system

Abstract

The system according to this embodiment aims to generate interfaces tailored to the engineer's level and knowledge using natural language. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a generated screen unit. The reception unit receives instructions in natural language. The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The execution unit executes the commands generated by the generation unit. The generated screen unit generates a user-specific settings screen based on information from the device.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that inexperienced engineers feel difficulty in handling CUI.

[0005] The system according to the embodiment aims to generate an interface adapted to the level and knowledge of engineers using natural language.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an execution unit, and a generated screen unit. The reception unit receives instructions in natural language. The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The execution unit executes the commands generated by the generation unit. The generated screen unit generates a user-specific settings screen based on information from the device. [Effects of the Invention]

[0007] The system according to this embodiment can generate interfaces tailored to the engineer's level and knowledge using natural language. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The interface generation system according to an embodiment of the present invention is a system that generates an interface tailored to the user's (engineer's) level, knowledge, and preferences using natural language. This interface generation system allows the user to input instructions in natural language, and the generating AI analyzes these instructions to generate and execute CUI-level commands. It can also generate a user-specific configuration screen based on information from the equipment. This makes it easy for engineers unfamiliar with CUI to operate the system. For example, if a user instructs, "Create a screen to set the VLAN IDs for all ports and display the current VLAN ID settings," the generating AI analyzes the instruction and generates appropriate commands. The generated commands are executed using a CUI-based system, and the results are output. The output results are recognized by the AI, and a configuration screen is generated. This configuration screen can be customized to suit the user's level and preferences. Furthermore, the same configuration screen can be provided for different models and manufacturers, and it can be used even with low-spec network equipment. Additionally, screens that have been configured can be saved as favorites and reused. This improves the engineer's work efficiency and prevents time loss due to typos and spelling errors. This system aims to improve the current situation where the command-line interface (CUI) is the main interface and eliminate the inconveniences of the graphical user interface (GUI). By utilizing AI-generated interfaces, it provides interfaces tailored to user needs, creating an environment that is easy for all system engineers to use. As a result, the interface generation system can provide interfaces that match the user's level, knowledge, and preferences.

[0029] The interface generation system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a generation screen unit. The reception unit receives instructions in natural language. These instructions include, but are not limited to, speech instructions and text instructions. The reception unit converts speech instructions to text using, for example, speech recognition technology. The reception unit also provides a text input interface, allowing the user to directly input text instructions. The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The generation unit analyzes the instructions using, for example, natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates complex commands based on user instructions using a generation AI. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze user instructions and generate appropriate commands. The generation unit can also generate commands including conditional branching based on user instructions using the generation AI. The execution unit executes the commands generated by the generation unit. The execution unit provides, for example, an environment for executing shell commands or scripts. The execution unit executes the generated commands and retrieves the results. For example, the execution unit executes commands in a shell environment and obtains their output. The execution unit can also execute scripts and save the results to a log file. The screen generation unit generates a user-specific settings screen based on information from the device. For example, the screen generation unit obtains sensor data and log information from the device and generates a settings screen based on it. The screen generation unit uses a generation AI to customize the settings screen to suit the user's level and preferences. For example, the screen generation unit adjusts the layout and colors of the settings screen based on the user's profile information and past settings history. Furthermore, the screen generation unit can provide the same settings screen for different models and manufacturers. For example, the screen generation unit generates a common settings screen that corresponds to different brands and models of devices. As a result, the interface generation system according to the embodiment can provide an interface tailored to the user's level, knowledge, and preferences. Some or all of the above-described processes in the screen generation unit may be performed using AI, for example, or without using AI.For example, the screen generation unit can input sensor data acquired from the device into the generation AI, and have the generation AI execute the generation of the settings screen.

[0030] The reception unit accepts instructions in natural language. These instructions include, but are not limited to, voice and text instructions. The reception unit converts voice instructions to text using, for example, speech recognition technology. Specifically, deep learning-based speech recognition models are often used as speech recognition technology. These models achieve high-precision speech recognition by learning from large amounts of voice data. When a voice instruction is input, the speech recognition model analyzes the voice data and converts it into corresponding text data. The reception unit also provides a text input interface, allowing users to directly input text instructions. This text input interface is provided, for example, as a form on a web browser or an input field in a mobile app. This allows users to give instructions to the system not only using voice but also using text. Furthermore, the reception unit analyzes the intent of the input text instructions using natural language processing technology. For example, morphological analysis is used to divide the text into words, and grammatical analysis is used to analyze the sentence structure. This allows the reception unit to accurately understand the user's instructions and pass them on to the next processing step.

[0031] The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The generation unit analyzes the instructions using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Specifically, morphological analysis divides the input text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and identifies sentence elements such as subject, predicate, and object. Semantic analysis understands the meaning of the sentence and grasps the user's intent. The generation unit uses a generation AI to generate complex commands based on the user's instructions. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's instructions and generate appropriate commands. LLM can understand and generate natural language by learning from large amounts of text data. If the user instructs "Copy files and create a backup," the generation AI analyzes this instruction and generates a specific shell command such as "cp file1 file2." The generation unit can also use the generation AI to generate commands that include conditional branching based on the user's instructions. For example, in response to an instruction such as "If there is little disk space left, delete old files and then create a backup," the generating AI will produce a complex command like "if [ $(df / | grep -v Filesystem | awk '{print $4}') -lt 100000 ]; then rm oldfile; fi; cp file1 file2". This allows the generation unit to analyze the user's natural language instructions and convert them into concrete commands.

[0032] The execution unit executes the commands generated by the generation unit. The execution unit provides an environment for executing, for example, shell commands or scripts. Specifically, the execution unit executes commands using a Linux or Windows shell environment. If the generated command is a shell command, the execution unit executes that command in the shell environment and retrieves its output. For example, if a command like "ls -l" is generated, the execution unit executes this command and retrieves a list of files in a directory. The execution unit can also execute scripts and save the results to a log file. For example, if a Python script is generated, the execution unit executes the script using a Python interpreter and saves the results to a log file. Furthermore, the execution unit can parse the execution results of commands and pass them to the next processing step. For example, if the command execution result is an error, the execution unit parses the error message and performs appropriate error handling. This ensures that the execution unit reliably executes the generated commands and processes the results appropriately.

[0033] The screen generation unit generates a user-specific settings screen based on information from the device. For example, it acquires sensor data and log information from the device and generates a settings screen based on that. Specifically, it acquires data from the device's temperature and humidity sensors and adjusts the layout and content of the settings screen based on that data. For example, if the device temperature is high, the screen generation unit will prioritize displaying the cooling fan settings. Furthermore, the screen generation unit uses generation AI to customize the settings screen to suit the user's level and preferences. For example, it adjusts the layout and colors of the settings screen based on the user's profile information and past setting history. If the user is a beginner, the screen generation unit can display only simple settings items and hide detailed settings items. Also, if the user has frequently changed certain settings in the past, those settings items can be prioritized. In addition, the screen generation unit can provide the same settings screen for different models and manufacturers. For example, it can generate a common settings screen that corresponds to different brands and models of devices. This allows users to have a consistent operating experience even when using different devices. Some or all of the above-described processing in the screen generation section may be performed using AI, for example, or without AI. For example, the screen generation section can input sensor data acquired from the device into the AI ​​generation system and have the AI ​​generation system perform the generation of the settings screen. The AI ​​generation system analyzes the sensor data and generates the optimal settings screen. This allows the screen generation section to provide the user with the optimal settings screen and improve the ease of operation.

[0034] The screen generation section can customize the settings screen to suit the user's level and preferences. For example, the screen generation section can adjust the layout and colors of the settings screen based on the user's profile information. For instance, it can provide a simple layout for beginners and detailed setting options for advanced users. The screen generation section can also generate a settings screen tailored to the user's preferences based on their past settings history. For example, it can automatically reflect the colors and layouts the user has previously selected. This improves usability by providing a settings screen that matches the user's level and preferences. Some or all of the above-described processes in the screen generation section may be performed using AI, or not. For example, the screen generation section can input user profile information into a generating AI and have the generating AI perform the customization of the settings screen.

[0035] The screen generation unit can provide the same settings screen for different models and manufacturers. For example, the screen generation unit generates a common settings screen that corresponds to different brands and models of equipment. For example, the screen generation unit provides the same settings screen for different models of network equipment. Furthermore, the screen generation unit can provide the same settings screen for equipment from different manufacturers. For example, the screen generation unit generates a common settings screen for routers and switches from different manufacturers. This ensures consistency of operation by providing the same settings screen for different models and manufacturers. Some or all of the above-described processes in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input equipment information for different models and manufacturers into a generation AI and have the generation AI execute the generation of a common settings screen.

[0036] The screen generation unit can generate configuration screens that can be used even by low-spec network devices. For example, it provides lightweight configuration screens even for devices with low CPU performance or memory capacity. For instance, it generates simple and lightweight configuration screens for low-spec routers and switches. Furthermore, it can generate configuration screens that provide only the minimum necessary functions even for low-spec devices. For example, it provides a configuration screen that displays only basic settings for low-spec devices. This allows for the generation of configuration screens usable by a wide range of devices. Some or all of the above-described processes in the screen generation unit may be performed using AI, or not. For example, the screen generation unit can input low-spec device information into a generation AI and have the generation AI generate a lightweight configuration screen.

[0037] The screen generation section allows users to save screens they have set as favorites and reuse them. The screen generation section provides a function to save screens set by the user. For example, the screen generation section saves the layout and settings of screens set by the user. The screen generation section can also provide a function to reuse saved settings screens. For example, the screen generation section can retrieve and display settings screens saved by the user. This improves the efficiency of the setting process by allowing users to save screens they have set as favorites and reuse them. Some or all of the above-described processes in the screen generation section may be performed using AI, for example, or without AI. For example, the screen generation section can input information from saved settings screens into a generation AI and have the generation AI generate settings screens for reuse.

[0038] The generation unit can generate complex commands based on user instructions. For example, the generation unit can generate commands that include multiple steps based on user instructions. For example, the generation unit can generate complex commands related to network settings. The generation unit can also generate commands that include conditional branching based on user instructions. For example, the generation unit can generate commands that include conditional branching related to security settings. This allows for detailed operations by generating complex commands based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user instructions into a generation AI and have the generation AI execute the generation of complex commands.

[0039] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit can automatically display instructions that the user has frequently entered in the past as candidates. For example, the reception unit can prioritize displaying frequently used commands from the past instruction history. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can prioritize displaying the voice input interface to a user who has frequently used voice input in the past. The reception unit can also predict and suggest instructions to be used during specific time periods based on the user's past instruction history. For example, the reception unit can suggest commands that are frequently used during specific time periods based on past instruction history. In this way, by analyzing past instruction history, the reception unit can provide the user with the most suitable reception method. 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 past instruction history data into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception unit can filter instructions based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving instructions related to the project the user is currently working on. For example, the reception unit suggests relevant commands based on the project's progress. The reception unit can also filter and display relevant instructions based on the user's areas of interest. For example, the reception unit prioritizes displaying commands related to the user's areas of interest. The reception unit can also suggest relevant instructions based on areas the user has shown interest in in the past. For example, the reception unit suggests relevant commands based on past areas of interest. This allows for the priority reception of highly relevant instructions by filtering based on the current project 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 project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving instructions related to that region. For example, the reception unit will prioritize displaying commands related to a specific region. Furthermore, if the user is on the move, the reception unit can suggest the most appropriate instructions based on their current location. For example, the reception unit will suggest commands related to the current location to a user who is on the move. Also, if the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. For example, the reception unit will prioritize displaying commands related to a specific location. This allows for the priority of receiving instructions that are highly relevant by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input geographical location information into a generating AI and have the generating AI select highly relevant instructions.

[0042] The reception unit can analyze the user's social media activity when receiving instructions and receive relevant instructions. For example, the reception unit can suggest relevant instructions based on information shared by the user on social media. For example, the reception unit can suggest relevant commands based on information shared on social media. The reception unit can also prioritize receiving instructions related to the user's areas of interest based on their social media activity. For example, the reception unit can analyze social media activity and prioritize displaying commands related to areas of interest. The reception unit can also suggest relevant instructions based on accounts the user follows on social media. For example, the reception unit can suggest commands related to followed accounts. This allows for the priority of receiving relevant instructions by analyzing social media activity. 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 social media activity data into a generating AI and have the generating AI select relevant instructions.

[0043] The generation unit can adjust the level of detail of the generated commands based on the importance of the instructions. For example, the generation unit generates detailed commands for important instructions. For example, it generates detailed commands for high-priority network settings. The generation unit can also generate commands with standard level of detail for general instructions. For example, it generates commands with standard level of detail for general system administration. The generation unit can also generate concise commands for simple instructions. For example, it generates concise commands for simple tasks. By adjusting the level of detail of the generated commands based on the importance of the instructions, commands with appropriate level of detail can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input instruction importance data into the generation AI and have the generation AI perform the level of detail adjustment.

[0044] The generation unit can apply different generation algorithms depending on the category of the instruction when generating commands. For example, in the case of instructions related to network settings, the generation unit can generate commands by applying a specific algorithm. For example, the generation unit can generate commands using an algorithm specialized for network settings. The generation unit can also generate commands by applying a different algorithm in the case of instructions related to security settings. For example, the generation unit can generate commands using an algorithm specialized for security settings. Furthermore, the generation unit can generate commands by applying yet another algorithm in the case of instructions related to system administration. For example, the generation unit can generate commands using an algorithm specialized for system administration. In this way, appropriate commands can be generated by applying different generation algorithms depending on the category of the instruction. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input instruction category data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0045] The generation unit can determine the generation priority based on the timing of instruction submission when generating commands. For example, in the case of urgent instructions, the generation unit generates commands with the highest priority. For example, the generation unit generates commands with the highest priority for tasks with high urgency. The generation unit can also generate commands with a standard priority for regular instructions. For example, the generation unit generates commands with a standard priority for regularly scheduled system maintenance. The generation unit can also postpone the generation of commands for long-term instructions. For example, the generation unit postpones the generation of commands for long-term projects. This allows commands to be generated at the appropriate time by determining the generation priority based on the timing of instruction submission. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input instruction submission timing data into the generation AI and have the generation AI perform the priority determination.

[0046] The generation unit can adjust the order of command generation based on the relevance of instructions. For example, the generation unit can prioritize the generation of commands related to important instructions. For example, it can prioritize the generation of commands related to high-priority tasks. The generation unit can also generate commands related to general instructions in a standard order. For example, it can generate commands related to general system administration in a standard order. The generation unit can also postpone the generation of commands related to simple instructions. For example, it can postpone the generation of commands related to simple tasks. This allows important instructions to be processed preferentially by adjusting the order of generation based on the relevance of instructions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input instruction relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0047] The execution unit can select the optimal execution method by referring to past execution history when executing a command. For example, the execution unit can select the optimal execution method based on past successful execution methods. The execution unit can also select an execution method that avoids risks by referring to past failures. The execution unit can also select the most efficient execution method from past execution history. For example, the execution unit can select the most efficient execution method based on past execution history. In this way, the optimal execution method can be selected by referring to past execution history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input past execution history data into a generating AI and have the generating AI select the optimal execution method.

[0048] The execution unit can customize the execution method based on the user's current situation when a command is executed. For example, if the user is in the office, the execution unit can provide the execution method best suited to the office environment. The execution unit can also provide the execution method best suited to the remote environment if the user is working remotely. The execution unit can also provide the execution method best suited to the mobile environment if the user is on the move. This allows the execution unit to provide the optimal execution method by customizing the execution method based on the user's current situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the execution method.

[0049] The execution unit can select the optimal execution method when executing a command, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit can provide the optimal execution method for that region. For example, the execution unit can execute a command related to that specific region. The execution unit can also suggest the optimal execution method based on the user's current location if the user is on the move. For example, the execution unit can suggest a command related to the current location for a user who is on the move. The execution unit can also provide the optimal execution method for a specific location if the user is in a specific location. For example, the execution unit can execute a command related to that specific location. In this way, the optimal execution method can be provided by taking geographical location information into consideration. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input geographical location information into a generating AI and have the generating AI select the optimal execution method.

[0050] The execution unit can analyze the user's social media activity and propose a means of execution when a command is executed. For example, the execution unit can propose the optimal method of execution based on information shared by the user on social media. The execution unit can also prioritize proposing execution methods related to the user's areas of interest based on their social media activity. For example, the execution unit analyzes social media activity and proposes execution methods related to areas of interest. The execution unit can also propose the optimal method of execution based on accounts the user follows on social media. For example, the execution unit proposes execution methods related to accounts followed. In this way, by analyzing social media activity, relevant execution methods can be proposed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input social media activity data into a generating AI and have the generating AI execute the proposal of execution methods.

[0051] The screen generation unit can select the optimal display method by referring to the user's past operation history when generating the settings screen. For example, the screen generation unit can prioritize displaying settings that the user has frequently used in the past. For example, the screen generation unit can prioritize displaying frequently used settings based on past operation history. The screen generation unit can also suggest the most efficient display method based on the user's past operation history. For example, the screen generation unit can suggest the most efficient display method based on past operation history. The screen generation unit can also automatically reflect settings that the user has customized in the past. For example, the screen generation unit can automatically reflect settings that have been customized in the past. This allows the system to provide the optimal display method by referring to past operation history. Some or all of the above processing in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input past operation history data into a generation AI and have the generation AI select the optimal display method.

[0052] The generation screen section can be customized based on the user's current project and areas of interest when generating the settings screen. For example, the generation screen section can prioritize displaying settings related to the project the user is currently working on. For example, the generation screen section can prioritize displaying settings related to the current project. The generation screen section can also customize and display relevant settings based on the user's areas of interest. For example, the generation screen section can customize and display settings related to areas of interest. The generation screen section can also suggest relevant settings based on areas the user has shown interest in in the past. For example, the generation screen section can suggest relevant settings based on past areas of interest. This allows for the provision of highly relevant settings by customizing based on the current project and areas of interest. Some or all of the above processing in the generation screen section may be performed using AI, for example, or without AI. For example, the generation screen section can input project information and area of ​​interest data into a generation AI and have the generation AI perform the customization.

[0053] The screen generation unit can select the optimal display method when generating a settings screen, taking into account the user's geographical location information. For example, if the user is in a specific region, the screen generation unit can prioritize displaying settings related to that region. The screen generation unit can also suggest the optimal settings based on the user's current location if the user is on the move. For example, if the user is in a specific location, the screen generation unit can prioritize displaying settings related to that location. This allows the system to provide the optimal display method by considering geographical location information. Some or all of the above processing in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input geographical location information into a generation AI and have the generation AI select the optimal display method.

[0054] The screen generation unit can analyze the user's social media activity and suggest the optimal display method when generating the settings screen. For example, the screen generation unit can suggest relevant settings based on information shared by the user on social media. The screen generation unit can also prioritize displaying settings related to the user's areas of interest based on their social media activity. For example, the screen generation unit analyzes social media activity and prioritizes displaying settings related to areas of interest. The screen generation unit can also suggest relevant settings based on accounts the user follows on social media. For example, the screen generation unit suggests settings related to accounts the user follows. In this way, relevant settings can be suggested by analyzing social media activity. Some or all of the above processing in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input social media activity data into a generation AI and have the generation AI suggest the optimal display method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The generation unit can generate complex commands based on user instructions. For example, it can generate commands that include multiple steps based on user instructions. It can also generate complex commands related to network settings. Furthermore, it can generate commands that include conditional branching based on user instructions. It can generate commands that include conditional branching related to security settings. This allows for detailed operations by generating complex commands based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user instructions into a generation AI and have the generation AI execute the generation of complex commands.

[0057] The screen generation section can customize the settings screen to suit the user's level and preferences. For example, it can adjust the layout and colors of the settings screen based on the user's profile information. It can provide a simple layout for beginners and detailed setting options for advanced users. It can also generate a settings screen tailored to the user's preferences based on their past settings history. It automatically reflects previously selected colors and layouts. This improves usability by providing a settings screen that matches the user's level and preferences. Some or all of the above processes in the screen generation section may be performed using AI or not. For example, the screen generation section can input the user's profile information into a generation AI and have the generation AI perform the customization of the settings screen.

[0058] The screen generation unit can provide the same settings screen for different models and manufacturers. For example, it can generate a common settings screen for devices of different brands and models. It can provide the same settings screen for network devices of different models. It can also provide the same settings screen for devices of different manufacturers. It can generate a common settings screen for routers and switches of different manufacturers. This ensures consistency of operation by providing the same settings screen for different models and manufacturers. Some or all of the above processing in the screen generation unit may be performed using AI or not. For example, the screen generation unit can input device information for different models and manufacturers into a generation AI and have the generation AI execute the generation of a common settings screen.

[0059] The screen generation section can generate configuration screens that can be used even with low-spec network equipment. For example, it can provide lightweight configuration screens even for devices with low CPU performance or memory capacity. It can generate simple and lightweight configuration screens for low-spec routers and switches. It can also generate configuration screens that provide only the minimum necessary functions for low-spec equipment. It can provide configuration screens that display only basic settings. By generating configuration screens that can be used even with low-spec network equipment, it becomes usable with a wide range of devices. Some or all of the above processing in the screen generation section may be performed using AI or not. For example, the screen generation section can input low-spec device information into a generation AI and have the generation AI execute the generation of a lightweight configuration screen.

[0060] The screen generation section allows users to save screens they have set as favorites and reuse them. For example, it can provide a function to save screens set by the user. It saves the layout and settings of the screen set by the user. It can also provide a function to reuse saved settings screens. It can recall and display the settings screen saved by the user. This improves the efficiency of the setting process by allowing users to save and reuse screens they have set once as favorites. Some or all of the above processing in the screen generation section may be performed using AI or not. For example, the screen generation section can input information from saved settings screens into a generation AI and have the generation AI generate settings screens for reuse.

[0061] The generation unit can adjust the level of detail generated based on the importance of the instruction when generating commands. For example, for important instructions, it can generate detailed commands. For example, it can generate detailed commands for high-priority network settings. For general instructions, it can also generate commands with standard level of detail. For example, it can generate standard level of detail commands for general system administration. For simple instructions, it can also generate concise commands. For example, it can generate concise commands for simple tasks. By adjusting the level of detail generated based on the importance of the instruction, commands with appropriate level of detail can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input instruction importance data into the generation AI and have the generation AI perform the level of detail adjustment.

[0062] The generation unit can apply different generation algorithms depending on the category of the instruction when generating commands. For example, in the case of instructions related to network settings, a specific algorithm is applied to generate the command. A command is generated using an algorithm specialized for network settings. Similarly, in the case of instructions related to security settings, a different algorithm can be applied to generate the command. A command is generated using an algorithm specialized for security settings. Furthermore, in the case of instructions related to system administration, yet another algorithm can be applied to generate the command. A command is generated using an algorithm specialized for system administration. This allows for the generation of appropriate commands by applying different generation algorithms depending on the category of the instruction. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input instruction category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives instructions in natural language. These instructions include voice and text. The reception desk uses speech recognition technology to convert voice instructions into text. It also provides a text input interface, allowing users to directly input text instructions. Step 2: The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The generation unit analyzes the instructions using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit uses a generation AI to generate complex commands based on the user's instructions. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's instructions and generate appropriate commands. The generation unit can also use the generation AI to generate commands that include conditional branching based on the user's instructions. Step 3: The execution unit executes the commands generated by the generation unit. The execution unit provides an environment for executing shell commands and scripts. The execution unit executes the generated commands and retrieves the results. For example, the execution unit executes a command in a shell environment and retrieves its output. The execution unit can also execute a script and save the results to a log file. Step 4: The screen generation unit generates a user-specific settings screen based on information from the device. The screen generation unit acquires sensor data and log information from the device and generates a settings screen based on that. The screen generation unit uses generation AI to customize the settings screen to suit the user's level and preferences. For example, the screen generation unit adjusts the layout and colors of the settings screen based on the user's profile information and past settings history. Furthermore, the screen generation unit can provide the same settings screen for different models and manufacturers. For example, the screen generation unit can generate a common settings screen that is compatible with devices of different brands and models.

[0065] (Example of form 2) The interface generation system according to an embodiment of the present invention is a system that generates an interface tailored to the user's (engineer's) level, knowledge, and preferences using natural language. This interface generation system allows the user to input instructions in natural language, and the generating AI analyzes these instructions to generate and execute CUI-level commands. It can also generate a user-specific configuration screen based on information from the equipment. This makes it easy for engineers unfamiliar with CUI to operate the system. For example, if a user instructs, "Create a screen to set the VLAN IDs for all ports and display the current VLAN ID settings," the generating AI analyzes the instruction and generates appropriate commands. The generated commands are executed using a CUI-based system, and the results are output. The output results are recognized by the AI, and a configuration screen is generated. This configuration screen can be customized to suit the user's level and preferences. Furthermore, the same configuration screen can be provided for different models and manufacturers, and it can be used even with low-spec network equipment. Additionally, screens that have been configured can be saved as favorites and reused. This improves the engineer's work efficiency and prevents time loss due to typos and spelling errors. This system aims to improve the current situation where the command-line interface (CUI) is the main interface and eliminate the inconveniences of the graphical user interface (GUI). By utilizing AI-generated interfaces, it provides interfaces tailored to user needs, creating an environment that is easy for all system engineers to use. As a result, the interface generation system can provide interfaces that match the user's level, knowledge, and preferences.

[0066] The interface generation system according to the embodiment comprises a reception unit, a generation unit, an execution unit, and a generation screen unit. The reception unit receives instructions in natural language. These instructions include, but are not limited to, speech instructions and text instructions. The reception unit converts speech instructions to text using, for example, speech recognition technology. The reception unit also provides a text input interface, allowing the user to directly input text instructions. The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The generation unit analyzes the instructions using, for example, natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates complex commands based on user instructions using a generation AI. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze user instructions and generate appropriate commands. The generation unit can also generate commands including conditional branching based on user instructions using the generation AI. The execution unit executes the commands generated by the generation unit. The execution unit provides, for example, an environment for executing shell commands or scripts. The execution unit executes the generated commands and retrieves the results. For example, the execution unit executes commands in a shell environment and obtains their output. The execution unit can also execute scripts and save the results to a log file. The screen generation unit generates a user-specific settings screen based on information from the device. For example, the screen generation unit obtains sensor data and log information from the device and generates a settings screen based on it. The screen generation unit uses a generation AI to customize the settings screen to suit the user's level and preferences. For example, the screen generation unit adjusts the layout and colors of the settings screen based on the user's profile information and past settings history. Furthermore, the screen generation unit can provide the same settings screen for different models and manufacturers. For example, the screen generation unit generates a common settings screen that corresponds to different brands and models of devices. As a result, the interface generation system according to the embodiment can provide an interface tailored to the user's level, knowledge, and preferences. Some or all of the above-described processes in the screen generation unit may be performed using AI, for example, or without using AI.For example, the screen generation unit can input sensor data acquired from the device into the generation AI, and have the generation AI execute the generation of the settings screen.

[0067] The reception unit accepts instructions in natural language. These instructions include, but are not limited to, voice and text instructions. The reception unit converts voice instructions to text using, for example, speech recognition technology. Specifically, deep learning-based speech recognition models are often used as speech recognition technology. These models achieve high-precision speech recognition by learning from large amounts of voice data. When a voice instruction is input, the speech recognition model analyzes the voice data and converts it into corresponding text data. The reception unit also provides a text input interface, allowing users to directly input text instructions. This text input interface is provided, for example, as a form on a web browser or an input field in a mobile app. This allows users to give instructions to the system not only using voice but also using text. Furthermore, the reception unit analyzes the intent of the input text instructions using natural language processing technology. For example, morphological analysis is used to divide the text into words, and grammatical analysis is used to analyze the sentence structure. This allows the reception unit to accurately understand the user's instructions and pass them on to the next processing step.

[0068] The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The generation unit analyzes the instructions using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Specifically, morphological analysis divides the input text into words and identifies the part of speech of each word. Grammatical analysis analyzes the structure of the sentence and identifies sentence elements such as subject, predicate, and object. Semantic analysis understands the meaning of the sentence and grasps the user's intent. The generation unit uses a generation AI to generate complex commands based on the user's instructions. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's instructions and generate appropriate commands. LLM can understand and generate natural language by learning from large amounts of text data. If the user instructs "Copy files and create a backup," the generation AI analyzes this instruction and generates a specific shell command such as "cp file1 file2." The generation unit can also use the generation AI to generate commands that include conditional branching based on the user's instructions. For example, in response to an instruction such as "If there is little disk space left, delete old files and then create a backup," the generating AI will produce a complex command like "if [ $(df / | grep -v Filesystem | awk '{print $4}') -lt 100000 ]; then rm oldfile; fi; cp file1 file2". This allows the generation unit to analyze the user's natural language instructions and convert them into concrete commands.

[0069] The execution unit executes the commands generated by the generation unit. The execution unit provides an environment for executing, for example, shell commands or scripts. Specifically, the execution unit executes commands using a Linux or Windows shell environment. If the generated command is a shell command, the execution unit executes that command in the shell environment and retrieves its output. For example, if a command like "ls -l" is generated, the execution unit executes this command and retrieves a list of files in a directory. The execution unit can also execute scripts and save the results to a log file. For example, if a Python script is generated, the execution unit executes the script using a Python interpreter and saves the results to a log file. Furthermore, the execution unit can parse the execution results of commands and pass them to the next processing step. For example, if the command execution result is an error, the execution unit parses the error message and performs appropriate error handling. This ensures that the execution unit reliably executes the generated commands and processes the results appropriately.

[0070] The screen generation unit generates a user-specific settings screen based on information from the device. For example, it acquires sensor data and log information from the device and generates a settings screen based on that. Specifically, it acquires data from the device's temperature and humidity sensors and adjusts the layout and content of the settings screen based on that data. For example, if the device temperature is high, the screen generation unit will prioritize displaying the cooling fan settings. Furthermore, the screen generation unit uses generation AI to customize the settings screen to suit the user's level and preferences. For example, it adjusts the layout and colors of the settings screen based on the user's profile information and past setting history. If the user is a beginner, the screen generation unit can display only simple settings items and hide detailed settings items. Also, if the user has frequently changed certain settings in the past, those settings items can be prioritized. In addition, the screen generation unit can provide the same settings screen for different models and manufacturers. For example, it can generate a common settings screen that corresponds to different brands and models of devices. This allows users to have a consistent operating experience even when using different devices. Some or all of the above-described processing in the screen generation section may be performed using AI, for example, or without AI. For example, the screen generation section can input sensor data acquired from the device into the AI ​​generation system and have the AI ​​generation system perform the generation of the settings screen. The AI ​​generation system analyzes the sensor data and generates the optimal settings screen. This allows the screen generation section to provide the user with the optimal settings screen and improve the ease of operation.

[0071] The screen generation section can customize the settings screen to suit the user's level and preferences. For example, the screen generation section can adjust the layout and colors of the settings screen based on the user's profile information. For instance, it can provide a simple layout for beginners and detailed setting options for advanced users. The screen generation section can also generate a settings screen tailored to the user's preferences based on their past settings history. For example, it can automatically reflect the colors and layouts the user has previously selected. This improves usability by providing a settings screen that matches the user's level and preferences. Some or all of the above-described processes in the screen generation section may be performed using AI, or not. For example, the screen generation section can input user profile information into a generating AI and have the generating AI perform the customization of the settings screen.

[0072] The screen generation unit can provide the same settings screen for different models and manufacturers. For example, the screen generation unit generates a common settings screen that corresponds to different brands and models of equipment. For example, the screen generation unit provides the same settings screen for different models of network equipment. Furthermore, the screen generation unit can provide the same settings screen for equipment from different manufacturers. For example, the screen generation unit generates a common settings screen for routers and switches from different manufacturers. This ensures consistency of operation by providing the same settings screen for different models and manufacturers. Some or all of the above-described processes in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input equipment information for different models and manufacturers into a generation AI and have the generation AI execute the generation of a common settings screen.

[0073] The screen generation unit can generate configuration screens that can be used even by low-spec network devices. For example, it provides lightweight configuration screens even for devices with low CPU performance or memory capacity. For instance, it generates simple and lightweight configuration screens for low-spec routers and switches. Furthermore, it can generate configuration screens that provide only the minimum necessary functions even for low-spec devices. For example, it provides a configuration screen that displays only basic settings for low-spec devices. This allows for the generation of configuration screens usable by a wide range of devices. Some or all of the above-described processes in the screen generation unit may be performed using AI, or not. For example, the screen generation unit can input low-spec device information into a generation AI and have the generation AI generate a lightweight configuration screen.

[0074] The screen generation section allows users to save screens they have set as favorites and reuse them. The screen generation section provides a function to save screens set by the user. For example, the screen generation section saves the layout and settings of screens set by the user. The screen generation section can also provide a function to reuse saved settings screens. For example, the screen generation section can retrieve and display settings screens saved by the user. This improves the efficiency of the setting process by allowing users to save screens they have set as favorites and reuse them. Some or all of the above-described processes in the screen generation section may be performed using AI, for example, or without AI. For example, the screen generation section can input information from saved settings screens into a generation AI and have the generation AI generate settings screens for reuse.

[0075] The generation unit can generate complex commands based on user instructions. For example, the generation unit can generate commands that include multiple steps based on user instructions. For example, the generation unit can generate complex commands related to network settings. The generation unit can also generate commands that include conditional branching based on user instructions. For example, the generation unit can generate commands that include conditional branching related to security settings. This allows for detailed operations by generating complex commands based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user instructions into a generation AI and have the generation AI execute the generation of complex commands.

[0076] The reception unit can estimate the user's emotions and adjust how natural language instructions are received based on the estimated emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. For example, the reception unit can provide concise input options to a stressed user. The reception unit can also provide detailed input options and suggest customizable input methods if the user is relaxed. For example, the reception unit can display detailed settings to a relaxed user. The reception unit can also prioritize voice input to allow for quick instruction input if the user is in a hurry. For example, the reception unit can provide a voice input interface to a user in a hurry. This allows for a more appropriate interface by adjusting how instructions are received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 desk can input user emotion data into a generating AI, which can then adjust the reception process based on those emotions.

[0077] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit can automatically display instructions that the user has frequently entered in the past as candidates. For example, the reception unit can prioritize displaying frequently used commands from the past instruction history. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can prioritize displaying the voice input interface to a user who has frequently used voice input in the past. The reception unit can also predict and suggest instructions to be used during specific time periods based on the user's past instruction history. For example, the reception unit can suggest commands that are frequently used during specific time periods based on past instruction history. In this way, by analyzing past instruction history, the reception unit can provide the user with the most suitable reception method. 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 past instruction history data into a generating AI and have the generating AI select the optimal reception method.

[0078] The reception unit can filter instructions based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving instructions related to the project the user is currently working on. For example, the reception unit suggests relevant commands based on the project's progress. The reception unit can also filter and display relevant instructions based on the user's areas of interest. For example, the reception unit prioritizes displaying commands related to the user's areas of interest. The reception unit can also suggest relevant instructions based on areas the user has shown interest in in the past. For example, the reception unit suggests relevant commands based on past areas of interest. This allows for the priority reception of highly relevant instructions by filtering based on the current project 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 project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0079] The reception unit can estimate the user's emotions and determine the priority of instructions to receive based on the estimated emotions. For example, if the user is nervous, the reception unit will prioritize important instructions. For instance, it will display high-priority commands to a nervous user. The reception unit can also prioritize detailed instructions if the user is relaxed. For example, it will display detailed settings to a relaxed user. The reception unit can also prioritize instructions that require quick processing if the user is in a hurry. For example, it will display commands that can be executed quickly to a user in a hurry. This allows important instructions to be processed preferentially by determining the priority of 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, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​determine priorities.

[0080] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize receiving instructions related to that region. For example, the reception unit will prioritize displaying commands related to a specific region. Furthermore, if the user is on the move, the reception unit can suggest the most appropriate instructions based on their current location. For example, the reception unit will suggest commands related to the current location to a user who is on the move. Also, if the user is in a specific location, the reception unit can prioritize receiving instructions related to that location. For example, the reception unit will prioritize displaying commands related to a specific location. This allows for the priority of receiving instructions that are highly relevant by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input geographical location information into a generating AI and have the generating AI select highly relevant instructions.

[0081] The reception unit can analyze the user's social media activity when receiving instructions and receive relevant instructions. For example, the reception unit can suggest relevant instructions based on information shared by the user on social media. For example, the reception unit can suggest relevant commands based on information shared on social media. The reception unit can also prioritize receiving instructions related to the user's areas of interest based on their social media activity. For example, the reception unit can analyze social media activity and prioritize displaying commands related to areas of interest. The reception unit can also suggest relevant instructions based on accounts the user follows on social media. For example, the reception unit can suggest commands related to followed accounts. This allows for the priority of receiving relevant instructions by analyzing social media activity. 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 social media activity data into a generating AI and have the generating AI select relevant instructions.

[0082] The generation unit can estimate the user's emotions and adjust the way commands are expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate commands that include detailed explanations. For example, the generation unit can generate commands with detailed explanations for a relaxed user. The generation unit can also generate concise and to-the-point commands if the user is in a hurry. For example, the generation unit can generate concise commands for a user in a hurry. The generation unit can also generate commands with visually stimulating effects if the user is excited. For example, the generation unit can generate commands with visually stimulating effects for an excited user. By adjusting the way commands are expressed according to the user's emotions, more appropriate commands can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the method of expression.

[0083] The generation unit can adjust the level of detail of the generated commands based on the importance of the instructions. For example, the generation unit generates detailed commands for important instructions. For example, it generates detailed commands for high-priority network settings. The generation unit can also generate commands with standard level of detail for general instructions. For example, it generates commands with standard level of detail for general system administration. The generation unit can also generate concise commands for simple instructions. For example, it generates concise commands for simple tasks. By adjusting the level of detail of the generated commands based on the importance of the instructions, commands with appropriate level of detail can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input instruction importance data into the generation AI and have the generation AI perform the level of detail adjustment.

[0084] The generation unit can apply different generation algorithms depending on the category of the instruction when generating commands. For example, in the case of instructions related to network settings, the generation unit can generate commands by applying a specific algorithm. For example, the generation unit can generate commands using an algorithm specialized for network settings. The generation unit can also generate commands by applying a different algorithm in the case of instructions related to security settings. For example, the generation unit can generate commands using an algorithm specialized for security settings. Furthermore, the generation unit can generate commands by applying yet another algorithm in the case of instructions related to system administration. For example, the generation unit can generate commands using an algorithm specialized for system administration. In this way, appropriate commands can be generated by applying different generation algorithms depending on the category of the instruction. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input instruction category data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the length of the commands it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise commands. For example, the generation unit can generate short, concise commands for a user in a hurry. The generation unit can also generate longer commands with detailed explanations for a relaxed user. For example, the generation unit can generate longer commands with detailed explanations for a relaxed user. The generation unit can also generate commands with visually stimulating effects for an excited user. For example, the generation unit can generate commands with visually stimulating effects for an excited user. By adjusting the length of commands according to the user's emotions, more appropriate commands can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, 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 generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the commands.

[0086] The generation unit can determine the generation priority based on the timing of instruction submission when generating commands. For example, in the case of urgent instructions, the generation unit generates commands with the highest priority. For example, the generation unit generates commands with the highest priority for tasks with high urgency. The generation unit can also generate commands with a standard priority for regular instructions. For example, the generation unit generates commands with a standard priority for regularly scheduled system maintenance. The generation unit can also postpone the generation of commands for long-term instructions. For example, the generation unit postpones the generation of commands for long-term projects. This allows commands to be generated at the appropriate time by determining the generation priority based on the timing of instruction submission. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input instruction submission timing data into the generation AI and have the generation AI perform the priority determination.

[0087] The generation unit can adjust the order of command generation based on the relevance of instructions. For example, the generation unit can prioritize the generation of commands related to important instructions. For example, it can prioritize the generation of commands related to high-priority tasks. The generation unit can also generate commands related to general instructions in a standard order. For example, it can generate commands related to general system administration in a standard order. The generation unit can also postpone the generation of commands related to simple instructions. For example, it can postpone the generation of commands related to simple tasks. This allows important instructions to be processed preferentially by adjusting the order of generation based on the relevance of instructions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input instruction relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0088] The execution unit can estimate the user's emotions and adjust the command execution method based on the estimated emotions. For example, if the user is nervous, the execution unit can provide a simple and highly visible display method. For example, if the user is nervous, the execution unit can provide a simple and highly visible display method for a nervous user. The execution unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the execution unit can provide a display method that includes detailed information. The execution unit can also provide a display method that gets to the point if the user is in a hurry. For example, if the execution unit provides a display method that gets to the point for a user in a hurry. By adjusting the command execution method according to the user's emotions, a more appropriate execution method can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into the generating AI and have the generating AI adjust the execution method.

[0089] The execution unit can select the optimal execution method by referring to past execution history when executing a command. For example, the execution unit can select the optimal execution method based on past successful execution methods. The execution unit can also select an execution method that avoids risks by referring to past failures. The execution unit can also select the most efficient execution method from past execution history. For example, the execution unit can select the most efficient execution method based on past execution history. In this way, the optimal execution method can be selected by referring to past execution history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input past execution history data into a generating AI and have the generating AI select the optimal execution method.

[0090] The execution unit can customize the execution method based on the user's current situation when a command is executed. For example, if the user is in the office, the execution unit can provide the execution method best suited to the office environment. The execution unit can also provide the execution method best suited to the remote environment if the user is working remotely. The execution unit can also provide the execution method best suited to the mobile environment if the user is on the move. This allows the execution unit to provide the optimal execution method by customizing the execution method based on the user's current situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the execution method.

[0091] The execution unit can estimate the user's emotions and determine the priority of commands to execute based on the estimated emotions. For example, if the user is tense, the execution unit will prioritize important commands. For example, the execution unit will prioritize high-priority commands for a tense user. The execution unit can also prioritize detailed commands for a relaxed user. For example, the execution unit will prioritize detailed commands for a relaxed user. The execution unit can also prioritize commands that require quick processing if the user is in a hurry. For example, the execution unit will prioritize commands that can be executed quickly for a hurried user. In this way, by determining the priority of commands according to the user's emotions, important commands can be prioritized. 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 execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input user emotion data into the generating AI and have the generating AI determine priorities.

[0092] The execution unit can select the optimal execution method when executing a command, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit can provide the optimal execution method for that region. For example, the execution unit can execute a command related to that specific region. The execution unit can also suggest the optimal execution method based on the user's current location if the user is on the move. For example, the execution unit can suggest a command related to the current location for a user who is on the move. The execution unit can also provide the optimal execution method for a specific location if the user is in a specific location. For example, the execution unit can execute a command related to that specific location. In this way, the optimal execution method can be provided by taking geographical location information into consideration. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI. For example, the execution unit can input geographical location information into a generating AI and have the generating AI select the optimal execution method.

[0093] The execution unit can analyze the user's social media activity and propose a means of execution when a command is executed. For example, the execution unit can propose the optimal method of execution based on information shared by the user on social media. The execution unit can also prioritize proposing execution methods related to the user's areas of interest based on their social media activity. For example, the execution unit analyzes social media activity and proposes execution methods related to areas of interest. The execution unit can also propose the optimal method of execution based on accounts the user follows on social media. For example, the execution unit proposes execution methods related to accounts followed. In this way, by analyzing social media activity, relevant execution methods can be proposed. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input social media activity data into a generating AI and have the generating AI execute the proposal of execution methods.

[0094] The generated screen section can estimate the user's emotions and adjust the display method of the settings screen based on the estimated emotions. For example, if the user is tense, the generated screen section can provide an interface with calming colors to reduce visual stress. For example, the generated screen section provides an interface with calming colors to tense users. Also, if the user is enjoying themselves, the generated screen section can provide an interface with bright colors to make the input process more enjoyable. For example, the generated screen section provides an interface with bright colors to enjoyable users. Also, if the user is tired, the generated screen section can provide a simple and highly visible interface to make the input process easier. For example, the generated screen section provides a simple and highly visible interface to tired users. In this way, by adjusting the display method of the settings screen according to the user's emotions, a more appropriate interface can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation screen section may be performed using AI, for example, or without AI. For example, the generation screen section can input user emotion data into the generation AI and have the generation AI adjust the display method.

[0095] The screen generation unit can select the optimal display method by referring to the user's past operation history when generating the settings screen. For example, the screen generation unit can prioritize displaying settings that the user has frequently used in the past. For example, the screen generation unit can prioritize displaying frequently used settings based on past operation history. The screen generation unit can also suggest the most efficient display method based on the user's past operation history. For example, the screen generation unit can suggest the most efficient display method based on past operation history. The screen generation unit can also automatically reflect settings that the user has customized in the past. For example, the screen generation unit can automatically reflect settings that have been customized in the past. This allows the system to provide the optimal display method by referring to past operation history. Some or all of the above processing in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input past operation history data into a generation AI and have the generation AI select the optimal display method.

[0096] The generation screen section can be customized based on the user's current project and areas of interest when generating the settings screen. For example, the generation screen section can prioritize displaying settings related to the project the user is currently working on. For example, the generation screen section can prioritize displaying settings related to the current project. The generation screen section can also customize and display relevant settings based on the user's areas of interest. For example, the generation screen section can customize and display settings related to areas of interest. The generation screen section can also suggest relevant settings based on areas the user has shown interest in in the past. For example, the generation screen section can suggest relevant settings based on past areas of interest. This allows for the provision of highly relevant settings by customizing based on the current project and areas of interest. Some or all of the above processing in the generation screen section may be performed using AI, for example, or without AI. For example, the generation screen section can input project information and area of ​​interest data into a generation AI and have the generation AI perform the customization.

[0097] The screen generation unit can estimate the user's emotions and determine the priority of the settings screen based on the estimated emotions. For example, if the user is nervous, the screen generation unit will prioritize displaying important settings. For example, if the user is nervous, the screen generation unit will prioritize displaying settings that are of high importance. The screen generation unit can also prioritize displaying detailed settings if the user is relaxed. For example, if the user is relaxed, the screen generation unit will prioritize displaying detailed settings. The screen generation unit can also prioritize displaying settings that require quick processing if the user is in a hurry. For example, if the user is in a hurry, the screen generation unit will prioritize displaying settings that can be quickly executed. In this way, by determining the priority of the settings screen according to the user's emotions, important settings can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation screen section may be performed using AI, for example, or without AI. For example, the generation screen section can input user emotion data into the generation AI and have the generation AI determine priorities.

[0098] The screen generation unit can select the optimal display method when generating a settings screen, taking into account the user's geographical location information. For example, if the user is in a specific region, the screen generation unit can prioritize displaying settings related to that region. The screen generation unit can also suggest the optimal settings based on the user's current location if the user is on the move. For example, if the user is in a specific location, the screen generation unit can prioritize displaying settings related to that location. This allows the system to provide the optimal display method by considering geographical location information. Some or all of the above processing in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input geographical location information into a generation AI and have the generation AI select the optimal display method.

[0099] The screen generation unit can analyze the user's social media activity and suggest the optimal display method when generating the settings screen. For example, the screen generation unit can suggest relevant settings based on information shared by the user on social media. The screen generation unit can also prioritize displaying settings related to the user's areas of interest based on their social media activity. For example, the screen generation unit analyzes social media activity and prioritizes displaying settings related to areas of interest. The screen generation unit can also suggest relevant settings based on accounts the user follows on social media. For example, the screen generation unit suggests settings related to accounts the user follows. In this way, relevant settings can be suggested by analyzing social media activity. Some or all of the above processing in the screen generation unit may be performed using AI, for example, or without AI. For example, the screen generation unit can input social media activity data into a generation AI and have the generation AI suggest the optimal display method.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception unit can estimate the user's emotions and adjust how natural language instructions are received based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided, minimizing the input steps. Concise input options can be offered to stressed users. If the user is relaxed, detailed input options can be offered, and customizable input methods can be suggested. Detailed settings can be displayed to relaxed users. If the user is in a hurry, voice input can be prioritized to allow for quick instruction input. A voice input interface can be provided to hurried users. This allows for a more appropriate interface by adjusting how instructions are received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the reception method based on emotions.

[0102] The generation unit can generate complex commands based on user instructions. For example, it can generate commands that include multiple steps based on user instructions. It can also generate complex commands related to network settings. Furthermore, it can generate commands that include conditional branching based on user instructions. It can generate commands that include conditional branching related to security settings. This allows for detailed operations by generating complex commands based on user instructions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input user instructions into a generation AI and have the generation AI execute the generation of complex commands.

[0103] The screen generation section can customize the settings screen to suit the user's level and preferences. For example, it can adjust the layout and colors of the settings screen based on the user's profile information. It can provide a simple layout for beginners and detailed setting options for advanced users. It can also generate a settings screen tailored to the user's preferences based on their past settings history. It automatically reflects previously selected colors and layouts. This improves usability by providing a settings screen that matches the user's level and preferences. Some or all of the above processes in the screen generation section may be performed using AI or not. For example, the screen generation section can input the user's profile information into a generation AI and have the generation AI perform the customization of the settings screen.

[0104] The screen generation unit can provide the same settings screen for different models and manufacturers. For example, it can generate a common settings screen for devices of different brands and models. It can provide the same settings screen for network devices of different models. It can also provide the same settings screen for devices of different manufacturers. It can generate a common settings screen for routers and switches of different manufacturers. This ensures consistency of operation by providing the same settings screen for different models and manufacturers. Some or all of the above processing in the screen generation unit may be performed using AI or not. For example, the screen generation unit can input device information for different models and manufacturers into a generation AI and have the generation AI execute the generation of a common settings screen.

[0105] The screen generation section can generate configuration screens that can be used even with low-spec network equipment. For example, it can provide lightweight configuration screens even for devices with low CPU performance or memory capacity. It can generate simple and lightweight configuration screens for low-spec routers and switches. It can also generate configuration screens that provide only the minimum necessary functions for low-spec equipment. It can provide configuration screens that display only basic settings. By generating configuration screens that can be used even with low-spec network equipment, it becomes usable with a wide range of devices. Some or all of the above processing in the screen generation section may be performed using AI or not. For example, the screen generation section can input low-spec device information into a generation AI and have the generation AI execute the generation of a lightweight configuration screen.

[0106] The screen generation section allows users to save screens they have set as favorites and reuse them. For example, it can provide a function to save screens set by the user. It saves the layout and settings of the screen set by the user. It can also provide a function to reuse saved settings screens. It can recall and display the settings screen saved by the user. This improves the efficiency of the setting process by allowing users to save and reuse screens they have set once as favorites. Some or all of the above processing in the screen generation section may be performed using AI or not. For example, the screen generation section can input information from saved settings screens into a generation AI and have the generation AI generate settings screens for reuse.

[0107] The generation unit can estimate the user's emotions and adjust the way commands are expressed based on the estimated emotions. For example, if the user is relaxed, it can generate commands that include detailed explanations. If the user is in a hurry, it can generate concise and to-the-point commands. If the user is excited, it can generate commands with visually stimulating effects. By adjusting the way commands are expressed according to the user's emotions, more appropriate commands can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the commands are expressed.

[0108] The generation unit can adjust the level of detail generated based on the importance of the instruction when generating commands. For example, for important instructions, it can generate detailed commands. For example, it can generate detailed commands for high-priority network settings. For general instructions, it can also generate commands with standard level of detail. For example, it can generate standard level of detail commands for general system administration. For simple instructions, it can also generate concise commands. For example, it can generate concise commands for simple tasks. By adjusting the level of detail generated based on the importance of the instruction, commands with appropriate level of detail can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input instruction importance data into the generation AI and have the generation AI perform the level of detail adjustment.

[0109] The generation unit can apply different generation algorithms depending on the category of the instruction when generating commands. For example, in the case of instructions related to network settings, a specific algorithm is applied to generate the command. A command is generated using an algorithm specialized for network settings. Similarly, in the case of instructions related to security settings, a different algorithm can be applied to generate the command. A command is generated using an algorithm specialized for security settings. Furthermore, in the case of instructions related to system administration, yet another algorithm can be applied to generate the command. A command is generated using an algorithm specialized for system administration. This allows for the generation of appropriate commands by applying different generation algorithms depending on the category of the instruction. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input instruction category data into a generation AI and have the generation AI execute the application of a generation algorithm.

[0110] The generation unit can estimate the user's emotions and adjust the length of the commands it generates based on the estimated emotions. For example, if the user is in a hurry, it can generate short, concise commands. It can also generate longer commands with detailed explanations if the user is relaxed. It can also generate longer commands with detailed explanations if the user is excited. By adjusting the command length according to the user's emotions, it is possible to generate more appropriate commands. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the command length.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives instructions in natural language. These instructions include voice and text. The reception desk uses speech recognition technology to convert voice instructions into text. It also provides a text input interface, allowing users to directly input text instructions. Step 2: The generation unit analyzes the instructions received by the reception unit and generates CUI-level commands. The generation unit analyzes the instructions using natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis. The generation unit uses a generation AI to generate complex commands based on the user's instructions. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the user's instructions and generate appropriate commands. The generation unit can also use the generation AI to generate commands that include conditional branching based on the user's instructions. Step 3: The execution unit executes the commands generated by the generation unit. The execution unit provides an environment for executing shell commands and scripts. The execution unit executes the generated commands and retrieves the results. For example, the execution unit executes a command in a shell environment and retrieves its output. The execution unit can also execute a script and save the results to a log file. Step 4: The screen generation unit generates a user-specific settings screen based on information from the device. The screen generation unit acquires sensor data and log information from the device and generates a settings screen based on that. The screen generation unit uses generation AI to customize the settings screen to suit the user's level and preferences. For example, the screen generation unit adjusts the layout and colors of the settings screen based on the user's profile information and past settings history. Furthermore, the screen generation unit can provide the same settings screen for different models and manufacturers. For example, the screen generation unit can generate a common settings screen that is compatible with devices of different brands and models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and generation screen unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives natural language instructions using the microphone 38B or touch panel 38A of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the instructions using natural language processing technology and generates CUI-level commands. The execution unit executes the commands generated by the specific processing unit 290 of the data processing unit 12 and obtains the results. The generation screen unit generates and displays a user-specific settings screen 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and generation screen unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives natural language instructions using the microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the instructions using natural language processing technology and generates CUI-level commands. The execution unit executes the commands generated by the specific processing unit 290 of the data processing unit 12 and obtains the results. The generation screen unit generates and displays a user-specific settings screen 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and generation screen unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives natural language instructions using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the instructions using natural language processing technology and generates CUI-level commands. The execution unit executes the commands generated by the specific processing unit 290 of the data processing unit 12 and obtains the results. The generation screen unit generates and displays a user-specific settings screen 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 various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, generation unit, execution unit, and generation screen unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives natural language instructions using the microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the instructions using natural language processing technology and generates CUI-level commands. The execution unit executes the commands generated by the specific processing unit 290 of the data processing unit 12 and obtains the results. The generation screen unit generates and displays a user-specific settings screen 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception desk that accepts instructions in natural language, A generation unit analyzes the instructions received by the reception unit and generates a CUI-level command, An execution unit that executes the commands generated by the generation unit, It includes a generation screen section that generates a user-specific settings screen based on information from the device. A system characterized by the following features. (Note 2) The aforementioned generation screen section is Customize the settings screen to suit the user's level and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned generation screen section is Provides the same settings screen across different models and manufacturers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned generation screen section is Generates a configuration screen that can be used even with low-spec network equipment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned generation screen section is Save a screen you've set up as a favorite and reuse it. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate complex commands based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how natural language instructions are received based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past instruction history and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of instructions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions, the system prioritizes accepting instructions 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 12) The aforementioned reception unit is When receiving instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the way commands are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating commands, adjust the level of detail based on the importance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating commands, different generation algorithms are applied depending on the category of the instruction. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the commands generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating commands, the generation priority is determined based on when the instructions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating commands, adjust the generation order based on the relevance of the instructions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, It estimates the user's emotions and adjusts how commands are executed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, When executing a command, the system refers to past execution history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, When a command is executed, the execution method is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, It estimates the user's emotions and determines the priority of commands to execute based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, When executing a command, the system selects the optimal execution method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, When a command is executed, the system analyzes the user's social media activity and suggests the appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned generation screen section is It estimates the user's emotions and adjusts how the settings screen is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned generation screen section is When generating the settings screen, 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 27) The aforementioned generation screen section is When generating the settings screen, it is customized based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned generation screen section is It estimates the user's emotions and determines the priority of the settings screen based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned generation screen section is When generating the settings screen, the optimal display method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned generation screen section is When generating the settings screen, the system analyzes the user's social media activity and suggests the optimal display method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 instructions in natural language, A generation unit analyzes the instructions received by the reception unit and generates a CUI-level command, An execution unit that executes the commands generated by the generation unit, It includes a generation screen section that generates a user-specific settings screen based on information from the device. A system characterized by the following features.

2. The aforementioned generation screen section is Customize the settings screen to suit the user's level and preferences. The system according to feature 1.

3. The aforementioned generation screen section is Provides the same settings screen across different models and manufacturers. The system according to feature 1.

4. The aforementioned generation screen section is Generates a configuration screen that can be used even with low-spec network equipment. The system according to feature 1.

5. The aforementioned generation screen section is Save a screen you've set up as a favorite and reuse it. The system according to feature 1.

6. The generating unit is Generate complex commands based on user instructions. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how natural language instructions are received based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past instruction history and select the optimal reception method. The system according to feature 1.

9. The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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

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