system

Abstract

The system according to this embodiment aims to enable small and medium-sized enterprises to select the optimal security measures within a limited budget. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives information from the user regarding the current network configuration, security measures, and budget. The analysis unit analyzes and evaluates the information received by the reception unit. The proposal unit proposes a security solution based on the evaluation results obtained by the analysis unit. The provision unit provides the solution proposed by the proposal unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for small and medium-sized enterprises to select optimal security measures within a limited budget.

[0005] The system according to the embodiment aims to enable small and medium-sized enterprises to select optimal security measures within a limited budget.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives information from the user regarding the current network configuration, security measures, and budget. The analysis unit analyzes and evaluates the information received by the reception unit. The proposal unit proposes security solutions based on the evaluation results obtained by the analysis unit. The provision unit provides the solutions proposed by the proposal unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows small and medium-sized enterprises to select the optimal security measures within a limited budget. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The security solution provision system according to an embodiment of the present invention is a system that provides optimal security solutions to individual small and medium-sized enterprises (SMEs) by inputting information such as the current network configuration, security measures, and budget into a generating AI. In this system, the user inputs information such as the current network configuration, security measures, and budget, and the generating AI analyzes the input information and performs a third-party evaluation. Based on this evaluation, the generating AI proposes the optimal security solution. The proposed solution is provided to the user, enabling them to implement the most effective security measures within their budget. For example, the user inputs information such as the presence or absence of a firewall, VPN usage, the status of antivirus software implementation as a security measure, and the annual security measures budget. The generating AI analyzes this information and evaluates the current security measures. Based on the evaluation results, the generating AI proposes the optimal security solution, such as strengthening the firewall, installing new antivirus software, or changing VPN settings. This tool allows SMEs to implement the most effective security measures within their limited budget, minimizing security risks. Thus, the security solution provision system can provide the optimal security solution based on user information.

[0029] The security solution provision system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives information from the user regarding the current network configuration, security measures, and budget. For example, the user inputs information such as the presence or absence of a firewall, VPN usage, the status of antivirus software installation as a security measure, and the annual security measures cost as a budget. The analysis unit analyzes and evaluates the information received by the reception unit. For example, the generating AI evaluates the current security measures based on the network configuration, security measures, and budget information entered by the user. The proposal unit proposes security solutions based on the evaluation results obtained by the analysis unit. For example, the generating AI proposes the optimal security solution, such as strengthening the firewall, installing new antivirus software, or changing VPN settings. The provision unit provides the solution proposed by the proposal unit to the user. For example, the generating AI provides the proposed security solution to the user, enabling the most effective security measures to be implemented within the budget. Thus, the security solution provision system according to this embodiment can provide the optimal security solution based on the user's information.

[0030] The reception desk can analyze the user's past input history and select an input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. This improves input efficiency by providing the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0031] The reception unit can filter information input based on the user's current work situation or areas of interest. For example, the reception unit prioritizes input of information related to the user's current ongoing projects. The reception unit can also filter and display relevant security information based on the user's areas of interest. Furthermore, the reception unit can prompt the user to input only the necessary information according to their work situation. This enables information input tailored to the user's work situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's work data into a generating AI and have the generating AI perform the filtering.

[0032] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception unit will prioritize inputting security information related to that region. The reception unit can also suggest optimal security measures based on the user's current location. Furthermore, the reception unit can prioritize inputting relevant security solutions based on the user's geographical location. This allows for the priority input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting relevant information.

[0033] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can extract and input security information of interest from the user's social media activity. The reception desk can also suggest relevant security solutions based on the user's social media posts. Furthermore, the reception desk can analyze the user's social media activity and input optimal security information. This allows relevant information to be input based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input the relevant information.

[0034] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. It can also perform a simplified analysis on less important information. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to network configuration information. It can also apply a different analysis algorithm to security measures information. Furthermore, it can apply an analysis algorithm specialized for budget management to budget information. By applying an analysis algorithm appropriate to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0036] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for the prioritization of the analysis of the most recent information by determining the analysis priority based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0037] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0038] The proposal unit can adjust the level of detail of its proposals based on the importance of the security solutions. For example, it can provide detailed proposals for important security solutions and simplified proposals for less important security solutions. Furthermore, the proposal unit can dynamically adjust the level of detail of its proposals according to the importance of the security solutions. This allows for efficient proposals by adjusting the level of detail according to the importance of the security solutions. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input security solution importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0039] The proposal unit can apply different proposal algorithms depending on the category of security solution when making a proposal. For example, the proposal unit can apply a dedicated proposal algorithm for strengthening firewalls. It can also apply a different proposal algorithm for installing antivirus software. Furthermore, it can apply a proposal algorithm specifically for VPNs for changing VPN settings. By applying a proposal algorithm appropriate to the category of security solution, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input security solution category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0040] The proposal department can determine the priority of proposals based on the submission timing of security solutions. For example, the proposal department may prioritize the most recent security solutions. It can also postpone the proposal of older security solutions. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the submission timing. This allows for the prioritization of the most recent solutions by determining the priority of proposals based on the submission timing of security solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input security solution submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0041] The proposal unit can adjust the order of proposals based on the relevance of the security solutions during the proposal process. For example, the proposal unit can prioritize proposing highly relevant security solutions. It can also postpone proposing less relevant security solutions. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the security solutions. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the security solutions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input security solution relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0042] The service provider can select the optimal delivery method by referring to the user's past feedback when providing a solution. For example, the service provider may prioritize providing the delivery method that the user has preferred in the past. The service provider can also select the optimal delivery method based on the user's past feedback. Furthermore, the service provider can analyze the user's past feedback and select the most effective delivery method. By selecting the optimal delivery method based on the user's past feedback, the service provider can provide a delivery method that is effective for the user. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0043] The service provider can customize the delivery method based on the user's current work situation when providing a solution. For example, if the user is busy, the service provider can select a concise and to-the-point delivery method. Conversely, if the user is relaxed, the service provider can select a delivery method that includes detailed explanations. Furthermore, the service provider can deliver the solution at the optimal time according to the user's work situation. This allows the service provider to deliver the solution at the optimal time for the user by customizing the delivery method according to the user's work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's work data into a generating AI and have the generating AI perform the customization of the delivery method.

[0044] The service provider can select a delivery method based on the user's geographical location when providing a solution. For example, if the user is in a specific region, the service provider will prioritize providing solutions relevant to that region. The service provider can also select the optimal delivery method based on the user's current location. Furthermore, the service provider can prioritize providing relevant solutions based on the user's geographical location. This allows for the provision of highly relevant solutions by selecting the optimal delivery method based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the delivery method.

[0045] The service provider can analyze the user's social media activity and propose delivery methods when providing solutions. For example, the service provider can extract and provide solutions of interest from the user's social media activity. The service provider can also propose relevant solutions based on the user's social media posts. Furthermore, the service provider can analyze the user's social media activity and propose the most suitable delivery method. This allows the service provider to provide highly relevant solutions to the user by proposing the most suitable delivery method based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute the proposal of delivery methods.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The reception desk can predict security risks in real time based on user input. For example, it can instantly assess current security risks based on network configuration information entered by the user and highlight high-risk areas. The reception desk can also generate a list of security measures that can be implemented within the budget based on budget information entered by the user, in real time. Furthermore, the reception desk can compare the security measures entered by the user with past data and suggest areas for improvement. This allows users to receive immediate feedback based on their input, enabling efficient information entry.

[0048] The reception desk can provide an automatic input completion function based on the user's past input history. For example, it can automatically complete network configuration information previously entered by the user, saving input effort. It can also automatically display recommended security measures based on security measures information previously used by the user. Furthermore, it can automatically generate a list of security measures that can be implemented within the budget based on budget information previously entered by the user. This streamlines the user's input process and enables quick information entry.

[0049] The reception system can dynamically adjust the priority of input based on the user's current work situation. For example, if a user is working on an important project, it can prompt them to prioritize entering security information related to that project. If the user is performing routine tasks, it can also prioritize entering general security information. Furthermore, if the user is handling an emergency, it can prompt them to prioritize entering only the most critical information. This enables optimal information entry tailored to the user's work situation.

[0050] The analysis unit can simulate security risks based on user input. For example, it can simulate hypothetical attack scenarios based on network configuration information entered by the user and identify high-risk areas. It can also simulate the effectiveness of security measures based on security measures information entered by the user and propose optimal countermeasures. Furthermore, it can simulate combinations of countermeasures that can be implemented within a budget based on budget information entered by the user. This allows users to understand specific risk scenarios and take effective countermeasures.

[0051] The analytics unit can analyze users' social media activity and predict security risk trends. For example, it can identify current security risk trends from information users share on social media. It can also analyze the activities of users' followers and friends to predict related risks. Furthermore, it can predict future risks based on security topics that users are interested in. This allows users to stay informed about the latest risks and take appropriate measures.

[0052] The analysis unit can dynamically adjust the display order of analysis results based on the importance of the information. For example, it can display important information first so that users can quickly check it. It can also display less important information later. Furthermore, it can adjust the display order of analysis results in real time according to the importance of the information. This allows users to prioritize checking important information, enabling efficient information management.

[0053] The analysis unit can apply different analysis methods depending on the category of information. For example, for network configuration information, it can perform topology analysis to identify network vulnerabilities. For security countermeasure information, it can perform benchmark analysis to evaluate the effectiveness of the countermeasures. Furthermore, for budget information, it can perform cost-benefit analysis to propose the optimal budget allocation. By applying the most appropriate analysis method for each category of information, the accuracy of the analysis is improved.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk receives information from the user regarding the current network configuration, security measures, and budget. For example, the user enters information such as the presence or absence of a firewall, VPN usage, the status of antivirus software installation as a security measure, and the annual security measures budget. Step 2: The analysis unit analyzes and evaluates the information received by the reception unit. For example, the generating AI evaluates the current security measures based on the network configuration, security measures, and budget information entered by the user. Step 3: The proposal department proposes security solutions based on the evaluation results obtained by the analysis department. For example, the generation AI proposes the optimal security solution, such as strengthening the firewall, installing new antivirus software, or changing VPN settings. Step 4: The supply department provides the user with the solution proposed by the proposal department. For example, the generation AI can provide the user with the proposed security solution and implement the most effective security measures within the budget.

[0056] (Example of form 2) The security solution provision system according to an embodiment of the present invention is a system that provides optimal security solutions to individual small and medium-sized enterprises (SMEs) by inputting information such as the current network configuration, security measures, and budget into a generating AI. In this system, the user inputs information such as the current network configuration, security measures, and budget, and the generating AI analyzes the input information and performs a third-party evaluation. Based on this evaluation, the generating AI proposes the optimal security solution. The proposed solution is provided to the user, enabling them to implement the most effective security measures within their budget. For example, the user inputs information such as the presence or absence of a firewall, VPN usage, the status of antivirus software implementation as a security measure, and the annual security measures budget. The generating AI analyzes this information and evaluates the current security measures. Based on the evaluation results, the generating AI proposes the optimal security solution, such as strengthening the firewall, installing new antivirus software, or changing VPN settings. This tool allows SMEs to implement the most effective security measures within their limited budget, minimizing security risks. Thus, the security solution provision system can provide the optimal security solution based on user information.

[0057] The security solution provision system according to this embodiment comprises a reception unit, an analysis unit, a proposal unit, and a provision unit. The reception unit receives information from the user regarding the current network configuration, security measures, and budget. For example, the user inputs information such as the presence or absence of a firewall, VPN usage, the status of antivirus software installation as a security measure, and the annual security measures cost as a budget. The analysis unit analyzes and evaluates the information received by the reception unit. For example, the generating AI evaluates the current security measures based on the network configuration, security measures, and budget information entered by the user. The proposal unit proposes security solutions based on the evaluation results obtained by the analysis unit. For example, the generating AI proposes the optimal security solution, such as strengthening the firewall, installing new antivirus software, or changing VPN settings. The provision unit provides the solution proposed by the proposal unit to the user. For example, the generating AI provides the proposed security solution to the user, enabling the most effective security measures to be implemented within the budget. Thus, the security solution provision system according to this embodiment can provide the optimal security solution based on the user's information.

[0058] The reception desk can estimate the user's emotions and adjust the timing of information input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can temporarily suspend input and display a relaxing interface. If the user is relaxed, the reception desk can also encourage them to continue inputting information. Furthermore, if the user is in a hurry, the reception desk can provide a simplified input form to allow for quick completion. This reduces user stress by adjusting the timing of information input according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0059] The reception desk can analyze the user's past input history and select an input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. This improves input efficiency by providing the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0060] The reception unit can filter information input based on the user's current work situation or areas of interest. For example, the reception unit prioritizes input of information related to the user's current ongoing projects. The reception unit can also filter and display relevant security information based on the user's areas of interest. Furthermore, the reception unit can prompt the user to input only the necessary information according to their work situation. This enables information input tailored to the user's work situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's work data into a generating AI and have the generating AI perform the filtering.

[0061] The reception desk can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prompt them to prioritize entering only important information. If the user is relaxed, the reception desk may also prompt them to enter detailed information. If the user is in a hurry, the reception desk may also prompt them to prioritize entering the most important information. This allows important information to be entered preferentially by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI determine the priority of information.

[0062] The reception unit can prioritize inputting highly relevant information by considering the user's geographical location when information is entered. For example, if the user is in a specific region, the reception unit will prioritize inputting security information related to that region. The reception unit can also suggest optimal security measures based on the user's current location. Furthermore, the reception unit can prioritize inputting relevant security solutions based on the user's geographical location. This allows for the priority input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI prioritize inputting relevant information.

[0063] The reception desk can analyze the user's social media activity and input relevant information when information is entered. For example, the reception desk can extract and input security information of interest from the user's social media activity. The reception desk can also suggest relevant security solutions based on the user's social media posts. Furthermore, the reception desk can analyze the user's social media activity and input optimal security information. This allows relevant information to be input based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input the relevant information.

[0064] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0065] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis on important information. It can also perform a simplified analysis on less important information. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0066] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a dedicated analysis algorithm to network configuration information. It can also apply a different analysis algorithm to security measures information. Furthermore, it can apply an analysis algorithm specialized for budget management to budget information. By applying an analysis algorithm appropriate to the category of information, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0067] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0068] The analysis unit can determine the priority of analysis based on the submission date of the information during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission date. This allows for the prioritization of the analysis of the most recent information by determining the analysis priority based on the submission date of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0069] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0070] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0071] The proposal unit can adjust the level of detail of its proposals based on the importance of the security solutions. For example, it can provide detailed proposals for important security solutions and simplified proposals for less important security solutions. Furthermore, the proposal unit can dynamically adjust the level of detail of its proposals according to the importance of the security solutions. This allows for efficient proposals by adjusting the level of detail according to the importance of the security solutions. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input security solution importance data into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0072] The proposal unit can apply different proposal algorithms depending on the category of security solution when making a proposal. For example, the proposal unit can apply a dedicated proposal algorithm for strengthening firewalls. It can also apply a different proposal algorithm for installing antivirus software. Furthermore, it can apply a proposal algorithm specifically for VPNs for changing VPN settings. By applying a proposal algorithm appropriate to the category of security solution, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input security solution category data into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0073] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0074] The proposal department can determine the priority of proposals based on the submission timing of security solutions. For example, the proposal department may prioritize the most recent security solutions. It can also postpone the proposal of older security solutions. Furthermore, the proposal department can dynamically adjust the priority of proposals based on the submission timing. This allows for the prioritization of the most recent solutions by determining the priority of proposals based on the submission timing of security solutions. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input security solution submission timing data into a generating AI and have the generating AI perform the determination of proposal priority.

[0075] The proposal unit can adjust the order of proposals based on the relevance of the security solutions during the proposal process. For example, the proposal unit can prioritize proposing highly relevant security solutions. It can also postpone proposing less relevant security solutions. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the security solutions. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the security solutions. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input security solution relevance data into a generating AI and have the generating AI perform the adjustment of the proposal order.

[0076] The service provider can estimate the user's emotions and adjust the way the solution is delivered based on those emotions. For example, if the user is nervous, the service provider can provide a simple and easy-to-understand delivery method. If the user is relaxed, the service provider can provide a more detailed delivery method. If the user is in a hurry, the service provider can provide a concise delivery method. By adjusting the solution delivery method according to the user's emotions, the service provider can provide a delivery method that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the solution is delivered.

[0077] The service provider can select the optimal delivery method by referring to the user's past feedback when providing a solution. For example, the service provider may prioritize providing the delivery method that the user has preferred in the past. The service provider can also select the optimal delivery method based on the user's past feedback. Furthermore, the service provider can analyze the user's past feedback and select the most effective delivery method. By selecting the optimal delivery method based on the user's past feedback, the service provider can provide a delivery method that is effective for the user. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0078] The service provider can customize the delivery method based on the user's current work situation when providing a solution. For example, if the user is busy, the service provider can select a concise and to-the-point delivery method. Conversely, if the user is relaxed, the service provider can select a delivery method that includes detailed explanations. Furthermore, the service provider can deliver the solution at the optimal time according to the user's work situation. This allows the service provider to deliver the solution at the optimal time for the user by customizing the delivery method according to the user's work situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's work data into a generating AI and have the generating AI perform the customization of the delivery method.

[0079] The service provider can estimate the user's emotions and determine the priority of solutions based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing important solutions. If the user is relaxed, the service provider may also provide detailed solutions. If the user is in a hurry, the service provider may also prioritize providing the most important solutions. This allows for the priority of providing important solutions by determining the priority of solutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of solutions.

[0080] The service provider can select a delivery method based on the user's geographical location when providing a solution. For example, if the user is in a specific region, the service provider will prioritize providing solutions relevant to that region. The service provider can also select the optimal delivery method based on the user's current location. Furthermore, the service provider can prioritize providing relevant solutions based on the user's geographical location. This allows for the provision of highly relevant solutions by selecting the optimal delivery method based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI perform the selection of the delivery method.

[0081] The service provider can analyze the user's social media activity and propose delivery methods when providing solutions. For example, the service provider can extract and provide solutions of interest from the user's social media activity. The service provider can also propose relevant solutions based on the user's social media posts. Furthermore, the service provider can analyze the user's social media activity and propose the most suitable delivery method. This allows the service provider to provide highly relevant solutions to the user by proposing the most suitable delivery method based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute the proposal of delivery methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives information from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the received information. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a security solution based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the proposed solution to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of information input based on the estimated emotions. Emotion estimation is performed using, for example, the camera 42 and microphone 38B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives information from the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the received information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes a security solution based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides the proposed solution to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of information input based on the estimated emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 238 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives information from the user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a security solution based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides the proposed solution to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of information input based on the estimated emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 238 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives information from the user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the received information. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes a security solution based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the proposed solution to the user. The reception unit can, for example, estimate the user's emotions and adjust the timing of information input based on the estimated emotions. Emotion estimation is performed, for example, using the camera 42 and microphone 238 of the robot 414.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The reception desk can predict security risks in real time based on user input. For example, it can instantly assess current security risks based on network configuration information entered by the user and highlight high-risk areas. The reception desk can also generate a list of security measures that can be implemented within the budget based on budget information entered by the user, in real time. Furthermore, the reception desk can compare the security measures entered by the user with past data and suggest areas for improvement. This allows users to receive immediate feedback based on their input, enabling efficient information entry.

[0084] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on those emotions. For example, if the user is stressed, it can display a simple interface with calming colors. If the user is relaxed, it can provide a colorful and interactive interface. Furthermore, if the user is in a hurry, the interface can be simplified to allow information to be entered with minimal clicks. This allows for the provision of an optimal input environment tailored to the user's emotions.

[0085] The reception desk can provide an automatic input completion function based on the user's past input history. For example, it can automatically complete network configuration information previously entered by the user, saving input effort. It can also automatically display recommended security measures based on security measures information previously used by the user. Furthermore, it can automatically generate a list of security measures that can be implemented within the budget based on budget information previously entered by the user. This streamlines the user's input process and enables quick information entry.

[0086] The reception system can dynamically adjust the priority of input based on the user's current work situation. For example, if a user is working on an important project, it can prompt them to prioritize entering security information related to that project. If the user is performing routine tasks, it can also prioritize entering general security information. Furthermore, if the user is handling an emergency, it can prompt them to prioritize entering only the most critical information. This enables optimal information entry tailored to the user's work situation.

[0087] The reception desk can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is stressed, it can emphasize positive feedback to boost their motivation. If the user is relaxed, it can provide detailed feedback to deepen understanding of the user. Furthermore, if the user is in a hurry, it can provide concise and to-the-point feedback. This allows for the provision of optimal feedback tailored to the user's emotions.

[0088] The analysis unit can simulate security risks based on user input. For example, it can simulate hypothetical attack scenarios based on network configuration information entered by the user and identify high-risk areas. It can also simulate the effectiveness of security measures based on security measures information entered by the user and propose optimal countermeasures. Furthermore, it can simulate combinations of countermeasures that can be implemented within a budget based on budget information entered by the user. This allows users to understand specific risk scenarios and take effective countermeasures.

[0089] The analytics unit can analyze users' social media activity and predict security risk trends. For example, it can identify current security risk trends from information users share on social media. It can also analyze the activities of users' followers and friends to predict related risks. Furthermore, it can predict future risks based on security topics that users are interested in. This allows users to stay informed about the latest risks and take appropriate measures.

[0090] The analysis unit can estimate the user's emotions and customize how the analysis results are presented based on those estimated emotions. For example, if the user is nervous, it can provide simple and visually easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results that get straight to the point. This allows the system to provide optimal analysis results tailored to the user's emotions.

[0091] The analysis unit can dynamically adjust the display order of analysis results based on the importance of the information. For example, it can display important information first so that users can quickly check it. It can also display less important information later. Furthermore, it can adjust the display order of analysis results in real time according to the importance of the information. This allows users to prioritize checking important information, enabling efficient information management.

[0092] The analysis unit can apply different analysis methods depending on the category of information. For example, for network configuration information, it can perform topology analysis to identify network vulnerabilities. For security countermeasure information, it can perform benchmark analysis to evaluate the effectiveness of the countermeasures. Furthermore, for budget information, it can perform cost-benefit analysis to propose the optimal budget allocation. By applying the most appropriate analysis method for each category of information, the accuracy of the analysis is improved.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The reception desk receives information from the user regarding the current network configuration, security measures, and budget. For example, the user enters information such as the presence or absence of a firewall, VPN usage, the status of antivirus software installation as a security measure, and the annual security measures budget. Step 2: The analysis unit analyzes and evaluates the information received by the reception unit. For example, the generating AI evaluates the current security measures based on the network configuration, security measures, and budget information entered by the user. Step 3: The proposal department proposes security solutions based on the evaluation results obtained by the analysis department. For example, the generation AI proposes the optimal security solution, such as strengthening the firewall, installing new antivirus software, or changing VPN settings. Step 4: The supply department provides the user with the solution proposed by the proposal department. For example, the generation AI can provide the user with the proposed security solution and implement the most effective security measures within the budget.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0097] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0106] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0107] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0108] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0109] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0139] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0150] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0151] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0152] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0156] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0157] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0158] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0159] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0160] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0161] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0163] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0164] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0165] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0166] [Explanation of Symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives information from users regarding the current network configuration, security measures, and budget, An analysis unit analyzes and evaluates the information received by the reception unit, Based on the evaluation results obtained by the analysis unit, the proposal unit proposes a security solution. The system comprises a provisioning unit that provides the solution proposed by the proposal unit to the user. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of information input based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system according to feature 1.

4. The aforementioned reception unit is When entering information, filtering is performed based on the user's current work situation or areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When entering information, relevant information is prioritized based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When entering information, the system analyzes the user's social media activity and inputs relevant information. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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