system

Abstract

The system according to the embodiment aims to quickly provide corporate customers with a plan for building zero trust security. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a corporate customer. The analysis unit analyzes the information received by the reception unit. The generation unit generates a zero trust security implementation plan based on the information analyzed by the analysis unit. The provision unit provides the implementation plan generated by the generation unit to the corporate customer.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technologies, it was difficult to quickly generate zero trust security implementation plans and provide them to corporate customers.

[0005] The system according to the embodiment aims to quickly provide corporate customers with a plan for building zero trust security. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a corporate customer. The analysis unit analyzes the information received by the reception unit. The generation unit generates a zero trust security implementation plan based on the information analyzed by the analysis unit. The provision unit provides the implementation plan generated by the generation unit to the corporate customer. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide corporate customers with a plan for building zero trust security. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) A zero trust security proposal system according to an embodiment of the present invention is a system in which corporate customers input the services they are currently using or considering via chat, and AI proposes optimal zero trust security implementation plans, services, estimated costs, implementation periods, etc. In this zero trust security proposal system, corporate customers input the services they are currently using or considering for each item via chat, and AI analyzes the input information and generates an optimal zero trust security implementation plan. The generated implementation plan includes specific services, estimated costs, implementation periods, etc. This mechanism allows for rapid zero trust security proposals even in situations where there are few security personnel. For example, corporate customers input the services they are currently using or considering for each item via chat. For example, they input the services they are currently using or considering for items such as network security, endpoint security, and access management. This information is input into AI. Next, AI analyzes the input information and generates an optimal zero trust security implementation plan. Based on the input information, AI proposes an optimal security implementation plan. For example, for network security, the system proposes the introduction of firewalls and VPNs, and for endpoint security, it proposes the introduction of antivirus software and endpoint detection and response (EDR) tools. The generated implementation proposal includes specific services, estimated costs, and implementation time. For example, it includes information such as how much a firewall implementation will cost and how long it will take. This information is provided to corporate customers. This system allows us to quickly propose zero trust security solutions, even in situations where there are few security personnel. Corporate customers can easily enter information in a chat format to obtain the optimal security implementation proposal. This is expected to accelerate market development and accelerate orders for zero trust security. The zero trust security proposal system allows corporate customers to easily enter information and quickly obtain the optimal security implementation proposal.

[0029] A zero trust security proposal system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a corporate customer. The information from the corporate customer includes, but is not limited to, services currently being used or services being considered. For example, the reception unit receives input from the corporate customer in chat format, with each item indicating the services currently being used or services being considered. The reception unit can also store the information entered by the corporate customer in a database. For example, the reception unit stores the information entered by the corporate customer in a database in real time so that the analysis unit can later use the information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the information entered by the corporate customer using data mining technology or machine learning technology. For example, the analysis unit provides data for generating an optimal zero trust security implementation plan based on the information entered by the corporate customer. The generation unit proposes a specific implementation plan, service, cost, and period based on the data provided by the analysis unit. The generation unit generates an optimal zero trust security implementation plan based on information entered by, for example, a corporate customer. For example, the generation unit proposes the introduction of a firewall or VPN for network security, and proposes the introduction of antivirus software or an endpoint detection and response (EDR) tool for endpoint security. The generation unit proposes information such as the cost of installing a firewall and the duration of the implementation. The provision unit provides the implementation plan generated by the generation unit to the corporate customer. For example, the provision unit sends the implementation plan generated by the generation unit to the corporate customer by email. The provision unit can also provide the implementation plan generated by the generation unit to the corporate customer through a web portal. For example, the provision unit allows the corporate customer to access the web portal and view the implementation plan generated by the generation unit. This allows the zero trust security proposal system according to the embodiment to efficiently accept and analyze information from corporate customers, and generate and provide an optimal zero trust security implementation plan.

[0030] The reception unit allows corporate customers to input services they are currently using or considering for each item in chat format. For example, the reception unit accepts corporate customers' input of services they are currently using or considering for each item in chat format. For example, corporate customers can input services they are currently using or considering for items such as network security, endpoint security, and access management. The reception unit can also store the information entered by corporate customers in a database. For example, the reception unit can store the information entered by corporate customers in a database in real time so that the analysis unit can later use it. This allows corporate customers to easily collect information by entering information in chat format. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the information entered by corporate customers into AI, which can analyze the information and store it in a database.

[0031] The analysis unit can analyze the information received by the reception unit and provide data for generating a zero trust security implementation plan. The analysis unit can analyze the information entered by the corporate customer using, for example, data mining or machine learning technology. For example, the analysis unit can provide data for generating an optimal zero trust security implementation plan based on the information entered by the corporate customer. For example, the analysis unit can provide data for proposing the introduction of a firewall or VPN for network security, or for proposing the introduction of antivirus software or endpoint detection and response (EDR) tools for endpoint security, based on the information entered by the corporate customer. The analysis unit can also provide information such as the cost and duration of firewall implementation based on the information entered by the corporate customer. This allows the analysis of the information received from the reception unit and the provision of data for generating an implementation plan, thereby enabling the generation of highly accurate implementation plans. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the information entered by the corporate customer into AI, which can then analyze the information and provide the data.

[0032] The generation unit can propose specific implementation plans, services, costs, and timeframes based on the data provided by the analysis unit. The generation unit generates optimal zero trust security implementation plans based on, for example, information input by corporate customers. For example, the generation unit proposes the introduction of firewalls and VPNs for network security, and the introduction of antivirus software and endpoint detection and response (EDR) tools for endpoint security. The generation unit proposes information such as the cost and duration of firewall implementation. The generation unit proposes optimal security implementation plans based on, for example, information input by corporate customers. This enables specific proposals to be made to corporate customers by proposing specific implementation plans based on the data provided by the analysis unit. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data provided by the analysis unit into AI, which then generates the optimal implementation plan.

[0033] The providing unit can provide the construction plan generated by the generating unit to the corporate customer. For example, the providing unit can send the construction plan generated by the generating unit to the corporate customer by email. The providing unit can also provide the construction plan generated by the generating unit to the corporate customer through a web portal. For example, the providing unit can allow the corporate customer to access the web portal and view the construction plan generated by the generating unit. This allows the construction plan generated by the generating unit to be provided to the corporate customer, thereby enabling the proposal to be made quickly. Some or all of the above-mentioned processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the construction plan generated by the generating unit into AI, which can generate information to be provided to the corporate customer.

[0034] The reception unit can analyze past input history and automatically generate an optimal input format. For example, the reception unit can automatically display services that a corporate customer has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the corporate customer has used in the past. Furthermore, the reception unit can predict and suggest services that the corporate customer will use during a specific time period based on the corporate customer's past input history. This improves input efficiency by providing an optimal input format based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history into AI, which then automatically generates an optimal input format.

[0035] The reception unit can present customized questions according to the business type and size of the corporate customer when the information is input. For example, if the corporate customer is in the IT industry, the reception unit can add questions about specific security requirements. The reception unit can also add questions about the budget if the corporate customer is a small or medium-sized enterprise. Furthermore, the reception unit can add questions about multiple departments if the corporate customer is a large corporation. This allows for more appropriate information to be collected by presenting questions according to the business type and size of the corporate customer. Some or all of the above-mentioned processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input information about the business type and size of the corporate customer into AI, which can then generate customized questions.

[0036] The reception unit can automatically add region-specific security requirements based on the geographic location information of the corporate customer when input. For example, if the corporate customer is located in a specific country, the reception unit can add questions about the security regulations of that country. Furthermore, if the corporate customer is located in an urban area, the reception unit can add questions about city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the reception unit can add questions about region-specific security risks. In this way, region-specific security requirements can be appropriately reflected by taking the geographic location information of the corporate customer into consideration. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographic location information of the corporate customer into AI, which can then automatically add region-specific security requirements.

[0037] The reception unit can analyze the corporate customer's social media activity at the time of input and suggest relevant security needs. For example, if the corporate customer frequently uses social media, the reception unit can add questions about social media security. Furthermore, if the corporate customer uses a specific social media platform, the reception unit can also add questions about security risks specific to that platform. Furthermore, if the corporate customer is not very active on social media, the reception unit can also add questions about basic security measures. This allows the analysis of the corporate customer's social media activity to appropriately suggest relevant security needs. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the corporate customer's social media activity into AI, which can then suggest relevant security needs.

[0038] During analysis, the analysis unit can perform risk assessment based on past security incident data. For example, the analysis unit can evaluate the current risk level based on the past security incident data. The analysis unit can also analyze past security incident data and predict future risks. Furthermore, the analysis unit can refer to past security incident data to propose countermeasures for specific risks. In this way, referring to past security incident data improves the accuracy of risk assessment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past security incident data into AI, which then performs risk assessment.

[0039] During analysis, the analysis unit can apply different analysis methods to each business type of the corporate customer. For example, the analysis unit can apply an analysis method specialized for network security to a corporate customer in the IT industry. The analysis unit can also apply an analysis method specialized for supply chain security to a corporate customer in the manufacturing industry. The analysis unit can also apply an analysis method specialized for data protection to a corporate customer in the financial industry. In this way, by applying an analysis method according to the business type of the corporate customer, the accuracy of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the business type of the corporate customer into AI, and the AI ​​can apply different analysis methods to each business type.

[0040] During the analysis, the analysis unit can evaluate region-specific risks based on the geographic location information of the corporate customer. For example, if the corporate customer is located in a specific country, the analysis unit evaluates the security risks of that country. Furthermore, if the corporate customer is located in an urban area, the analysis unit can evaluate city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the analysis unit can evaluate region-specific security risks. This allows region-specific risks to be appropriately evaluated by taking the geographic location information of the corporate customer into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographic location information of the corporate customer into AI, which can then evaluate region-specific risks.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis based on benchmark data of related industries. The analysis unit can improve the accuracy of the analysis by, for example, referring to benchmark data of the IT industry. The analysis unit can also improve the accuracy of the analysis by referring to benchmark data of the manufacturing industry. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to benchmark data of the financial industry. In this way, by referring to benchmark data of related industries, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input benchmark data of related industries into AI, which can improve the accuracy of the analysis.

[0042] At the time of generation, the generation unit can propose an optimal construction plan based on the corporate customer's past security measure history. The generation unit proposes an optimal construction plan, for example, based on security measures that the corporate customer has implemented in the past. The generation unit can also analyze the corporate customer's past security measure history and propose improvements. Furthermore, the generation unit can refer to the corporate customer's past security measure history to propose the most effective construction plan. In this way, the optimal construction plan can be proposed by referring to the corporate customer's past security measure history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the corporate customer's past security measure history into AI, which then proposes the optimal construction plan.

[0043] At the time of generation, the generation unit can generate a configuration plan customized according to the industry and scale of the corporate customer. For example, the generation unit generates a configuration plan specialized for network security for a corporate customer in the IT industry. The generation unit can also generate a configuration plan specialized for supply chain security for a corporate customer in the manufacturing industry. Furthermore, the generation unit can generate a configuration plan specialized for data protection for a corporate customer in the financial industry. This enables more appropriate proposals by generating a configuration plan tailored to the industry and scale of the corporate customer. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input information about the industry and scale of the corporate customer into AI, which then generates a customized configuration plan.

[0044] At the time of generation, the generation unit can propose region-specific security measures based on the geographical location information of the corporate customer. For example, if the corporate customer is located in a specific country, the generation unit can propose measures based on the security regulations of that country. Furthermore, if the corporate customer is located in an urban area, the generation unit can propose measures to address city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the generation unit can propose measures to address region-specific security risks. In this way, region-specific security measures can be appropriately proposed by taking the geographical location information of the corporate customer into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the corporate customer into AI, which can then propose region-specific security measures.

[0045] During generation, the generation unit can improve the accuracy of the proposal content based on benchmark data of the related industry. The generation unit can improve the accuracy of the proposal content by referring to benchmark data of the IT industry, for example. The generation unit can also improve the accuracy of the proposal content by referring to benchmark data of the manufacturing industry. Furthermore, the generation unit can improve the accuracy of the proposal content by referring to benchmark data of the financial industry. As a result, the accuracy of the proposal content is improved by referring to benchmark data of the related industry. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input benchmark data of the related industry into AI, which can improve the accuracy of the proposal content.

[0046] At the time of information provision, the provision unit can select the optimal information provision method based on the corporate customer's past feedback. For example, the provision unit selects the optimal information provision method based on feedback provided in the past by the corporate customer. The provision unit can also analyze the corporate customer's past feedback and select an information provision method that reflects areas for improvement. Furthermore, the provision unit can select the most effective information provision method by referring to the corporate customer's past feedback. In this way, the optimal information provision method can be selected by referring to the corporate customer's past feedback. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the corporate customer's past feedback into AI, which can select the optimal information provision method.

[0047] The provision unit can provide information customized according to the industry and scale of the corporate customer at the time of provision. For example, the provision unit can provide information on network security to a corporate customer in the IT industry. The provision unit can also provide information on supply chain security to a corporate customer in the manufacturing industry. Furthermore, the provision unit can provide information on data protection to a corporate customer in the financial industry. This makes it possible to provide more appropriate information by providing information according to the industry and scale of the corporate customer. Some or all of the above-mentioned processing in the provision unit may be performed using, or without, AI, for example. For example, the provision unit can input information on the industry and scale of the corporate customer into AI, which can generate customized information.

[0048] At the time of providing the information, the providing unit can provide region-specific information based on the geographical location information of the corporate customer. For example, if the corporate customer is located in a specific country, the providing unit can provide information about security regulations in that country. Furthermore, if the corporate customer is located in an urban area, the providing unit can provide information about city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the providing unit can provide information about region-specific security risks. This makes it possible to appropriately provide region-specific information by taking the geographical location information of the corporate customer into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the geographical location information of the corporate customer into AI, which can generate region-specific information.

[0049] The provision unit can analyze the social media activities of corporate customers and provide relevant information at the time of provision. For example, if the corporate customer frequently uses social media, the provision unit can provide information about social media security. In addition, if the corporate customer uses a specific social media platform, the provision unit can also provide information about security risks specific to that platform. Furthermore, if the corporate customer is not very active on social media, the provision unit can also provide information about basic security measures. This allows for appropriate provision of relevant information by analyzing the social media activities of corporate customers. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the social media activities of corporate customers into AI, which then generates relevant information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can automatically obtain benchmark data for related industries based on the information entered by corporate customers and provide it to the analysis unit. For example, benchmark data for the IT industry can be obtained based on information entered by corporate customers in the IT industry and provided to the analysis unit. Also, benchmark data for the manufacturing industry can be obtained based on information entered by corporate customers in the manufacturing industry and provided to the analysis unit. Furthermore, benchmark data for the financial industry can be obtained based on information entered by corporate customers in the financial industry and provided to the analysis unit. In this way, the reception unit can provide benchmark data according to the corporate customers' industries, allowing the analysis unit to perform more accurate analysis.

[0052] The reception unit can automatically obtain relevant legal and regulatory information based on the information entered by the corporate customer and provide it to the analysis unit. For example, if the corporate customer is located in a specific country, it can obtain security legal and regulatory information for that country and provide it to the analysis unit. Also, if the corporate customer belongs to a specific industry, it can obtain security legal and regulatory information for that industry and provide it to the analysis unit. Furthermore, if the corporate customer is a company of a specific size, it can obtain security legal and regulatory information appropriate for that size and provide it to the analysis unit. In this way, the reception unit can provide legal and regulatory information for the corporate customer, allowing the analysis unit to perform a more appropriate analysis.

[0053] The reception unit can automatically obtain relevant security news and trend information based on the corporate customer's input and provide it to the provision unit. For example, when a corporate customer inputs information about network security, the latest network security news and trend information can be obtained and provided to the provision unit. Also, when a corporate customer inputs information about endpoint security, the latest endpoint security news and trend information can be obtained and provided to the provision unit. Furthermore, when a corporate customer inputs information about access management, the latest access management news and trend information can be obtained and provided to the provision unit. In this way, the reception unit can provide relevant security news and trend information based on the corporate customer's input, allowing the provision unit to provide more appropriate information.

[0054] The reception unit can automatically obtain information on related security vendors based on the corporate customer's input and provide it to the provision unit. For example, when a corporate customer inputs information related to network security, information on related network security vendors can be obtained and provided to the provision unit. Also, when a corporate customer inputs information related to endpoint security, information on related endpoint security vendors can be obtained and provided to the provision unit. Furthermore, when a corporate customer inputs information related to access management, information on related access management vendors can be obtained and provided to the provision unit. In this way, the reception unit can provide information on related security vendors based on the corporate customer's input, allowing the provision unit to provide more appropriate information.

[0055] The analysis unit can predict associated security risks based on the information entered by the corporate customer. For example, when a corporate customer enters information related to network security, the analysis unit can predict associated network security risks. When a corporate customer enters information related to endpoint security, the analysis unit can predict associated endpoint security risks. When a corporate customer enters information related to access management, the analysis unit can predict associated access management risks. This allows the analysis unit to provide more appropriate analysis results by predicting associated security risks based on the information entered by the corporate customer.

[0056] The generation unit can predict the effectiveness of related security measures based on the input content of the corporate customer. For example, when a corporate customer inputs information about network security, the generation unit can predict the effectiveness of related network security measures. Also, when a corporate customer inputs information about endpoint security, the generation unit can predict the effectiveness of related endpoint security measures. Furthermore, when a corporate customer inputs information about access management, the generation unit can predict the effectiveness of related access management measures. As a result, the generation unit can make more appropriate proposals by predicting the effectiveness of related security measures based on the input content of the corporate customer.

[0057] The provision unit can provide information on related security training based on the corporate customer's input. For example, when a corporate customer inputs information related to network security, information on related network security training can be provided. Also, when a corporate customer inputs information related to endpoint security, information on related endpoint security training can be provided. Furthermore, when a corporate customer inputs information related to access management, information on related access management training can be provided. In this way, the provision unit can provide more appropriate information by providing information on related security training based on the corporate customer's input.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception department accepts information from corporate customers. This information includes services currently being used and services under consideration. The reception department accepts corporate customers' input of the services currently being used or services under consideration for each item in chat format. The reception department also saves the information entered by corporate customers in a database in real time, making it available for later use by the analysis department. Step 2: The analysis unit analyzes the information received by the reception unit. Using data mining and machine learning technologies, the analysis unit analyzes the information entered by the corporate customer and provides data to generate an optimal zero trust security implementation plan. Step 3: The generation unit generates specific zero trust security implementation plans based on the data provided by the analysis unit. For network security, the generation unit proposes the introduction of firewalls and VPNs, and for endpoint security, it proposes the introduction of antivirus software and endpoint detection and response (EDR) tools. It also provides information such as the cost and time required to implement firewalls. Step 4: The provision department provides the construction plan generated by the generation department to the corporate customer. The provision department sends the construction plan generated by the generation department to the corporate customer by email or provides it through a web portal. The corporate customer can access the web portal to view the construction plan generated by the generation department.

[0060] (Example 2) A zero trust security proposal system according to an embodiment of the present invention is a system in which corporate customers input the services they are currently using or considering via chat, and AI proposes optimal zero trust security implementation plans, services, estimated costs, implementation periods, etc. In this zero trust security proposal system, corporate customers input the services they are currently using or considering for each item via chat, and AI analyzes the input information and generates an optimal zero trust security implementation plan. The generated implementation plan includes specific services, estimated costs, implementation periods, etc. This mechanism allows for rapid zero trust security proposals even in situations where there are few security personnel. For example, corporate customers input the services they are currently using or considering for each item via chat. For example, they input the services they are currently using or considering for items such as network security, endpoint security, and access management. This information is input into AI. Next, AI analyzes the input information and generates an optimal zero trust security implementation plan. Based on the input information, AI proposes an optimal security implementation plan. For example, for network security, the system proposes the introduction of firewalls and VPNs, and for endpoint security, it proposes the introduction of antivirus software and endpoint detection and response (EDR) tools. The generated implementation proposal includes specific services, estimated costs, and implementation time. For example, it includes information such as how much a firewall implementation will cost and how long it will take. This information is provided to corporate customers. This system allows us to quickly propose zero trust security solutions, even in situations where there are few security personnel. Corporate customers can easily enter information in a chat format to obtain the optimal security implementation proposal. This is expected to accelerate market development and accelerate orders for zero trust security. The zero trust security proposal system allows corporate customers to easily enter information and quickly obtain the optimal security implementation proposal.

[0061] A zero trust security proposal system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives information from a corporate customer. The information from the corporate customer includes, but is not limited to, services currently being used or services being considered. For example, the reception unit receives input from the corporate customer in chat format, with each item indicating the services currently being used or services being considered. The reception unit can also store the information entered by the corporate customer in a database. For example, the reception unit stores the information entered by the corporate customer in a database in real time so that the analysis unit can later use the information. The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the information entered by the corporate customer using data mining technology or machine learning technology. For example, the analysis unit provides data for generating an optimal zero trust security implementation plan based on the information entered by the corporate customer. The generation unit proposes a specific implementation plan, service, cost, and period based on the data provided by the analysis unit. The generation unit generates an optimal zero trust security implementation plan based on information entered by, for example, a corporate customer. For example, the generation unit proposes the introduction of a firewall or VPN for network security, and proposes the introduction of antivirus software or an endpoint detection and response (EDR) tool for endpoint security. The generation unit proposes information such as the cost of installing a firewall and the duration of the implementation. The provision unit provides the implementation plan generated by the generation unit to the corporate customer. For example, the provision unit sends the implementation plan generated by the generation unit to the corporate customer by email. The provision unit can also provide the implementation plan generated by the generation unit to the corporate customer through a web portal. For example, the provision unit allows the corporate customer to access the web portal and view the implementation plan generated by the generation unit. This allows the zero trust security proposal system according to the embodiment to efficiently accept and analyze information from corporate customers, and generate and provide an optimal zero trust security implementation plan.

[0062] The reception unit allows corporate customers to input services they are currently using or considering for each item in chat format. For example, the reception unit accepts corporate customers' input of services they are currently using or considering for each item in chat format. For example, corporate customers can input services they are currently using or considering for items such as network security, endpoint security, and access management. The reception unit can also store the information entered by corporate customers in a database. For example, the reception unit can store the information entered by corporate customers in a database in real time so that the analysis unit can later use it. This allows corporate customers to easily collect information by entering information in chat format. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the information entered by corporate customers into AI, which can analyze the information and store it in a database.

[0063] The analysis unit can analyze the information received by the reception unit and provide data for generating a zero trust security implementation plan. The analysis unit can analyze the information entered by the corporate customer using, for example, data mining or machine learning technology. For example, the analysis unit can provide data for generating an optimal zero trust security implementation plan based on the information entered by the corporate customer. For example, the analysis unit can provide data for proposing the introduction of a firewall or VPN for network security, or for proposing the introduction of antivirus software or endpoint detection and response (EDR) tools for endpoint security, based on the information entered by the corporate customer. The analysis unit can also provide information such as the cost and duration of firewall implementation based on the information entered by the corporate customer. This allows the analysis of the information received from the reception unit and the provision of data for generating an implementation plan, thereby enabling the generation of highly accurate implementation plans. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the information entered by the corporate customer into AI, which can then analyze the information and provide the data.

[0064] The generation unit can propose specific implementation plans, services, costs, and timeframes based on the data provided by the analysis unit. The generation unit generates optimal zero trust security implementation plans based on, for example, information input by corporate customers. For example, the generation unit proposes the introduction of firewalls and VPNs for network security, and the introduction of antivirus software and endpoint detection and response (EDR) tools for endpoint security. The generation unit proposes information such as the cost and duration of firewall implementation. The generation unit proposes optimal security implementation plans based on, for example, information input by corporate customers. This enables specific proposals to be made to corporate customers by proposing specific implementation plans based on the data provided by the analysis unit. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input data provided by the analysis unit into AI, which then generates the optimal implementation plan.

[0065] The providing unit can provide the construction plan generated by the generating unit to the corporate customer. For example, the providing unit can send the construction plan generated by the generating unit to the corporate customer by email. The providing unit can also provide the construction plan generated by the generating unit to the corporate customer through a web portal. For example, the providing unit can allow the corporate customer to access the web portal and view the construction plan generated by the generating unit. This allows the construction plan generated by the generating unit to be provided to the corporate customer, thereby enabling the proposal to be made quickly. Some or all of the above-mentioned processing by the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the construction plan generated by the generating unit into AI, which can generate information to be provided to the corporate customer.

[0066] The reception unit can estimate the corporate customer's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the corporate customer is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the corporate customer is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the corporate customer is in a hurry, the reception unit can prioritize voice input to enable quick information input. This improves usability by optimizing the input interface according to the corporate customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the corporate customer's emotions into AI, which can then estimate the emotions and dynamically change the input interface.

[0067] The reception unit can analyze past input history and automatically generate an optimal input format. For example, the reception unit can automatically display services that a corporate customer has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the corporate customer has used in the past. Furthermore, the reception unit can predict and suggest services that the corporate customer will use during a specific time period based on the corporate customer's past input history. This improves input efficiency by providing an optimal input format based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past input history into AI, which then automatically generates an optimal input format.

[0068] The reception unit can present customized questions according to the business type and size of the corporate customer when the information is input. For example, if the corporate customer is in the IT industry, the reception unit can add questions about specific security requirements. The reception unit can also add questions about the budget if the corporate customer is a small or medium-sized enterprise. Furthermore, the reception unit can add questions about multiple departments if the corporate customer is a large corporation. This allows for more appropriate information to be collected by presenting questions according to the business type and size of the corporate customer. Some or all of the above-mentioned processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input information about the business type and size of the corporate customer into AI, which can then generate customized questions.

[0069] The reception unit can estimate the corporate customer's emotions and dynamically change the priority of input based on the estimated emotions. For example, if the corporate customer is feeling stressed, the reception unit can prioritize input of important items. Furthermore, if the corporate customer is relaxed, the reception unit can sequentially input detailed items. Furthermore, if the corporate customer is in a hurry, the reception unit can input only the most important items. This enables efficient information collection by changing the priority of input according to the corporate customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the corporate customer's emotions into an AI, which can then estimate the emotions and dynamically change the priority of input.

[0070] The reception unit can automatically add region-specific security requirements based on the geographic location information of the corporate customer when input. For example, if the corporate customer is located in a specific country, the reception unit can add questions about the security regulations of that country. Furthermore, if the corporate customer is located in an urban area, the reception unit can add questions about city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the reception unit can add questions about region-specific security risks. In this way, region-specific security requirements can be appropriately reflected by taking the geographic location information of the corporate customer into consideration. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the geographic location information of the corporate customer into AI, which can then automatically add region-specific security requirements.

[0071] The reception unit can analyze the corporate customer's social media activity at the time of input and suggest relevant security needs. For example, if the corporate customer frequently uses social media, the reception unit can add questions about social media security. Furthermore, if the corporate customer uses a specific social media platform, the reception unit can also add questions about security risks specific to that platform. Furthermore, if the corporate customer is not very active on social media, the reception unit can also add questions about basic security measures. This allows the analysis of the corporate customer's social media activity to appropriately suggest relevant security needs. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the corporate customer's social media activity into AI, which can then suggest relevant security needs.

[0072] The analysis unit can estimate the corporate customer's emotions and dynamically adjust the parameters of the analysis algorithm based on the estimated emotions. For example, if the corporate customer is feeling stressed, the analysis unit can simplify the parameters of the analysis algorithm and provide results quickly. Furthermore, if the corporate customer is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. Furthermore, if the corporate customer is in a hurry, the analysis unit can perform an analysis based on the most important data. This improves the accuracy of the analysis results by adjusting the parameters of the analysis algorithm according to the corporate customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the corporate customer's emotions into AI, which can then estimate the emotions and dynamically adjust the parameters of the analysis algorithm.

[0073] During analysis, the analysis unit can perform risk assessment based on past security incident data. For example, the analysis unit can evaluate the current risk level based on the past security incident data. The analysis unit can also analyze past security incident data and predict future risks. Furthermore, the analysis unit can refer to past security incident data to propose countermeasures for specific risks. In this way, referring to past security incident data improves the accuracy of risk assessment. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past security incident data into AI, which then performs risk assessment.

[0074] During analysis, the analysis unit can apply different analysis methods to each business type of the corporate customer. For example, the analysis unit can apply an analysis method specialized for network security to a corporate customer in the IT industry. The analysis unit can also apply an analysis method specialized for supply chain security to a corporate customer in the manufacturing industry. The analysis unit can also apply an analysis method specialized for data protection to a corporate customer in the financial industry. In this way, by applying an analysis method according to the business type of the corporate customer, the accuracy of the analysis results is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the business type of the corporate customer into AI, and the AI ​​can apply different analysis methods to each business type.

[0075] The analysis unit can estimate the corporate customer's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the corporate customer is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the corporate customer is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the corporate customer is in a hurry, the analysis unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the analysis results according to the corporate customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the corporate customer's emotions into AI, which can estimate the emotions and adjust the display method of the analysis results.

[0076] During the analysis, the analysis unit can evaluate region-specific risks based on the geographic location information of the corporate customer. For example, if the corporate customer is located in a specific country, the analysis unit evaluates the security risks of that country. Furthermore, if the corporate customer is located in an urban area, the analysis unit can evaluate city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the analysis unit can evaluate region-specific security risks. This allows region-specific risks to be appropriately evaluated by taking the geographic location information of the corporate customer into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographic location information of the corporate customer into AI, which can then evaluate region-specific risks.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis based on benchmark data of related industries. The analysis unit can improve the accuracy of the analysis by, for example, referring to benchmark data of the IT industry. The analysis unit can also improve the accuracy of the analysis by referring to benchmark data of the manufacturing industry. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to benchmark data of the financial industry. In this way, by referring to benchmark data of related industries, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input benchmark data of related industries into AI, which can improve the accuracy of the analysis.

[0078] The generation unit can estimate the corporate customer's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, if the corporate customer is feeling stressed, the generation unit can provide a simple and intuitive presentation. Furthermore, if the corporate customer is relaxed, the generation unit can provide a presentation that includes detailed information. Furthermore, if the corporate customer is in a hurry, the generation unit can provide a presentation that focuses on the main points. This adjusts the way the proposal is presented based on the corporate customer's emotions, thereby increasing the likelihood of the proposal being accepted. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the corporate customer's emotions into an AI, which can then estimate the emotions and adjust the way the proposal is presented.

[0079] At the time of generation, the generation unit can propose an optimal construction plan based on the corporate customer's past security measure history. The generation unit proposes an optimal construction plan, for example, based on security measures that the corporate customer has implemented in the past. The generation unit can also analyze the corporate customer's past security measure history and propose improvements. Furthermore, the generation unit can refer to the corporate customer's past security measure history to propose the most effective construction plan. In this way, the optimal construction plan can be proposed by referring to the corporate customer's past security measure history. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the corporate customer's past security measure history into AI, which then proposes the optimal construction plan.

[0080] At the time of generation, the generation unit can generate a configuration plan customized according to the industry and scale of the corporate customer. For example, the generation unit generates a configuration plan specialized for network security for a corporate customer in the IT industry. The generation unit can also generate a configuration plan specialized for supply chain security for a corporate customer in the manufacturing industry. Furthermore, the generation unit can generate a configuration plan specialized for data protection for a corporate customer in the financial industry. This enables more appropriate proposals by generating a configuration plan tailored to the industry and scale of the corporate customer. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input information about the industry and scale of the corporate customer into AI, which then generates a customized configuration plan.

[0081] The generation unit can estimate the corporate customer's emotions and prioritize the proposals based on the estimated emotions. For example, if the corporate customer is feeling stressed, the generation unit can prioritize the most important proposals. Furthermore, if the corporate customer is relaxed, the generation unit can sequentially display detailed proposals. Furthermore, if the corporate customer is in a hurry, the generation unit can display only the most important proposals. This enables efficient proposals by prioritizing the proposals based on the corporate customer's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the corporate customer's emotions into an AI, which can then estimate the emotions and prioritize the proposals.

[0082] At the time of generation, the generation unit can propose region-specific security measures based on the geographical location information of the corporate customer. For example, if the corporate customer is located in a specific country, the generation unit can propose measures based on the security regulations of that country. Furthermore, if the corporate customer is located in an urban area, the generation unit can propose measures to address city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the generation unit can propose measures to address region-specific security risks. In this way, region-specific security measures can be appropriately proposed by taking the geographical location information of the corporate customer into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the geographical location information of the corporate customer into AI, which can then propose region-specific security measures.

[0083] During generation, the generation unit can improve the accuracy of the proposal content based on benchmark data of the related industry. The generation unit can improve the accuracy of the proposal content by referring to benchmark data of the IT industry, for example. The generation unit can also improve the accuracy of the proposal content by referring to benchmark data of the manufacturing industry. Furthermore, the generation unit can improve the accuracy of the proposal content by referring to benchmark data of the financial industry. As a result, the accuracy of the proposal content is improved by referring to benchmark data of the related industry. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input benchmark data of the related industry into AI, which can improve the accuracy of the proposal content.

[0084] The providing unit can estimate the corporate customer's emotions and adjust the display method of the information to be provided based on the estimated emotions. For example, if the corporate customer is feeling stressed, the providing unit can provide a simple, highly visible display method. Furthermore, if the corporate customer is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the corporate customer is in a hurry, the providing unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of information according to the corporate customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the corporate customer's emotions into AI, which can estimate the emotions and adjust the display method of information.

[0085] At the time of information provision, the provision unit can select the optimal information provision method based on the corporate customer's past feedback. For example, the provision unit selects the optimal information provision method based on feedback provided in the past by the corporate customer. The provision unit can also analyze the corporate customer's past feedback and select an information provision method that reflects areas for improvement. Furthermore, the provision unit can select the most effective information provision method by referring to the corporate customer's past feedback. In this way, the optimal information provision method can be selected by referring to the corporate customer's past feedback. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the corporate customer's past feedback into AI, which can select the optimal information provision method.

[0086] The provision unit can provide information customized according to the industry and scale of the corporate customer at the time of provision. For example, the provision unit can provide information on network security to a corporate customer in the IT industry. The provision unit can also provide information on supply chain security to a corporate customer in the manufacturing industry. Furthermore, the provision unit can provide information on data protection to a corporate customer in the financial industry. This makes it possible to provide more appropriate information by providing information according to the industry and scale of the corporate customer. Some or all of the above-mentioned processing in the provision unit may be performed using, or without, AI, for example. For example, the provision unit can input information on the industry and scale of the corporate customer into AI, which can generate customized information.

[0087] The providing unit can estimate the corporate customer's emotions and determine the priority of information to provide based on the estimated emotions. For example, if the corporate customer is feeling stressed, the providing unit can prioritize the most important information. Furthermore, if the corporate customer is relaxed, the providing unit can sequentially provide detailed information. Furthermore, if the corporate customer is in a hurry, the providing unit can provide only the most important information. This enables efficient information provision by determining the priority of information according to the corporate customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the corporate customer's emotions into AI, which can then estimate the emotions and determine the priority of information.

[0088] At the time of providing the information, the providing unit can provide region-specific information based on the geographical location information of the corporate customer. For example, if the corporate customer is located in a specific country, the providing unit can provide information about security regulations in that country. Furthermore, if the corporate customer is located in an urban area, the providing unit can provide information about city-specific security risks. Furthermore, if the corporate customer is located in a rural area, the providing unit can provide information about region-specific security risks. This makes it possible to appropriately provide region-specific information by taking the geographical location information of the corporate customer into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the geographical location information of the corporate customer into AI, which can generate region-specific information.

[0089] The provision unit can analyze the social media activities of corporate customers and provide relevant information at the time of provision. For example, if the corporate customer frequently uses social media, the provision unit can provide information about social media security. In addition, if the corporate customer uses a specific social media platform, the provision unit can also provide information about security risks specific to that platform. Furthermore, if the corporate customer is not very active on social media, the provision unit can also provide information about basic security measures. This allows for appropriate provision of relevant information by analyzing the social media activities of corporate customers. Some or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input the social media activities of corporate customers into AI, which then generates relevant information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives information entered by corporate customers in a chat format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using data mining technology or machine learning technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal zero trust security construction plan. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated construction plan to the corporate customer. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives information entered by a corporate customer in a chat format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using data mining technology or machine learning technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal zero trust security construction plan. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated construction plan to the corporate customer. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives information entered by corporate customers in chat format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using data mining technology or machine learning technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal zero trust security construction plan. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated construction plan to the corporate customer. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information entered by corporate customers in a chat format. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information using data mining technology or machine learning technology. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an optimal zero trust security construction plan. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated construction plan to the corporate customer.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can automatically obtain benchmark data for related industries based on the information entered by corporate customers and provide it to the analysis unit. For example, benchmark data for the IT industry can be obtained based on information entered by corporate customers in the IT industry and provided to the analysis unit. Also, benchmark data for the manufacturing industry can be obtained based on information entered by corporate customers in the manufacturing industry and provided to the analysis unit. Furthermore, benchmark data for the financial industry can be obtained based on information entered by corporate customers in the financial industry and provided to the analysis unit. In this way, the reception unit can provide benchmark data according to the corporate customers' industries, allowing the analysis unit to perform more accurate analysis.

[0092] The reception unit can automatically obtain relevant legal and regulatory information based on the information entered by the corporate customer and provide it to the analysis unit. For example, if the corporate customer is located in a specific country, it can obtain security legal and regulatory information for that country and provide it to the analysis unit. Also, if the corporate customer belongs to a specific industry, it can obtain security legal and regulatory information for that industry and provide it to the analysis unit. Furthermore, if the corporate customer is a company of a specific size, it can obtain security legal and regulatory information appropriate for that size and provide it to the analysis unit. In this way, the reception unit can provide legal and regulatory information for the corporate customer, allowing the analysis unit to perform a more appropriate analysis.

[0093] The analysis unit can estimate the corporate customer's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the corporate customer is feeling stressed, a simple, highly visible display method can be provided. Alternatively, if the corporate customer is relaxed, a display method including detailed information can be provided. Furthermore, if the corporate customer is in a hurry, a display method that focuses on the main points can be provided. This improves visibility by adjusting the display method of the analysis results according to the corporate customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the corporate customer's emotions into AI, which can then estimate the emotions and adjust the display method of the analysis results.

[0094] The generation unit can estimate the corporate customer's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, if the corporate customer is feeling stressed, a simple and intuitive presentation can be provided. If the corporate customer is relaxed, a presentation that includes detailed information can be provided. If the corporate customer is in a hurry, a presentation that focuses on the main points can be provided. By adjusting the presentation of the proposal based on the corporate customer's emotions, the likelihood of the proposal being accepted increases. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the corporate customer's emotions into an AI, which can then estimate the emotions and adjust the way the proposal is presented.

[0095] The providing unit can estimate the corporate customer's emotions and adjust the display method of the information to be provided based on the estimated emotions. For example, if the corporate customer is feeling stressed, a simple, highly visible display method can be provided. Alternatively, if the corporate customer is relaxed, a display method including detailed information can be provided. Furthermore, if the corporate customer is in a hurry, a display method that focuses on the main points can be provided. This improves visibility by adjusting the display method of information according to the corporate customer's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the corporate customer's emotions into AI, which can then estimate the emotions and adjust the display method of information.

[0096] The reception unit can automatically obtain relevant security news and trend information based on the corporate customer's input and provide it to the provision unit. For example, when a corporate customer inputs information about network security, the latest network security news and trend information can be obtained and provided to the provision unit. Also, when a corporate customer inputs information about endpoint security, the latest endpoint security news and trend information can be obtained and provided to the provision unit. Furthermore, when a corporate customer inputs information about access management, the latest access management news and trend information can be obtained and provided to the provision unit. In this way, the reception unit can provide relevant security news and trend information based on the corporate customer's input, allowing the provision unit to provide more appropriate information.

[0097] The reception unit can automatically obtain information on related security vendors based on the corporate customer's input and provide it to the provision unit. For example, when a corporate customer inputs information related to network security, information on related network security vendors can be obtained and provided to the provision unit. Also, when a corporate customer inputs information related to endpoint security, information on related endpoint security vendors can be obtained and provided to the provision unit. Furthermore, when a corporate customer inputs information related to access management, information on related access management vendors can be obtained and provided to the provision unit. In this way, the reception unit can provide information on related security vendors based on the corporate customer's input, allowing the provision unit to provide more appropriate information.

[0098] The analysis unit can predict associated security risks based on the information entered by the corporate customer. For example, when a corporate customer enters information related to network security, the analysis unit can predict associated network security risks. When a corporate customer enters information related to endpoint security, the analysis unit can predict associated endpoint security risks. When a corporate customer enters information related to access management, the analysis unit can predict associated access management risks. This allows the analysis unit to provide more appropriate analysis results by predicting associated security risks based on the information entered by the corporate customer.

[0099] The generation unit can predict the effectiveness of related security measures based on the input content of the corporate customer. For example, when a corporate customer inputs information about network security, the generation unit can predict the effectiveness of related network security measures. Also, when a corporate customer inputs information about endpoint security, the generation unit can predict the effectiveness of related endpoint security measures. Furthermore, when a corporate customer inputs information about access management, the generation unit can predict the effectiveness of related access management measures. As a result, the generation unit can make more appropriate proposals by predicting the effectiveness of related security measures based on the input content of the corporate customer.

[0100] The provision unit can provide information on related security training based on the corporate customer's input. For example, when a corporate customer inputs information related to network security, information on related network security training can be provided. Also, when a corporate customer inputs information related to endpoint security, information on related endpoint security training can be provided. Furthermore, when a corporate customer inputs information related to access management, information on related access management training can be provided. In this way, the provision unit can provide more appropriate information by providing information on related security training based on the corporate customer's input.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception department accepts information from corporate customers. This information includes services currently being used and services under consideration. The reception department accepts corporate customers' input of the services currently being used or services under consideration for each item in chat format. The reception department also saves the information entered by corporate customers in a database in real time, making it available for later use by the analysis department. Step 2: The analysis unit analyzes the information received by the reception unit. Using data mining and machine learning technologies, the analysis unit analyzes the information entered by the corporate customer and provides data to generate an optimal zero trust security implementation plan. Step 3: The generation unit generates specific zero trust security implementation plans based on the data provided by the analysis unit. For network security, the generation unit proposes the introduction of firewalls and VPNs, and for endpoint security, it proposes the introduction of antivirus software and endpoint detection and response (EDR) tools. It also provides information such as the cost and time required to implement firewalls. Step 4: The provision department provides the construction plan generated by the generation department to the corporate customer. The provision department sends the construction plan generated by the generation department to the corporate customer by email or provides it through a web portal. The corporate customer can access the web portal to view the construction plan generated by the generation department.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception department that accepts information from corporate customers, an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a zero trust security construction plan based on the information analyzed by the analysis unit; a providing unit that provides the construction plan generated by the generating unit to the corporate customer. A system characterized by:

2. The reception unit Corporate customers enter the services they are currently using or considering for each item in chat format.

2. The system of claim 1.

3. The analysis unit Analyzing the information received by the reception unit and providing data for generating a zero trust security construction plan 2. The system of claim 1.

4. The generation unit Based on the data provided by the analysis department, we propose specific construction plans, services, costs, and timeframes.

2. The system of claim 1.

5. The providing unit Providing the construction plan generated by the generation unit to corporate customers 2. The system of claim 1.

6. The reception unit Estimate the emotions of corporate customers and dynamically change the design of the input interface based on the estimated emotions.

2. The system of claim 1.

7. The reception unit Analyze past input history and automatically generate input formats 2. The system of claim 1.

8. The reception unit When entering data, questions customized to the business type and size of the corporate customer are displayed.

2. The system of claim 1.

Images