eye movements. The user's eye movements include the direction of gaze and the frequency of blinking. The detection unit uses an infrared sensor and a camera to monitor the user's eye movements in real time and collect data. For example, the infrared sensor detects eye movements with high accuracy, and the camera records the eye movements as video. Step 2: The selection unit uses machine learning or deep learning to analyze the eye movement data collected by the detection unit and determine which character the user selected. For example, a support vector machine or a random forest can be used as the machine learning algorithm, and a convolutional neural network or a recurrent neural network can be used as the deep learning algorithm. Step 3: The confirmation unit confirms the characters selected by the selection unit. The confirmation unit learns the user's personal preferences and daily conversations, and prioritizes displaying characters, pictures, and images that the user frequently uses. For example, based on the user's past selection history and conversation history, the confirmation unit prioritizes displaying frequently used characters, pictures, and images. Step 4: The display unit displays the characters, pictures, or images confirmed by the confirmation unit on the attached speaker or on the opposite side of the goggles. For example, the selected characters are output as voice from the speaker, and the selected pictures or images are displayed on the opposite side of the goggles. This allows the user to communicate the selection to others.

[0062] (Example 2) The goggle-type screen system according to the embodiment of the present invention is a system that aims to use AI to speed up the mechanism of detecting the movement of the user's eyes, displaying characters, and determining the characters by the movement of the eyes, and put it into practical use. This system detects the movement of the user's eyes, selects characters based on the movement, and the AI ​​analyzes the user's eye movement to speed up the determination of the characters. Furthermore, by having the AI ​​learn the user's personal preferences and daily conversations, it is possible to select not only characters but also pictures and images, thereby speeding up user input. Finally, it is a mechanism for displaying the selected characters, pictures, and images on the attached speaker or the opposite side of the goggles, and for communication. For example, an infrared sensor or a camera built into the goggles monitors the user's eye movement in real time and collects the data. When the user fixes his / her gaze on a specific character, that character is selected. Next, the AI ​​analyzes the collected data on the eye movement using machine learning or deep learning to determine which character the user selected. This speeds up the determination of the characters. For example, the character is determined by analyzing the time the user fixed his / her gaze on a specific character and the pattern of the gaze movement. In addition, the AI ​​learns the user's past input data and conversation history, and prioritizes displaying characters, pictures, and images that the user frequently uses. This speeds up user input. For example, by prioritizing display of emojis and images that the user frequently uses, selection becomes easier. Finally, the selected characters, pictures, and images are displayed on the attached speaker or on the opposite side of the goggles. This allows the user to communicate the selection to others. For example, the selected characters are output as voice from the speaker, allowing the user to communicate by voice. In addition, visual communication is also possible by displaying the selected pictures and images on the opposite side of the goggles. This allows the goggle-type screen system to speed up user input by selecting, confirming, and displaying characters based on the user's eye movements.

[0063] The goggle-type screen system according to the embodiment includes a detection unit, a selection unit, a determination unit, and a display unit. The detection unit detects the user's eye movement. The user's eye movement includes, for example, the direction of gaze and the frequency of blinking, but is not limited to such examples. The detection unit monitors the user's eye movement in real time using, for example, an infrared sensor or a camera, and collects data. For example, the infrared sensor detects the eye movement with high accuracy, and the camera records the eye movement as an image. The selection unit uses machine learning or deep learning to analyze the eye movement data collected by the detection unit and determine which character the user selected. For example, a support vector machine or a random forest can be used as a machine learning algorithm. A convolutional neural network or a recurrent neural network can be used as a deep learning algorithm. The determination unit determines the character selected by the selection unit. The determination unit learns the user's personal preferences and daily conversations, and preferentially displays characters, pictures, and images that the user frequently uses. For example, frequently used characters, pictures, and images are preferentially displayed based on the user's past selection history and conversation history. The display unit displays the characters, pictures, and images confirmed by the confirmation unit on the attached speaker or the opposite side of the goggles. For example, the selected characters are output as sound from the speaker. Also, the selected pictures and images are displayed on the opposite side of the goggles. This allows the user to communicate the content of the selection to others. As a result, the goggle-type screen system according to the embodiment can speed up user input by selecting, confirming, and displaying characters based on the user's eye movements.

[0064] The detection unit detects the user's eye movement. The user's eye movement includes, for example, the direction of gaze and the frequency of blinking, but is not limited to such examples. The detection unit monitors the user's eye movement in real time using, for example, an infrared sensor or a camera, and collects data. Specifically, the infrared sensor detects the eye movement with high accuracy, and the camera records the eye movement as an image. The infrared sensor tracks the position and movement of the eye pupil with high accuracy, and can accurately grasp the direction of gaze. This makes it possible to detect in real time which direction the user is looking. In addition, the camera can analyze in detail the frequency of blinking and the open / closed state of the eyes by recording the eye movement as an image. This makes it possible to collect various data regarding the user's eye movement and improve the accuracy of the analysis. Furthermore, the detection unit can process the collected data in real time and provide feedback according to the user's eye movement. For example, the cursor on the screen can be moved according to the direction of gaze. This allows the user to intuitively operate objects on the screen with eye movements. The detection unit detects the user's eye movements with high accuracy and collects data in real time, thereby improving the performance of the entire system.

[0065] The selection unit uses machine learning or deep learning to analyze the eye movement data collected by the detection unit and to determine which character the user selected. Specifically, a support vector machine or a random forest can be used as a machine learning algorithm. The support vector machine can determine the character selected by the user with high accuracy based on the feature amount of the eye movement. The random forest can combine multiple decision trees to analyze the eye movement data and predict the user's selection with high accuracy. A convolutional neural network or a recurrent neural network can be used as a deep learning algorithm. The convolutional neural network can analyze the image data of the eye movement and recognize the direction of gaze and the blinking pattern with high accuracy. The recurrent neural network is suitable for analyzing temporal data and can analyze the continuous pattern of the eye movement to predict the user's selection. This allows the selection unit to analyze the eye movement data with high accuracy using machine learning or deep learning and quickly determine which character the user selected. Furthermore, the selection unit can learn the user's past selection history and patterns and continuously improve the accuracy of the selection. This allows the selection unit to quickly and accurately select characters based on the user's eye movements.

[0066] The confirmation unit confirms the characters selected by the selection unit. The confirmation unit learns the user's personal tastes and daily conversations, and preferentially displays characters, pictures, and images that the user frequently uses. Specifically, based on the user's past selection history and conversation history, the confirmation unit preferentially displays frequently used characters, pictures, and images. For example, by preferentially displaying words and phrases that the user frequently uses, the efficiency of input can be improved. The confirmation unit analyzes the user's selection history and learns characters, pictures, and images that the user frequently uses. As a result, it is possible to predict characters, pictures, and images that the user is likely to select next, and display them preferentially. Furthermore, the confirmation unit can learn the user's tastes and daily conversation patterns, and provide the optimal options for the user. For example, if the user is interested in a specific theme or topic, it is possible to preferentially display characters, pictures, and images related to that theme. As a result, the confirmation unit can provide the optimal options according to the individual needs of the user, and improve the efficiency and accuracy of input. The confirmation unit confirms the characters selected by the selection unit, and learns the user's tastes and daily conversations, and can provide the optimal options for the user.

[0067] The display unit displays the characters, pictures, and images confirmed by the confirmation unit on the attached speaker or on the opposite side of the goggles. Specifically, the selected characters are output as voice from the speaker. The selected pictures and images are also displayed on the opposite side of the goggles. For example, the selected characters are output as voice from the speaker, so that the user can communicate the selected content to others. Furthermore, the selected pictures and images are displayed on the opposite side of the goggles, so that the user can visually confirm the selected content. The display unit can display the characters, pictures, and images confirmed by the confirmation unit in real time. This allows the user to immediately confirm the selected content and correct it as necessary. Furthermore, the display unit can provide an optimal display method according to the user's visual and auditory senses. For example, it is possible to provide audio output preferentially to a visually impaired user, and visual display preferentially to a hearing impaired user. This allows the display unit to provide an optimal display method according to the individual needs of the user, thereby improving the user's convenience. The display unit displays the characters, pictures, and images confirmed by the confirmation unit in real time, and provides an optimal display method according to the user's visual and auditory senses, thereby improving user convenience.

[0068] The detection unit can monitor the user's eye movement in real time using an infrared sensor or a camera and collect data. The infrared sensor includes, for example, an arrangement and a detection range for detecting eye movement with high accuracy. For example, the infrared sensor is arranged inside the goggles and accurately detects the user's eye movement. The camera includes, for example, a resolution and a frame rate. For example, the camera records the eye movement as an image with high resolution and adjusts the frame rate to monitor the eye movement in real time. In this way, the eye movement can be accurately detected and data can be collected by using the infrared sensor or the camera. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input data acquired by the infrared sensor or the camera to the generation AI and cause the generation AI to detect the eye movement.

[0069] The selection unit can analyze the collected eye movement data using machine learning or deep learning to determine which character the user selected. Machine learning algorithms include, for example, support vector machines and random forests. For example, a support vector machine classifies the eye movement data and determines which character the user selected. A random forest analyzes the eye movement data with multiple decision trees to determine which character the user selected. Deep learning algorithms include, for example, convolutional neural networks and recurrent neural networks. For example, a convolutional neural network filters the eye movement data, extracts features, and analyzes them. A recurrent neural network analyzes temporal changes in the eye movement data and determines which character the user selected. In this way, by using machine learning or deep learning, the eye movement data can be accurately analyzed and the selection of the character can be determined. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the eye movement data to a generation AI and cause the generation AI to select the character.

[0070] The determination unit can learn the user's personal taste or daily conversation and preferentially display characters, pictures, and images that the user frequently uses. The taste includes, for example, past selection history and survey results. For example, the determination unit analyzes the user's past selection history and preferentially displays frequently used characters, pictures, and images. Based on the survey results, the user's taste is identified and the display content is adjusted. The daily conversation includes, for example, voice recognition and text analysis. For example, the determination unit analyzes the user's daily conversation using voice recognition technology and learns frequently used words and phrases. Using text analysis technology, the user's conversation history is analyzed and frequently used characters, pictures, and images are preferentially displayed. In this way, by learning the user's personal taste and daily conversation, frequently used characters, pictures, and images can be preferentially displayed. Part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on the user's taste and daily conversation to the generation AI and cause the generation AI to execute the priority order of the display content.

[0071] The display unit can display the selected characters, pictures, or images on the other side of the attached speaker or goggles, enabling the user to communicate with others. The speaker includes, for example, volume and sound quality. For example, the speaker outputs the selected characters as sound, enabling the user to communicate with others. The volume is adjusted to output sound at an appropriate volume. The sound quality is adjusted to provide clear sound. The goggles include, for example, a viewing angle and resolution. For example, the goggles display the selected picture or image on the other side, enabling the user to communicate with others. The viewing angle is widened to make the display content easier to see. The resolution is increased to make the display content clearer. This allows the user to communicate information to others by displaying the selected characters, pictures, or images. Some 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 selected characters, pictures, or images to a generation AI, and cause the generation AI to generate the display content.

[0072] The detection unit can estimate the user's emotion and adjust the detection accuracy of the eye movement based on the estimated emotion of the user. For example, when the user is nervous, the detection unit can increase the detection accuracy to detect minute changes in the eye movement. Also, when the user is relaxed, the detection accuracy can be adjusted to detect rough changes in the eye movement. Furthermore, when the user is tired, the detection accuracy of the eye movement can be moderately adjusted to prevent erroneous detection. As a result, the detection accuracy of the eye movement is improved by adjusting the detection accuracy based on the user's emotion. The estimation of the emotion 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 detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the detection accuracy of the eye movement.

[0073] The detection unit can analyze the user's past eye movement data and select an appropriate detection algorithm. The detection unit selects an optimal detection algorithm based on, for example, eye movement patterns frequently performed by the user in the past. The detection unit can also cluster the user's past eye movement data and apply an optimal detection algorithm to each cluster. Furthermore, the user's past eye movement data can be analyzed by time period and an optimal detection algorithm according to the time period can be selected. In this way, the optimal detection algorithm can be selected by analyzing the past eye movement data. A part or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input the past eye movement data to the generation AI and cause the generation AI to select the optimal detection algorithm.

[0074] The detection unit can perform filtering based on the user's current visual state or ambient light when detecting eye movements. For example, when the user is in a dark environment, the detection unit detects eye movements using an infrared sensor. Also, when the user is in a bright environment, the detection unit can adjust the exposure of the camera to detect eye movements. Furthermore, when the user is visually tired, the detection accuracy of eye movements can be appropriately adjusted to prevent erroneous detection. As a result, the detection accuracy of eye movements is improved by filtering based on the visual state and ambient light. Part or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data on the visual state and ambient light to the generation AI and cause the generation AI to adjust the filtering.

[0075] The detection unit can estimate the user's emotion and determine the priority of the eye movement to be detected based on the estimated user's emotion. For example, when the user is excited, the detection unit preferentially detects large changes in the eye movement. In addition, when the user is relaxed, the detection unit can also preferentially detect subtle changes in the eye movement. Furthermore, when the user is nervous, the detection unit can also preferentially detect stable patterns of the eye movement. This improves the detection accuracy by determining the priority of the eye movement based on 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 detection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detection 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 the eye movement.

[0076] The detection unit can detect highly relevant movements preferentially based on the geographical location information of the user when detecting eye movements. For example, when the user is in a specific location, the detection unit preferentially detects eye movements related to the location. Also, when the user is moving, the detection unit can preferentially detect eye movements related to the movement. Furthermore, when the user is at home, the detection unit can preferentially detect daily eye movements. In this way, by taking the geographical location information into consideration, highly relevant movements can be preferentially detected. Part or all of the above-mentioned processing in the detection unit may be performed, for example, using AI or may be performed without using AI. For example, the detection unit can input data of geographical location information to the generation AI and cause the generation AI to execute detection of highly relevant movements.

[0077] When detecting eye movements, the detection unit can detect related movements based on the user's social media activities. For example, the detection unit preferentially detects eye movements related to emojis that the user frequently uses on social media. The detection unit can also preferentially detect eye movements related to images that the user frequently sees on social media. Furthermore, eye movements can be detected based on the time period during which the user is active on social media. In this way, related movements can be detected by analyzing social media activities. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, AI, or may be performed without using AI. For example, the detection unit can input data of social media activities to the generation AI and cause the generation AI to detect related movements.

[0078] The selection unit can estimate the user's emotion and adjust the character selection algorithm based on the estimated user's emotion. For example, when the user is relaxed, the selection unit applies a detailed character selection algorithm. Also, when the user is in a hurry, the selection unit can apply a simplified character selection algorithm. Furthermore, when the user is excited, the selection unit can apply a visually stimulating character selection algorithm. This improves the selection accuracy by adjusting the character selection algorithm based on 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 selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the character selection algorithm.

[0079] The selection unit can select an appropriate selection method based on the user's past selection history when selecting characters. For example, the selection unit preferentially displays characters that the user has frequently selected in the past. The selection unit can also analyze the user's past selection history and suggest an optimal selection method. Furthermore, the selection unit can predict and suggest characters to be used in a specific time period from the user's past selection history. This makes it possible to select an optimal selection method by referring to the past selection history. A part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can input data of the past selection history to the generation AI and cause the generation AI to select the optimal selection method.

[0080] The selection unit can improve the accuracy of selection based on the user's current visual state or ambient light when selecting characters. For example, when the user is in a dark environment, the selection unit can improve the accuracy of selection by increasing the contrast of the characters. Also, when the user is in a bright environment, the selection unit can improve the accuracy of selection by increasing the size of the characters. Furthermore, when the user is visually tired, the display time of the characters can be extended to improve the accuracy of selection. This improves the accuracy of selection by improving the accuracy of selection based on the visual state and ambient light. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can input data on the visual state and ambient light to the generation AI and cause the generation AI to improve the accuracy of selection.

[0081] The selection unit can estimate the user's emotion and determine the priority order of characters to be selected based on the estimated user's emotion. For example, when the user is excited, the selection unit can preferentially display frequently used emojis. Also, when the user is relaxed, the selection unit can preferentially display detailed characters. Furthermore, when the user is nervous, the selection unit can preferentially display simple characters. This improves the selection accuracy by determining the priority order of characters based on the user's emotion. The estimation of emotion 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 selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection 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 characters.

[0082] The selection unit can, when selecting characters, preferentially select characters with high relevance based on the geographical location information of the user. For example, when the user is in a specific location, the selection unit preferentially selects characters related to the location. In addition, when the user is moving, the selection unit can preferentially select characters related to movement. Furthermore, when the user is at home, the selection unit can preferentially select everyday characters. In this way, by taking the geographical location information into consideration, characters with high relevance can be preferentially selected. Part or all of the above-mentioned processing in the selection unit may be performed, for example, using AI, or may be performed without using AI. For example, the selection unit can input data of geographical location information to the generation AI and cause the generation AI to select characters with high relevance.

[0083] The selection unit can select related characters based on the user's social media activity when selecting characters. For example, the selection unit preferentially selects emojis that the user frequently uses on social media. The selection unit can also preferentially select characters related to images that the user often sees on social media. Furthermore, the selection unit can select characters based on the time period during which the user is active on social media. In this way, related characters can be selected by analyzing social media activities. A part or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input data on social media activities to the generation AI and cause the generation AI to select related characters.

[0084] The determination unit can estimate the user's emotion and adjust the character determination algorithm based on the estimated user's emotion. For example, when the user is relaxed, the determination unit applies a detailed character determination algorithm. Also, when the user is in a hurry, the determination unit can apply a simplified character determination algorithm. Furthermore, when the user is excited, a visually stimulating character determination algorithm can be applied. This improves the determination accuracy by adjusting the character determination algorithm based on the user's emotion. The estimation of emotion 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the character determination algorithm.

[0085] The confirmation unit can select an appropriate confirmation method based on the user's past confirmation history when confirming characters. For example, the confirmation unit preferentially displays characters that the user has frequently confirmed in the past. The confirmation unit can also analyze the user's past confirmation history and suggest an optimal confirmation method. Furthermore, it can predict and suggest characters to be used in a specific time period from the user's past confirmation history. This makes it possible to select an optimal confirmation method by referring to the past confirmation history. Some or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit can input data of the past confirmation history to the generation AI and cause the generation AI to select an optimal confirmation method.

[0086] The confirmation unit can improve the accuracy of confirmation based on the user's current visual state or ambient light when confirming characters. For example, when the user is in a dark environment, the confirmation unit can improve the accuracy of confirmation by increasing the contrast of the characters. Also, when the user is in a bright environment, the confirmation unit can improve the accuracy of confirmation by increasing the size of the characters. Furthermore, when the user is visually tired, the display time of the characters can be extended to improve the accuracy of confirmation. This improves the accuracy of confirmation by improving the accuracy of confirmation based on the visual state and ambient light. Part or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or may be performed without using AI. For example, the confirmation unit can input data on the visual state and ambient light to the generation AI and cause the generation AI to improve the accuracy of confirmation.

[0087] The determination unit can estimate the user's emotion and determine the priority of characters to be determined based on the estimated user's emotion. For example, when the user is excited, the determination unit can preferentially display frequently used emojis. Also, when the user is relaxed, detailed characters can be preferentially displayed. Furthermore, when the user is nervous, simple characters can be preferentially displayed. This improves the accuracy of determination by determining the priority of characters based on the user's emotion. The estimation of emotion 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination 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 characters.

[0088] The confirmation unit can, when confirming characters, prioritize the confirmation of highly relevant characters based on the geographical location information of the user. For example, when the user is in a specific location, the confirmation unit prioritizes the confirmation of characters related to the location. In addition, when the user is moving, the confirmation unit can also prioritize the confirmation of characters related to the movement. Furthermore, when the user is at home, the confirmation unit can also prioritize the confirmation of everyday characters. In this way, by taking into account the geographical location information, highly relevant characters can be prioritized. Part or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI, or may be performed without using AI. For example, the confirmation unit can input data of geographical location information to the generation AI and cause the generation AI to execute the confirmation of highly relevant characters.

[0089] The determination unit can determine related characters based on the user's social media activity when determining characters. For example, the determination unit preferentially determines emojis that the user frequently uses on social media. The determination unit can also preferentially determine characters related to images that the user often sees on social media. Furthermore, the determination unit can also determine characters based on the time period during which the user is active on social media. In this way, related characters can be determined by analyzing social media activities. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on social media activities to the generation AI and cause the generation AI to perform the determination of related characters.

[0090] The display unit can estimate the user's emotion and adjust the priority of the display contents according to the estimated user's emotion. For example, when the user is relaxed, the display unit can preferentially display detailed information. Also, when the user is in a hurry, the display unit can preferentially display information that covers the main points. Furthermore, when the user is excited, the display unit can preferentially display visually stimulating information. This improves the display accuracy by adjusting the priority of the display contents based on 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 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 priority of the display contents.

[0091] The display unit can select an appropriate display method based on the user's past display history at the time of display. For example, the display unit preferentially displays information that the user has frequently displayed in the past. The display unit can also analyze the user's past display history and propose an optimal display method. Furthermore, the display unit can predict and propose information to be displayed in a specific time period from the user's past display history. This makes it possible to select an optimal display method by referring to the past display history. 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 data of the past display history to the generation AI and cause the generation AI to select an optimal display method.

[0092] The display unit can improve the accuracy of the display based on the user's current visual state or ambient light when displaying. For example, when the user is in a dark environment, the display unit can increase the contrast of the display to improve visibility. Also, when the user is in a bright environment, the display size can be increased to improve visibility. Furthermore, when the user is visually tired, the display time can be extended to improve visibility. This improves the display accuracy by improving the accuracy of the display based on the visual state or ambient light. 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 data on the visual state or ambient light to the generation AI and cause the generation AI to improve the accuracy of the display.

[0093] The display unit can estimate the user's emotion and determine the priority of the contents to be displayed based on the estimated user's emotion. For example, when the user is excited, the display unit can prioritize displaying frequently used emoticons. Also, when the user is relaxed, detailed information can be prioritized. Furthermore, when the user is nervous, simple information can be prioritized. This improves the display accuracy by determining the priority of the display contents based on 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 execute the determination of the priority of the display contents.

[0094] The display unit can display highly relevant content preferentially based on the geographical location information of the user at the time of display. For example, when the user is in a specific location, the display unit can display information related to the location preferentially. In addition, when the user is moving, the display unit can also display information related to the movement preferentially. Furthermore, when the user is at home, the display unit can display daily information preferentially. In this way, by taking the geographical location information into consideration, highly relevant content can be displayed preferentially. 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 data of the geographical location information to the generation AI and cause the generation AI to execute the display of highly relevant content.

[0095] The display unit can display related content based on the user's social media activity at the time of display. For example, the display unit can preferentially display emojis that the user frequently uses on social media. The display unit can also preferentially display information related to images that the user often sees on social media. Furthermore, the display unit can display information based on the time period during which the user is active on social media. This makes it possible to display related content by analyzing social media activity. A part or all of the above-mentioned processing in the display unit can be performed, for example, using AI or can be performed without using AI. For example, the display unit can input data on social media activity to a generation AI and cause the generation AI to display related content.

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

[0097] The goggle-type screen system can further include a voice recognition unit. The voice recognition unit can recognize a user's voice command and assist in character selection and confirmation. For example, when a user selects a specific character, the user can say "this character" by voice, and the selection unit can preferentially select that character. Also, the user can say "confirm" by voice, and the confirmation unit can confirm that character. Furthermore, the voice recognition unit can analyze the user's voice command and estimate the user's intention, thereby achieving smoother operation. As a result, the use of voice recognition makes user operation more intuitive and efficient.

[0098] The goggle-type screen system can further include a vibration feedback unit. The vibration feedback unit can generate minute vibrations when a user selects a specific character to confirm the selection. For example, when a user fixes his / her gaze on a specific character, the goggles vibrate lightly to inform the user that the selection is complete. In addition, vibration feedback can be provided when the confirmation unit confirms the character, to inform the user that the confirmation is complete. Furthermore, by adjusting the strength and pattern of the vibration, different feedback can be provided for different operations. As a result, the use of vibration feedback makes it possible to confirm an operation using a sense other than vision.

[0099] The goggle-type screen system may further include a temperature feedback unit. The temperature feedback unit may generate a temperature change when the user performs a specific operation, thereby allowing the user to confirm the operation. For example, when the user selects a character, a part of the goggles may become warm to indicate the completion of the selection. Also, when the confirmation unit confirms the character, a part of the goggles may become cold to indicate the completion of the confirmation. Furthermore, by adjusting the speed and range of the temperature change, different feedback may be provided for different operations. Thus, by using temperature feedback, it becomes possible to confirm an operation using a sense other than sight or touch.

[0100] The goggle-type screen system can further include a scent feedback unit. The scent feedback unit can emit a scent when the user performs a specific operation to confirm the operation. For example, when the user selects a character, a specific scent is emitted to notify the completion of the selection. Also, when the confirmation unit confirms the character, a different scent can be emitted to notify the completion of the confirmation. Furthermore, by adjusting the type and strength of the scent, different feedback can be provided for different operations. As a result, the scent feedback can be used to confirm an operation using the sense of smell.

[0101] The goggle-type screen system may further include a tactile feedback unit. The tactile feedback unit may generate a tactile stimulus when the user performs a specific operation, thereby allowing the user to confirm the operation. For example, when the user selects a character, the user may feel a part of the goggles being lightly pressed to indicate the completion of the selection. When the confirmation unit confirms the character, the user may feel a part of the goggles being lightly pulled to indicate the completion of the confirmation. Furthermore, by adjusting the strength and pattern of the tactile stimulus, different feedback may be provided for different operations. Thus, the use of tactile feedback allows the user to confirm an operation using senses other than sight and hearing.

[0102] The goggle-type screen system can estimate the user's emotions and dynamically change the display content based on the estimated emotions. For example, if the user is feeling stressed, the system can display images and text that have a relaxing effect. If the user is excited, the system can switch to a display of calming colors. Furthermore, if the user is tired, the system can increase the size of the text to make it easier to read. This makes it possible to adjust the display content according to the user's emotions, providing a more comfortable user experience.

[0103] The goggle-type screen system can estimate the user's emotions and adjust the audio feedback based on the estimated emotions. For example, if the user is relaxed, the system can provide feedback in a calm voice. If the user is nervous, the system can provide encouraging words in the voice. Furthermore, if the user is excited, the system can provide feedback in a calm tone. This allows the adjustment of audio feedback according to the user's emotions, providing a more personalized user experience.

[0104] The goggle-type screen system can estimate the user's emotions and adjust the difficulty of operations based on the estimated emotions. For example, if the user is feeling stressed, the system can simplify the operations and reduce the number of options, thereby reducing the burden of operations. Also, if the user is relaxed, the system can provide detailed operations and enable fine adjustments according to the user's intentions. Furthermore, if the user is excited, the system can speed up the response of operations and support rapid operations. This makes it possible to adjust the difficulty of operations according to the user's emotions, providing a more comfortable user experience.

[0105] The goggle-type screen system can estimate the user's emotions and adjust the frequency of notifications based on the estimated emotions. For example, if the user is relaxed, the system can reduce the frequency of notifications to avoid disturbing the user's concentration. Also, if the user is busy, the system can prioritize displaying only important notifications. Furthermore, if the user is excited, the system can change the tone of notifications to a calmer one to stabilize the user's emotions. This makes it possible to adjust the frequency of notifications according to the user's emotions, providing a more personalized user experience.

[0106] The goggle-type screen system can estimate the user's emotions and dynamically change the interface design based on the estimated emotions. For example, if the user is stressed, the system can switch to a simple and calm design. If the user is relaxed, the system can change to a colorful and fun design. Furthermore, if the user is excited, the system can change to a less visually stimulating design to stabilize the user's emotions. This makes it possible to adjust the interface design according to the user's emotions, providing a more comfortable user experience.

[0107] The process flow of the second embodiment will be briefly described below.

[0108] Step 1: The detection unit detects the user's eye movements. The user's eye movements include the direction of gaze and the frequency of blinking. The detection unit uses an infrared sensor and a camera to monitor the user's eye movements in real time and collect data. For example, the infrared sensor detects eye movements with high accuracy, and the camera records the eye movements as video. Step 2: The selection unit uses machine learning or deep learning to analyze the eye movement data collected by the detection unit and determine which character the user selected. For example, a support vector machine or a random forest can be used as the machine learning algorithm, and a convolutional neural network or a recurrent neural network can be used as the deep learning algorithm. Step 3: The confirmation unit confirms the characters selected by the selection unit. The confirmation unit learns the user's personal preferences and daily conversations, and prioritizes displaying characters, pictures, and images that the user frequently uses. For example, based on the user's past selection history and conversation history, the confirmation unit prioritizes displaying frequently used characters, pictures, and images. Step 4: The display unit displays the characters, pictures, or images confirmed by the confirmation unit on the attached speaker or on the opposite side of the goggles. For example, the selected characters are output as voice from the speaker, and the selected pictures or images are displayed on the opposite side of the goggles. This allows the user to communicate the selection to others.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements including the above-mentioned detection unit, selection unit, confirmation unit, and display unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the detection unit monitors the user's eye movement in real time using an infrared sensor or a camera 42 of the smart device 14 to collect data. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the eye movement data using machine learning or deep learning to determine which character the user selected. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and learns the user's past selection history and conversation history, and preferentially displays frequently used characters, pictures, and images. The display unit displays characters, pictures, and images confirmed using the output device 40 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements including the above-mentioned detection unit, selection unit, determination unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit monitors the user's eye movement in real time using the infrared sensor and the camera 42 of the smart glasses 214 to collect data. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the eye movement data using machine learning and deep learning to determine which character the user selected. The determination unit is realized by the specific processing unit 290 of the data processing device 12, and learns the user's past selection history and conversation history, and preferentially displays frequently used characters, pictures, and images. The display unit displays the determined characters, pictures, and images using the speaker 240 and display of the smart glasses 214. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements including the above-mentioned detection unit, selection unit, confirmation unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the detection unit monitors the user's eye movement in real time using the infrared sensor and the camera 42 of the headset type terminal 314 to collect data. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the data of the eye movement using machine learning and deep learning to determine which character the user selected. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and learns the user's past selection history and conversation history, and preferentially displays frequently used characters, pictures, and images. The display unit displays the confirmed characters, pictures, and images using the display 343 and the speaker 240 of the headset type terminal 314. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements including the above-mentioned detection unit, selection unit, confirmation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit monitors the user's eye movement in real time using the infrared sensor and camera 42 of the robot 414 to collect data. The selection unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the eye movement data using machine learning and deep learning to determine which character the user selected. The confirmation unit is realized by the specific processing unit 290 of the data processing device 12, and learns the user's past selection history and conversation history, and preferentially displays frequently used characters, pictures, and images. The display unit displays the confirmed characters, pictures, and images using the speaker 240 and display of the robot 414. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] These emotions are distributed in the three 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Appendix 1) A detection unit that detects the movement of a user's eyes; a selection unit that selects a character based on the eye movement detected by the detection unit; a confirmation unit that confirms the character selected by the selection unit; a display unit that displays the character, picture, or image confirmed by the confirmation unit; A system characterized by: (Appendix 2) The detection unit is Use an infrared sensor or camera to monitor the user's eye movements in real time and collect data. 2. The system of claim 1, (Appendix 3) The selection unit is Using machine learning or deep learning to analyze collected eye movement data to determine which letters the user selected 2. The system of claim 1, (Appendix 4) The determination unit is Learns the user's personal preferences or daily conversations, and prioritizes displaying characters, pictures, and images that the user frequently uses 2. The system of claim 1, (Appendix 5) The display unit is The selected text, picture, or image is displayed on the other side of the attached speaker or goggles, allowing the user to communicate it to others. 2. The system of claim 1, (Appendix 6) The detection unit is Estimate the user's emotion and adjust the accuracy of eye movement detection according to the estimated user's emotion. 2. The system of claim 1, (Appendix 7) The detection unit is Analyze the user's past eye movement data and select an appropriate detection algorithm 2. The system of claim 1, (Appendix 8) The detection unit is When detecting eye movements, filtering is performed based on the user's current visual state or ambient light 2. The system of claim 1, (Appendix 9) The detection unit is Estimate the user's emotion and determine the priority of eye movements to be detected according to the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The detection unit is When detecting eye movements, prioritize detection of relevant movements based on the user's geographic location information. 2. The system of claim 1, (Appendix 11) The detection unit is Upon detecting eye movements, detect related movements based on the user's social media activity 2. The system of claim 1, (Appendix 12) The selection unit is Infer user emotions and adjust the character selection algorithm according to the estimated user emotions. 2. The system of claim 1, (Appendix 13) The selection unit is When selecting characters, choose the appropriate selection method based on the user's past selection history. 2. The system of claim 1, (Appendix 14) The selection unit is When selecting characters, improve the accuracy of the selection based on the user's current visual state or ambient light 2. The system of claim 1, (Appendix 15) The selection unit is A user's emotion is estimated, and a priority order of characters to be selected is determined according to the estimated user's emotion. 2. The system of claim 1, (Appendix 16) The selection unit is When selecting characters, prioritize characters that are more relevant based on the user's geographic location information 2. The system of claim 1, (Appendix 17) The selection unit is Upon character selection, select related characters based on the user's social media activity 2. The system of claim 1, (Appendix 18) The determination unit is Estimate the user's emotions and adjust the character confirmation algorithm according to the estimated user emotions. 2. The system of claim 1, (Appendix 19) The determination unit is When confirming characters, an appropriate confirmation method is selected based on the user's past confirmation history. 2. The system of claim 1, (Appendix 20) The determination unit is When confirming characters, improve the accuracy of the confirmation based on the user's current visual state or ambient light 2. The system of claim 1, (Appendix 21) The determination unit is The user's emotions are estimated, and the priority of characters to be confirmed is determined according to the estimated user's emotions. 2. The system of claim 1, (Appendix 22) The determination unit is When confirming characters, prioritize characters that are highly relevant based on the user's geographic location information. 2. The system of claim 1, (Appendix 23) The determination unit is When a character is confirmed, related characters are confirmed based on the user's social media activity. 2. The system of claim 1, (Appendix 24) The display unit is Estimate the user's emotions and adjust the priority of the displayed content according to the estimated user's emotions. 2. The system of claim 1, (Appendix 25) The display unit is When displaying, the appropriate display method is selected based on the user's past display history. 2. The system of claim 1, (Appendix 26) The display unit is At display time, improve the accuracy of the display based on the user's current visual condition or ambient lighting 2. The system of claim 1, (Appendix 27) The display unit is Estimate the user's emotions and prioritize the content to be displayed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 28) The display unit is At display time, content that is relevant to the user is prioritized based on the user's geographic location. 2. The system of claim 1, (Appendix 29) The display unit is At view time, show relevant content based on the user's social media activity 2. The system of claim 1, [Explanation of symbols]

[0181] 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 detection unit that detects the movement of a user's eyes; a selection unit that selects a character based on the eye movement detected by the detection unit; a confirmation unit that confirms the character selected by the selection unit; a display unit that displays the character, picture, or image confirmed by the confirmation unit; A system characterized by:

2. The detection unit is Use an infrared sensor or camera to monitor the user's eye movements in real time and collect data.

2. The system of claim 1 .

3. The selection unit is Using machine learning or deep learning to analyze collected eye movement data to determine which letters the user selected 2. The system of claim 1 .

4. The determination unit is Learns the user's personal preferences or daily conversations, and prioritizes displaying characters, pictures, and images that the user frequently uses 2. The system of claim 1 .

5. The display unit is The selected text, picture, or image is displayed on the other side of the attached speaker or goggles, allowing the user to communicate it to others.

2. The system of claim 1 .

6. The detection unit is Estimate the user's emotion and adjust the accuracy of eye movement detection according to the estimated user's emotion.

2. The system of claim 1 .

7. The detection unit is Analyze the user's past eye movement data and select an appropriate detection algorithm 2. The system of claim 1 .

8. The detection unit is When detecting eye movements, filtering is performed based on the user's current visual state or ambient light 2. The system of claim 1 .

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