system

Abstract

The system according to this embodiment aims to enable users with visual or hearing impairments to use smartphones more comfortably. [Solution] The system according to the embodiment comprises a voice guidance unit, a gesture support unit, and a customization unit. The voice guidance unit provides voice descriptions of elements on the screen. The gesture support unit supports gesture operations performed by the user on the screen. The customization unit customizes the content of the support according to the user's settings.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when a user with visual or hearing impairments uses a smartphone, sufficient support is not provided, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user with visual or hearing impairments to use a smartphone more comfortably.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a voice guidance unit, a gesture support unit, and a customization unit. The voice guidance unit provides voice descriptions of elements on the screen. The gesture support unit supports gesture operations performed by the user on the screen. The customization unit customizes the support content according to the user's settings. [Effects of the Invention]

[0007] The system according to this embodiment can enable users with visual or hearing impairments to use smartphones more comfortably. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The AI ​​assistant for smartphone accessibility support according to an embodiment of the present invention is a system aimed at assisting users with visual or hearing impairments. This system has a voice guide unit that explains elements on the screen by voice, and when the user touches a specific element on the screen, it explains the content and function of that element by voice. It also has a gesture support unit that supports gesture operations performed by the user on the screen, and when the user speaks the operation they want to perform, it provides voice guidance on the gesture operation to achieve the desired operation based on the content of the speech. Furthermore, it has a collection unit that collects sounds around the user and a conversion unit that converts the collected sounds into text and displays it on a display unit, supporting the user's movement. Furthermore, it has a customization unit that customizes the content of the support according to the user's settings, and the speed and volume of the voice guide and the font size of the text conversion can be set according to the user's preferences. Finally, it also has a function to customize the content of the voice guide based on the user's current time of day and location. As a result, the AI ​​assistant for smartphone accessibility support can make smartphones easier to use for users with visual or hearing impairments.

[0029] The AI ​​assistant for smartphone accessibility support according to this embodiment comprises a voice guidance unit, a gesture support unit, and a customization unit. The voice guidance unit provides voice descriptions of elements on the screen. For example, when a user touches a specific element on the screen, the voice guidance unit provides voice descriptions of the content and function of that element. The voice guidance unit can provide voice descriptions of elements such as buttons, text fields, and images. The gesture support unit supports gesture operations performed by the user on the screen. For example, when a user speaks about an operation they want to perform, the gesture support unit provides voice guidance on the gesture operation to achieve the desired operation based on the spoken content. The gesture support unit can support gesture operations such as scrolling, tapping, and swiping. The customization unit customizes the support content according to the user's settings. For example, the customization unit can set the speed and volume of the voice guidance and the font size of the text conversion according to the user's preferences. The customization unit can, for example, increase the speed of the voice guidance, increase the volume, or increase the font size of the text conversion. This allows the AI ​​assistant for smartphone accessibility support according to the embodiment to make smartphones easier to use for users with visual or hearing impairments.

[0030] The voice guide unit provides voice descriptions of elements on the screen. For example, when a user touches a specific element on the screen, the voice guide unit provides a voice description of that element's content and function. Specifically, when a user moves their finger on the smartphone's touchscreen, the voice guide unit detects the element at the location of the finger and reads aloud the element's name and function. For example, when a user touches an icon on the home screen, the voice guide unit explains which application the icon represents. Similarly, when an option in the settings menu is touched, the voice guide unit provides voice guidance on the option's function and the results of selecting it. The voice guide unit can provide voice descriptions of elements such as buttons, text fields, and images. Furthermore, the voice guide unit can use AI to learn the user's operation history and context to provide more appropriate voice guidance. For example, for applications and functions frequently used by the user, it can omit detailed explanations and provide concise guidance. The voice guide unit is also multilingual and can provide voice guidance in different languages ​​depending on the user's settings. This allows the voice guide unit to assist visually impaired users in intuitively operating their smartphones, improving usability.

[0031] The gesture support unit assists users with gesture operations on the screen. For example, when a user speaks the desired operation, the gesture support unit provides voice guidance on the gesture operation to perform that operation based on the spoken content. Specifically, if a user says "scroll down," the gesture support unit will provide voice guidance on the scroll-down gesture operation, assisting the user in performing the operation. The gesture support unit can support gesture operations such as scrolling, tapping, and swiping. Furthermore, the gesture support unit uses AI to learn the user's gesture operation patterns, enabling it to recognize operations more accurately and quickly. For example, if a user frequently uses a particular gesture, it will prioritize recognizing that gesture, improving the response time of the operation. In addition, the gesture support unit can analyze the user's spoken content using natural language processing technology and handle complex operation instructions. For example, it can handle instructions that include multiple operations, such as "move to the next page and tap the first link," helping the user to operate smoothly. As a result, the gesture support unit can enable users with visual or hearing impairments to operate smartphones efficiently, improving accessibility.

[0032] The customization section customizes the support content according to the user's settings. For example, the customization section can set the speed and volume of the voice guide and the font size of the text conversion to suit the user's preferences. Specifically, if a user wants to speed up the voice guide, the customization section adjusts the speed through the settings menu, providing the voice guide at a speed that meets the user's request. Similarly, the volume can be adjusted according to the environment, such as lowering the volume in a quiet environment and increasing it in a noisy environment. Furthermore, the font size of the text conversion can be adjusted to a larger size depending on the degree of visual impairment. The customization section can learn the user's operation history and settings to provide support content that is optimal for each individual user. For example, if a user frequently changes a particular setting, that setting will be displayed preferentially and made easily accessible. The customization section can also collect user feedback and use it to improve the support content. For example, if the content of the voice guide or the guidance for gesture operation is difficult to understand, improvements will be made based on user feedback to provide a more user-friendly interface. In this way, the customization section can provide flexible support that meets the individual needs of users and improve the accessibility of smartphones.

[0033] The audio guide unit can provide audio explanations of the content and function of specific elements on the screen when the user touches them. For example, when the user touches a button, the audio guide unit will provide an audio explanation of that button's function. It can also provide audio explanations of the content of a text field when the user touches it. Furthermore, when the user touches an image, the audio guide unit can provide an audio explanation of that image. This makes it easier for users to understand the elements on the screen.

[0034] The gesture support unit can, when a user speaks the desired operation, provide voice guidance for the gesture operation to perform that operation based on the user's utterance. For example, if the user says "scroll," the gesture support unit will provide voice guidance for the scroll operation. It can also provide voice guidance for the tap operation if the user says "tap." Furthermore, if the user says "swipe," the gesture support unit can provide voice guidance for the swipe operation. This allows the user to control operations by voice. Some or all of the above-described processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input the user's utterance into a generating AI and have the generating AI execute guidance for the gesture operation based on the utterance.

[0035] The customization section allows users to set the speed and volume of the voice guide and the font size of the text conversion according to their preferences. For example, if the user wants to speed up the voice guide, the customization section can adjust the speed. Similarly, if the user wants to increase the volume of the voice guide, the customization section can adjust the volume. Furthermore, if the user wants to increase the font size of the text conversion, the customization section can adjust the font size. This allows users to change settings to their liking. Some or all of the above-described processes in the customization section may be performed using AI, for example, or without AI. For example, the customization section can input the user's settings into a generating AI and have the generating AI perform customizations based on those settings.

[0036] The AI ​​assistant for smartphone accessibility support according to this embodiment includes a collection unit that collects sounds around the user, and a conversion unit that converts the collected sounds into text and displays them on a display unit. The collection unit collects sounds around the user. For example, the collection unit can collect ambient sounds, conversation sounds, alarm sounds, etc. The collection unit collects ambient sounds using, for example, a microphone. The conversion unit converts the sounds collected by the collection unit into text and displays them on a display unit. For example, the conversion unit converts sounds into text using speech recognition technology. For example, the conversion unit can convert collected conversation sounds into text and display them on a display unit. The conversion unit can also convert collected alarm sounds into text and display them on a display unit. This allows the user to confirm ambient sounds in text. Some or all of the above-described processes in the collection unit and conversion unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the collected sound data into a generating AI and have the generating AI perform the conversion from sound to text.

[0037] The audio guide unit can customize the content of the audio guide based on the user's current time of day and location. For example, if the user is using it at night, the audio guide unit will provide an audio guide suitable for nighttime. Furthermore, if the user is in a specific location, the audio guide unit can provide location-related information in the audio guide. This allows for the provision of an audio guide tailored to the user's situation. Some or all of the above processing in the audio guide unit may be performed using AI, for example, or without AI. For example, the audio guide unit can input information about the user's current time of day and location into a generating AI and have the generating AI execute a customized audio guide.

[0038] The voice guidance unit can optimize the content of the voice guidance based on the user's past operation history. For example, the voice guidance unit can prioritize guiding users through functions they have frequently used in the past. Furthermore, the voice guidance unit can predict functions that the user will use during specific time periods based on their past operation history and guide users through them using voice guidance. In addition, the voice guidance unit can analyze the user's past operation history and guide users through the most efficient operation procedures using voice guidance. This allows the system to provide optimal voice guidance based on the user's past operation history. Some or all of the above-described processes in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can input the user's past operation history data into a generating AI and have the generating AI perform the optimization of the voice guidance based on the operation history.

[0039] The voice guidance unit can dynamically change the content of the voice guidance according to the user's current activity and situation. For example, if the user is walking, the voice guidance unit can provide voice guidance suitable for walking. It can also provide voice guidance suitable for driving if the user is driving a car. Furthermore, if the user is relaxing at home, the voice guidance unit can provide voice guidance suitable for relaxation. This allows for the provision of voice guidance tailored to the user's current situation. Some or all of the above processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can input data on the user's current activity and situation into a generating AI, and cause the generating AI to dynamically change the voice guidance according to the activity and situation.

[0040] The audio guide unit can customize the content of the audio guide based on the user's language settings and cultural background. For example, the audio guide unit can automatically set the language of the audio guide based on the user's language settings. The audio guide unit can also provide an audio guide using appropriate expressions and examples based on the user's cultural background. Furthermore, the audio guide unit can provide a language switching function if the user uses multiple languages. This allows for the provision of an audio guide tailored to the user's language settings and cultural background. Some or all of the above processing in the audio guide unit may be performed using AI, for example, or without AI. For example, the audio guide unit can input data on the user's language settings and cultural background into a generating AI, and have the generating AI customize the audio guide based on those settings and cultural background.

[0041] The voice guidance unit can switch the content of the voice guidance to an energy-saving mode depending on the battery level of the user's device. For example, if the device's battery level is low, the voice guidance unit can reduce the frequency of voice guidance and switch to energy-saving mode. The voice guidance unit can also provide normal voice guidance when the device's battery level is sufficient. Furthermore, if the device's battery level is very low, the voice guidance unit can provide only essential information through voice guidance. This allows the voice guidance to be provided in an energy-saving mode according to the device's battery level. Some or all of the above processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can input device battery level data into a generating AI and cause the generating AI to switch the voice guidance to an energy-saving mode according to the battery level.

[0042] The gesture support unit can optimize gesture guidance based on the user's past gesture history. For example, the gesture support unit prioritizes guiding the user to gestures they have frequently used in the past. The gesture support unit can also predict and guide the user to gestures they will use at specific times based on their past gesture history. Furthermore, the gesture support unit can analyze the user's past gesture history and guide the user to the most efficient procedure. This allows the unit to provide optimal gesture guidance based on the user's past gesture history. Some or all of the above processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input the user's past gesture history data into a generating AI and have the generating AI optimize gesture guidance based on the gesture history.

[0043] The gesture support unit can dynamically change the gesture guidance according to the user's current hand position and movement. For example, if the user's hand is on the left side of the screen, the gesture support unit will provide gesture guidance suitable for the left side. Furthermore, if the user's hand is moving quickly, the gesture support unit can provide rapid gesture guidance. Additionally, if the user's hand is in the center of the screen, the gesture support unit can provide gesture guidance suitable for the center. This allows the gesture support unit to provide gesture guidance tailored to the user's current hand position and movement. Some or all of the above processing in the gesture support unit may be performed using AI, or without AI. For example, the gesture support unit can input data on the user's hand position and movement into a generating AI, causing the generating AI to dynamically change the gesture guidance according to the hand position and movement.

[0044] The gesture support unit can customize gesture guidance according to the screen size and resolution of the user's device. For example, if the user is using a smartphone, the gesture support unit can provide gesture guidance that is appropriate for the screen size. Furthermore, if the user is using a tablet, the gesture support unit can provide gesture guidance optimized for a larger screen. Additionally, if the user is using a smartwatch, the gesture support unit can provide concise and highly visible gesture guidance. This allows the gesture support unit to provide gesture guidance tailored to the screen size and resolution of the user's device. Some or all of the above processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input device screen size and resolution data into a generating AI and have the generating AI customize the gesture guidance according to the screen size and resolution.

[0045] The gesture support unit can optimize gesture guidance based on the sensor information of the user's device. For example, the gesture support unit can provide optimal gesture guidance based on the acceleration sensor information of the user's device. It can also provide optimal gesture guidance based on the gyroscope sensor information of the user's device. Furthermore, it can provide optimal gesture guidance based on the proximity sensor information of the user's device. This enables the provision of optimal gesture guidance based on the sensor information of the user's device. Some or all of the above processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input the device's sensor information into a generating AI and have the generating AI perform the optimization of gesture guidance based on the sensor information.

[0046] The customization unit can optimize custom settings based on the user's past setting history. For example, the customization unit prioritizes providing settings that the user has frequently used in the past. It can also predict and provide settings that the user will use during specific time periods based on their past setting history. Furthermore, the customization unit can analyze the user's past setting history and provide the most efficient settings. This allows for the provision of optimal custom settings based on the user's past setting history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past setting history data into a generating AI and have the generating AI perform the optimization of custom settings based on the setting history.

[0047] The customization unit can dynamically change the customization settings according to the user's current usage and environment. For example, the customization unit can provide a bright screen setting when the user is outdoors. It can also provide a dark screen setting when the user is indoors. Furthermore, the customization unit can provide a night mode when the user is using it at night. This allows for the provision of optimal customization settings according to the user's current usage and environment. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data on the user's current usage and environment into a generating AI, and cause the generating AI to dynamically change the customization settings according to the usage and environment.

[0048] The customization unit can optimize the custom settings according to the hardware characteristics of the user's device. For example, if the user is using a smartphone, the customization unit can provide settings tailored to the device's hardware characteristics. It can also provide settings optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the customization unit can provide concise and highly visible settings. This allows for the provision of optimal custom settings tailored to the hardware characteristics of the user's device. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data on the device's hardware characteristics into a generating AI and have the generating AI perform the optimization of the custom settings according to the hardware characteristics.

[0049] The customization unit can optimize custom settings based on the user's application usage history. For example, the customization unit prioritizes providing settings for applications the user has frequently used in the past. It can also predict and provide settings to be used during specific time periods based on the user's application usage history. Furthermore, the customization unit can analyze the user's application usage history and provide the most efficient settings. This allows for the provision of optimal custom settings based on the user's application usage history. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's application usage history data into a generating AI and have the generating AI perform the optimization of custom settings based on the usage history.

[0050] The sound collection unit can optimize sound collection based on the user's past sound environment history. For example, the collection unit can provide the optimal sound collection method based on the sound environment of places the user has frequently visited in the past. The collection unit can also predict and optimize the sounds to be collected at specific time periods based on the user's past sound environment history. Furthermore, the collection unit can analyze the user's past sound environment history and provide the most efficient sound collection method. This enables the provision of an optimal sound collection method based on the user's past sound environment history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past sound environment history data into a generating AI and have the generating AI perform the optimization of the sound collection method based on the sound environment history.

[0051] The sound collection unit can dynamically change its sound collection method according to the user's current location and situation. For example, if the user is outdoors, the sound collection unit can adjust its sound collection method considering the surrounding noise. Furthermore, if the user is indoors, the sound collection unit can provide a sound collection method suitable for a quiet environment. Additionally, if the user is moving, the sound collection unit can provide a sound collection method suitable for movement. This allows for the provision of an optimal sound collection method tailored to the user's current location and situation. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input data on the user's current location and situation into a generating AI, causing the generating AI to dynamically change the sound collection method according to the location and situation.

[0052] The sound collection unit can optimize sound collection according to the microphone characteristics of the user's device. For example, if the user is using a smartphone, the sound collection unit can provide a sound collection method that matches the microphone characteristics of the device. Furthermore, if the user is using a tablet, the sound collection unit can provide a sound collection method optimized for larger devices. Additionally, if the user is using a smartwatch, the sound collection unit can provide a simple and highly visible sound collection method. This allows for the provision of an optimal sound collection method tailored to the microphone characteristics of the user's device. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input data on the device's microphone characteristics into a generating AI, causing the generating AI to optimize the sound collection method according to the microphone characteristics.

[0053] The sound collection unit can optimize sound collection based on the noise level around the user. For example, if the user is in a noisy environment, the sound collection unit can optimize sound collection using a noise-canceling function. The sound collection unit can also collect ambient sounds in detail if the user is in a quiet environment. Furthermore, if the user is moving, the sound collection unit can provide a sound collection method suitable for movement. This allows for the provision of an optimal sound collection method according to the noise level around the user. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input ambient noise level data into a generating AI and have the generating AI optimize the sound collection method based on the noise level.

[0054] The conversion unit can optimize the conversion from sound to text based on the user's past conversion history. For example, the conversion unit can prioritize providing conversion methods that the user has frequently used in the past. It can also predict and provide conversion methods to be used during specific time periods based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and provide the most efficient conversion method. This allows for the provision of the optimal sound-to-text conversion method based on the user's past conversion history. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's past conversion history data into a generating AI and have the generating AI optimize the sound-to-text conversion method based on the conversion history.

[0055] The conversion unit can dynamically change the sound-to-text conversion according to the user's current sound environment. For example, if the user is in a noisy environment, the conversion unit can optimize the sound-to-text conversion using a noise-canceling function. The conversion unit can also provide a detailed sound-to-text conversion if the user is in a quiet environment. Furthermore, if the user is on the move, the conversion unit can provide a sound-to-text conversion suitable for movement. This allows the conversion unit to provide the optimal sound-to-text conversion method according to the user's current sound environment. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's current sound environment data into a generating AI, causing the generating AI to dynamically change the sound-to-text conversion method according to the sound environment.

[0056] The conversion unit can customize the conversion from sound to text based on the user's language settings and cultural background. For example, the conversion unit can automatically set the language for the conversion from sound to text based on the user's language settings. The conversion unit can also provide a conversion from sound to text using appropriate expressions and examples based on the user's cultural background. Furthermore, the conversion unit can provide a language switching function if the user uses multiple languages. This allows the conversion unit to provide the optimal method of conversion from sound to text according to the user's language settings and cultural background. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input data on the user's language settings and cultural background into a generating AI and have the generating AI perform customization of the conversion method from sound to text based on the language settings and cultural background.

[0057] The conversion unit can optimize the conversion from sound to text according to the display characteristics of the user's device. For example, if the user is using a smartphone, the conversion unit can provide text conversion that matches the device's display characteristics. Furthermore, if the user is using a tablet, the conversion unit can provide text conversion optimized for a larger display. Additionally, if the user is using a smartwatch, the conversion unit can provide concise and highly legible text conversion. This allows for the provision of an optimal sound-to-text conversion method tailored to the display characteristics of the user's device. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input data on the device's display characteristics into a generating AI, causing the generating AI to optimize the sound-to-text conversion method according to the display characteristics.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The voice guidance unit can be optimized based on the user's past operation history. For example, the voice guidance unit can prioritize guiding users through functions they have frequently used in the past. It can also predict functions that the user will use during specific time periods based on their past operation history and guide them through those functions. Furthermore, the voice guidance unit can analyze the user's past operation history and guide users through the most efficient operation procedures. This allows for the provision of optimal voice guidance based on the user's past operation history. Some or all of the above processing in the voice guidance unit may be performed using AI or not. For example, the voice guidance unit can input the user's past operation history data into a generating AI and have the generating AI optimize the voice guidance based on the operation history.

[0060] The gesture support unit can optimize gesture guidance based on the user's past gesture history. For example, the gesture support unit prioritizes guiding the user to gestures they have frequently used in the past. Furthermore, the gesture support unit can predict and guide the user to gestures they will use at specific times based on their past gesture history. In addition, the gesture support unit can analyze the user's past gesture history and guide the user to the most efficient procedure. This allows the unit to provide optimal gesture guidance based on the user's past gesture history. Some or all of the above processing in the gesture support unit may be performed using AI or not. For example, the gesture support unit can input the user's past gesture history data into a generating AI and have the generating AI optimize gesture guidance based on the gesture history.

[0061] The customization unit can optimize custom settings based on the user's past setting history. For example, the customization unit prioritizes providing settings that the user has frequently used in the past. It can also predict and provide settings that the user will use during specific time periods based on their past setting history. Furthermore, the customization unit can analyze the user's past setting history and provide the most efficient settings. This allows for the provision of optimal custom settings based on the user's past setting history. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's past setting history data into a generating AI and have the generating AI perform the optimization of custom settings based on the setting history.

[0062] The sound collection unit can optimize sound collection based on the user's past sound environment history. For example, the collection unit can provide the optimal sound collection method based on the sound environment of places the user has frequently visited in the past. The collection unit can also predict and optimize the sounds to be collected at specific time periods based on the user's past sound environment history. Furthermore, the collection unit can analyze the user's past sound environment history and provide the most efficient sound collection method. This enables the provision of an optimal sound collection method based on the user's past sound environment history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past sound environment history data into a generating AI and have the generating AI perform the optimization of the sound collection method based on the sound environment history.

[0063] The conversion unit can optimize the conversion from sound to text based on the user's past conversion history. For example, the conversion unit can prioritize providing conversion methods that the user has frequently used in the past. It can also predict and provide conversion methods to be used during specific time periods based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and provide the most efficient conversion method. This allows for the provision of the optimal sound-to-text conversion method based on the user's past conversion history. Some or all of the above-described processes in the conversion unit may be performed using AI or not. For example, the conversion unit can input the user's past conversion history data into a generating AI and have the generating AI perform the optimization of the sound-to-text conversion method based on the conversion history.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The audio guide unit provides voice descriptions of elements on the screen. For example, when a user touches a specific element on the screen, the audio guide unit provides a voice description of that element's content and function. The audio guide unit can provide voice descriptions of elements such as buttons, text fields, and images. Step 2: The gesture support unit supports gesture operations performed by the user on the screen. For example, if the user speaks the action they want to perform, the unit will provide voice guidance on the gesture operation to achieve that action based on the spoken content. The gesture support unit can support gesture operations such as scrolling, tapping, and swiping. Step 3: The customization section customizes the support content according to the user's settings. For example, the user can set the speed and volume of the voice guide and the font size of the text conversion to suit their preferences. The customization section can increase the speed of the voice guide, increase the volume, and increase the font size of the text conversion.

[0066] (Example of form 2) The AI ​​assistant for smartphone accessibility support according to an embodiment of the present invention is a system aimed at assisting users with visual or hearing impairments. This system has a voice guide unit that explains elements on the screen by voice, and when the user touches a specific element on the screen, it explains the content and function of that element by voice. It also has a gesture support unit that supports gesture operations performed by the user on the screen, and when the user speaks the operation they want to perform, it provides voice guidance on the gesture operation to achieve the desired operation based on the content of the speech. Furthermore, it has a collection unit that collects sounds around the user and a conversion unit that converts the collected sounds into text and displays it on a display unit, supporting the user's movement. Furthermore, it has a customization unit that customizes the content of the support according to the user's settings, and the speed and volume of the voice guide and the font size of the text conversion can be set according to the user's preferences. Finally, it also has a function to customize the content of the voice guide based on the user's current time of day and location. As a result, the AI ​​assistant for smartphone accessibility support can make smartphones easier to use for users with visual or hearing impairments.

[0067] The AI ​​assistant for smartphone accessibility support according to this embodiment comprises a voice guidance unit, a gesture support unit, and a customization unit. The voice guidance unit provides voice descriptions of elements on the screen. For example, when a user touches a specific element on the screen, the voice guidance unit provides voice descriptions of the content and function of that element. The voice guidance unit can provide voice descriptions of elements such as buttons, text fields, and images. The gesture support unit supports gesture operations performed by the user on the screen. For example, when a user speaks about an operation they want to perform, the gesture support unit provides voice guidance on the gesture operation to achieve the desired operation based on the spoken content. The gesture support unit can support gesture operations such as scrolling, tapping, and swiping. The customization unit customizes the support content according to the user's settings. For example, the customization unit can set the speed and volume of the voice guidance and the font size of the text conversion according to the user's preferences. The customization unit can, for example, increase the speed of the voice guidance, increase the volume, or increase the font size of the text conversion. This allows the AI ​​assistant for smartphone accessibility support according to the embodiment to make smartphones easier to use for users with visual or hearing impairments.

[0068] The voice guide unit provides voice descriptions of elements on the screen. For example, when a user touches a specific element on the screen, the voice guide unit provides a voice description of that element's content and function. Specifically, when a user moves their finger on the smartphone's touchscreen, the voice guide unit detects the element at the location of the finger and reads aloud the element's name and function. For example, when a user touches an icon on the home screen, the voice guide unit explains which application the icon represents. Similarly, when an option in the settings menu is touched, the voice guide unit provides voice guidance on the option's function and the results of selecting it. The voice guide unit can provide voice descriptions of elements such as buttons, text fields, and images. Furthermore, the voice guide unit can use AI to learn the user's operation history and context to provide more appropriate voice guidance. For example, for applications and functions frequently used by the user, it can omit detailed explanations and provide concise guidance. The voice guide unit is also multilingual and can provide voice guidance in different languages ​​depending on the user's settings. This allows the voice guide unit to assist visually impaired users in intuitively operating their smartphones, improving usability.

[0069] The gesture support unit assists users with gesture operations on the screen. For example, when a user speaks the desired operation, the gesture support unit provides voice guidance on the gesture operation to perform that operation based on the spoken content. Specifically, if a user says "scroll down," the gesture support unit will provide voice guidance on the scroll-down gesture operation, assisting the user in performing the operation. The gesture support unit can support gesture operations such as scrolling, tapping, and swiping. Furthermore, the gesture support unit uses AI to learn the user's gesture operation patterns, enabling it to recognize operations more accurately and quickly. For example, if a user frequently uses a particular gesture, it will prioritize recognizing that gesture, improving the response time of the operation. In addition, the gesture support unit can analyze the user's spoken content using natural language processing technology and handle complex operation instructions. For example, it can handle instructions that include multiple operations, such as "move to the next page and tap the first link," helping the user to operate smoothly. As a result, the gesture support unit can enable users with visual or hearing impairments to operate smartphones efficiently, improving accessibility.

[0070] The customization section customizes the support content according to the user's settings. For example, the customization section can set the speed and volume of the voice guide and the font size of the text conversion to suit the user's preferences. Specifically, if a user wants to speed up the voice guide, the customization section adjusts the speed through the settings menu, providing the voice guide at a speed that meets the user's request. Similarly, the volume can be adjusted according to the environment, such as lowering the volume in a quiet environment and increasing it in a noisy environment. Furthermore, the font size of the text conversion can be adjusted to a larger size depending on the degree of visual impairment. The customization section can learn the user's operation history and settings to provide support content that is optimal for each individual user. For example, if a user frequently changes a particular setting, that setting will be displayed preferentially and made easily accessible. The customization section can also collect user feedback and use it to improve the support content. For example, if the content of the voice guide or the guidance for gesture operation is difficult to understand, improvements will be made based on user feedback to provide a more user-friendly interface. In this way, the customization section can provide flexible support that meets the individual needs of users and improve the accessibility of smartphones.

[0071] The audio guide unit can provide audio explanations of the content and function of specific elements on the screen when the user touches them. For example, when the user touches a button, the audio guide unit will provide an audio explanation of that button's function. It can also provide audio explanations of the content of a text field when the user touches it. Furthermore, when the user touches an image, the audio guide unit can provide an audio explanation of that image. This makes it easier for users to understand the elements on the screen.

[0072] The gesture support unit can, when a user speaks the desired operation, provide voice guidance for the gesture operation to perform that operation based on the user's utterance. For example, if the user says "scroll," the gesture support unit will provide voice guidance for the scroll operation. It can also provide voice guidance for the tap operation if the user says "tap." Furthermore, if the user says "swipe," the gesture support unit can provide voice guidance for the swipe operation. This allows the user to control operations by voice. Some or all of the above-described processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input the user's utterance into a generating AI and have the generating AI execute guidance for the gesture operation based on the utterance.

[0073] The customization section allows users to set the speed and volume of the voice guide and the font size of the text conversion according to their preferences. For example, if the user wants to speed up the voice guide, the customization section can adjust the speed. Similarly, if the user wants to increase the volume of the voice guide, the customization section can adjust the volume. Furthermore, if the user wants to increase the font size of the text conversion, the customization section can adjust the font size. This allows users to change settings to their liking. Some or all of the above-described processes in the customization section may be performed using AI, for example, or without AI. For example, the customization section can input the user's settings into a generating AI and have the generating AI perform customizations based on those settings.

[0074] The AI ​​assistant for smartphone accessibility support according to this embodiment includes a collection unit that collects sounds around the user, and a conversion unit that converts the collected sounds into text and displays them on a display unit. The collection unit collects sounds around the user. For example, the collection unit can collect ambient sounds, conversation sounds, alarm sounds, etc. The collection unit collects ambient sounds using, for example, a microphone. The conversion unit converts the sounds collected by the collection unit into text and displays them on a display unit. For example, the conversion unit converts sounds into text using speech recognition technology. For example, the conversion unit can convert collected conversation sounds into text and display them on a display unit. The conversion unit can also convert collected alarm sounds into text and display them on a display unit. This allows the user to confirm ambient sounds in text. Some or all of the above-described processes in the collection unit and conversion unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the collected sound data into a generating AI and have the generating AI perform the conversion from sound to text.

[0075] The audio guide unit can customize the content of the audio guide based on the user's current time of day and location. For example, if the user is using it at night, the audio guide unit will provide an audio guide suitable for nighttime. Furthermore, if the user is in a specific location, the audio guide unit can provide location-related information in the audio guide. This allows for the provision of an audio guide tailored to the user's situation. Some or all of the above processing in the audio guide unit may be performed using AI, for example, or without AI. For example, the audio guide unit can input information about the user's current time of day and location into a generating AI and have the generating AI execute a customized audio guide.

[0076] The voice guide unit can estimate the user's emotions and adjust the tone and content of the voice guide based on the estimated emotions. For example, if the user is nervous, the voice guide unit can provide a calm tone of voice guide to give a sense of security. Conversely, if the user is relaxed, the voice guide unit can provide a bright tone of voice guide to create a friendly atmosphere. Furthermore, if the user is in a hurry, the voice guide unit can provide a quick and concise voice guide to ensure efficient guidance. This allows for the provision of voice guides tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the voice guide unit may be performed using AI, or not. For example, the voice guide unit can input user emotion data into the generative AI and have the generative AI adjust the tone and content of the voice guide based on the emotion.

[0077] The voice guidance unit can optimize the content of the voice guidance based on the user's past operation history. For example, the voice guidance unit can prioritize guiding users through functions they have frequently used in the past. Furthermore, the voice guidance unit can predict functions that the user will use during specific time periods based on their past operation history and guide users through them using voice guidance. In addition, the voice guidance unit can analyze the user's past operation history and guide users through the most efficient operation procedures using voice guidance. This allows the system to provide optimal voice guidance based on the user's past operation history. Some or all of the above-described processes in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can input the user's past operation history data into a generating AI and have the generating AI perform the optimization of the voice guidance based on the operation history.

[0078] The voice guidance unit can dynamically change the content of the voice guidance according to the user's current activity and situation. For example, if the user is walking, the voice guidance unit can provide voice guidance suitable for walking. It can also provide voice guidance suitable for driving if the user is driving a car. Furthermore, if the user is relaxing at home, the voice guidance unit can provide voice guidance suitable for relaxation. This allows for the provision of voice guidance tailored to the user's current situation. Some or all of the above processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can input data on the user's current activity and situation into a generating AI, and cause the generating AI to dynamically change the voice guidance according to the activity and situation.

[0079] The voice guidance unit can estimate the user's emotions and adjust the start timing of the voice guidance based on the estimated emotions. For example, if the user is feeling stressed, the voice guidance unit can delay the start of the voice guidance to provide time to relax. Conversely, if the user is relaxed, the voice guidance unit can start the voice guidance earlier to provide smoother guidance. Furthermore, if the user is in a hurry, the voice guidance unit can start the voice guidance immediately to provide quick guidance. This allows the voice guidance to be provided at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice guidance unit may be performed using AI, or not using AI. For example, the voice guidance unit can input user emotion data into the generative AI and have the generative AI adjust the start timing of the voice guidance based on the emotions.

[0080] The audio guide unit can customize the content of the audio guide based on the user's language settings and cultural background. For example, the audio guide unit can automatically set the language of the audio guide based on the user's language settings. The audio guide unit can also provide an audio guide using appropriate expressions and examples based on the user's cultural background. Furthermore, the audio guide unit can provide a language switching function if the user uses multiple languages. This allows for the provision of an audio guide tailored to the user's language settings and cultural background. Some or all of the above processing in the audio guide unit may be performed using AI, for example, or without AI. For example, the audio guide unit can input data on the user's language settings and cultural background into a generating AI, and have the generating AI customize the audio guide based on those settings and cultural background.

[0081] The voice guidance unit can switch the content of the voice guidance to an energy-saving mode depending on the battery level of the user's device. For example, if the device's battery level is low, the voice guidance unit can reduce the frequency of voice guidance and switch to energy-saving mode. The voice guidance unit can also provide normal voice guidance when the device's battery level is sufficient. Furthermore, if the device's battery level is very low, the voice guidance unit can provide only essential information through voice guidance. This allows the voice guidance to be provided in an energy-saving mode according to the device's battery level. Some or all of the above processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can input device battery level data into a generating AI and cause the generating AI to switch the voice guidance to an energy-saving mode according to the battery level.

[0082] The gesture support unit can estimate the user's emotions and adjust the gesture guidance method based on the estimated user emotions. For example, if the user is nervous, the gesture support unit can provide simple and easy-to-understand gesture guidance. If the user is relaxed, the gesture support unit can also provide detailed gesture guidance. Furthermore, if the user is in a hurry, the gesture support unit can provide quick and concise gesture guidance. This allows for the provision of gesture guidance that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the gesture support unit may be performed using AI, or not using AI. For example, the gesture support unit can input user emotion data into the generative AI and have the generative AI adjust the gesture guidance method based on the emotions.

[0083] The gesture support unit can optimize gesture guidance based on the user's past gesture history. For example, the gesture support unit prioritizes guiding the user to gestures they have frequently used in the past. The gesture support unit can also predict and guide the user to gestures they will use at specific times based on their past gesture history. Furthermore, the gesture support unit can analyze the user's past gesture history and guide the user to the most efficient procedure. This allows the unit to provide optimal gesture guidance based on the user's past gesture history. Some or all of the above processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input the user's past gesture history data into a generating AI and have the generating AI optimize gesture guidance based on the gesture history.

[0084] The gesture support unit can dynamically change the gesture guidance according to the user's current hand position and movement. For example, if the user's hand is on the left side of the screen, the gesture support unit will provide gesture guidance suitable for the left side. Furthermore, if the user's hand is moving quickly, the gesture support unit can provide rapid gesture guidance. Additionally, if the user's hand is in the center of the screen, the gesture support unit can provide gesture guidance suitable for the center. This allows the gesture support unit to provide gesture guidance tailored to the user's current hand position and movement. Some or all of the above processing in the gesture support unit may be performed using AI, or without AI. For example, the gesture support unit can input data on the user's hand position and movement into a generating AI, causing the generating AI to dynamically change the gesture guidance according to the hand position and movement.

[0085] The gesture support unit can estimate the user's emotions and determine the priority of gesture operations based on the estimated emotions. For example, if the user is tense, the gesture support unit will prioritize important gesture operations. If the user is relaxed, the gesture support unit can also guide the user to more detailed gesture operations. Furthermore, if the user is in a hurry, the gesture support unit can prioritize gesture operations that can be performed quickly. This allows the system to provide gesture operation guidance with priorities tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the gesture support unit may be performed using AI, or not. For example, the gesture support unit can input user emotion data into a generative AI and have the generative AI determine the priority of gesture operations based on emotions.

[0086] The gesture support unit can customize gesture guidance according to the screen size and resolution of the user's device. For example, if the user is using a smartphone, the gesture support unit can provide gesture guidance that is appropriate for the screen size. Furthermore, if the user is using a tablet, the gesture support unit can provide gesture guidance optimized for a larger screen. Additionally, if the user is using a smartwatch, the gesture support unit can provide concise and highly visible gesture guidance. This allows the gesture support unit to provide gesture guidance tailored to the screen size and resolution of the user's device. Some or all of the above processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input device screen size and resolution data into a generating AI and have the generating AI customize the gesture guidance according to the screen size and resolution.

[0087] The gesture support unit can optimize gesture guidance based on the sensor information of the user's device. For example, the gesture support unit can provide optimal gesture guidance based on the acceleration sensor information of the user's device. It can also provide optimal gesture guidance based on the gyroscope sensor information of the user's device. Furthermore, it can provide optimal gesture guidance based on the proximity sensor information of the user's device. This enables the provision of optimal gesture guidance based on the sensor information of the user's device. Some or all of the above processing in the gesture support unit may be performed using AI, for example, or without AI. For example, the gesture support unit can input the device's sensor information into a generating AI and have the generating AI perform the optimization of gesture guidance based on the sensor information.

[0088] The customization unit can estimate the user's emotions and dynamically change the customization settings based on the estimated emotions. For example, if the user is tense, the customization unit can provide an interface with calming colors. It can also provide an interface with bright colors if the user is relaxed. Furthermore, if the user is in a hurry, the customization unit can provide a simple and highly visible interface. This allows for the provision of customization settings that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI dynamically change the customization settings based on the emotions.

[0089] The customization unit can optimize custom settings based on the user's past setting history. For example, the customization unit prioritizes providing settings that the user has frequently used in the past. It can also predict and provide settings that the user will use during specific time periods based on their past setting history. Furthermore, the customization unit can analyze the user's past setting history and provide the most efficient settings. This allows for the provision of optimal custom settings based on the user's past setting history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's past setting history data into a generating AI and have the generating AI perform the optimization of custom settings based on the setting history.

[0090] The customization unit can dynamically change the customization settings according to the user's current usage and environment. For example, the customization unit can provide a bright screen setting when the user is outdoors. It can also provide a dark screen setting when the user is indoors. Furthermore, the customization unit can provide a night mode when the user is using it at night. This allows for the provision of optimal customization settings according to the user's current usage and environment. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data on the user's current usage and environment into a generating AI, and cause the generating AI to dynamically change the customization settings according to the usage and environment.

[0091] The customization unit can estimate the user's emotions and determine the priority of customization settings based on the estimated emotions. For example, if the user is stressed, the customization unit can prioritize important settings. It can also provide detailed settings if the user is relaxed. Furthermore, if the user is in a hurry, it can prioritize settings that can be executed quickly. This allows customization settings to be provided with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI determine the priority of customization settings based on emotions.

[0092] The customization unit can optimize the custom settings according to the hardware characteristics of the user's device. For example, if the user is using a smartphone, the customization unit can provide settings tailored to the device's hardware characteristics. It can also provide settings optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the customization unit can provide concise and highly visible settings. This allows for the provision of optimal custom settings tailored to the hardware characteristics of the user's device. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data on the device's hardware characteristics into a generating AI and have the generating AI perform the optimization of the custom settings according to the hardware characteristics.

[0093] The customization unit can optimize custom settings based on the user's application usage history. For example, the customization unit prioritizes providing settings for applications the user has frequently used in the past. It can also predict and provide settings to be used during specific time periods based on the user's application usage history. Furthermore, the customization unit can analyze the user's application usage history and provide the most efficient settings. This allows for the provision of optimal custom settings based on the user's application usage history. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's application usage history data into a generating AI and have the generating AI perform the optimization of custom settings based on the usage history.

[0094] The sound collection unit can estimate the user's emotions and adjust the sound collection method based on the estimated emotions. For example, if the user is tense, the collection unit can collect ambient sounds in more detail to provide reassuring information. If the user is relaxed, the collection unit can also collect ambient sounds in moderation to provide information that helps maintain relaxation. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important sounds to provide information quickly. This allows for the provision of an optimal sound collection method tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user emotion data into the generative AI and have the generative AI adjust the sound collection method based on the emotions.

[0095] The sound collection unit can optimize sound collection based on the user's past sound environment history. For example, the collection unit can provide the optimal sound collection method based on the sound environment of places the user has frequently visited in the past. The collection unit can also predict and optimize the sounds to be collected at specific time periods based on the user's past sound environment history. Furthermore, the collection unit can analyze the user's past sound environment history and provide the most efficient sound collection method. This enables the provision of an optimal sound collection method based on the user's past sound environment history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past sound environment history data into a generating AI and have the generating AI perform the optimization of the sound collection method based on the sound environment history.

[0096] The sound collection unit can dynamically change its sound collection method according to the user's current location and situation. For example, if the user is outdoors, the sound collection unit can adjust its sound collection method considering the surrounding noise. Furthermore, if the user is indoors, the sound collection unit can provide a sound collection method suitable for a quiet environment. Additionally, if the user is moving, the sound collection unit can provide a sound collection method suitable for movement. This allows for the provision of an optimal sound collection method tailored to the user's current location and situation. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input data on the user's current location and situation into a generating AI, causing the generating AI to dynamically change the sound collection method according to the location and situation.

[0097] The sound collection unit can estimate the user's emotions and determine the priority of sounds to collect based on the estimated emotions. For example, if the user is tense, the sound collection unit will prioritize collecting sounds that provide a sense of security. It can also prioritize collecting sounds that maintain relaxation if the user is relaxed. Furthermore, if the user is in a hurry, the sound collection unit can prioritize collecting important sounds. This allows for the collection of sounds with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sound collection unit may be performed using AI, or not. For example, the sound collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of sounds based on emotions.

[0098] The sound collection unit can optimize sound collection according to the microphone characteristics of the user's device. For example, if the user is using a smartphone, the sound collection unit can provide a sound collection method that matches the microphone characteristics of the device. Furthermore, if the user is using a tablet, the sound collection unit can provide a sound collection method optimized for larger devices. Additionally, if the user is using a smartwatch, the sound collection unit can provide a simple and highly visible sound collection method. This allows for the provision of an optimal sound collection method tailored to the microphone characteristics of the user's device. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input data on the device's microphone characteristics into a generating AI, causing the generating AI to optimize the sound collection method according to the microphone characteristics.

[0099] The sound collection unit can optimize sound collection based on the noise level around the user. For example, if the user is in a noisy environment, the sound collection unit can optimize sound collection using a noise-canceling function. The sound collection unit can also collect ambient sounds in detail if the user is in a quiet environment. Furthermore, if the user is moving, the sound collection unit can provide a sound collection method suitable for movement. This allows for the provision of an optimal sound collection method according to the noise level around the user. Some or all of the above processing in the sound collection unit may be performed using AI, for example, or without AI. For example, the sound collection unit can input ambient noise level data into a generating AI and have the generating AI optimize the sound collection method based on the noise level.

[0100] The conversion unit can estimate the user's emotions and adjust the sound-to-text conversion method based on the estimated emotions. For example, if the user is tense, the conversion unit can provide a concise and easy-to-understand text conversion. It can also provide a detailed text conversion if the user is relaxed. Furthermore, if the user is in a hurry, the conversion unit can provide a quick and concise text conversion. This allows for the provision of the optimal sound-to-text conversion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the conversion unit may be performed using AI, or not. For example, the conversion unit can input user emotion data into the generative AI and have the generative AI adjust the sound-to-text conversion method based on the emotions.

[0101] The conversion unit can optimize the conversion from sound to text based on the user's past conversion history. For example, the conversion unit can prioritize providing conversion methods that the user has frequently used in the past. It can also predict and provide conversion methods to be used during specific time periods based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and provide the most efficient conversion method. This allows for the provision of the optimal sound-to-text conversion method based on the user's past conversion history. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's past conversion history data into a generating AI and have the generating AI optimize the sound-to-text conversion method based on the conversion history.

[0102] The conversion unit can dynamically change the sound-to-text conversion according to the user's current sound environment. For example, if the user is in a noisy environment, the conversion unit can optimize the sound-to-text conversion using a noise-canceling function. The conversion unit can also provide a detailed sound-to-text conversion if the user is in a quiet environment. Furthermore, if the user is on the move, the conversion unit can provide a sound-to-text conversion suitable for movement. This allows the conversion unit to provide the optimal sound-to-text conversion method according to the user's current sound environment. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the user's current sound environment data into a generating AI, causing the generating AI to dynamically change the sound-to-text conversion method according to the sound environment.

[0103] The transformation unit can estimate the user's emotions and adjust the display method of the transformation results based on the estimated emotions. For example, if the user is tense, the transformation unit can provide a simple and highly visible display method. If the user is relaxed, the transformation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the transformation unit can provide a concise display method. This allows for the provision of the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the transformation unit may be performed using AI, for example, or not using AI. For example, the transformation unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the transformation results based on the emotions.

[0104] The conversion unit can customize the conversion from sound to text based on the user's language settings and cultural background. For example, the conversion unit can automatically set the language for the conversion from sound to text based on the user's language settings. The conversion unit can also provide a conversion from sound to text using appropriate expressions and examples based on the user's cultural background. Furthermore, the conversion unit can provide a language switching function if the user uses multiple languages. This allows the conversion unit to provide the optimal method of conversion from sound to text according to the user's language settings and cultural background. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input data on the user's language settings and cultural background into a generating AI and have the generating AI perform customization of the conversion method from sound to text based on the language settings and cultural background.

[0105] The conversion unit can optimize the conversion from sound to text according to the display characteristics of the user's device. For example, if the user is using a smartphone, the conversion unit can provide text conversion that matches the device's display characteristics. Furthermore, if the user is using a tablet, the conversion unit can provide text conversion optimized for a larger display. Additionally, if the user is using a smartwatch, the conversion unit can provide concise and highly legible text conversion. This allows for the provision of an optimal sound-to-text conversion method tailored to the display characteristics of the user's device. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input data on the device's display characteristics into a generating AI, causing the generating AI to optimize the sound-to-text conversion method according to the display characteristics.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The voice guide unit can estimate the user's emotions and adjust the content of the voice guide based on those emotions. For example, if the user is nervous, the voice guide unit can explain in a calm tone to provide reassurance. If the user is relaxed, the voice guide unit can explain in a bright tone to create a friendly atmosphere. Furthermore, if the user is in a hurry, the voice guide unit can provide quick and concise explanations to offer efficient guidance. This allows for the provision of optimal voice guidance tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice guide unit may be performed using AI or not. For example, the voice guide unit can input user emotion data into the generative AI and have the generative AI adjust the content of the voice guide based on those emotions.

[0108] The gesture support unit can estimate the user's emotions and adjust the gesture guidance method based on the estimated emotions. For example, if the user is nervous, the gesture support unit can provide simple and easy-to-understand gesture guidance. If the user is relaxed, it can also provide detailed gesture guidance. Furthermore, if the user is in a hurry, it can provide quick and concise gesture guidance. This allows for gesture guidance tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the gesture support unit may be performed using AI or not. For example, the gesture support unit can input user emotion data into the generative AI and have the generative AI adjust the gesture guidance method based on the emotions.

[0109] The customization unit can estimate the user's emotions and dynamically change the customization settings based on the estimated emotions. For example, if the user is tense, the customization unit can provide an interface with calming colors. If the user is relaxed, it can provide an interface with bright colors. Furthermore, if the user is in a hurry, it can provide a simple and highly visible interface. This allows for the provision of customization settings that correspond to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into a generative AI and have the generative AI dynamically change the customization settings based on the emotions.

[0110] The sound collection unit can estimate the user's emotions and adjust the sound collection method based on the estimated emotions. For example, if the user is tense, the collection unit can collect ambient sounds in more detail to provide reassuring information. If the user is relaxed, the collection unit can also collect ambient sounds in moderation to provide information that maintains relaxation. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important sounds to provide information quickly. This allows for the provision of an optimal sound collection method tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI adjust the sound collection method based on the emotions.

[0111] The conversion unit can estimate the user's emotions and adjust the sound-to-text conversion method based on the estimated emotions. For example, if the user is nervous, the conversion unit can provide a concise and easy-to-understand text conversion. If the user is relaxed, it can also provide a detailed text conversion. Furthermore, if the user is in a hurry, the conversion unit can provide a quick and concise text conversion. This allows for the provision of the optimal sound-to-text conversion method according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit can input user emotion data into a generative AI and have the generative AI adjust the sound-to-text conversion method based on the emotions.

[0112] The voice guidance unit can be optimized based on the user's past operation history. For example, the voice guidance unit can prioritize guiding users through functions they have frequently used in the past. It can also predict functions that the user will use during specific time periods based on their past operation history and guide them through those functions. Furthermore, the voice guidance unit can analyze the user's past operation history and guide users through the most efficient operation procedures. This allows for the provision of optimal voice guidance based on the user's past operation history. Some or all of the above processing in the voice guidance unit may be performed using AI or not. For example, the voice guidance unit can input the user's past operation history data into a generating AI and have the generating AI optimize the voice guidance based on the operation history.

[0113] The gesture support unit can optimize gesture guidance based on the user's past gesture history. For example, the gesture support unit prioritizes guiding the user to gestures they have frequently used in the past. Furthermore, the gesture support unit can predict and guide the user to gestures they will use at specific times based on their past gesture history. In addition, the gesture support unit can analyze the user's past gesture history and guide the user to the most efficient procedure. This allows the unit to provide optimal gesture guidance based on the user's past gesture history. Some or all of the above processing in the gesture support unit may be performed using AI or not. For example, the gesture support unit can input the user's past gesture history data into a generating AI and have the generating AI optimize gesture guidance based on the gesture history.

[0114] The customization unit can optimize custom settings based on the user's past setting history. For example, the customization unit prioritizes providing settings that the user has frequently used in the past. It can also predict and provide settings that the user will use during specific time periods based on their past setting history. Furthermore, the customization unit can analyze the user's past setting history and provide the most efficient settings. This allows for the provision of optimal custom settings based on the user's past setting history. Some or all of the above processes in the customization unit may be performed using AI or not. For example, the customization unit can input the user's past setting history data into a generating AI and have the generating AI perform the optimization of custom settings based on the setting history.

[0115] The sound collection unit can optimize sound collection based on the user's past sound environment history. For example, the collection unit can provide the optimal sound collection method based on the sound environment of places the user has frequently visited in the past. The collection unit can also predict and optimize the sounds to be collected at specific time periods based on the user's past sound environment history. Furthermore, the collection unit can analyze the user's past sound environment history and provide the most efficient sound collection method. This enables the provision of an optimal sound collection method based on the user's past sound environment history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's past sound environment history data into a generating AI and have the generating AI perform the optimization of the sound collection method based on the sound environment history.

[0116] The conversion unit can optimize the conversion from sound to text based on the user's past conversion history. For example, the conversion unit can prioritize providing conversion methods that the user has frequently used in the past. It can also predict and provide conversion methods to be used during specific time periods based on the user's past conversion history. Furthermore, the conversion unit can analyze the user's past conversion history and provide the most efficient conversion method. This allows for the provision of the optimal sound-to-text conversion method based on the user's past conversion history. Some or all of the above-described processes in the conversion unit may be performed using AI or not. For example, the conversion unit can input the user's past conversion history data into a generating AI and have the generating AI perform the optimization of the sound-to-text conversion method based on the conversion history.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The audio guide unit provides voice descriptions of elements on the screen. For example, when a user touches a specific element on the screen, the audio guide unit provides a voice description of that element's content and function. The audio guide unit can provide voice descriptions of elements such as buttons, text fields, and images. Step 2: The gesture support unit supports gesture operations performed by the user on the screen. For example, if the user speaks the action they want to perform, the unit will provide voice guidance on the gesture operation to achieve that action based on the spoken content. The gesture support unit can support gesture operations such as scrolling, tapping, and swiping. Step 3: The customization section customizes the support content according to the user's settings. For example, the user can set the speed and volume of the voice guide and the font size of the text conversion to suit their preferences. The customization section can increase the speed of the voice guide, increase the volume, and increase the font size of the text conversion.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] For example, the voice guidance unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the gesture support unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the data collection unit is implemented by the microphone 38B of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the conversion unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] For example, the voice guidance unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the gesture support unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the data collection unit is implemented by the microphone 238 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the conversion unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] For example, the voice guidance unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the gesture support unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the data collection unit is implemented by the microphone 238 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the conversion unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] For example, the voice guidance unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the gesture support unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the customization unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the data collection unit is implemented by the microphone 238 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the conversion unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A voice guide unit that explains the elements on the screen using voice, A gesture support unit that supports gesture operations performed by the user on the screen, It includes a customization unit that customizes the content of the support according to the user's settings. A system characterized by the following features. (Note 2) The aforementioned audio guide unit is When a user touches a specific element on the screen, the content and function of that element are explained in audio. The system described in Appendix 1, characterized by the features described herein. (Note 3) The gesture support unit is When a user speaks the action they want to perform, the system provides voice guidance on the gesture operation required to perform that action, based on the spoken content. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned customization unit is Users can customize the speed and volume of the voice guide and the font size of the text input to their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) A sound collection unit that collects sounds from the user's surroundings, The system includes a conversion unit that converts the sound collected by the collection unit into characters and displays them on a display unit. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned audio guide unit is Customize the audio guide content based on the user's current time of day and location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned audio guide unit is The system estimates the user's emotions and adjusts the tone and content of the voice guide based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned audio guide unit is The content of the voice guide is optimized based on the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned audio guide unit is The content of the audio guide is dynamically changed according to the user's current activities and situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned audio guide unit is It estimates the user's emotions and adjusts the start timing of the voice guide based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned audio guide unit is Customize the audio guide content based on the user's language settings and cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned audio guide unit is The audio guide content will switch to power-saving mode depending on the user's device's battery level. The system described in Appendix 1, characterized by the features described herein. (Note 13) The gesture support unit is It estimates the user's emotions and adjusts the gesture guidance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The gesture support unit is Optimize gesture guidance based on the user's past gesture history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The gesture support unit is The gesture control instructions are dynamically changed according to the user's current hand position and movement. The system described in Appendix 1, characterized by the features described herein. (Note 16) The gesture support unit is It estimates the user's emotions and determines the priority of gesture actions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The gesture support unit is Customize gesture control instructions according to the user's device screen size and resolution. The system described in Appendix 1, characterized by the features described herein. (Note 18) The gesture support unit is Optimize gesture control guidance based on sensor information from the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned customization unit is It estimates the user's emotions and dynamically changes customization settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned customization unit is Optimize customization settings based on the user's past settings history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned customization unit is Dynamically change customization settings based on the user's current usage and environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned customization unit is It estimates the user's emotions and prioritizes customization settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned customization unit is Optimize custom settings according to the hardware characteristics of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned customization unit is Optimize customization settings based on the user's application usage history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is The system estimates the user's emotions and adjusts the sound collection method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is Optimize sound collection based on the user's past sound environment history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is Dynamically change sound collection based on the user's current location and situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned collection unit is It estimates the user's emotions and determines the priority of sounds to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned collection unit is Optimize sound collection according to the microphone characteristics of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned collection unit is Optimize sound collection based on the noise level around the user. The system described in Appendix 1, characterized by the features described herein. (Note 31) The conversion unit is It estimates the user's emotions and adjusts the sound-to-text conversion method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The conversion unit is The conversion from sound to text is optimized based on the user's past conversion history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The conversion unit is The conversion from sound to text is dynamically changed according to the user's current sound environment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The conversion unit is It estimates the user's emotions and adjusts how the conversion results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The conversion unit is Customize the conversion from sound to text based on the user's language settings and cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 36) The conversion unit is The conversion from sound to text is optimized according to the display characteristics of the user's device. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A voice guide unit that explains the elements on the screen using voice, A gesture support unit that supports gesture operations performed by the user on the screen, It includes a customization unit that customizes the content of the support according to the user's settings. A system characterized by the following features.

2. The aforementioned audio guide unit is When a user touches a specific element on the screen, the content and function of that element are explained in audio. The system according to feature 1.

3. The gesture support unit is When a user speaks the action they want to perform, the system provides voice guidance on the gesture operation required to perform that action, based on the spoken content. The system according to feature 1.

4. The aforementioned customization unit is Users can customize the speed and volume of the voice guide and the font size of the text input to their preferences. The system according to feature 1.

5. A sound collection unit that collects sounds from the user's surroundings, The system includes a conversion unit that converts the sound collected by the collection unit into characters and displays them on a display unit. The system according to feature 1.

6. The aforementioned audio guide unit is Customize the audio guide content based on the user's current time of day and location. The system according to feature 1.

7. The aforementioned audio guide unit is The system estimates the user's emotions and adjusts the tone and content of the voice guide based on those emotions. The system according to feature 1.

8. The aforementioned audio guide unit is The content of the voice guide is optimized based on the user's past operation history. The system according to feature 1.

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