System

Abstract

To easily set up network devices without specialized knowledge by using the system according to the embodiment.SOLUTION: A system according to the embodiment has a reception unit, a generation unit, and a provision unit. The reception unit accepts input from the user. The generation unit analyzes the information received by the receiving unit and generates the settings of the network device. The provision unit provides the settings generated by the generation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] With conventional technology, specialized knowledge is required to configure network devices, and a shortage of engineers is an issue for small and medium-sized enterprises.

[0005] The system according to the embodiment aims to enable a user to easily configure network devices without specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives an input from a user. The generating unit analyzes the information received by the receiving unit and generates a configuration for the network device. The providing unit provides the configuration generated by the generating unit to the user. Effect of the Invention

[0007] The system according to the embodiment allows the user to easily configure network devices without any specialized knowledge. [Brief description of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A network device maintenance system according to an embodiment of the present invention is a system that improves the efficiency of network device maintenance in small and medium-sized enterprises. In this system, a user inputs necessary information in a simplified format such as Excel, and an AI analyzes the information to automatically generate settings for the network device. The generated settings are provided to the user and applied to the network device. This allows even people without network knowledge to easily handle maintenance. This system improves the efficiency of network device maintenance in small and medium-sized enterprises and resolves the problem of a shortage of engineers. In addition, the AI ​​automatically handles issues such as security measures, so the system can be used with peace of mind. For example, a user inputs information such as basic network settings and security policies in a simplified format such as Excel. This information is input to the AI. Next, the AI ​​analyzes the input information and automatically generates settings for the network device. The AI ​​generates optimal settings based on knowledge of network settings that it has learned in advance. For example, it automatically generates settings for routers and switches, firewall rules, etc. The generated settings are provided to the user and applied to the network device. The user can check the generated settings and make corrections as necessary. This allows even people without network knowledge to easily handle maintenance. This system will streamline network equipment maintenance in small and medium-sized enterprises, resolving the problem of a shortage of engineers. In addition, the AI ​​will automatically handle issues such as security measures, so the system can be used with peace of mind. As a result, the network equipment maintenance system will streamline network equipment maintenance in small and medium-sized enterprises, resolving the problem of a shortage of engineers.

[0029] The network device maintenance system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input from a user. The input from the user includes, but is not limited to, for example, a text input, a voice input, and an image input. The reception unit receives information such as a basic network setting and a security policy input in Excel format. The reception unit can also convert the contents dictated by the user into text using voice input and accept the contents as input. For example, the voice is converted into text using a voice recognition technology. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a network device setting. The generation unit analyzes the input information using, for example, a data mining technology or a natural language processing technology. The generation AI generates an optimal setting based on knowledge of network settings learned in advance. For example, the generation unit automatically generates settings for routers and switches, firewall rules, and the like. The generation unit uses the generation AI to generate a network device setting. For example, the generation AI generates a network device setting using a text generation AI (for example, LLM). The generation unit can also use the generation AI to generate a network device setting. For example, the generation AI generates optimal settings based on knowledge of network settings. The providing unit provides the settings generated by the generation unit to the user. The providing unit provides the generated settings to the user in text format, for example. The providing unit can also provide the generated settings to the user in graphical format. For example, the network topology is visually displayed. The providing unit can also provide the generated settings to the user in voice format. For example, the setting contents are provided by voice using voice synthesis technology. As a result, the network device maintenance system according to the embodiment accepts and analyzes user input, and provides the generated settings, making it possible for even people without network knowledge to handle maintenance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, when providing the generated settings to the user in text format, the providing unit can generate text using the generation AI.

[0030] The reception unit receives input from a user. Examples of input from a user include, but are not limited to, text input, voice input, and image input. The reception unit receives information such as basic network settings and security policies input in Excel format. Specifically, the reception unit can receive detailed setting information such as IP addresses, subnet masks, gateways, and DNS server information entered by the user in an Excel sheet, firewall rules, and access control lists (ACLs). The reception unit can also convert the contents dictated by the user into text using voice input and accept the text as input. For example, speech is converted into text using voice recognition technology. For the voice recognition technology, a voice recognition model using deep learning is used, and the user's speech can be converted into text with high accuracy. Furthermore, image input can also be received. For example, screenshots of the setting screen of a network device or handwritten notes can be uploaded as images, and the reception unit can analyze these images and extract necessary information. For image analysis, OCR (optical character recognition) technology is used to convert handwritten characters or printed characters into digital text. This enables the receiving unit to receive input in a variety of formats, improving user convenience.

[0031] The generation unit uses the generation AI to analyze the information received by the reception unit and generate network device settings. The generation unit analyzes the input information using, for example, data mining technology or natural language processing technology. The generation AI generates optimal settings based on the knowledge of network settings learned in advance. For example, it automatically generates router and switch settings, firewall rules, etc. The generation unit uses the generation AI to generate network device settings. For example, the generation AI generates network settings using a text generation AI (for example, LLM). Specifically, the generation AI analyzes the basic network setting information entered by the user and generates an optimal router setting file. For example, it automatically sets the IP address of the router, sets the DHCP server, sets the NAT (network address translation), etc. In addition, the switch's VLAN (virtual LAN) settings and port security settings are also automatically generated by the generation AI. In addition, the generation AI also sets firewall rules and automatically sets access control for specific IP addresses and ports. The generation AI learns past setting data and best practices and can propose optimal settings. For example, it generates appropriate settings by considering security policies according to specific industries and uses. This enables the generation unit to quickly and accurately generate network device settings based on information input by the user, thereby improving the operational efficiency of the network.

[0032] The providing unit provides the user with the settings generated by the generating unit. The providing unit provides the user with the generated settings in text format, for example. The providing unit can also provide the user with the generated settings in graphical format. For example, the network topology is visually displayed. Specifically, the generated settings of the router and switch can be downloaded to the user as a text file. Furthermore, a network topology diagram is generated to enable the user to visually check the network configuration. The network topology diagram includes the arrangement and connection status of each network device, the configuration of VLANs, and the like, and the user can intuitively grasp the overall picture of the network. The providing unit can also provide the user with the generated settings in audio format. For example, the settings are provided by voice using voice synthesis technology. The voice synthesis technology uses a text-to-speech engine, and the generated settings can be conveyed to the user in a natural voice. This allows users with visual impairments or who are busy to check the settings. Furthermore, the providing unit can store the generated settings on the cloud so that the user can access them at any time. For example, the user can log in to refer to the settings generated in the past or download them again. This allows the providing unit to provide the generated settings to the user in a variety of formats, improving convenience.

[0033] The network device maintenance system includes a security unit that performs security measures. The security unit performs security measures. The security measures include, but are not limited to, for example, antivirus measures, firewall settings, and access control. The security unit protects the network device using, for example, antivirus software. The security unit can also automatically perform firewall settings. For example, the security unit automatically generates firewall rules and applies them to the network device. The security unit can also automatically generate an access control list and apply them to the network device. For example, the security unit generates rules that restrict access from a specific IP address. This allows the security measures to be performed automatically, allowing the device to be used with peace of mind. A part or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the settings of the antivirus software to the generation AI and cause the generation AI to execute the optimal settings.

[0034] The network device maintenance system includes a correction unit that corrects the generated settings. The correction unit corrects the generated settings. The correction includes, for example, manual correction, automatic correction, and a range of correction, but is not limited to such examples. The correction unit, for example, provides an interface for a user to manually correct the settings. The correction unit can also automatically correct the generated settings. For example, a part of the settings is automatically corrected and adjusted to an optimal setting. The correction unit can also specify the range of correction. For example, only a specific setting item is corrected. This makes it possible to correct the generated settings according to the user's request. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or may be performed without using AI. For example, the correction unit can input the generated settings to the generation AI and cause the generation AI to execute the correction content.

[0035] The network device maintenance system includes an application unit that applies the generated settings. The application unit applies the generated settings. The application includes, for example, the timing of application, the scope of application, and a method of confirming the application, but is not limited to such examples. The application unit, for example, adjusts the timing of applying the generated settings to the network devices. The application unit can also specify the scope of application of the generated settings. For example, the settings are applied only to a specific network device. The application unit can also provide a method of confirming the application. For example, an interface is provided for confirming whether the settings have been correctly applied. This allows the generated settings to be automatically applied, thereby making the maintenance of the network devices more efficient. A part or all of the above-mentioned processing in the application unit may be performed, for example, using AI, or may be performed without using AI. For example, the application unit can input the generated settings to the generation AI and cause the generation AI to execute the application content.

[0036] The reception unit can analyze the user's past input history and propose an optimal input format. For example, the reception unit automatically proposes an optimal input format based on information frequently input by the user in the past. The reception unit can also preferentially propose an input method (Excel, text, etc.) that the user has used in the past. The reception unit can also propose an input format suitable for a specific task from the user's past input history. This makes input work more efficient by proposing an optimal input format based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to execute the proposal of an optimal input format.

[0037] The reception unit can filter the input contents based on the user's current work situation and field of interest when acquiring the input contents. For example, the reception unit preferentially acquires information related to a project that the user is currently working on. The reception unit can also filter and acquire highly relevant information based on the user's field of interest. The reception unit can also acquire only necessary information and simplify the input contents according to the user's work situation. As a result, by filtering the input contents based on the user's work situation and field of interest, only necessary information is acquired and the input contents are simplified. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data to the generation AI and cause the generation AI to execute the filtering process.

[0038] The reception unit can preferentially acquire information with high relevance based on the geographical location information of the user when acquiring the input contents. The reception unit, for example, preferentially acquires information related to the location where the user is currently located. The reception unit can also suggest optimal input contents based on the geographical location information of the user. The reception unit can also preferentially acquire information specific to the region based on the user's location information. As a result, it is possible to suggest optimal input contents by preferentially acquiring information with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to acquire information with high relevance.

[0039] The reception unit can analyze the user's social media activity and acquire related information when acquiring the input contents. The reception unit can, for example, analyze the user's social media posting contents and acquire related information. The reception unit can also suggest optimal input contents based on the user's social media interests. The reception unit can also acquire highly relevant information based on the user's social media activity history. In this way, by analyzing the user's social media activities, highly relevant information can be acquired and optimal input contents can be suggested. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related information.

[0040] The generating unit can adjust the level of detail of the setting based on the importance of the network when generating the setting. For example, the generating unit generates a detailed setting for an important network device. The generating unit can also generate a standard setting for a general network device. The generating unit can also generate a simplified setting for a network device with low importance. In this way, the level of detail of the setting can be adjusted based on the importance of the network, thereby generating appropriate setting content. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating unit can input the importance data of the network device to the generating AI and cause the generating AI to adjust the level of detail of the setting.

[0041] The generation unit can apply different generation algorithms based on the network category when generating settings. For example, the generation unit applies a generation algorithm dedicated to routers to router settings. The generation unit can also apply a generation algorithm dedicated to switches to switch settings. The generation unit can also apply a generation algorithm dedicated to firewalls to firewall settings. In this way, optimal setting contents can be generated by applying different generation algorithms depending on the network category. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of network devices to the generation AI and cause the generation AI to apply the generation algorithm.

[0042] The generating unit can determine the priority of settings based on the network configuration time when generating settings. For example, for a newly configured network, the generating unit preferentially generates the latest settings. For an existing network, the generating unit can also preferentially generate an update based on the current settings. For an old network, the generating unit can also preferentially generate settings that take compatibility into consideration. This allows appropriate setting content to be generated by determining the priority of settings based on the network configuration time. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating unit can input network configuration time data to the generating AI and cause the generating AI to determine the priority of settings.

[0043] The generating unit can adjust the order of settings based on the relevance of the network when generating settings. For example, the generating unit generates settings for important network devices first. The generating unit can also generate settings for highly relevant network devices in succession. The generating unit can also postpone the settings for less relevant network devices. In this way, appropriate setting contents can be generated by adjusting the order of settings based on the relevance of the network. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating unit can input the relevance data of the network devices to the generating AI and cause the generating AI to adjust the order of settings.

[0044] The providing unit can select an appropriate providing method based on the user's past setting confirmation history when providing settings. The providing unit selects the optimal providing method based on, for example, setting contents confirmed by the user in the past. The providing unit can also select the most efficient providing method from the user's past setting confirmation history. The providing unit can also analyze the user's past setting confirmation history and select the optimal providing method. This enables efficient provision by selecting the optimal providing method based on the user's past setting confirmation history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past setting confirmation history data to the generating AI and cause the generating AI to select the optimal providing method.

[0045] The providing unit can customize the provided contents based on the user's current work situation when providing the settings. For example, the providing unit preferentially provides the setting contents related to the project the user is currently working on. The providing unit can also provide only the necessary setting contents according to the user's work situation. The providing unit can also customize and provide the optimal setting contents based on the user's work situation. As a result, by customizing the provided contents according to the user's work situation, only the necessary setting contents can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's work situation data to the generating AI and cause the generating AI to customize the provided contents.

[0046] The providing unit can select an appropriate providing method based on the geographical location information of the user when providing the settings. For example, the providing unit preferentially provides setting contents related to the location where the user is currently located. The providing unit can also select an optimal providing method based on the geographical location information of the user. The providing unit can also preferentially provide setting contents specific to a region based on the user's location information. This allows highly relevant setting contents to be provided by selecting an optimal providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0047] The providing unit can analyze the user's social media activity and adjust the provided content when providing the settings. For example, the providing unit can analyze the user's social media posting content and provide related setting content. The providing unit can also provide optimal setting content based on the user's social media interests. The providing unit can also provide highly relevant setting content based on the user's social media activity history. In this way, highly relevant setting content can be provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to adjust the provided content.

[0048] The security unit can select appropriate countermeasures based on past security incident data when taking security measures. For example, the security unit selects countermeasures to prevent similar incidents based on past security incident data. The security unit can also select the most effective countermeasures from past security incident data. The security unit can also analyze past security incident data and select optimal security countermeasures. In this way, similar incidents can be prevented by selecting optimal countermeasures based on past security incident data. Some or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input past security incident data to a generation AI and cause the generation AI to select optimal countermeasures.

[0049] The security unit can select an appropriate security measure based on the user's geographical location information when taking security measures. For example, the security unit prioritizes security measures related to the user's current location. The security unit can also select the optimal security measure based on the user's geographical location information. The security unit can also prioritize region-specific security measures based on the user's location information. This allows highly relevant security measures to be taken by selecting the optimal security measure based on the user's geographical location information. Some or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal measure.

[0050] The correction unit can select an appropriate correction method based on the past correction history at the time of correction. For example, the correction unit selects the optimal correction method for solving a similar problem based on the past correction history. The correction unit can also select the most effective correction method from the past correction history. The correction unit can also analyze the past correction history and select the optimal correction method. In this way, by selecting the optimal correction method based on the past correction history, a similar problem can be effectively solved. A part or all of the above-mentioned processing in the correction unit may be performed, for example, using AI, or may be performed without using AI. For example, the correction unit can input past correction history data to the generation AI and cause the generation AI to select the optimal correction method.

[0051] The correction unit can select an appropriate correction method based on the geographical location information of the user at the time of correction. For example, the correction unit prioritizes correction content related to the user's current location. The correction unit can also select an optimal correction method based on the geographical location information of the user. The correction unit can also prioritize region-specific correction content based on the user's location information. In this way, by selecting an optimal correction method based on the user's geographical location information, highly relevant correction content can be implemented. Part or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or may be performed without using AI. For example, the correction unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal correction method.

[0052] The application unit can select an appropriate application method based on past application history when applying settings. The application unit selects an optimal method for applying similar settings based on, for example, past application history. The application unit can also select the most effective application method from the past application history. The application unit can also analyze the past application history and select the optimal application method. As a result, similar settings can be effectively applied by selecting the optimal application method based on the past application history. Some or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input past application history data to the generation AI and cause the generation AI to select the optimal application method.

[0053] The application unit can select an appropriate application method based on the user's geographical location information when applying the settings. For example, the application unit preferentially applies setting contents related to the user's current location. The application unit can also select an optimal application method based on the user's geographical location information. The application unit can also preferentially apply region-specific setting contents based on the user's location information. This allows highly relevant setting contents to be applied by selecting an optimal application method based on the user's geographical location information. A part or all of the above-mentioned processing in the application unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the application unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal application method.

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

[0055] When accepting input contents from a user, the reception unit can analyze the user's past input history and suggest an optimal input format. For example, the reception unit can automatically suggest an optimal input format based on information frequently input by the user in the past. The reception unit can also preferentially suggest input methods (Excel, text, etc.) that the user has used in the past. Furthermore, the reception unit can also suggest an input format suitable for a specific task from the user's past input history. In this way, input work can be made more efficient by suggesting an optimal input format based on the user's past input history.

[0056] When acquiring the input contents, the reception unit can filter the input contents based on the user's current work situation and field of interest. For example, information related to a project that the user is currently working on is preferentially acquired. The reception unit can also filter and acquire highly relevant information based on the user's field of interest. Furthermore, the reception unit can acquire only necessary information and simplify the input contents according to the user's work situation. In this way, by filtering the input contents based on the user's work situation and field of interest, only necessary information is acquired and the input contents are simplified.

[0057] The generating unit can adjust the level of detail of the settings based on the importance of the network when generating the settings. For example, detailed settings are generated for important network devices. The generating unit can also generate standard settings for general network devices. Furthermore, the generating unit can also generate simplified settings for network devices with low importance. In this way, appropriate setting content can be generated by adjusting the level of detail of the settings based on the importance of the network.

[0058] The providing unit can select an appropriate providing method based on the user's past setting confirmation history when providing settings. For example, the optimal providing method is selected based on the setting contents confirmed by the user in the past. The providing unit can also select the most efficient providing method from the user's past setting confirmation history. Furthermore, the providing unit can analyze the user's past setting confirmation history and select the optimal providing method. This enables efficient provision by selecting the optimal providing method based on the user's past setting confirmation history.

[0059] When implementing security measures, the security department can select appropriate measures based on past security incident data. For example, measures to prevent similar incidents are selected based on past security incident data. The security department can also select the most effective measures from past security incident data. Furthermore, the security department can analyze past security incident data and select optimal security measures. In this way, similar incidents can be prevented by selecting optimal measures based on past security incident data.

[0060] At the time of correction, the correction unit can select an appropriate correction method based on the past correction history. For example, the correction unit selects the optimal correction method for solving a similar problem based on the past correction history. The correction unit can also select the most effective correction method from the past correction history. Furthermore, the correction unit can analyze the past correction history and select the optimal correction method. In this way, similar problems can be effectively solved by selecting the optimal correction method based on the past correction history.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit receives input from the user. Input from the user includes text input, voice input, image input, etc. For example, information such as basic network settings and security policies entered in Excel format is received. In addition, contents dictated by the user using voice input can be converted into text and accepted as input contents. Voice recognition technology is used to convert voice to text. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and generate network device settings. The generation unit analyzes the input information using data mining and natural language processing technologies. The generation AI generates optimal settings based on the knowledge of network settings it has learned in advance. For example, it automatically generates router and switch settings, firewall rules, etc. Step 3: The providing unit provides the configuration generated by the generating unit to the user. The providing unit can provide the generated configuration to the user in a text format, a graphical format, or an audio format. For example, the providing unit can visually display the network topology or provide the configuration content by voice using a voice synthesis technology.

[0063] (Example 2) A network device maintenance system according to an embodiment of the present invention is a system that improves the efficiency of network device maintenance in small and medium-sized enterprises. In this system, a user inputs necessary information in a simplified format such as Excel, and an AI analyzes the information to automatically generate settings for the network device. The generated settings are provided to the user and applied to the network device. This allows even people without network knowledge to easily handle maintenance. This system improves the efficiency of network device maintenance in small and medium-sized enterprises and resolves the problem of a shortage of engineers. In addition, the AI ​​automatically handles issues such as security measures, so the system can be used with peace of mind. For example, a user inputs information such as basic network settings and security policies in a simplified format such as Excel. This information is input to the AI. Next, the AI ​​analyzes the input information and automatically generates settings for the network device. The AI ​​generates optimal settings based on knowledge of network settings that it has learned in advance. For example, it automatically generates settings for routers and switches, firewall rules, etc. The generated settings are provided to the user and applied to the network device. The user can check the generated settings and make corrections as necessary. This allows even people without network knowledge to easily handle maintenance. This system will streamline network equipment maintenance in small and medium-sized enterprises, resolving the problem of a shortage of engineers. In addition, the AI ​​will automatically handle issues such as security measures, so the system can be used with peace of mind. As a result, the network equipment maintenance system will streamline network equipment maintenance in small and medium-sized enterprises, resolving the problem of a shortage of engineers.

[0064] The network device maintenance system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input from a user. The input from the user includes, but is not limited to, for example, a text input, a voice input, and an image input. The reception unit receives information such as a basic network setting and a security policy input in Excel format. The reception unit can also convert the contents dictated by the user into text using voice input and accept the contents as input. For example, the voice is converted into text using a voice recognition technology. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a network device setting. The generation unit analyzes the input information using, for example, a data mining technology or a natural language processing technology. The generation AI generates an optimal setting based on knowledge of network settings learned in advance. For example, the generation unit automatically generates settings for routers and switches, firewall rules, and the like. The generation unit uses the generation AI to generate a network device setting. For example, the generation AI generates a network device setting using a text generation AI (for example, LLM). The generation unit can also use the generation AI to generate a network device setting. For example, the generation AI generates optimal settings based on knowledge of network settings. The providing unit provides the settings generated by the generation unit to the user. The providing unit provides the generated settings to the user in text format, for example. The providing unit can also provide the generated settings to the user in graphical format. For example, the network topology is visually displayed. The providing unit can also provide the generated settings to the user in voice format. For example, the setting contents are provided by voice using voice synthesis technology. As a result, the network device maintenance system according to the embodiment accepts and analyzes user input, and provides the generated settings, making it possible for even people without network knowledge to handle maintenance. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, when providing the generated settings to the user in text format, the providing unit can generate text using the generation AI.

[0065] The reception unit receives input from a user. Examples of input from a user include, but are not limited to, text input, voice input, and image input. The reception unit receives information such as basic network settings and security policies input in Excel format. Specifically, the reception unit can receive detailed setting information such as IP addresses, subnet masks, gateways, and DNS server information entered by the user in an Excel sheet, firewall rules, and access control lists (ACLs). The reception unit can also convert the contents dictated by the user into text using voice input and accept the text as input. For example, speech is converted into text using voice recognition technology. For the voice recognition technology, a voice recognition model using deep learning is used, and the user's speech can be converted into text with high accuracy. Furthermore, image input can also be received. For example, screenshots of the setting screen of a network device or handwritten notes can be uploaded as images, and the reception unit can analyze these images and extract necessary information. For image analysis, OCR (optical character recognition) technology is used to convert handwritten characters or printed characters into digital text. This enables the receiving unit to receive input in a variety of formats, improving user convenience.

[0066] The generation unit uses the generation AI to analyze the information received by the reception unit and generate network device settings. The generation unit analyzes the input information using, for example, data mining technology or natural language processing technology. The generation AI generates optimal settings based on the knowledge of network settings learned in advance. For example, it automatically generates router and switch settings, firewall rules, etc. The generation unit uses the generation AI to generate network device settings. For example, the generation AI generates network settings using a text generation AI (for example, LLM). Specifically, the generation AI analyzes the basic network setting information entered by the user and generates an optimal router setting file. For example, it automatically sets the IP address of the router, sets the DHCP server, sets the NAT (network address translation), etc. In addition, the switch's VLAN (virtual LAN) settings and port security settings are also automatically generated by the generation AI. In addition, the generation AI also sets firewall rules and automatically sets access control for specific IP addresses and ports. The generation AI learns past setting data and best practices and can propose optimal settings. For example, it generates appropriate settings by considering security policies according to specific industries and uses. This enables the generation unit to quickly and accurately generate network device settings based on information input by the user, thereby improving the operational efficiency of the network.

[0067] The providing unit provides the user with the settings generated by the generating unit. The providing unit provides the user with the generated settings in text format, for example. The providing unit can also provide the user with the generated settings in graphical format. For example, the network topology is visually displayed. Specifically, the generated settings of the router and switch can be downloaded to the user as a text file. Furthermore, a network topology diagram is generated to enable the user to visually check the network configuration. The network topology diagram includes the arrangement and connection status of each network device, the configuration of VLANs, and the like, and the user can intuitively grasp the overall picture of the network. The providing unit can also provide the user with the generated settings in audio format. For example, the settings are provided by voice using voice synthesis technology. The voice synthesis technology uses a text-to-speech engine, and the generated settings can be conveyed to the user in a natural voice. This allows users with visual impairments or who are busy to check the settings. Furthermore, the providing unit can store the generated settings on the cloud so that the user can access them at any time. For example, the user can log in to refer to the settings generated in the past or download them again. This allows the providing unit to provide the generated settings to the user in a variety of formats, improving convenience.

[0068] The network device maintenance system includes a security unit that performs security measures. The security unit performs security measures. The security measures include, but are not limited to, for example, antivirus measures, firewall settings, and access control. The security unit protects the network device using, for example, antivirus software. The security unit can also automatically perform firewall settings. For example, the security unit automatically generates firewall rules and applies them to the network device. The security unit can also automatically generate an access control list and apply them to the network device. For example, the security unit generates rules that restrict access from a specific IP address. This allows the security measures to be performed automatically, allowing the device to be used with peace of mind. A part or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the settings of the antivirus software to the generation AI and cause the generation AI to execute the optimal settings.

[0069] The network device maintenance system includes a correction unit that corrects the generated settings. The correction unit corrects the generated settings. The correction includes, for example, manual correction, automatic correction, and a range of correction, but is not limited to such examples. The correction unit, for example, provides an interface for a user to manually correct the settings. The correction unit can also automatically correct the generated settings. For example, a part of the settings is automatically corrected and adjusted to an optimal setting. The correction unit can also specify the range of correction. For example, only a specific setting item is corrected. This makes it possible to correct the generated settings according to the user's request. Some or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or may be performed without using AI. For example, the correction unit can input the generated settings to the generation AI and cause the generation AI to execute the correction content.

[0070] The network device maintenance system includes an application unit that applies the generated settings. The application unit applies the generated settings. The application includes, for example, the timing of application, the scope of application, and a method of confirming the application, but is not limited to such examples. The application unit, for example, adjusts the timing of applying the generated settings to the network devices. The application unit can also specify the scope of application of the generated settings. For example, the settings are applied only to a specific network device. The application unit can also provide a method of confirming the application. For example, an interface is provided for confirming whether the settings have been correctly applied. This allows the generated settings to be automatically applied, thereby making the maintenance of the network devices more efficient. A part or all of the above-mentioned processing in the application unit may be performed, for example, using AI, or may be performed without using AI. For example, the application unit can input the generated settings to the generation AI and cause the generation AI to execute the application content.

[0071] The reception unit can estimate the user's emotion and adjust the timing of prompting the user to confirm the input contents based on the estimated user's emotion. For example, when the user is stressed, the reception unit quickly confirms the input contents, thereby reducing the burden on the user. In addition, when the user is relaxed, the reception unit can also perform detailed confirmation to improve the accuracy of the input contents. In addition, when the user is in a hurry, the reception unit can perform minimal confirmation and quickly proceed to the next step. In this way, the burden on the user is reduced by adjusting the timing of confirmation of the input contents according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0072] The reception unit can analyze the user's past input history and propose an optimal input format. For example, the reception unit automatically proposes an optimal input format based on information frequently input by the user in the past. The reception unit can also preferentially propose an input method (Excel, text, etc.) that the user has used in the past. The reception unit can also propose an input format suitable for a specific task from the user's past input history. This makes input work more efficient by proposing an optimal input format based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to execute the proposal of an optimal input format.

[0073] The reception unit can filter the input contents based on the user's current work situation and field of interest when acquiring the input contents. For example, the reception unit preferentially acquires information related to a project that the user is currently working on. The reception unit can also filter and acquire highly relevant information based on the user's field of interest. The reception unit can also acquire only necessary information and simplify the input contents according to the user's work situation. As a result, by filtering the input contents based on the user's work situation and field of interest, only necessary information is acquired and the input contents are simplified. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's work situation data to the generation AI and cause the generation AI to execute the filtering process.

[0074] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit prioritizes checking important input contents. In addition, when the user is relaxed, the reception unit can also prioritize checking detailed input contents. In addition, when the user is in a hurry, the reception unit can also prioritize checking minimum input contents. In this way, by determining the priority of the input contents according to the user's emotion, important input contents can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can preferentially acquire information with high relevance based on the geographical location information of the user when acquiring the input contents. The reception unit, for example, preferentially acquires information related to the location where the user is currently located. The reception unit can also suggest optimal input contents based on the geographical location information of the user. The reception unit can also preferentially acquire information specific to the region based on the user's location information. As a result, it is possible to suggest optimal input contents by preferentially acquiring information with high relevance based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information data of the user to the generation AI and cause the generation AI to acquire information with high relevance.

[0076] The reception unit can analyze the user's social media activity and acquire related information when acquiring the input contents. The reception unit can, for example, analyze the user's social media posting contents and acquire related information. The reception unit can also suggest optimal input contents based on the user's social media interests. The reception unit can also acquire highly relevant information based on the user's social media activity history. In this way, by analyzing the user's social media activities, highly relevant information can be acquired and optimal input contents can be suggested. A part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to acquire related information.

[0077] The generation unit can estimate the user's emotion and adjust the expression method of the generated settings based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates detailed setting contents. In addition, when the user is in a hurry, the generation unit can also generate simple setting contents. In addition, when the user is stressed, the generation unit can also generate visually easy-to-understand setting contents. In this way, by adjusting the expression method of the settings according to the user's emotion, it is possible to provide setting contents that are easy for the user to understand. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The generating unit can adjust the level of detail of the setting based on the importance of the network when generating the setting. For example, the generating unit generates a detailed setting for an important network device. The generating unit can also generate a standard setting for a general network device. The generating unit can also generate a simplified setting for a network device with low importance. In this way, the level of detail of the setting can be adjusted based on the importance of the network, thereby generating appropriate setting content. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating unit can input the importance data of the network device to the generating AI and cause the generating AI to adjust the level of detail of the setting.

[0079] The generation unit can apply different generation algorithms based on the network category when generating settings. For example, the generation unit applies a generation algorithm dedicated to routers to router settings. The generation unit can also apply a generation algorithm dedicated to switches to switch settings. The generation unit can also apply a generation algorithm dedicated to firewalls to firewall settings. In this way, optimal setting contents can be generated by applying different generation algorithms depending on the network category. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of network devices to the generation AI and cause the generation AI to apply the generation algorithm.

[0080] The generation unit can estimate the user's emotion and adjust the length of the setting to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short setting that covers the main points. In addition, when the user is relaxed, the generation unit can also generate a longer setting that includes detailed explanations. In addition, when the user is excited, the generation unit can also generate a setting that adds visually stimulating effects. In this way, by adjusting the length of the setting according to the user's emotion, it is possible to provide a setting content that is appropriate for the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The generating unit can determine the priority of settings based on the network configuration time when generating settings. For example, for a newly configured network, the generating unit preferentially generates the latest settings. For an existing network, the generating unit can also preferentially generate an update based on the current settings. For an old network, the generating unit can also preferentially generate settings that take compatibility into consideration. This allows appropriate setting content to be generated by determining the priority of settings based on the network configuration time. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating unit can input network configuration time data to the generating AI and cause the generating AI to determine the priority of settings.

[0082] The generating unit can adjust the order of settings based on the relevance of the network when generating settings. For example, the generating unit generates settings for important network devices first. The generating unit can also generate settings for highly relevant network devices in succession. The generating unit can also postpone the settings for less relevant network devices. In this way, appropriate setting contents can be generated by adjusting the order of settings based on the relevance of the network. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generating unit can input the relevance data of the network devices to the generating AI and cause the generating AI to adjust the order of settings.

[0083] The providing unit can estimate the user's emotion and adjust the setting providing method based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible providing method. Furthermore, when the user is relaxed, the providing unit can also provide a providing method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a providing method that focuses on the main points. In this way, by adjusting the setting providing method according to the user's emotion, a providing method that is easy for the user to understand can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The providing unit can select an appropriate providing method based on the user's past setting confirmation history when providing settings. The providing unit selects the optimal providing method based on, for example, setting contents confirmed by the user in the past. The providing unit can also select the most efficient providing method from the user's past setting confirmation history. The providing unit can also analyze the user's past setting confirmation history and select the optimal providing method. This enables efficient provision by selecting the optimal providing method based on the user's past setting confirmation history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the user's past setting confirmation history data to the generating AI and cause the generating AI to select the optimal providing method.

[0085] The providing unit can customize the provided contents based on the user's current work situation when providing the settings. For example, the providing unit preferentially provides the setting contents related to the project the user is currently working on. The providing unit can also provide only the necessary setting contents according to the user's work situation. The providing unit can also customize and provide the optimal setting contents based on the user's work situation. As a result, by customizing the provided contents according to the user's work situation, only the necessary setting contents can be provided. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's work situation data to the generating AI and cause the generating AI to customize the provided contents.

[0086] The providing unit can estimate the user's emotion and determine the order of providing the settings based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides important setting contents with priority. In addition, when the user is relaxed, the providing unit can also provide detailed setting contents with priority. In addition, when the user is in a hurry, the providing unit can also provide minimum setting contents with priority. In this way, by determining the order of providing the settings according to the user's emotion, important setting contents can be provided with priority. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The providing unit can select an appropriate providing method based on the geographical location information of the user when providing the settings. For example, the providing unit preferentially provides setting contents related to the location where the user is currently located. The providing unit can also select an optimal providing method based on the geographical location information of the user. The providing unit can also preferentially provide setting contents specific to a region based on the user's location information. This allows highly relevant setting contents to be provided by selecting an optimal providing method based on the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the geographical location information data of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0088] The providing unit can analyze the user's social media activity and adjust the provided content when providing the settings. For example, the providing unit can analyze the user's social media posting content and provide related setting content. The providing unit can also provide optimal setting content based on the user's social media interests. The providing unit can also provide highly relevant setting content based on the user's social media activity history. In this way, highly relevant setting content can be provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the providing unit can be performed, for example, using AI or can be performed without using AI. For example, the providing unit can input the user's social media activity data to the generating AI and cause the generating AI to adjust the provided content.

[0089] The security unit can estimate the user's emotion and determine the priority of security measures based on the estimated user's emotion. For example, when the user is nervous, the security unit prioritizes important security measures. Also, when the user is relaxed, the security unit can prioritize detailed security measures. Also, when the user is in a hurry, the security unit can prioritize minimum security measures. In this way, by determining the priority of security measures according to the user's emotion, important security measures can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the security unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the security unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The security unit can select appropriate countermeasures based on past security incident data when taking security measures. For example, the security unit selects countermeasures to prevent similar incidents based on past security incident data. The security unit can also select the most effective countermeasures from past security incident data. The security unit can also analyze past security incident data and select optimal security countermeasures. In this way, similar incidents can be prevented by selecting optimal countermeasures based on past security incident data. Some or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input past security incident data to a generation AI and cause the generation AI to select optimal countermeasures.

[0091] The security unit can estimate the user's emotion and adjust the display method of the security measures based on the estimated user's emotion. For example, when the user is nervous, the security unit provides a simple and highly visible display method. Also, when the user is relaxed, the security unit can provide a display method including detailed information. Also, when the user is in a hurry, the security unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the security measures according to the user's emotion, a display method that is easy for the user to understand can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the security unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the security unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The security unit can select an appropriate security measure based on the user's geographical location information when taking security measures. For example, the security unit prioritizes security measures related to the user's current location. The security unit can also select the optimal security measure based on the user's geographical location information. The security unit can also prioritize region-specific security measures based on the user's location information. This allows highly relevant security measures to be taken by selecting the optimal security measure based on the user's geographical location information. Some or all of the above-mentioned processing in the security unit may be performed using, for example, AI, or may be performed without using AI. For example, the security unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal measure.

[0093] The correction unit can estimate the user's emotion and determine the priority of the correction contents based on the estimated user's emotion. For example, when the user is nervous, the correction unit prioritizes important correction contents. Also, when the user is relaxed, the correction unit can prioritize detailed correction contents. Also, when the user is in a hurry, the correction unit can prioritize minimum correction contents. In this way, by determining the priority of the correction contents according to the user's emotion, important correction contents can be prioritized. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0094] The correction unit can select an appropriate correction method based on the past correction history at the time of correction. For example, the correction unit selects the optimal correction method for solving a similar problem based on the past correction history. The correction unit can also select the most effective correction method from the past correction history. The correction unit can also analyze the past correction history and select the optimal correction method. In this way, by selecting the optimal correction method based on the past correction history, a similar problem can be effectively solved. A part or all of the above-mentioned processing in the correction unit may be performed, for example, using AI, or may be performed without using AI. For example, the correction unit can input past correction history data to the generation AI and cause the generation AI to select the optimal correction method.

[0095] The correction unit can estimate the user's emotion and adjust the display method of the correction contents based on the estimated user's emotion. For example, when the user is nervous, the correction unit provides a simple and highly visible display method. In addition, when the user is relaxed, the correction unit can also provide a display method including detailed information. In addition, when the user is in a hurry, the correction unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the correction contents according to the user's emotion, a display method that is easy for the user to understand can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the emotion estimation.

[0096] The correction unit can select an appropriate correction method based on the geographical location information of the user at the time of correction. For example, the correction unit prioritizes correction content related to the user's current location. The correction unit can also select an optimal correction method based on the geographical location information of the user. The correction unit can also prioritize region-specific correction content based on the user's location information. In this way, by selecting an optimal correction method based on the user's geographical location information, highly relevant correction content can be implemented. Part or all of the above-mentioned processing in the correction unit may be performed, for example, using AI or may be performed without using AI. For example, the correction unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal correction method.

[0097] The application unit can estimate the user's emotion and adjust the timing of setting application based on the estimated user's emotion. For example, when the user is nervous, the application unit applies important setting contents with priority. Also, when the user is relaxed, the application unit can also apply detailed setting contents with priority. Also, when the user is in a hurry, the application unit can apply minimum setting contents with priority. In this way, by adjusting the timing of setting application according to the user's emotion, important setting contents can be applied at an appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the application unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the application unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0098] The application unit can select an appropriate application method based on past application history when applying settings. The application unit selects an optimal method for applying similar settings based on, for example, past application history. The application unit can also select the most effective application method from the past application history. The application unit can also analyze the past application history and select the optimal application method. As a result, similar settings can be effectively applied by selecting the optimal application method based on the past application history. Some or all of the above-mentioned processing in the application unit may be performed using, for example, AI, or may be performed without using AI. For example, the application unit can input past application history data to the generation AI and cause the generation AI to select the optimal application method.

[0099] The application unit can estimate the user's emotion and determine the priority of setting application based on the estimated user's emotion. For example, when the user is nervous, the application unit applies important setting contents with priority. Also, when the user is relaxed, the application unit can also apply detailed setting contents with priority. Also, when the user is in a hurry, the application unit can apply minimum setting contents with priority. In this way, by determining the priority of setting application according to the user's emotion, important setting contents can be applied with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the application unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the application unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0100] The application unit can select an appropriate application method based on the user's geographical location information when applying the settings. For example, the application unit preferentially applies setting contents related to the user's current location. The application unit can also select an optimal application method based on the user's geographical location information. The application unit can also preferentially apply region-specific setting contents based on the user's location information. This allows highly relevant setting contents to be applied by selecting an optimal application method based on the user's geographical location information. A part or all of the above-mentioned processing in the application unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the application unit can input the user's geographical location information data to the generation AI and cause the generation AI to select the optimal application method.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] When accepting input contents from a user, the reception unit can analyze the user's past input history and suggest an optimal input format. For example, the reception unit can automatically suggest an optimal input format based on information frequently input by the user in the past. The reception unit can also preferentially suggest input methods (Excel, text, etc.) that the user has used in the past. Furthermore, the reception unit can also suggest an input format suitable for a specific task from the user's past input history. In this way, input work can be made more efficient by suggesting an optimal input format based on the user's past input history.

[0103] The security unit can estimate the user's emotions and determine the priority order of security measures based on the estimated user's emotions. For example, if the user is nervous, important security measures are implemented with priority. The security unit can also implement detailed security measures with priority when the user is relaxed. Furthermore, if the user is in a hurry, the security unit can also implement minimum security measures with priority. In this way, by determining the priority order of security measures according to the user's emotions, important security measures can be implemented with priority.

[0104] The correction unit can estimate the user's emotions and determine the priority of correction contents based on the estimated user's emotions. For example, if the user is nervous, important correction contents are implemented with priority. In addition, the correction unit can also prioritize detailed correction contents when the user is relaxed. Furthermore, if the user is in a hurry, the correction unit can also prioritize minimal correction contents. In this way, by determining the priority of correction contents according to the user's emotions, important correction contents can be implemented with priority.

[0105] The application unit can estimate the user's emotion and adjust the timing of applying the settings based on the estimated user's emotion. For example, if the user is nervous, the application unit can prioritize application of important setting contents. Also, if the user is relaxed, the application unit can prioritize application of detailed setting contents. Furthermore, if the user is in a hurry, the application unit can prioritize application of minimum setting contents. In this way, by adjusting the timing of applying the settings according to the user's emotion, important setting contents can be applied at an appropriate time.

[0106] The providing unit can estimate the user's emotions and adjust the method of providing settings based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible method of providing settings is provided. In addition, the providing unit can also provide a method of providing settings including detailed information if the user is relaxed. Furthermore, if the user is in a hurry, a method of providing settings that focuses on the main points can be provided. In this way, by adjusting the method of providing settings according to the user's emotions, a method of providing settings that is easy for the user to understand can be provided.

[0107] When acquiring the input contents, the reception unit can filter the input contents based on the user's current work situation and field of interest. For example, information related to a project that the user is currently working on is preferentially acquired. The reception unit can also filter and acquire highly relevant information based on the user's field of interest. Furthermore, the reception unit can acquire only necessary information and simplify the input contents according to the user's work situation. In this way, by filtering the input contents based on the user's work situation and field of interest, only necessary information is acquired and the input contents are simplified.

[0108] The generating unit can adjust the level of detail of the settings based on the importance of the network when generating the settings. For example, detailed settings are generated for important network devices. The generating unit can also generate standard settings for general network devices. Furthermore, the generating unit can also generate simplified settings for network devices with low importance. In this way, appropriate setting content can be generated by adjusting the level of detail of the settings based on the importance of the network.

[0109] The providing unit can select an appropriate providing method based on the user's past setting confirmation history when providing settings. For example, the optimal providing method is selected based on the setting contents confirmed by the user in the past. The providing unit can also select the most efficient providing method from the user's past setting confirmation history. Furthermore, the providing unit can analyze the user's past setting confirmation history and select the optimal providing method. This enables efficient provision by selecting the optimal providing method based on the user's past setting confirmation history.

[0110] When implementing security measures, the security department can select appropriate measures based on past security incident data. For example, measures to prevent similar incidents are selected based on past security incident data. The security department can also select the most effective measures from past security incident data. Furthermore, the security department can analyze past security incident data and select optimal security measures. In this way, similar incidents can be prevented by selecting optimal measures based on past security incident data.

[0111] At the time of correction, the correction unit can select an appropriate correction method based on the past correction history. For example, the correction unit selects the optimal correction method for solving a similar problem based on the past correction history. The correction unit can also select the most effective correction method from the past correction history. Furthermore, the correction unit can analyze the past correction history and select the optimal correction method. In this way, similar problems can be effectively solved by selecting the optimal correction method based on the past correction history.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The reception unit receives input from the user. Input from the user includes text input, voice input, image input, etc. For example, information such as basic network settings and security policies entered in Excel format is received. In addition, contents dictated by the user using voice input can be converted into text and accepted as input contents. Voice recognition technology is used to convert voice to text. Step 2: The generation unit uses the generation AI to analyze the information received by the reception unit and generate network device settings. The generation unit analyzes the input information using data mining and natural language processing technologies. The generation AI generates optimal settings based on the knowledge of network settings it has learned in advance. For example, it automatically generates router and switch settings, firewall rules, etc. Step 3: The providing unit provides the configuration generated by the generating unit to the user. The providing unit can provide the generated configuration to the user in a text format, a graphical format, or an audio format. For example, the providing unit can visually display the network topology or provide the configuration content by voice using a voice synthesis technology.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, security unit, correction unit, and application unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input from a user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes input information to generate settings for the network device. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated settings to the user. The security unit is realized by the specific processing unit 290 of the data processing device 12 and takes security measures. The correction unit is realized by the control unit 46A of the smart device 14 and corrects the generated settings. The application unit is realized by the specific processing unit 290 of the data processing device 12 and applies the generated settings to the network device. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, security unit, correction unit, and application unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information to generate settings for the network device. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated settings to the user by voice. The security unit is realized by the specific processing unit 290 of the data processing device 12 and takes security measures. The correction unit is realized by the control unit 46A of the smart glasses 214 and corrects the generated settings. The application unit is realized by the specific processing unit 290 of the data processing device 12 and applies the generated settings to the network device. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, security unit, correction unit, and application unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information to generate settings for the network device. The provision unit is realized by the display 343 of the headset type terminal 314 and visually provides the generated settings to the user. The security unit is realized by the specific processing unit 290 of the data processing device 12 and takes security measures. The correction unit is realized by the control unit 46A of the headset type terminal 314 and corrects the generated settings. The application unit is realized by the specific processing unit 290 of the data processing device 12 and applies the generated settings to the network device. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, security unit, correction unit, and application unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information to generate settings for the network device. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated settings to the user by voice. The security unit is realized by the specific processing unit 290 of the data processing device 12 and takes security measures. The correction unit is realized by the control unit 46A of the robot 414 and corrects the generated settings. The application unit is realized by the specific processing unit 290 of the data processing device 12 and applies the generated settings to the network device. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) A reception unit that receives an input from a user; a generating unit that analyzes the information received by the receiving unit and generates settings for the network device; A provision unit that provides the setting generated by the generation unit to a user. A system characterized by: (Appendix 2) Equipping a security department to implement security measures 2. The system of claim 1, (Appendix 3) Provides a correction section to correct the generated configuration 2. The system of claim 1, (Appendix 4) An application unit that applies the generated settings 2. The system of claim 1, (Appendix 5) The reception unit is Infer the user's emotions and adjust the timing to prompt the user to confirm the input content based on the estimated user emotions. 2. The system of claim 1, (Appendix 6) The reception unit is Analyze the user's past input history and suggest appropriate input formats 2. The system of claim 1, (Appendix 7) The reception unit is As input is captured, filter it based on the user's current work situation or interests 2. The system of claim 1, (Appendix 8) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 9) The reception unit is When capturing input, prioritize the most relevant information based on the user's geographic location 2. The system of claim 1, (Appendix 10) The reception unit is As input is captured, the user's social media activity is analyzed to retrieve relevant information 2. The system of claim 1, (Appendix 11) The generation unit is Estimate the user's emotions and adjust the expression of the generated settings based on the estimated user emotions. 2. The system of claim 1, (Appendix 12) The generation unit is When generating configuration, adjust the level of detail depending on the importance of the network. 2. The system of claim 1, (Appendix 13) The generation unit is During configuration generation, different generation algorithms are applied based on the network category. 2. The system of claim 1, (Appendix 14) The generation unit is Estimate the user's emotion and adjust the length of the generated setting based on the estimated user's emotion. 2. The system of claim 1, (Appendix 15) The generation unit is When generating configuration, prioritize configuration depending on when the network was configured 2. The system of claim 1, (Appendix 16) The generation unit is When generating configuration, adjust the order of configurations according to network relevance 2. The system of claim 1, (Appendix 17) The providing unit is Inferring a user's emotions and adjusting the way settings are presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 18) The providing unit is When providing settings, the appropriate provision method is selected based on the user's past setting confirmation history. 2. The system of claim 1, (Appendix 19) The providing unit is When providing settings, customize the content provided according to the user's current work situation. 2. The system of claim 1, (Appendix 20) The providing unit is A user's emotion is estimated, and a setting provision order is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 21) The providing unit is When providing settings, select the appropriate provision method based on the user's geographic location information 2. The system of claim 1, (Appendix 22) The providing unit is When you set up a service, analyze your social media activity to tailor the service to your needs 2. The system of claim 1, (Appendix 23) The security unit includes: Inferring user emotions and prioritizing security measures based on the inferred user emotions 3. The system of claim 2, (Appendix 24) The security unit includes: When implementing security measures, select appropriate measures based on past security incident data. 3. The system of claim 2, (Appendix 25) The security unit includes: Inferring user emotions and adjusting the way security measures are displayed based on the inferred user emotions 3. The system of claim 2, (Appendix 26) The security unit includes: When implementing security measures, select appropriate measures based on the user's geographic location information 3. The system of claim 2, (Appendix 27) The correction unit is Inferring user sentiment and prioritizing corrections based on the estimated user sentiment 4. The system of claim 3. (Appendix 28) The correction unit is When making corrections, select the appropriate correction method based on past correction history. 4. The system of claim 3. (Appendix 29) The correction unit is Inferring a user's emotions and adjusting the way in which corrections are presented based on the inferred user emotions 4. The system of claim 3. (Appendix 30) The correction unit is During remediation, the appropriate remediation method is selected based on the user's geographic location. 4. The system of claim 3. (Appendix 31) The application unit includes: To estimate a user's emotion and adjust the timing of applying settings based on the estimated user's emotion. 5. The system of claim 4. (Appendix 32) The application unit includes: When applying settings, the appropriate application method is selected based on past application history. 5. The system of claim 4. (Appendix 33) The application unit includes: A user's emotion is estimated, and a priority order for setting application is determined based on the estimated user's emotion. 5. The system of claim 4. (Appendix 34) The application unit includes: When applying settings, the appropriate application method is selected based on the user's geographic location information. 5. The system of claim 4. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives an input from a user; a generating unit that analyzes the information received by the receiving unit and generates settings for the network device; A provision unit that provides the setting generated by the generation unit to a user. A system characterized by:

2. Equipping a security department to implement security measures 2. The system of claim 1 .

3. Provides a correction section to correct the generated configuration 2. The system of claim 1 .

4. An application unit that applies the generated settings 2. The system of claim 1 .

5. The reception unit is Infer the user's emotions and adjust the timing to prompt the user to confirm the input content based on the estimated user emotions.

2. The system of claim 1 .

6. The reception unit is Analyze the user's past input history and suggest appropriate input formats 2. The system of claim 1 .

7. The reception unit is As input is captured, filter it based on the user's current work situation or interests 2. The system of claim 1 .

8. The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1 .

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