system

Abstract

The system according to this embodiment aims to enable senior users who are not comfortable with smartphone operation to complete operations using only voice commands. [Solution] The system according to the embodiment comprises a reception unit, a transmission unit, an adjustment unit, a reading unit, and a navigation unit. The reception unit receives voice instructions. The transmission unit transmits a message based on the voice instructions received by the reception unit. The adjustment unit adjusts the font size based on the voice instructions received by the reception unit. The reading unit reads the text aloud based on the voice instructions received by the reception unit. The navigation unit navigates the operation based on the voice instructions received by the reception unit.

Description

Technical Field

[0006] , , ,

[0005] , , ,

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for senior users who are not good at smartphone operations to perform operations easily.

[0005] The system according to the embodiment aims to enable senior users who are not good at smartphone operations to complete operations only by voice instructions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a transmission unit, an adjustment unit, a reading unit, and a navigation unit. The reception unit receives voice instructions. The transmission unit transmits a message based on the voice instructions received by the reception unit. The adjustment unit adjusts the font size based on the voice instructions received by the reception unit. The reading unit reads the text aloud based on the voice instructions received by the reception unit. The navigation unit navigates the operation based on the voice instructions received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment allows even senior users who are not comfortable with smartphone operation to complete operations using only voice commands. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system for a specific messaging app developed to fully support senior users who are not comfortable with smartphone operation. This AI agent system not only enables easy sending of voice messages, adjustment of font size, and text-to-speech, but also gently guides the user through operations, allowing them to complete operations solely through voice commands. It features a visually clear, interactive, and user-friendly interface. For example, if a user wants to send a voice message, the AI ​​agent system asks, "Who would you like to send this to?" and when the user answers with the recipient's name, the message is sent to that person. For example, if the user says, "I want to ask my son when he's coming over next," the AI ​​agent system confirms, "Shall I send a message to Makoto saying, 'When are you coming over next?'" and when the user answers, "Yes!", the message is sent. Next, with the font size adjustment function, when the user gives the voice command, "Make the text bigger," the AI ​​agent system adjusts the font size. Furthermore, with the text-to-speech function, when the user gives the command, "Read the message," the AI ​​agent system reads the message aloud in a natural voice. In addition, the AI ​​agent system learns the user's operation patterns and automatically optimizes the settings. For example, the system prioritizes displaying frequently used functions and simplifies operations. Furthermore, interactive guidance features allow the AI ​​agent system to provide visual instructions and assist with voice commands and operations. In this way, smartphones become easier for senior users to use, preventing isolation from the digital world. For instance, even users with impaired vision can send and receive messages using only voice commands, allowing them to enjoy communication without stress. It also strengthens connections with family members living far away, enabling them to confidently utilize digital technology. Ultimately, the AI ​​agent system enables senior users to operate their smartphones using only voice commands.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a transmission unit, an adjustment unit, a reading unit, and a navigation unit. The reception unit receives voice instructions. For example, the reception unit receives a voice instruction when a user says, "I want to send a message." The transmission unit sends a message based on the voice instruction received by the reception unit. For example, the transmission unit sends a message when a user says, "I want to ask my son when he's coming next." The adjustment unit adjusts the font size based on the voice instruction received by the reception unit. For example, the adjustment unit adjusts the font size when a user says, "Make the font bigger." The reading unit reads the text aloud based on the voice instruction received by the reception unit. For example, the reading unit reads the message aloud when a user says, "Read the message." The navigation unit navigates the user through operations based on the voice instructions received by the reception unit. For example, the navigation unit navigates the user through operations when a user says, "What should I do next?" As a result, the AI ​​agent system according to this embodiment allows senior users to operate their smartphones using only voice instructions. Some or all of the above-described processes in the reception unit, transmission unit, adjustment unit, reading unit, and navigation unit may be performed using AI, for example, or without AI. For example, the reception unit can input voice instructions into the AI ​​and have the AI ​​analyze the voice instructions. The transmission unit can have the AI ​​send messages. The adjustment unit can have the AI ​​adjust the font size. The reading unit can have the AI ​​read text aloud. The navigation unit can have the AI ​​navigate the user's actions.

[0030] The reception desk receives voice commands. Specifically, if a user says, "I want to send a message," the reception desk receives that voice command. The reception desk uses high-precision speech recognition technology to convert the user's voice into text data. This speech recognition technology has a noise-canceling function, which removes ambient noise and can clearly recognize the user's voice. Furthermore, the speech recognition engine learns the differences in the user's pronunciation and accent, achieving speech recognition optimized for each individual user. For example, if a user says, "I want to send a message," the reception desk analyzes the voice and recognizes the command "I want to send a message" as text data. This speech recognition process is performed in real time, allowing for an immediate response the moment the user gives a command. The reception desk is responsible for analyzing the content of the voice command and sending the command to the appropriate processing department. As a result, users can operate the system using only voice commands, providing a user-friendly interface, especially for senior users.

[0031] The sending unit transmits messages based on voice commands received by the receiving unit. Specifically, if the user says, "I want to ask my son when he's coming next," the sending unit will transmit that message. The sending unit uses natural language processing technology to analyze voice commands and generate appropriate messages. For example, it converts the user's voice command into text data and generates a message based on that text data. In this process, AI understands the context and generates appropriate message content. The generated message is sent to the recipient specified by the user. The sending unit can refer to the user's contact list to identify the recipient of the message. For example, if the user says, "to my son," the sending unit identifies the contact for "son" from the user's contact list and sends the message to that contact. The sending unit monitors the message transmission status in real time and provides feedback to the user on whether the transmission was successful. This allows the user to check the message transmission status and resend or correct the message as needed. The sending unit supports user communication by transmitting messages quickly and accurately based on the user's voice commands.

[0032] The adjustment unit adjusts the font size based on voice commands received by the reception unit. Specifically, if a user says "make the text larger," the adjustment unit adjusts the font size. The adjustment unit analyzes the voice command and generates instructions to change the font size of the user interface. In this process, AI understands the user's instructions and sets an appropriate font size. For example, if a user says "make the text larger," the adjustment unit checks the current font size and changes it to an appropriate size based on that. The adjustment unit can flexibly adjust the font size according to the user's visual needs. For example, for users with impaired vision, the font size can be increased to improve readability. The adjustment unit provides feedback to the user that the font size change has been applied, allowing the user to confirm the changes. This allows users to adjust the font size to suit their visual needs and use the system comfortably. The adjustment unit quickly and accurately adjusts the font size based on the user's voice commands, improving user convenience.

[0033] The text-to-speech unit reads text aloud based on voice commands received by the reception unit. Specifically, if a user says "Read the message," the unit reads that message aloud. The text-to-speech unit converts text data into speech using speech synthesis technology. This speech synthesis technology achieves natural pronunciation and intonation, generating voices that are easy for users to understand. For example, if a user says "Read the message," the text-to-speech unit retrieves the text data of that message and converts it into natural-sounding speech using a speech synthesis engine. The text-to-speech unit can read text at the appropriate time based on user instructions. For example, if a user says "Read the next message," the text-to-speech unit retrieves the next message, converts it into speech, and reads it aloud. The text-to-speech unit responds flexibly to user voice commands and quickly provides the information the user needs. Furthermore, the text-to-speech unit can adjust the speed and volume of the voice according to the user's preferences. This allows users to change the voice settings to suit their preferences and listen to information comfortably. The text-to-speech unit reads text quickly and accurately based on user voice commands, improving user convenience.

[0034] The navigation unit guides the user through the operation based on voice instructions received by the reception unit. Specifically, if the user says, "What should I do next?", the navigation unit will guide them through the operation. The navigation unit analyzes the user's voice instructions and generates instructions to guide the user through the next operation. In this process, AI understands the user's current situation and operation history to provide appropriate navigation. For example, if the user says, "What should I do next?", the navigation unit checks the current operation status and guides the user through the next operation. The navigation unit can provide visual and audio navigation according to the user interface guidelines. For example, by displaying the next operation steps on the screen and providing voice guidance simultaneously, the user can proceed with the operation without getting lost. The navigation unit responds flexibly to the user's voice instructions and quickly provides the information the user needs. Furthermore, the navigation unit can learn the user's operation history and provide navigation optimized for each individual user. This allows users to receive navigation tailored to their own operating style, making the system more comfortable to use. The navigation unit guides the user through the operation quickly and accurately based on voice commands, improving user convenience.

[0035] The learning unit can learn the user's operation patterns and automatically optimize settings. For example, the learning unit can prioritize displaying functions that the user frequently uses. For example, if the user says, "I want to send a message," the learning unit will prioritize accepting that voice command. For example, if the user says, "Make the text bigger," the learning unit will prioritize accepting that voice command. This makes operation easier by learning the user's operation patterns and optimizing settings. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's operation patterns into the AI ​​and have the AI ​​learn the operation patterns.

[0036] The guidance unit can provide visual instructions. For example, if a user asks, "What should I do next?", the guidance unit will visually guide them through the operation. For example, if a user says, "I want to send a message," the guidance unit will visually guide them through the operation. For example, if a user says, "Make the text bigger," the guidance unit will visually guide them through the operation. By providing visual instructions, the operation becomes easier to understand. Some or all of the above-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input visual instructions into AI and have AI generate the instructions.

[0037] The transmitting unit can send messages based on voice commands. For example, if the user says, "I want to ask my son when he's coming next," the transmitting unit will send that message. For example, if the user says, "I want to thank my friend," the transmitting unit will send that message. For example, if the user says, "I want to make an appointment with the doctor," the transmitting unit will send that message. This simplifies operation by sending messages based on voice commands. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or not using AI. For example, the transmitting unit can input voice commands into AI and have the AI ​​send the messages.

[0038] The adjustment unit can adjust the font size based on voice commands. For example, the adjustment unit adjusts the font size when the user says, "Make the text bigger." For example, the adjustment unit adjusts the font size when the user says, "Make the text smaller." For example, the adjustment unit adjusts the font size when the user says, "Make the text easier to read." By adjusting the font size based on voice commands, visibility is improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input voice commands to the AI ​​and have the AI ​​perform the font size adjustment.

[0039] The text-to-speech unit can read text aloud based on voice commands. For example, if the user says, "Read the message," the text-to-speech unit will read the message. For example, if the user says, "Read the news," the text-to-speech unit will read the news. For example, if the user says, "Read the weather forecast," the text-to-speech unit will read the weather forecast. This simplifies operation by reading text aloud based on voice commands. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input voice commands into AI and have the AI ​​perform the text-to-speech.

[0040] The navigation unit can guide operations based on voice commands. For example, if the user says, "What should I do next?", the navigation unit will guide the user through the operation. For example, if the user says, "I want to send a message," the navigation unit will guide the user through the operation. For example, if the user says, "Make the text larger," the navigation unit will guide the user through the operation. This simplifies operation by guiding users through operations based on voice commands. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input voice commands into AI and have the AI ​​perform the operation navigation.

[0041] The reception unit can analyze the user's past voice instruction history and select the optimal reception method. For example, the reception unit prioritizes receiving voice instructions that the user has frequently used in the past. For example, the reception unit can predict and receive instructions to be used during a specific time period based on the user's past voice instruction history. For example, the reception unit can analyze the user's past voice instruction history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past voice instruction history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the voice instruction history into AI and have the AI ​​perform the history analysis.

[0042] The reception unit can filter voice commands based on the user's current situation and areas of interest. For example, the reception unit prioritizes receiving voice commands that are highly relevant to the user's current situation. For example, the reception unit filters and receives relevant voice commands based on the user's areas of interest. For example, the reception unit receives the most appropriate voice command considering the user's current situation and areas of interest. This streamlines the operation by filtering voice commands based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0043] The reception unit can prioritize receiving highly relevant instructions by considering the user's geographical location when receiving voice instructions. For example, if the user is in a specific location, the reception unit will prioritize receiving voice instructions related to that location. For example, the reception unit will filter and receive highly relevant voice instructions based on the user's geographical location. For example, if the user is on the move, the reception unit will receive the most appropriate voice instructions based on the user's current location. In this way, by considering geographical location, highly relevant instructions can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​perform the filtering of highly relevant instructions.

[0044] The reception unit can analyze the user's social media activity and receive relevant instructions when it receives a voice command. For example, the reception unit can analyze the user's social media activity and prioritize receiving relevant voice commands. For example, the reception unit can receive relevant voice commands based on what the user has mentioned on social media. For example, the reception unit can receive the most appropriate voice command considering the user's social media activity. In this way, relevant commands can be received by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into AI and have AI perform the task of receiving relevant commands.

[0045] The sending unit can adjust the level of detail of a message based on the importance of the recipient. For example, the sending unit sends a detailed message to an important recipient. For example, it sends a concise message to a general recipient. The sending unit adjusts the level of detail of the message according to the importance of the recipient. This allows the message to be sent with the appropriate level of detail by adjusting the level of detail based on the importance of the recipient. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the importance of the recipient into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the message.

[0046] The transmission unit can apply different transmission algorithms depending on the category of the message content when sending a message. For example, the transmission unit may apply a security-enhanced transmission algorithm to messages containing important information. For example, the transmission unit may apply a standard transmission algorithm to messages containing general information. The transmission unit may select the optimal transmission algorithm depending on the category of the message content. This allows the transmission unit to send messages using the appropriate algorithm by applying the appropriate algorithm according to the category of the message content. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit may input the category of the message content to the AI ​​and have the AI ​​perform the application of the transmission algorithm.

[0047] The sending unit can determine the priority of message transmission based on the recipient's submission timing when sending a message. For example, the sending unit will send messages to urgent recipients with priority. For example, the sending unit will send messages to general recipients with normal priority. The sending unit will determine the priority of transmission based on the recipient's submission timing. This allows messages to be sent with appropriate priority by determining the priority of transmission based on the recipient's submission timing. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the recipient's submission timing into AI and have AI perform the determination of the transmission priority.

[0048] The sending unit can adjust the order of messages based on the relevance of their content when sending messages. For example, the sending unit may prioritize sending messages containing important content. For example, the sending unit may send messages containing general content in the normal order. The sending unit adjusts the order of messages based on the relevance of their content. This allows messages to be sent in an appropriate order by adjusting the order of messages based on the relevance of their content. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the relevance of the message content into the AI ​​and have the AI ​​perform the adjustment of the sending order.

[0049] The adjustment unit can select the optimal font size by considering the user's visual acuity information when adjusting the font size. For example, the adjustment unit selects the optimal font size based on the user's visual acuity information. For example, if the user's visual acuity is poor, the adjustment unit increases the font size. For example, if the user's visual acuity is good, the adjustment unit maintains the normal font size. In this way, the optimal font size can be selected by considering the user's visual acuity information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's visual acuity information into AI and have AI perform the selection of the optimal font size.

[0050] The adjustment unit can apply different adjustment algorithms depending on the user's usage when adjusting the font size. For example, if the user uses the font frequently, the adjustment unit may increase the font size to improve readability. If the user uses the font infrequently, the adjustment unit may maintain the normal font size. The adjustment unit may apply the optimal font size adjustment algorithm depending on the user's usage. This allows the adjustment unit to provide the optimal font size according to the user's usage. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's usage into the AI ​​and have the AI ​​execute the application of the adjustment algorithm.

[0051] The adjustment unit can select the optimal font size by considering the user's device information when adjusting the font size. For example, if the user is using a smartphone, the adjustment unit will select a font size that matches the screen size. For example, if the user is using a tablet, the adjustment unit will select a font size optimized for a large screen. For example, if the user is using a smartwatch, the adjustment unit will select a font size that is highly visible. In this way, the adjustment unit can provide the optimal font size by considering the user's device information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's device information into the AI ​​and have the AI ​​perform the selection of the optimal font size.

[0052] The adjustment unit can improve the accuracy of font size adjustments by referring to the user's usage history. For example, the adjustment unit selects the optimal font size based on the user's past usage history. For example, the adjustment unit improves the accuracy of adjustments by referring to font sizes the user has adjusted in the past. For example, the adjustment unit analyzes the user's usage history and selects the most suitable font size. This improves the accuracy of adjustments by referring to the user's usage history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's usage history into AI and have AI perform the adjustment accuracy improvement.

[0053] The text-to-speech unit can adjust the level of detail in the reading based on the importance of the content being read. For example, the reading unit will provide detailed reading for text containing important information. For example, the reading unit will provide concise reading for text containing general information. The reading unit adjusts the level of detail in the reading according to the importance of the content being read. This allows the text to be read with an appropriate level of detail by adjusting the level of detail based on the importance of the content being read. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the importance of the content being read into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the reading.

[0054] The text-to-speech unit can apply different reading algorithms depending on the category of the content being read. For example, the text-to-speech unit applies a precise reading algorithm to text containing important information. For example, the text-to-speech unit applies a normal reading algorithm to text containing general information. For example, the text-to-speech unit selects the optimal reading algorithm depending on the category of the content being read. This allows the text to be read using an appropriate algorithm by applying the optimal reading algorithm according to the category of the content being read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the category of the content being read into the AI ​​and have the AI ​​perform the application of the reading algorithm.

[0055] The text-to-speech unit can determine the reading priority based on the submission date of the content to be read. For example, the text-to-speech unit may prioritize reading text containing urgent content. For example, the text-to-speech unit may read text containing general content with normal priority. The text-to-speech unit may determine the reading priority based on the submission date of the content to be read. This allows the text to be read with appropriate priority by determining the priority based on the submission date of the content to be read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the submission date of the content to be read into the AI ​​and have the AI ​​perform the determination of the reading priority.

[0056] The text-to-speech unit can adjust the reading order based on the relevance of the content being read. For example, the text-to-speech unit may prioritize reading text containing important information. For example, the text-to-speech unit may read text containing general information in the normal order. The text-to-speech unit may adjust the reading order based on the relevance of the content being read. This allows the text to be read in an appropriate order by adjusting the order based on the relevance of the content being read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the relevance of the content being read into the AI ​​and have the AI ​​perform the adjustment of the reading order.

[0057] The navigation unit can select the optimal navigation method by referring to the user's past operation history during navigation. For example, the navigation unit selects the optimal method based on the navigation methods the user has used in the past. For example, the navigation unit proposes the most efficient navigation method from the user's past operation history. For example, the navigation unit analyzes the user's past operation history and selects the optimal navigation method. In this way, the optimal navigation method can be selected by referring to the user's past operation history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's operation history into AI and have the AI ​​perform the selection of the optimal navigation method.

[0058] The navigation unit can customize the navigation method based on the user's current situation during navigation. For example, the navigation unit provides the user with the optimal navigation method according to their current situation. For example, the navigation unit customizes the navigation method considering the user's current situation. For example, the navigation unit selects the optimal navigation method based on the user's current situation. This allows for navigation using the appropriate method by customizing the navigation method based on the user's current situation. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's current situation into AI and have AI perform the customization of the navigation method.

[0059] The navigation unit can select the optimal navigation method when navigating, taking into account the user's geographical location information. For example, if the user is in a specific location, the navigation unit provides a navigation method relevant to that location. For example, the navigation unit selects the optimal navigation method based on the user's geographical location information. For example, if the user is on the move, the navigation unit provides the optimal navigation method based on the user's current location. This allows the navigation unit to select the optimal navigation method by taking into account the user's geographical location information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's geographical location information into AI and have the AI ​​select the optimal navigation method.

[0060] The navigation unit can analyze the user's social media activity and suggest navigation methods during navigation. For example, the navigation unit analyzes the user's social media activity and suggests relevant navigation methods. For example, the navigation unit provides the optimal navigation method based on what the user has mentioned on social media. For example, the navigation unit suggests navigation methods considering the user's social media activity. In this way, the optimal navigation method can be suggested by analyzing the user's social media activity. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media activity into AI and have AI perform the navigation method suggestion.

[0061] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can optimize the learning algorithm by referring to the user's past learning history. For example, the learning unit can analyze past learning data and apply the most efficient learning algorithm. In this way, the optimal learning algorithm can be applied by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0062] The learning unit can weight the training data based on the timing of voice command submissions during training. For example, the learning unit may assign a higher weight to training data based on urgent voice commands. For example, the learning unit may assign a normal weight to training data based on general voice commands. For example, the learning unit may adjust the weighting of the training data based on the timing of voice command submissions. This allows for training with appropriate weighting by weighting the training data based on the timing of voice command submissions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit may input the timing of voice command submissions into the AI ​​and have the AI ​​perform the weighting of the training data.

[0063] The guidance unit can select the optimal display method by referring to the user's past operation history when displaying visual instructions. For example, the guidance unit displays the optimal instructions based on the display method the user has used in the past. For example, the guidance unit suggests the most efficient display method from the user's past operation history. For example, the guidance unit analyzes the user's past operation history and displays the optimal instructions. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's operation history into AI and have AI perform the selection of the optimal display method.

[0064] The guidance unit can select the optimal display method when displaying visual instructions, taking into account the user's device information. For example, if the user is using a smartphone, the guidance unit will display instructions that are adapted to the screen size. If the user is using a tablet, the guidance unit will display instructions optimized for a larger screen. If the user is using a smartwatch, the guidance unit will display concise and highly visible instructions. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

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

[0066] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, it can prioritize receiving voice commands that the user has frequently used in the past. It can also predict and receive commands that the user will use during specific time periods based on their past voice command history. Furthermore, it can analyze the user's past voice command history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the voice command history into AI and have the AI ​​perform the history analysis.

[0067] The sending unit can adjust the level of detail of a message based on the importance of the recipient. For example, it can send a detailed message to an important recipient, and a concise message to a general recipient. Furthermore, it can adjust the level of detail of the message according to the importance of the recipient. This allows the message to be sent with an appropriate level of detail by adjusting the level of detail based on the importance of the recipient. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the importance of the recipient into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the message.

[0068] The adjustment unit can select the optimal font size by considering the user's visual acuity information when adjusting the font size. For example, it can select the optimal font size based on the user's visual acuity information. Furthermore, if the user's visual acuity is poor, the adjustment unit can increase the font size. Moreover, if the user's visual acuity is good, the adjustment unit can maintain the normal font size. In this way, the optimal font size can be selected by considering the user's visual acuity information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's visual acuity information into AI and have AI perform the selection of the optimal font size.

[0069] The text-to-speech unit can adjust the level of detail in the text based on the importance of the content being read. For example, it can read text in detail for important content, and concisely for general content. Furthermore, it can adjust the level of detail according to the importance of the content being read. This allows the text to be read at an appropriate level of detail by adjusting the level of detail based on the importance of the content being read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the importance of the content being read into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the text.

[0070] The navigation unit can select the optimal navigation method by referring to the user's past operation history during navigation. For example, it can select the optimal method based on navigation methods the user has used in the past. It can also suggest the most efficient navigation method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and select the optimal navigation method. In this way, the optimal navigation method can be selected by referring to the user's past operation history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit can input the user's operation history into AI and have the AI ​​perform the selection of the optimal navigation method.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk receives voice commands. For example, if a user says, "I want to send a message," the reception desk receives that voice command. Step 2: The transmitting unit sends a message based on the voice command received by the receiving unit. For example, if the user says, "I want to ask my son when he's coming next," the transmitting unit will send that message. Step 3: The adjustment unit adjusts the font size based on the voice instructions received by the reception unit. For example, if the user says "make the text bigger," the adjustment unit adjusts the font size. Step 4: The reading unit reads the text aloud based on the voice instructions received by the reception unit. For example, if the user says "Read the message," the unit will read the message aloud. Step 5: The navigation unit guides the user through the operation based on the voice instructions received by the reception unit. For example, if the user says, "What should I do next?", the navigation unit will guide them through the operation.

[0073] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for a specific messaging app developed to fully support senior users who are not comfortable with smartphone operation. This AI agent system not only enables easy sending of voice messages, adjustment of font size, and text-to-speech, but also gently guides the user through operations, allowing them to complete operations solely through voice commands. It features a visually clear, interactive, and user-friendly interface. For example, if a user wants to send a voice message, the AI ​​agent system asks, "Who would you like to send this to?" and when the user answers with the recipient's name, the message is sent to that person. For example, if the user says, "I want to ask my son when he's coming over next," the AI ​​agent system confirms, "Shall I send a message to Makoto saying, 'When are you coming over next?'" and when the user answers, "Yes!", the message is sent. Next, with the font size adjustment function, when the user gives the voice command, "Make the text bigger," the AI ​​agent system adjusts the font size. Furthermore, with the text-to-speech function, when the user gives the command, "Read the message," the AI ​​agent system reads the message aloud in a natural voice. In addition, the AI ​​agent system learns the user's operation patterns and automatically optimizes the settings. For example, the system prioritizes displaying frequently used functions and simplifies operations. Furthermore, interactive guidance features allow the AI ​​agent system to provide visual instructions and assist with voice commands and operations. In this way, smartphones become easier for senior users to use, preventing isolation from the digital world. For instance, even users with impaired vision can send and receive messages using only voice commands, allowing them to enjoy communication without stress. It also strengthens connections with family members living far away, enabling them to confidently utilize digital technology. Ultimately, the AI ​​agent system enables senior users to operate their smartphones using only voice commands.

[0074] The AI ​​agent system according to this embodiment comprises a reception unit, a transmission unit, an adjustment unit, a reading unit, and a navigation unit. The reception unit receives voice instructions. For example, the reception unit receives a voice instruction when a user says, "I want to send a message." The transmission unit sends a message based on the voice instruction received by the reception unit. For example, the transmission unit sends a message when a user says, "I want to ask my son when he's coming next." The adjustment unit adjusts the font size based on the voice instruction received by the reception unit. For example, the adjustment unit adjusts the font size when a user says, "Make the font bigger." The reading unit reads the text aloud based on the voice instruction received by the reception unit. For example, the reading unit reads the message aloud when a user says, "Read the message." The navigation unit navigates the user through operations based on the voice instructions received by the reception unit. For example, the navigation unit navigates the user through operations when a user says, "What should I do next?" As a result, the AI ​​agent system according to this embodiment allows senior users to operate their smartphones using only voice instructions. Some or all of the above-described processes in the reception unit, transmission unit, adjustment unit, reading unit, and navigation unit may be performed using AI, for example, or without AI. For example, the reception unit can input voice instructions into the AI ​​and have the AI ​​analyze the voice instructions. The transmission unit can have the AI ​​send messages. The adjustment unit can have the AI ​​adjust the font size. The reading unit can have the AI ​​read text aloud. The navigation unit can have the AI ​​navigate the user's actions.

[0075] The reception desk receives voice commands. Specifically, if a user says, "I want to send a message," the reception desk receives that voice command. The reception desk uses high-precision speech recognition technology to convert the user's voice into text data. This speech recognition technology has a noise-canceling function, which removes ambient noise and can clearly recognize the user's voice. Furthermore, the speech recognition engine learns the differences in the user's pronunciation and accent, achieving speech recognition optimized for each individual user. For example, if a user says, "I want to send a message," the reception desk analyzes the voice and recognizes the command "I want to send a message" as text data. This speech recognition process is performed in real time, allowing for an immediate response the moment the user gives a command. The reception desk is responsible for analyzing the content of the voice command and sending the command to the appropriate processing department. As a result, users can operate the system using only voice commands, providing a user-friendly interface, especially for senior users.

[0076] The sending unit transmits messages based on voice commands received by the receiving unit. Specifically, if the user says, "I want to ask my son when he's coming next," the sending unit will transmit that message. The sending unit uses natural language processing technology to analyze voice commands and generate appropriate messages. For example, it converts the user's voice command into text data and generates a message based on that text data. In this process, AI understands the context and generates appropriate message content. The generated message is sent to the recipient specified by the user. The sending unit can refer to the user's contact list to identify the recipient of the message. For example, if the user says, "to my son," the sending unit identifies the contact for "son" from the user's contact list and sends the message to that contact. The sending unit monitors the message transmission status in real time and provides feedback to the user on whether the transmission was successful. This allows the user to check the message transmission status and resend or correct the message as needed. The sending unit supports user communication by transmitting messages quickly and accurately based on the user's voice commands.

[0077] The adjustment unit adjusts the font size based on voice commands received by the reception unit. Specifically, if a user says "make the text larger," the adjustment unit adjusts the font size. The adjustment unit analyzes the voice command and generates instructions to change the font size of the user interface. In this process, AI understands the user's instructions and sets an appropriate font size. For example, if a user says "make the text larger," the adjustment unit checks the current font size and changes it to an appropriate size based on that. The adjustment unit can flexibly adjust the font size according to the user's visual needs. For example, for users with impaired vision, the font size can be increased to improve readability. The adjustment unit provides feedback to the user that the font size change has been applied, allowing the user to confirm the changes. This allows users to adjust the font size to suit their visual needs and use the system comfortably. The adjustment unit quickly and accurately adjusts the font size based on the user's voice commands, improving user convenience.

[0078] The text-to-speech unit reads text aloud based on voice commands received by the reception unit. Specifically, if a user says "Read the message," the unit reads that message aloud. The text-to-speech unit converts text data into speech using speech synthesis technology. This speech synthesis technology achieves natural pronunciation and intonation, generating voices that are easy for users to understand. For example, if a user says "Read the message," the text-to-speech unit retrieves the text data of that message and converts it into natural-sounding speech using a speech synthesis engine. The text-to-speech unit can read text at the appropriate time based on user instructions. For example, if a user says "Read the next message," the text-to-speech unit retrieves the next message, converts it into speech, and reads it aloud. The text-to-speech unit responds flexibly to user voice commands and quickly provides the information the user needs. Furthermore, the text-to-speech unit can adjust the speed and volume of the voice according to the user's preferences. This allows users to change the voice settings to suit their preferences and listen to information comfortably. The text-to-speech unit reads text quickly and accurately based on user voice commands, improving user convenience.

[0079] The navigation unit guides the user through the operation based on voice instructions received by the reception unit. Specifically, if the user says, "What should I do next?", the navigation unit will guide them through the operation. The navigation unit analyzes the user's voice instructions and generates instructions to guide the user through the next operation. In this process, AI understands the user's current situation and operation history to provide appropriate navigation. For example, if the user says, "What should I do next?", the navigation unit checks the current operation status and guides the user through the next operation. The navigation unit can provide visual and audio navigation according to the user interface guidelines. For example, by displaying the next operation steps on the screen and providing voice guidance simultaneously, the user can proceed with the operation without getting lost. The navigation unit responds flexibly to the user's voice instructions and quickly provides the information the user needs. Furthermore, the navigation unit can learn the user's operation history and provide navigation optimized for each individual user. This allows users to receive navigation tailored to their own operating style, making the system more comfortable to use. The navigation unit guides the user through the operation quickly and accurately based on voice commands, improving user convenience.

[0080] The learning unit can learn the user's operation patterns and automatically optimize settings. For example, the learning unit can prioritize displaying functions that the user frequently uses. For example, if the user says, "I want to send a message," the learning unit will prioritize accepting that voice command. For example, if the user says, "Make the text bigger," the learning unit will prioritize accepting that voice command. This makes operation easier by learning the user's operation patterns and optimizing settings. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's operation patterns into the AI ​​and have the AI ​​learn the operation patterns.

[0081] The guidance unit can provide visual instructions. For example, if a user asks, "What should I do next?", the guidance unit will visually guide them through the operation. For example, if a user says, "I want to send a message," the guidance unit will visually guide them through the operation. For example, if a user says, "Make the text bigger," the guidance unit will visually guide them through the operation. By providing visual instructions, the operation becomes easier to understand. Some or all of the above-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input visual instructions into AI and have AI generate the instructions.

[0082] The transmitting unit can send messages based on voice commands. For example, if the user says, "I want to ask my son when he's coming next," the transmitting unit will send that message. For example, if the user says, "I want to thank my friend," the transmitting unit will send that message. For example, if the user says, "I want to make an appointment with the doctor," the transmitting unit will send that message. This simplifies operation by sending messages based on voice commands. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or not using AI. For example, the transmitting unit can input voice commands into AI and have the AI ​​send the messages.

[0083] The adjustment unit can adjust the font size based on voice commands. For example, the adjustment unit adjusts the font size when the user says, "Make the text bigger." For example, the adjustment unit adjusts the font size when the user says, "Make the text smaller." For example, the adjustment unit adjusts the font size when the user says, "Make the text easier to read." By adjusting the font size based on voice commands, visibility is improved. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input voice commands to the AI ​​and have the AI ​​perform the font size adjustment.

[0084] The text-to-speech unit can read text aloud based on voice commands. For example, if the user says, "Read the message," the text-to-speech unit will read the message. For example, if the user says, "Read the news," the text-to-speech unit will read the news. For example, if the user says, "Read the weather forecast," the text-to-speech unit will read the weather forecast. This simplifies operation by reading text aloud based on voice commands. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input voice commands into AI and have the AI ​​perform the text-to-speech.

[0085] The navigation unit can guide operations based on voice commands. For example, if the user says, "What should I do next?", the navigation unit will guide the user through the operation. For example, if the user says, "I want to send a message," the navigation unit will guide the user through the operation. For example, if the user says, "Make the text larger," the navigation unit will guide the user through the operation. This simplifies operation by guiding users through operations based on voice commands. Some or all of the above-described processes in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input voice commands into AI and have the AI ​​perform the operation navigation.

[0086] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the AI ​​in the reception unit will delay the timing of voice command reception, giving the user time to relax. For example, if the user is relaxed, the AI ​​in the reception unit will speed up the timing of voice command reception, facilitating smooth operation. For example, if the user is in a hurry, the AI ​​in the reception unit will make the timing of voice command reception immediate, enabling quick operation. In this way, operation becomes smoother by adjusting the timing of voice command reception 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into the AI ​​and have the AI ​​perform emotion estimation.

[0087] The reception unit can analyze the user's past voice instruction history and select the optimal reception method. For example, the reception unit prioritizes receiving voice instructions that the user has frequently used in the past. For example, the reception unit can predict and receive instructions to be used during a specific time period based on the user's past voice instruction history. For example, the reception unit can analyze the user's past voice instruction history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past voice instruction history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the voice instruction history into AI and have the AI ​​perform the history analysis.

[0088] The reception unit can filter voice commands based on the user's current situation and areas of interest. For example, the reception unit prioritizes receiving voice commands that are highly relevant to the user's current situation. For example, the reception unit filters and receives relevant voice commands based on the user's areas of interest. For example, the reception unit receives the most appropriate voice command considering the user's current situation and areas of interest. This streamlines the operation by filtering voice commands based on the user's current situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0089] The reception unit can estimate the user's emotions and determine the priority of voice instructions to receive based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize important voice instructions. For example, if the user is relaxed, the reception unit will prioritize normal voice instructions. For example, if the user is in a hurry, the reception unit will prioritize urgent voice instructions. This ensures that important instructions are received preferentially by prioritizing voice instructions 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 reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0090] The reception unit can prioritize receiving highly relevant instructions by considering the user's geographical location when receiving voice instructions. For example, if the user is in a specific location, the reception unit will prioritize receiving voice instructions related to that location. For example, the reception unit will filter and receive highly relevant voice instructions based on the user's geographical location. For example, if the user is on the move, the reception unit will receive the most appropriate voice instructions based on the user's current location. In this way, by considering geographical location, highly relevant instructions can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into AI and have the AI ​​perform the filtering of highly relevant instructions.

[0091] The reception unit can analyze the user's social media activity and receive relevant instructions when it receives a voice command. For example, the reception unit can analyze the user's social media activity and prioritize receiving relevant voice commands. For example, the reception unit can receive relevant voice commands based on what the user has mentioned on social media. For example, the reception unit can receive the most appropriate voice command considering the user's social media activity. In this way, relevant commands can be received by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity into AI and have AI perform the task of receiving relevant commands.

[0092] The sending unit can estimate the user's emotions and adjust the way messages are expressed based on the estimated emotions. For example, if the user is relaxed, the sending unit will send a message using polite language. If the user is in a hurry, the sending unit will send a message using concise language. If the user is excited, the sending unit will send a message using language that reflects those emotions. This allows the sending unit to deliver messages with appropriate language by adjusting the way messages are expressed 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-described processes in the sending unit may be performed using AI or not. For example, the sending unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0093] The sending unit can adjust the level of detail of a message based on the importance of the recipient. For example, the sending unit sends a detailed message to an important recipient. For example, it sends a concise message to a general recipient. The sending unit adjusts the level of detail of the message according to the importance of the recipient. This allows the message to be sent with the appropriate level of detail by adjusting the level of detail based on the importance of the recipient. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the importance of the recipient into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the message.

[0094] The transmission unit can apply different transmission algorithms depending on the category of the message content when sending a message. For example, the transmission unit may apply a security-enhanced transmission algorithm to messages containing important information. For example, the transmission unit may apply a standard transmission algorithm to messages containing general information. The transmission unit may select the optimal transmission algorithm depending on the category of the message content. This allows the transmission unit to send messages using the appropriate algorithm by applying the appropriate algorithm according to the category of the message content. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit may input the category of the message content to the AI ​​and have the AI ​​perform the application of the transmission algorithm.

[0095] The sending unit can estimate the user's emotions and adjust the length of the message based on the estimated emotions. For example, if the user is relaxed, the sending unit will send a longer message. For example, if the user is in a hurry, the sending unit will send a shorter message. For example, if the user is excited, the sending unit will send a message of a length that reflects their emotions. This allows for the sending of messages of appropriate length by adjusting the message length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI or not. For example, the sending unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0096] The sending unit can determine the priority of message transmission based on the recipient's submission timing when sending a message. For example, the sending unit will send messages to urgent recipients with priority. For example, the sending unit will send messages to general recipients with normal priority. The sending unit will determine the priority of transmission based on the recipient's submission timing. This allows messages to be sent with appropriate priority by determining the priority of transmission based on the recipient's submission timing. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the recipient's submission timing into AI and have AI perform the determination of the transmission priority.

[0097] The sending unit can adjust the order of messages based on the relevance of their content when sending messages. For example, the sending unit may prioritize sending messages containing important content. For example, the sending unit may send messages containing general content in the normal order. The sending unit adjusts the order of messages based on the relevance of their content. This allows messages to be sent in an appropriate order by adjusting the order of messages based on the relevance of their content. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the relevance of the message content into the AI ​​and have the AI ​​perform the adjustment of the sending order.

[0098] The adjustment unit can estimate the user's emotions and change the font size adjustment method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit can increase the font size to improve readability. For example, if the user is relaxed, the adjustment unit can maintain the normal font size. For example, if the user is in a hurry, the adjustment unit can decrease the font size to increase the amount of information displayed. This improves readability by changing the font size adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0099] The adjustment unit can select the optimal font size by considering the user's visual acuity information when adjusting the font size. For example, the adjustment unit selects the optimal font size based on the user's visual acuity information. For example, if the user's visual acuity is poor, the adjustment unit increases the font size. For example, if the user's visual acuity is good, the adjustment unit maintains the normal font size. In this way, the optimal font size can be selected by considering the user's visual acuity information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's visual acuity information into AI and have AI perform the selection of the optimal font size.

[0100] The adjustment unit can apply different adjustment algorithms depending on the user's usage when adjusting the font size. For example, if the user uses the font frequently, the adjustment unit may increase the font size to improve readability. If the user uses the font infrequently, the adjustment unit may maintain the normal font size. The adjustment unit may apply the optimal font size adjustment algorithm depending on the user's usage. This allows the adjustment unit to provide the optimal font size according to the user's usage. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's usage into the AI ​​and have the AI ​​execute the application of the adjustment algorithm.

[0101] The adjustment unit can estimate the user's emotions and determine the frequency of font size adjustments based on the estimated emotions. For example, if the user is stressed, the adjustment unit adjusts the font size frequently. For example, if the user is relaxed, the adjustment unit adjusts the font size at a normal frequency. For example, if the user is in a hurry, the adjustment unit reduces the frequency of font size adjustments. This improves readability by determining the frequency of font size adjustments 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0102] The adjustment unit can select the optimal font size by considering the user's device information when adjusting the font size. For example, if the user is using a smartphone, the adjustment unit will select a font size that matches the screen size. For example, if the user is using a tablet, the adjustment unit will select a font size optimized for a large screen. For example, if the user is using a smartwatch, the adjustment unit will select a font size that is highly visible. In this way, the adjustment unit can provide the optimal font size by considering the user's device information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's device information into the AI ​​and have the AI ​​perform the selection of the optimal font size.

[0103] The adjustment unit can improve the accuracy of font size adjustments by referring to the user's usage history. For example, the adjustment unit selects the optimal font size based on the user's past usage history. For example, the adjustment unit improves the accuracy of adjustments by referring to font sizes the user has adjusted in the past. For example, the adjustment unit analyzes the user's usage history and selects the most suitable font size. This improves the accuracy of adjustments by referring to the user's usage history. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's usage history into AI and have AI perform the adjustment accuracy improvement.

[0104] The text-to-speech unit can estimate the user's emotions and adjust its reading style based on the estimated emotions. For example, if the user is relaxed, the text-to-speech unit will read in a calm voice. If the user is in a hurry, the text-to-speech unit will read quickly and concisely. If the user is excited, the text-to-speech unit will read with expressions that reflect those emotions. By adjusting the reading style according to the user's emotions, the text can be read with appropriate expression. 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 text-to-speech unit may be performed using AI, or not using AI. For example, the text-to-speech unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0105] The text-to-speech unit can adjust the level of detail in the reading based on the importance of the content being read. For example, the reading unit will provide detailed reading for text containing important information. For example, the reading unit will provide concise reading for text containing general information. The reading unit adjusts the level of detail in the reading according to the importance of the content being read. This allows the text to be read with an appropriate level of detail by adjusting the level of detail based on the importance of the content being read. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the importance of the content being read into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the reading.

[0106] The text-to-speech unit can apply different reading algorithms depending on the category of the content being read. For example, the text-to-speech unit applies a precise reading algorithm to text containing important information. For example, the text-to-speech unit applies a normal reading algorithm to text containing general information. For example, the text-to-speech unit selects the optimal reading algorithm depending on the category of the content being read. This allows the text to be read using an appropriate algorithm by applying the optimal reading algorithm according to the category of the content being read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the category of the content being read into the AI ​​and have the AI ​​perform the application of the reading algorithm.

[0107] The text-to-speech unit can estimate the user's emotions and adjust the reading speed based on the estimated emotions. For example, if the user is relaxed, the text-to-speech unit will read slowly. For example, if the user is in a hurry, the text-to-speech unit will read quickly. For example, if the user is excited, the text-to-speech unit will read at a speed that reflects the emotion. This allows the text to be read at an appropriate speed by adjusting the reading speed 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 text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0108] The text-to-speech unit can determine the reading priority based on the submission date of the content to be read. For example, the text-to-speech unit may prioritize reading text containing urgent content. For example, the text-to-speech unit may read text containing general content with normal priority. The text-to-speech unit may determine the reading priority based on the submission date of the content to be read. This allows the text to be read with appropriate priority by determining the priority based on the submission date of the content to be read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the submission date of the content to be read into the AI ​​and have the AI ​​perform the determination of the reading priority.

[0109] The text-to-speech unit can adjust the reading order based on the relevance of the content being read. For example, the text-to-speech unit may prioritize reading text containing important information. For example, the text-to-speech unit may read text containing general information in the normal order. The text-to-speech unit may adjust the reading order based on the relevance of the content being read. This allows the text to be read in an appropriate order by adjusting the order based on the relevance of the content being read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the relevance of the content being read into the AI ​​and have the AI ​​perform the adjustment of the reading order.

[0110] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, if the user is relaxed, the navigation unit provides a gentle navigation method. For example, if the user is in a hurry, the navigation unit provides a fast navigation method. For example, if the user is excited, the navigation unit provides an emotion-reflecting navigation method. This allows for appropriate navigation by adjusting the navigation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI or not using AI. For example, the navigation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0111] The navigation unit can select the optimal navigation method by referring to the user's past operation history during navigation. For example, the navigation unit selects the optimal method based on the navigation methods the user has used in the past. For example, the navigation unit proposes the most efficient navigation method from the user's past operation history. For example, the navigation unit analyzes the user's past operation history and selects the optimal navigation method. In this way, the optimal navigation method can be selected by referring to the user's past operation history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's operation history into AI and have the AI ​​perform the selection of the optimal navigation method.

[0112] The navigation unit can customize the navigation method based on the user's current situation during navigation. For example, the navigation unit provides the user with the optimal navigation method according to their current situation. For example, the navigation unit customizes the navigation method considering the user's current situation. For example, the navigation unit selects the optimal navigation method based on the user's current situation. This allows for navigation using the appropriate method by customizing the navigation method based on the user's current situation. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's current situation into AI and have AI perform the customization of the navigation method.

[0113] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated emotions. For example, if the user is stressed, the navigation unit will prioritize important navigation. For example, if the user is relaxed, the navigation unit will provide normal navigation. For example, if the user is in a hurry, the navigation unit will prioritize urgent navigation. This allows for the priority of important navigation by determining navigation priorities 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 navigation unit may be performed using AI or not using AI. For example, the navigation unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0114] The navigation unit can select the optimal navigation method when navigating, taking into account the user's geographical location information. For example, if the user is in a specific location, the navigation unit provides a navigation method relevant to that location. For example, the navigation unit selects the optimal navigation method based on the user's geographical location information. For example, if the user is on the move, the navigation unit provides the optimal navigation method based on the user's current location. This allows the navigation unit to select the optimal navigation method by taking into account the user's geographical location information. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's geographical location information into AI and have the AI ​​select the optimal navigation method.

[0115] The navigation unit can analyze the user's social media activity and suggest navigation methods during navigation. For example, the navigation unit analyzes the user's social media activity and suggests relevant navigation methods. For example, the navigation unit provides the optimal navigation method based on what the user has mentioned on social media. For example, the navigation unit suggests navigation methods considering the user's social media activity. In this way, the optimal navigation method can be suggested by analyzing the user's social media activity. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media activity into AI and have AI perform the navigation method suggestion.

[0116] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit will select training data with calming content. For example, if the user is in a hurry, the learning unit will select data that allows for rapid learning. For example, if the user is excited, the learning unit will select training data that reflects those emotions. This allows for learning with appropriate data by selecting training data 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0117] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can optimize the learning algorithm by referring to the user's past learning history. For example, the learning unit can analyze past learning data and apply the most efficient learning algorithm. In this way, the optimal learning algorithm can be applied by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0118] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit increases the learning frequency when the user is relaxed. For example, the learning unit decreases the learning frequency when the user is in a hurry. For example, the learning unit learns at a frequency that reflects the emotion when the user is excited. In this way, learning can be performed at an appropriate frequency by adjusting the learning frequency 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 learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0119] The learning unit can weight the training data based on the timing of voice command submissions during training. For example, the learning unit may assign a higher weight to training data based on urgent voice commands. For example, the learning unit may assign a normal weight to training data based on general voice commands. For example, the learning unit may adjust the weighting of the training data based on the timing of voice command submissions. This allows for training with appropriate weighting by weighting the training data based on the timing of voice command submissions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit may input the timing of voice command submissions into the AI ​​and have the AI ​​perform the weighting of the training data.

[0120] The guidance unit can estimate the user's emotions and adjust the display method of the visual guide based on the estimated user emotions. For example, if the user is relaxed, the guidance unit will display a guide in calm colors. If the user is in a hurry, the guidance unit will display a concise and highly visible guide. If the user is excited, the guidance unit will display a guide that reflects the emotion. This allows the guidance to be provided in an appropriate way by adjusting the display method of the visual guide 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 guidance unit may be performed using AI or not using AI. For example, the guidance unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0121] The guidance unit can select the optimal display method by referring to the user's past operation history when displaying visual instructions. For example, the guidance unit displays the optimal instructions based on the display method the user has used in the past. For example, the guidance unit suggests the most efficient display method from the user's past operation history. For example, the guidance unit analyzes the user's past operation history and displays the optimal instructions. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's operation history into AI and have AI perform the selection of the optimal display method.

[0122] The guidance unit can estimate the user's emotions and adjust the visual guide's operating procedures based on the estimated user emotions. For example, if the user is relaxed, the guidance unit provides detailed operating procedures. For example, if the user is in a hurry, the guidance unit provides concise operating procedures. For example, if the user is excited, the guidance unit provides operating procedures that reflect the emotion. This allows the guidance to be provided with appropriate procedures by adjusting the visual guide's operating procedures 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 guidance unit may be performed using AI or not using AI. For example, the guidance unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0123] The guidance unit can select the optimal display method when displaying visual instructions, taking into account the user's device information. For example, if the user is using a smartphone, the guidance unit will display instructions that are adapted to the screen size. If the user is using a tablet, the guidance unit will display instructions optimized for a larger screen. If the user is using a smartwatch, the guidance unit will display concise and highly visible instructions. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

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

[0125] The reception unit can estimate the user's emotions and adjust the timing of voice command reception based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of voice command reception, giving the user time to relax. If the user is relaxed, the reception unit can speed up the timing of voice command reception, facilitating smooth operation. Furthermore, if the user is in a hurry, the reception unit can make the timing of voice command reception instantaneous, enabling quick operation. In this way, adjusting the timing of voice command reception according to the user's emotions makes operation smoother. 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0126] The sending unit can estimate the user's emotions and adjust the message delivery method based on the estimated emotions. For example, if the user is relaxed, the sending unit can send a message using polite language. If the user is in a hurry, the sending unit can send a message using concise language. Furthermore, if the user is excited, the sending unit can send a message using language that reflects those emotions. In this way, by adjusting the message delivery method according to the user's emotions, the message can be delivered with appropriate language. 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 sending unit may be performed using AI, or not using AI. For example, the sending unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0127] The adjustment unit can estimate the user's emotions and change the font size adjustment method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit can increase the font size to improve readability. If the user is relaxed, the adjustment unit can maintain the normal font size. Furthermore, if the user is in a hurry, the adjustment unit can decrease the font size to increase the amount of information. In this way, readability is improved by changing the font size adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0128] The text-to-speech unit can estimate the user's emotions and adjust its reading style based on the estimated emotions. For example, if the user is relaxed, the text-to-speech unit can read in a calm voice. If the user is in a hurry, the text-to-speech unit can read quickly and concisely. Furthermore, if the user is excited, the text-to-speech unit can read with expressions that reflect those emotions. By adjusting the reading style according to the user's emotions, the text can be read with appropriate expression. 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 text-to-speech unit may be performed using AI, or not using AI. For example, the text-to-speech unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0129] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, if the user is relaxed, the navigation unit can provide a gentle navigation method. If the user is in a hurry, the navigation unit can provide a fast navigation method. Furthermore, if the user is excited, the navigation unit can provide a navigation method that reflects those emotions. This allows for appropriate navigation by adjusting the navigation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, or not using AI. For example, the navigation unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0130] The reception unit can analyze the user's past voice command history and select the optimal reception method. For example, it can prioritize receiving voice commands that the user has frequently used in the past. It can also predict and receive commands that the user will use during specific time periods based on their past voice command history. Furthermore, it can analyze the user's past voice command history and select the most efficient reception method. In this way, the optimal reception method can be selected by analyzing the past voice command history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the voice command history into AI and have the AI ​​perform the history analysis.

[0131] The sending unit can adjust the level of detail of a message based on the importance of the recipient. For example, it can send a detailed message to an important recipient, and a concise message to a general recipient. Furthermore, it can adjust the level of detail of the message according to the importance of the recipient. This allows the message to be sent with an appropriate level of detail by adjusting the level of detail based on the importance of the recipient. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can input the importance of the recipient into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the message.

[0132] The adjustment unit can select the optimal font size by considering the user's visual acuity information when adjusting the font size. For example, it can select the optimal font size based on the user's visual acuity information. Furthermore, if the user's visual acuity is poor, the adjustment unit can increase the font size. Moreover, if the user's visual acuity is good, the adjustment unit can maintain the normal font size. In this way, the optimal font size can be selected by considering the user's visual acuity information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's visual acuity information into AI and have AI perform the selection of the optimal font size.

[0133] The text-to-speech unit can adjust the level of detail in the text based on the importance of the content being read. For example, it can read text in detail for important content, and concisely for general content. Furthermore, it can adjust the level of detail according to the importance of the content being read. This allows the text to be read at an appropriate level of detail by adjusting the level of detail based on the importance of the content being read. Some or all of the above processing in the text-to-speech unit may be performed using AI, for example, or without AI. For example, the text-to-speech unit can input the importance of the content being read into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the text.

[0134] The navigation unit can select the optimal navigation method by referring to the user's past operation history during navigation. For example, it can select the optimal method based on navigation methods the user has used in the past. It can also suggest the most efficient navigation method based on the user's past operation history. Furthermore, it can analyze the user's past operation history and select the optimal navigation method. In this way, the optimal navigation method can be selected by referring to the user's past operation history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without using AI. For example, the navigation unit can input the user's operation history into AI and have the AI ​​perform the selection of the optimal navigation method.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The reception desk receives voice commands. For example, if a user says, "I want to send a message," the reception desk receives that voice command. Step 2: The transmitting unit sends a message based on the voice command received by the receiving unit. For example, if the user says, "I want to ask my son when he's coming next," the transmitting unit will send that message. Step 3: The adjustment unit adjusts the font size based on the voice instructions received by the reception unit. For example, if the user says "make the text bigger," the adjustment unit adjusts the font size. Step 4: The reading unit reads the text aloud based on the voice instructions received by the reception unit. For example, if the user says "Read the message," the unit will read the message aloud. Step 5: The navigation unit guides the user through the operation based on the voice instructions received by the reception unit. For example, if the user says, "What should I do next?", the navigation unit will guide them through the operation.

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, transmission unit, adjustment unit, reading unit, navigation unit, learning unit, and guidance unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives voice instructions using the microphone 38B of the smart device 14 and processes them using the control unit 46A. The transmission unit transmits messages using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the font size using the control unit 46A of the smart device 14. The reading unit reads the text aloud using the speaker 40B of the smart device 14. The navigation unit navigates the operation using the display 40A of the smart device 14. The learning unit learns the user's operation patterns using the specific processing unit 290 of the data processing unit 12 and optimizes the settings. The guidance unit provides visual guidance using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0154] 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.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0156] Each of the multiple elements described above, including the reception unit, transmission unit, adjustment unit, reading unit, navigation unit, learning unit, and guidance unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives voice instructions using the microphone 238 of the smart glasses 214 and processes them with the control unit 46A. The transmission unit transmits messages using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the font size using the control unit 46A of the smart glasses 214. The reading unit reads the text aloud using the speaker 240 of the smart glasses 214. The navigation unit navigates the operation using the display of the smart glasses 214. The learning unit learns the user's operation patterns using the specific processing unit 290 of the data processing unit 12 and optimizes the settings. The guidance unit provides visual guidance using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0172] Each of the multiple elements described above, including the reception unit, transmission unit, adjustment unit, reading unit, navigation unit, learning unit, and guidance unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives voice instructions using the microphone 238 of the headset terminal 314 and processes them using the control unit 46A. The transmission unit transmits messages using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the character size using the control unit 46A of the headset terminal 314. The reading unit reads the text aloud using the speaker 240 of the headset terminal 314. The navigation unit navigates the operation using the display 343 of the headset terminal 314. The learning unit learns the user's operation patterns using the specific processing unit 290 of the data processing unit 12 and optimizes the settings. The guidance unit provides visual guidance using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0187] 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.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0189] Each of the multiple elements described above, including the reception unit, transmission unit, adjustment unit, reading unit, navigation unit, learning unit, and guidance unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice instructions using the microphone 238 of the robot 414 and processes them using the control unit 46A. The transmission unit transmits messages using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the character size using the control unit 46A of the robot 414. The reading unit reads the text aloud using the speaker 240 of the robot 414. The navigation unit navigates the operation using the display of the robot 414. The learning unit learns the user's operation patterns using the specific processing unit 290 of the data processing unit 12 and optimizes the settings. The guidance unit provides visual guidance using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A reception desk that accepts voice commands, A transmitting unit that transmits a message based on a voice instruction received by the aforementioned receiving unit, An adjustment unit that adjusts the font size based on voice instructions received by the reception unit, A reading unit that reads out text based on voice instructions received by the reception unit, The system includes a navigation unit that guides the user through operations based on voice instructions received by the reception unit. A system characterized by the following features. (Note 2) It also features a learning unit that learns user operation patterns and automatically optimizes settings. The system described in Appendix 1, characterized by the features described herein. (Note 3) It also includes a guidance section that provides visual instructions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned transmitting unit Send a message based on voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, Adjust font size based on voice instructions The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reading unit, The text will be read aloud based on the voice instructions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned navigation unit is, Navigate operations based on voice instructions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice command acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving voice commands, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of voice commands to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving voice commands, the system prioritizes receiving commands that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving voice commands, the system analyzes the user's social media activity and accepts relevant commands. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned transmitting unit It estimates the user's emotions and adjusts the way messages are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned transmitting unit When sending a message, adjust the level of detail based on the recipient's importance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned transmitting unit When sending a message, different sending algorithms are applied depending on the category of the message content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned transmitting unit It estimates the user's emotions and adjusts the length of the message based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned transmitting unit When sending a message, the sending priority is determined based on the recipient's submission schedule. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned transmitting unit When sending messages, the sending order is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, It estimates the user's emotions and changes the font size adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, When adjusting font size, the optimal size is selected considering the user's visual acuity information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, When adjusting font size, different adjustment algorithms are applied depending on the user's usage. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, The system estimates the user's emotions and determines the frequency of adjusting the font size based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, When adjusting font size, the optimal size is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, When adjusting font size, the system references the user's usage history to improve the accuracy of the adjustment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reading unit, It estimates the user's emotions and adjusts the way the text is read aloud based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reading unit, When reading text aloud, adjust the level of detail based on the importance of the content being read. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reading unit, When text-to-speech, different reading algorithms are applied depending on the category of the content being read. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reading unit, It estimates the user's emotions and adjusts the reading speed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reading unit, When reading text aloud, the priority of the text to be read is determined based on when the content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reading unit, When reading text aloud, the reading order is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned navigation unit is, It estimates the user's emotions and adjusts the navigation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned navigation unit is, During navigation, the system selects the optimal navigation method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned navigation unit is, During navigation, customize the navigation method based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned navigation unit is, It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned navigation unit is, During navigation, the system selects the optimal navigation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned navigation unit is, During navigation, the system analyzes the user's social media activity and suggests navigation methods. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned learning unit, During training, the training data is weighted based on when the voice instructions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned guidance unit, It estimates the user's emotions and adjusts how the visual guide is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned guidance unit, When displaying visual instructions, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned guidance unit, It estimates the user's emotions and adjusts the operation steps of the visual guide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned guidance unit, When displaying visual instructions, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 reception desk that accepts voice commands, A transmitting unit that transmits a message based on a voice instruction received by the aforementioned receiving unit, An adjustment unit that adjusts the font size based on voice instructions received by the reception unit, A reading unit that reads out text based on voice instructions received by the reception unit, The system includes a navigation unit that guides the user through operations based on voice instructions received by the reception unit. A system characterized by the following features.

2. It also features a learning unit that learns user operation patterns and automatically optimizes settings. The system according to feature 1.

3. It also includes a guidance section that provides visual instructions. The system according to feature 1.

4. The aforementioned transmitting unit Send a message based on voice commands. The system according to feature 1.

5. The adjustment unit is, Adjust font size based on voice instructions The system according to feature 1.

6. The aforementioned reading unit, The text will be read aloud based on the voice instructions. The system according to feature 1.

7. The aforementioned navigation unit is, Navigate operations based on voice instructions. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of voice command acceptance based on the estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is The system analyzes the user's past voice command history and selects the optimal reception method. The system according to feature 1.

10. The aforementioned reception unit is When receiving voice commands, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

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