system

Abstract

The system according to this embodiment aims to automate and streamline the process of creating system configuration diagrams. [Solution] The system according to the embodiment comprises an input unit, a generation unit, and an output unit. The input unit receives system information as input. The generation unit generates a system configuration diagram based on the information input by the input unit. The output unit outputs the system configuration diagram generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of creating a system configuration diagram requires labor and time and is difficult to perform efficiently.

[0005] The system according to the embodiment aims to automate and improve the efficiency of the process of creating a system configuration diagram.

Means for Solving the Problems

[0006] The system according to the embodiment includes an input unit, a generation unit, and an output unit. The input unit inputs information of the system. The generation unit generates a system configuration diagram based on the information input by the input unit. The output unit outputs the system configuration diagram generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and streamline the process of creating system configuration diagrams. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52. <于

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated system configuration diagram generation system according to an embodiment of the present invention is a system that automatically creates system configuration diagrams using a generation AI. This system trains the generation AI with many system configuration diagrams created in the past and inputs system information (IP address, hostname, system name, role, etc.) into the generation AI. When a system configuration diagram is needed, the requirements such as the intended use and the desired level of detail in the diagram are communicated to the generation AI. Based on this information, the generation AI automatically generates an appropriate system configuration diagram. The output format can be diverse, including PowerPoint, CAD, and images, and can be flexibly adapted to the user's needs. This mechanism significantly streamlines the system configuration diagram creation process, saving time and effort. Furthermore, the learning ability of the generation AI provides more accurate configuration diagrams, improving productivity and enabling quick and accurate explanations and reports using system configuration diagrams. For example, the generation AI learns various types of system configuration diagrams, such as diagrams representing the entire system that can be used in proposal documents, system configuration diagrams used in design documents, and network configuration diagrams that show communication paths. This allows the generation AI to respond to diverse needs. For example, the generating AI analyzes the information entered by the user (such as intended use and the required level of detail in the system diagram) and generates an appropriate system diagram. If a user enters "I need a document that provides an overview of the entire business," the generating AI will generate a diagram showing the general system connections. On the other hand, if a user enters "I need a network diagram," the generating AI will generate a diagram including detailed information such as equipment, IP addresses, and hostnames at the unit level. This allows the generating AI to automatically generate system diagrams tailored to the user's needs. This significantly streamlines the system diagram creation process, saving time and effort. Furthermore, the AI's learning capabilities provide more accurate diagrams, improving productivity and enabling quick and accurate explanations and reports using system diagrams. In summary, the automated system diagram generation system streamlines the system diagram creation process, saving time and effort.

[0029] The automated system configuration diagram generation system according to the embodiment comprises an input unit, a generation unit, and an output unit. The input unit receives system information as input. System information includes, but is not limited to, an IP address, hostname, system name, and role. The input unit can receive system information by methods such as text input, voice input, and file upload. The generation unit generates a system configuration diagram based on the information input by the input unit. The generation unit generates the system configuration diagram using a generation AI. The generation AI has learned from many system configuration diagrams created in the past and generates an appropriate system configuration diagram based on the input information. The generation unit can improve the accuracy of generation, for example, by having the generation AI learn from past system configuration diagrams. The generation unit can also have the generation AI analyze requirements such as the intended use and the desired level of detail in the configuration diagram, and generate a system configuration diagram that meets the user's needs. For example, the generation unit uses a generation AI to analyze user input information and, if a document providing an overview of the entire business is needed, generates a configuration diagram showing the general system connections. If a network configuration diagram is needed, it generates a configuration diagram including detailed information such as equipment, IP addresses, and hostnames at the unit level. The output unit outputs the system configuration diagram generated by the generation unit. The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, or image. For example, the output unit can output the generated system configuration diagram in PDF format. The output unit can also output the generated system configuration diagram as an image file. Furthermore, the output unit can print the generated system configuration diagram. This allows the automated system configuration diagram generation system according to the embodiment to streamline the process of inputting, generating, and outputting system information. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input information input by the input unit into the generation AI and cause the generation AI to generate the system configuration diagram.Some or all of the above-described processing in the output unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the output unit can input the generated system configuration diagram into the generation AI and have the generation AI select the output format.

[0030] The input unit receives system information. This system information includes, but is not limited to, IP addresses, hostnames, system names, and roles. The input unit can receive system information through methods such as text input, voice input, and file uploads. Specifically, with text input, users can directly input information using a keyboard. With voice input, speech recognition technology can be used to convert what the user says into text data and incorporate it as system information. With file uploads, users can upload files containing system information they have created in advance, allowing the input unit to analyze the contents and extract the necessary information. This enables the input unit to efficiently collect system information in various ways, improving user convenience. Furthermore, the input unit has a function to check the integrity and accuracy of the input information and can issue warnings if incorrect information is entered. For example, if the IP address format is incorrect or hostnames are duplicated, a message prompting the user to correct it will be displayed. This allows the input unit to collect accurate and reliable system information.

[0031] The generation unit generates a system configuration diagram based on the information input by the input unit. The generation unit generates the system configuration diagram using a generation AI. The generation AI has learned from many system configuration diagrams created in the past and generates an appropriate system configuration diagram based on the input information. Specifically, the generation AI analyzes the input information such as IP addresses, hostnames, system names, and roles, and determines how each element is related. Based on past learning data, the generation AI selects the optimal layout and connection method and generates a visually easy-to-understand system configuration diagram. For example, when generating a network configuration diagram, the generation AI optimizes the placement and connection method of each device to create a diagram that is easy to understand visually. In addition, the generation AI can adjust the level of detail and precision of the system configuration diagram according to the user's requirements. For example, if a document that provides an overview of the entire business is needed, it generates a configuration diagram that shows the general connections of the systems, and if a network configuration diagram is needed, it generates a configuration diagram that includes detailed information on each device. In this way, the generation unit can provide flexible system configuration diagrams that meet the user's needs. Furthermore, the generation unit can periodically update the training data of the generation AI, enabling it to generate system configuration diagrams that reflect the latest technologies and trends. This allows the generation unit to consistently provide highly accurate and up-to-date system configuration diagrams, thereby improving the user's operational efficiency.

[0032] The output unit outputs the system configuration diagram generated by the generation unit. The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, and image. Specifically, the output unit can output the generated system configuration diagram in PDF format. PDF is a widely used file format with high compatibility across different devices and platforms. The output unit can also output the generated system configuration diagram as an image file. Image files are easy to share and display, making them suitable for use in presentations and reports. Furthermore, the output unit can print the generated system configuration diagram. Printed system configuration diagrams are convenient for use in meetings and discussions, and are useful for visually sharing information. The output unit allows users to flexibly select the output format according to their requirements. For example, if a user is creating presentation materials, they can output in PowerPoint format; if they are creating detailed technical documents, they can output in CAD format. This allows the output unit to meet diverse user needs and effectively utilize the generated system configuration diagram. Furthermore, the output unit has a function to check the quality of the generated system configuration diagram and make corrections or adjustments as needed. This allows the output unit to consistently provide high-quality system configuration diagrams, thereby improving user satisfaction.

[0033] The generation unit can learn from many system configuration diagrams created in the past using a generation AI. The generation unit improves the accuracy of generation by having the generation AI learn from previously created system configuration diagrams. For example, the generation unit has the generation AI learn system configuration diagrams such as network configuration diagrams and software architecture diagrams. Based on the system configuration diagrams learned by the generation AI, the generation unit generates an appropriate system configuration diagram according to the input information. In this way, the accuracy of generation improves as the generation AI learns from past system configuration diagrams. The generation AI learns system configuration diagrams using techniques such as deep learning and reinforcement learning. Some or all of the above processing in the generation unit may be performed using the generation AI, or without using the generation AI. For example, the generation unit can input previously created system configuration diagrams into the generation AI and have the generation AI learn from them.

[0034] The generation unit, using a generation AI, can analyze requirements such as intended use and the desired level of detail in the configuration diagram, and generate an appropriate system configuration diagram. For example, if the generation AI analyzes user input information and a document providing an overview of the entire business is needed, the generation unit can generate a configuration diagram showing the general system connections. Furthermore, if a network configuration diagram is required, the generation unit can generate a configuration diagram including detailed information such as equipment, IP addresses, and hostnames at the unit level. This allows the generation AI to analyze requirements and generate a system configuration diagram that meets the user's needs. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input user information into the generation AI and have the generation AI perform the requirements analysis and system configuration diagram generation.

[0035] The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, or image. For example, the output unit can output the generated system configuration diagram in PDF format. The output unit can also output the generated system configuration diagram as an image file. Furthermore, the output unit can print the generated system configuration diagram. This allows for a wide range of output formats for the system configuration diagram, flexibly responding to user needs. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input the generated system configuration diagram into a generation AI and have the generation AI select the output format.

[0036] The generation unit can generate various types of system configuration diagrams, such as diagrams representing the entire system that can be used in proposal documents, system configuration diagrams for use in design documents, and network configuration diagrams that show communication paths. For example, the generation unit's generation AI can generate a diagram representing the entire system that can be used in proposal documents. The generation unit can also generate system configuration diagrams for use in design documents. Furthermore, the generation unit can also generate network configuration diagrams that show communication paths. This allows the generation unit to meet diverse needs by generating various types of system configuration diagrams. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can have the generation AI generate a diagram representing the entire system that can be used in proposal documents.

[0037] The generation unit can generate an appropriate system configuration diagram based on the information entered by the user. For example, if the generation AI analyzes the user's input information and a diagram showing the general system connections is needed to provide an overview of the entire business, the generation unit can generate a diagram that includes detailed information such as equipment, IP addresses, and hostnames at the unit level if a network configuration diagram is needed. In this way, by generating an appropriate system configuration diagram based on the user's input information, a diagram that meets the user's needs can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit can input the user's input information into the generation AI and have the generation AI perform the generation of the system configuration diagram.

[0038] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). Furthermore, the input unit can predict and suggest input methods to be used during specific time periods based on the user's past input history. In addition, the input unit can provide input assistance functions by referring to information the user has previously entered. This allows the system to provide the user with the most suitable input method by analyzing past input history. Some or all of the above-described processes in the input unit may be performed using AI, or not. For example, the input unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0039] The input unit can filter system information based on the user's current projects and areas of interest when it is entered. For example, the input unit can display only system information related to the project the user is currently working on. The input unit can also prioritize the display of highly relevant system information based on the user's areas of interest. Furthermore, the input unit can automatically filter the necessary system information according to the user's project progress. This allows the system to provide highly relevant information by filtering based on the user's projects and areas of interest. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0040] The input unit can prioritize inputting highly relevant information when entering system information, taking into account the user's geographical location. For example, if the user is in a specific region, the input unit will prioritize inputting system information related to that region. Furthermore, if the user is on the move, the input unit can also input highly relevant system information based on their current location. Additionally, if the user is inside a specific facility, the input unit can prioritize inputting system information related to that facility. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant information.

[0041] The input unit can analyze the user's social media activity and input relevant information when system information is entered. For example, the input unit can input relevant system information based on information shared by the user on social media. The input unit can also analyze the activities of the user's social media followers and friends and input relevant system information. Furthermore, the input unit can input relevant system information based on topics the user has shown interest in on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0042] The generation unit can adjust the level of detail in the generated diagram based on the importance of the system information during generation. For example, the generation unit can display highly important system information in detail and simplify less important information. It can also highlight highly important system information and place less important information in the background. Furthermore, it can place highly important system information in the center and less important information around it. By adjusting the level of detail based on the importance of the system information, it is possible to provide a diagram that highlights important information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input system information importance data into a generation AI and have the generation AI perform the level of detail adjustment.

[0043] The generation unit can apply different generation algorithms depending on the system category during generation. For example, in the case of a network configuration diagram, the generation unit can apply a generation algorithm that emphasizes communication paths. Furthermore, in the case of a diagram representing the entire system, the generation unit can apply a generation algorithm that emphasizes the roles of the systems. Additionally, in the case of a system configuration diagram used in design documents, the generation unit can apply a generation algorithm that emphasizes detailed technical information. This allows for the generation of appropriate configuration diagrams by applying the appropriate generation algorithm according to the system category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input system category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine the priority of the configuration diagrams to be generated based on the submission timing of the system information during generation. For example, the generation unit can prioritize the generation of system information with an approaching submission deadline. It can also postpone the generation of system information with a distant submission deadline. Furthermore, the generation unit can automatically adjust the priority of the system information according to the submission deadline. This allows the generation unit to provide configuration diagrams that correspond to the submission deadlines by determining the priority based on the submission timing of the system information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input system information submission timing data into the generation AI and have the generation AI perform the priority determination.

[0045] The generation unit can adjust the order of the generated configuration diagrams based on the relationships between system information during generation. For example, the generation unit can prioritize the display of highly relevant system information. It can also postpone the display of less relevant system information. Furthermore, the generation unit can automatically adjust the display order of system information according to its relevance. This allows for the priority display of highly relevant information by adjusting the order based on the relationships between system information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relationship data of system information into a generation AI and have the generation AI perform the order adjustment.

[0046] The output unit can select the optimal output method by referring to the user's past output history when outputting. For example, the output unit may prioritize suggesting output formats that the user has frequently used in the past (PowerPoint, CAD, images, etc.). The output unit can also predict and suggest output formats to be used during specific time periods based on the user's past output history. Furthermore, the output unit can provide output assistance functions by referring to information that the user has output in the past. This allows the output unit to provide the user with the optimal output method by referring to past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into a generating AI and have the generating AI select the optimal output method.

[0047] The output unit can customize the output format based on the user's current projects and areas of interest during output. For example, the output unit may prioritize providing output formats relevant to the user's current projects. It can also suggest highly relevant output formats based on the user's areas of interest. Furthermore, the output unit can automatically customize the necessary output formats according to the user's project progress. This allows for the provision of highly relevant output by customizing the output format based on the user's projects and areas of interest. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's project information and areas of interest data into a generating AI and have the generating AI perform the customization of the output format.

[0048] The output unit can select the optimal output method when outputting, taking into account the user's geographical location information. For example, if the user is in a specific region, the output unit can prioritize providing an output format related to that region. Furthermore, if the user is on the move, the output unit can also provide a highly relevant output format based on their current location. Additionally, if the user is inside a specific facility, the output unit can prioritize providing an output format related to that facility. This allows for the provision of highly relevant output methods by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal output method.

[0049] The output unit can analyze the user's social media activity and suggest an output format at the time of output. For example, the output unit can suggest a relevant output format based on information shared by the user on social media. It can also analyze the activities of the user's social media followers and friends and suggest a relevant output format. Furthermore, the output unit can suggest a relevant output format based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it is possible to provide a highly relevant output format. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into a generating AI and have the generating AI perform the output format suggestion.

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

[0051] The input unit can analyze the user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). Furthermore, the input unit can predict and suggest input methods to be used during specific time periods based on the user's past input history. In addition, the input unit can provide input assistance functions by referring to information the user has previously entered. This allows the system to provide the user with the most suitable input method by analyzing their past input history.

[0052] The generation unit can adjust the level of detail in the generated diagram based on the importance of the system information. For example, it can display highly important system information in detail and simplify less important information. The generation unit can also highlight highly important system information and place less important information in the background. Furthermore, it can place highly important system information in the center and less important information around it. By adjusting the level of detail based on the importance of the system information, it can provide a diagram that highlights important information.

[0053] The generation unit can apply different generation algorithms depending on the system category during generation. For example, in the case of a network configuration diagram, a generation algorithm that emphasizes communication paths can be applied. Similarly, for a diagram representing the entire system, a generation algorithm that emphasizes the system's role can be applied. Furthermore, for system configuration diagrams used in design documents, a generation algorithm that emphasizes detailed technical information can be applied. This allows for the generation of appropriate configuration diagrams by applying the appropriate generation algorithm according to the system category.

[0054] The generation unit can determine the priority of the configuration diagrams to be generated based on the submission timing of the system information. For example, it can prioritize the generation of system information with an approaching submission deadline. It can also postpone the generation of system information with a distant submission deadline. Furthermore, it can automatically adjust the priority of system information according to the submission deadline. As a result, by determining the priority based on the submission timing of the system information, it can provide configuration diagrams that are appropriate for the submission deadline.

[0055] The generation unit can adjust the order of the generated configuration diagrams based on the relationships between system information during generation. For example, it can prioritize the display of highly relevant system information. It can also postpone the display of less relevant system information. Furthermore, it can automatically adjust the display order of system information according to its relevance. This allows for the priority display of highly relevant information by adjusting the order based on the relationships between system information.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The input unit receives system information. This information includes, for example, IP address, hostname, system name, and role. The input unit can receive system information through methods such as text input, voice input, and file upload. Step 2: The generation unit generates a system configuration diagram based on the information input by the input unit. The generation unit generates system configuration diagrams using generation AI and has learned from many system configuration diagrams created in the past. The generation unit analyzes requirements such as the intended use and the desired level of detail in the configuration diagram, and generates a system configuration diagram that meets the user's needs. For example, if a document that provides an overview of the entire business is needed, it generates a configuration diagram that shows the general connections of the systems, and if a network configuration diagram is needed, it generates a configuration diagram that includes detailed information such as equipment, IP addresses, and hostnames at the unit level. Step 3: The output unit outputs the system configuration diagram generated by the generation unit. The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, and image. For example, the generated system configuration diagram can be output in PDF format, as an image file, or printed.

[0058] (Example of form 2) The automated system configuration diagram generation system according to an embodiment of the present invention is a system that automatically creates system configuration diagrams using a generation AI. This system trains the generation AI with many system configuration diagrams created in the past and inputs system information (IP address, hostname, system name, role, etc.) into the generation AI. When a system configuration diagram is needed, the requirements such as the intended use and the desired level of detail in the diagram are communicated to the generation AI. Based on this information, the generation AI automatically generates an appropriate system configuration diagram. The output format can be diverse, including PowerPoint, CAD, and images, and can be flexibly adapted to the user's needs. This mechanism significantly streamlines the system configuration diagram creation process, saving time and effort. Furthermore, the learning ability of the generation AI provides more accurate configuration diagrams, improving productivity and enabling quick and accurate explanations and reports using system configuration diagrams. For example, the generation AI learns various types of system configuration diagrams, such as diagrams representing the entire system that can be used in proposal documents, system configuration diagrams used in design documents, and network configuration diagrams that show communication paths. This allows the generation AI to respond to diverse needs. For example, the generating AI analyzes the information entered by the user (such as intended use and the required level of detail in the system diagram) and generates an appropriate system diagram. If a user enters "I need a document that provides an overview of the entire business," the generating AI will generate a diagram showing the general system connections. On the other hand, if a user enters "I need a network diagram," the generating AI will generate a diagram including detailed information such as equipment, IP addresses, and hostnames at the unit level. This allows the generating AI to automatically generate system diagrams tailored to the user's needs. This significantly streamlines the system diagram creation process, saving time and effort. Furthermore, the AI's learning capabilities provide more accurate diagrams, improving productivity and enabling quick and accurate explanations and reports using system diagrams. In summary, the automated system diagram generation system streamlines the system diagram creation process, saving time and effort.

[0059] The automated system configuration diagram generation system according to the embodiment comprises an input unit, a generation unit, and an output unit. The input unit receives system information as input. System information includes, but is not limited to, an IP address, hostname, system name, and role. The input unit can receive system information by methods such as text input, voice input, and file upload. The generation unit generates a system configuration diagram based on the information input by the input unit. The generation unit generates the system configuration diagram using a generation AI. The generation AI has learned from many system configuration diagrams created in the past and generates an appropriate system configuration diagram based on the input information. The generation unit can improve the accuracy of generation, for example, by having the generation AI learn from past system configuration diagrams. The generation unit can also have the generation AI analyze requirements such as the intended use and the desired level of detail in the configuration diagram, and generate a system configuration diagram that meets the user's needs. For example, the generation unit uses a generation AI to analyze user input information and, if a document providing an overview of the entire business is needed, generates a configuration diagram showing the general system connections. If a network configuration diagram is needed, it generates a configuration diagram including detailed information such as equipment, IP addresses, and hostnames at the unit level. The output unit outputs the system configuration diagram generated by the generation unit. The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, or image. For example, the output unit can output the generated system configuration diagram in PDF format. The output unit can also output the generated system configuration diagram as an image file. Furthermore, the output unit can print the generated system configuration diagram. This allows the automated system configuration diagram generation system according to the embodiment to streamline the process of inputting, generating, and outputting system information. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input information input by the input unit into the generation AI and cause the generation AI to generate the system configuration diagram.Some or all of the above-described processing in the output unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the output unit can input the generated system configuration diagram into the generation AI and have the generation AI select the output format.

[0060] The input unit receives system information. This system information includes, but is not limited to, IP addresses, hostnames, system names, and roles. The input unit can receive system information through methods such as text input, voice input, and file uploads. Specifically, with text input, users can directly input information using a keyboard. With voice input, speech recognition technology can be used to convert what the user says into text data and incorporate it as system information. With file uploads, users can upload files containing system information they have created in advance, allowing the input unit to analyze the contents and extract the necessary information. This enables the input unit to efficiently collect system information in various ways, improving user convenience. Furthermore, the input unit has a function to check the integrity and accuracy of the input information and can issue warnings if incorrect information is entered. For example, if the IP address format is incorrect or hostnames are duplicated, a message prompting the user to correct it will be displayed. This allows the input unit to collect accurate and reliable system information.

[0061] The generation unit generates a system configuration diagram based on the information input by the input unit. The generation unit generates the system configuration diagram using a generation AI. The generation AI has learned from many system configuration diagrams created in the past and generates an appropriate system configuration diagram based on the input information. Specifically, the generation AI analyzes the input information such as IP addresses, hostnames, system names, and roles, and determines how each element is related. Based on past learning data, the generation AI selects the optimal layout and connection method and generates a visually easy-to-understand system configuration diagram. For example, when generating a network configuration diagram, the generation AI optimizes the placement and connection method of each device to create a diagram that is easy to understand visually. In addition, the generation AI can adjust the level of detail and precision of the system configuration diagram according to the user's requirements. For example, if a document that provides an overview of the entire business is needed, it generates a configuration diagram that shows the general connections of the systems, and if a network configuration diagram is needed, it generates a configuration diagram that includes detailed information on each device. In this way, the generation unit can provide flexible system configuration diagrams that meet the user's needs. Furthermore, the generation unit can periodically update the training data of the generation AI, enabling it to generate system configuration diagrams that reflect the latest technologies and trends. This allows the generation unit to consistently provide highly accurate and up-to-date system configuration diagrams, thereby improving the user's operational efficiency.

[0062] The output unit outputs the system configuration diagram generated by the generation unit. The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, and image. Specifically, the output unit can output the generated system configuration diagram in PDF format. PDF is a widely used file format with high compatibility across different devices and platforms. The output unit can also output the generated system configuration diagram as an image file. Image files are easy to share and display, making them suitable for use in presentations and reports. Furthermore, the output unit can print the generated system configuration diagram. Printed system configuration diagrams are convenient for use in meetings and discussions, and are useful for visually sharing information. The output unit allows users to flexibly select the output format according to their requirements. For example, if a user is creating presentation materials, they can output in PowerPoint format; if they are creating detailed technical documents, they can output in CAD format. This allows the output unit to meet diverse user needs and effectively utilize the generated system configuration diagram. Furthermore, the output unit has a function to check the quality of the generated system configuration diagram and make corrections or adjustments as needed. This allows the output unit to consistently provide high-quality system configuration diagrams, thereby improving user satisfaction.

[0063] The generation unit can learn from many system configuration diagrams created in the past using a generation AI. The generation unit improves the accuracy of generation by having the generation AI learn from previously created system configuration diagrams. For example, the generation unit has the generation AI learn system configuration diagrams such as network configuration diagrams and software architecture diagrams. Based on the system configuration diagrams learned by the generation AI, the generation unit generates an appropriate system configuration diagram according to the input information. In this way, the accuracy of generation improves as the generation AI learns from past system configuration diagrams. The generation AI learns system configuration diagrams using techniques such as deep learning and reinforcement learning. Some or all of the above processing in the generation unit may be performed using the generation AI, or without using the generation AI. For example, the generation unit can input previously created system configuration diagrams into the generation AI and have the generation AI learn from them.

[0064] The generation unit, using a generation AI, can analyze requirements such as intended use and the desired level of detail in the configuration diagram, and generate an appropriate system configuration diagram. For example, if the generation AI analyzes user input information and a document providing an overview of the entire business is needed, the generation unit can generate a configuration diagram showing the general system connections. Furthermore, if a network configuration diagram is required, the generation unit can generate a configuration diagram including detailed information such as equipment, IP addresses, and hostnames at the unit level. This allows the generation AI to analyze requirements and generate a system configuration diagram that meets the user's needs. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can input user information into the generation AI and have the generation AI perform the requirements analysis and system configuration diagram generation.

[0065] The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, or image. For example, the output unit can output the generated system configuration diagram in PDF format. The output unit can also output the generated system configuration diagram as an image file. Furthermore, the output unit can print the generated system configuration diagram. This allows for a wide range of output formats for the system configuration diagram, flexibly responding to user needs. Some or all of the above processing in the output unit may be performed using, for example, a generation AI, or without a generation AI. For example, the output unit can input the generated system configuration diagram into a generation AI and have the generation AI select the output format.

[0066] The generation unit can generate various types of system configuration diagrams, such as diagrams representing the entire system that can be used in proposal documents, system configuration diagrams for use in design documents, and network configuration diagrams that show communication paths. For example, the generation unit's generation AI can generate a diagram representing the entire system that can be used in proposal documents. The generation unit can also generate system configuration diagrams for use in design documents. Furthermore, the generation unit can also generate network configuration diagrams that show communication paths. This allows the generation unit to meet diverse needs by generating various types of system configuration diagrams. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can have the generation AI generate a diagram representing the entire system that can be used in proposal documents.

[0067] The generation unit can generate an appropriate system configuration diagram based on the information entered by the user. For example, if the generation AI analyzes the user's input information and a diagram showing the general system connections is needed to provide an overview of the entire business, the generation unit can generate a diagram that includes detailed information such as equipment, IP addresses, and hostnames at the unit level if a network configuration diagram is needed. In this way, by generating an appropriate system configuration diagram based on the user's input information, a diagram that meets the user's needs can be provided. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without using the generation AI. For example, the generation unit can input the user's input information into the generation AI and have the generation AI perform the generation of the system configuration diagram.

[0068] The input unit can estimate the user's emotions and adjust the timing of system information input based on the estimated emotions. For example, if the user is stressed, the input unit can delay the timing of system information input to allow for relaxed input. Conversely, if the user is focused, the input unit can speed up the timing of system information input to enable efficient input. Furthermore, if the user is tired, the input unit can adjust the timing of system information input to allow for breaks during input. This allows for efficient system information input by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation and input timing adjustment.

[0069] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). Furthermore, the input unit can predict and suggest input methods to be used during specific time periods based on the user's past input history. In addition, the input unit can provide input assistance functions by referring to information the user has previously entered. This allows the system to provide the user with the most suitable input method by analyzing past input history. Some or all of the above-described processes in the input unit may be performed using AI, or not. For example, the input unit can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0070] The input unit can filter system information based on the user's current projects and areas of interest when it is entered. For example, the input unit can display only system information related to the project the user is currently working on. The input unit can also prioritize the display of highly relevant system information based on the user's areas of interest. Furthermore, the input unit can automatically filter the necessary system information according to the user's project progress. This allows the system to provide highly relevant information by filtering based on the user's projects and areas of interest. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0071] The input unit can estimate the user's emotions and determine the priority of system information to be input based on the estimated emotions. For example, if the user is stressed, the input unit will postpone inputting less important system information and prioritize inputting more important information. If the user is relaxed, the input unit can input all system information equally. Furthermore, if the user is in a hurry, the input unit can prioritize inputting the most important system information. This enables efficient information input by determining the priority of input information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not using AI. For example, the input unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and determine the priority of input information.

[0072] The input unit can prioritize inputting highly relevant information when entering system information, taking into account the user's geographical location. For example, if the user is in a specific region, the input unit will prioritize inputting system information related to that region. Furthermore, if the user is on the move, the input unit can also input highly relevant system information based on their current location. Additionally, if the user is inside a specific facility, the input unit can prioritize inputting system information related to that facility. This allows for the priority input of highly relevant information by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant information.

[0073] The input unit can analyze the user's social media activity and input relevant information when system information is entered. For example, the input unit can input relevant system information based on information shared by the user on social media. The input unit can also analyze the activities of the user's social media followers and friends and input relevant system information. Furthermore, the input unit can input relevant system information based on topics the user has shown interest in on social media. This allows for efficient input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media activity data into a generating AI and have the generating AI select relevant information.

[0074] The generation unit can estimate the user's emotions and adjust the representation of the generated system diagram based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a system diagram with visually calming colors. If the user is in a hurry, the generation unit can also generate a system diagram that highlights important information. Furthermore, if the user is excited, the generation unit can generate a system diagram with visually stimulating effects. This allows for the provision of a more appropriate diagram by adjusting the representation of the system diagram according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and adjustment of the representation.

[0075] The generation unit can adjust the level of detail in the generated diagram based on the importance of the system information during generation. For example, the generation unit can display highly important system information in detail and simplify less important information. It can also highlight highly important system information and place less important information in the background. Furthermore, it can place highly important system information in the center and less important information around it. By adjusting the level of detail based on the importance of the system information, it is possible to provide a diagram that highlights important information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input system information importance data into a generation AI and have the generation AI perform the level of detail adjustment.

[0076] The generation unit can apply different generation algorithms depending on the system category during generation. For example, in the case of a network configuration diagram, the generation unit can apply a generation algorithm that emphasizes communication paths. Furthermore, in the case of a diagram representing the entire system, the generation unit can apply a generation algorithm that emphasizes the roles of the systems. Additionally, in the case of a system configuration diagram used in design documents, the generation unit can apply a generation algorithm that emphasizes detailed technical information. This allows for the generation of appropriate configuration diagrams by applying the appropriate generation algorithm according to the system category. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input system category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0077] The generation unit can estimate the user's emotions and adjust the length of the generated system diagram based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise system diagram. If the user is relaxed, the generation unit can also generate a longer system diagram with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a system diagram with visually stimulating effects. This allows for the provision of a system diagram of appropriate length by adjusting its length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation and adjust the length of the system diagram.

[0078] The generation unit can determine the priority of the configuration diagrams to be generated based on the submission timing of the system information during generation. For example, the generation unit can prioritize the generation of system information with an approaching submission deadline. It can also postpone the generation of system information with a distant submission deadline. Furthermore, the generation unit can automatically adjust the priority of the system information according to the submission deadline. This allows the generation unit to provide configuration diagrams that correspond to the submission deadlines by determining the priority based on the submission timing of the system information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input system information submission timing data into the generation AI and have the generation AI perform the priority determination.

[0079] The generation unit can adjust the order of the generated configuration diagrams based on the relationships between system information during generation. For example, the generation unit can prioritize the display of highly relevant system information. It can also postpone the display of less relevant system information. Furthermore, the generation unit can automatically adjust the display order of system information according to its relevance. This allows for the priority display of highly relevant information by adjusting the order based on the relationships between system information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input relationship data of system information into a generation AI and have the generation AI perform the order adjustment.

[0080] The output unit can estimate the user's emotions and adjust the format of the output system diagram based on the estimated user emotions. For example, if the user is relaxed, the output unit can output a system diagram with visually calming colors. If the user is in a hurry, the output unit can also output a system diagram that highlights important information. Furthermore, if the user is excited, the output unit can output a system diagram with visually stimulating effects. In this way, a more appropriate system diagram can be provided by adjusting the output format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, or not using AI. For example, the output unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation and output format adjustment.

[0081] The output unit can select the optimal output method by referring to the user's past output history when outputting. For example, the output unit may prioritize suggesting output formats that the user has frequently used in the past (PowerPoint, CAD, images, etc.). The output unit can also predict and suggest output formats to be used during specific time periods based on the user's past output history. Furthermore, the output unit can provide output assistance functions by referring to information that the user has output in the past. This allows the output unit to provide the user with the optimal output method by referring to past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into a generating AI and have the generating AI select the optimal output method.

[0082] The output unit can customize the output format based on the user's current projects and areas of interest during output. For example, the output unit may prioritize providing output formats relevant to the user's current projects. It can also suggest highly relevant output formats based on the user's areas of interest. Furthermore, the output unit can automatically customize the necessary output formats according to the user's project progress. This allows for the provision of highly relevant output by customizing the output format based on the user's projects and areas of interest. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's project information and areas of interest data into a generating AI and have the generating AI perform the customization of the output format.

[0083] The output unit can estimate the user's emotions and determine the priority of the system configuration diagrams to output based on the estimated user emotions. For example, if the user is stressed, the output unit will postpone outputting less important system configuration diagrams and prioritize outputting more important ones. If the user is relaxed, the output unit can output all system configuration diagrams equally. Furthermore, if the user is in a hurry, the output unit can prioritize outputting the most important system configuration diagrams. In this way, by determining the output priority according to the user's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and output priority determination.

[0084] The output unit can select the optimal output method when outputting, taking into account the user's geographical location information. For example, if the user is in a specific region, the output unit can prioritize providing an output format related to that region. Furthermore, if the user is on the move, the output unit can also provide a highly relevant output format based on their current location. Additionally, if the user is inside a specific facility, the output unit can prioritize providing an output format related to that facility. This allows for the provision of highly relevant output methods by considering the user's geographical location information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal output method.

[0085] The output unit can analyze the user's social media activity and suggest an output format at the time of output. For example, the output unit can suggest a relevant output format based on information shared by the user on social media. It can also analyze the activities of the user's social media followers and friends and suggest a relevant output format. Furthermore, the output unit can suggest a relevant output format based on topics the user has shown interest in on social media. In this way, by analyzing the user's social media activity, it is possible to provide a highly relevant output format. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's social media activity data into a generating AI and have the generating AI perform the output format suggestion.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The input unit can estimate the user's emotions and adjust the method of inputting system information based on those emotions. For example, if the user is stressed, the input unit can suggest voice input; if the user is relaxed, it can suggest text input. Furthermore, if the user is focused, the input unit can suggest file uploading. This allows for efficient system information input by providing the optimal input method according to the user's emotions.

[0088] The generation unit uses generation AI to estimate the user's emotions and adjust the style of the generated system diagram based on those emotions. For example, if the user is relaxed, the generation unit will generate a system diagram with visually calming colors. If the user is in a hurry, the generation unit can also generate a system diagram that highlights important information. Furthermore, if the user is excited, the generation unit can generate a system diagram with visually stimulating effects. By adjusting the style of the system diagram according to the user's emotions, a more appropriate diagram can be provided.

[0089] The output unit can estimate the user's emotions and adjust the format of the system diagram output based on those emotions. For example, if the user is relaxed, the output unit will output a system diagram with visually calming colors. If the user is in a hurry, the output unit can output a system diagram that highlights important information. Furthermore, if the user is excited, the output unit can output a system diagram with visually stimulating effects. In this way, by adjusting the output format according to the user's emotions, a more appropriate system diagram can be provided.

[0090] The input unit can estimate the user's emotions and prioritize the system information to be entered based on those emotions. For example, if the user is stressed, the input unit will postpone less important system information and prioritize more important information. If the user is relaxed, it can input all system information equally. Furthermore, if the user is in a hurry, it can prioritize the most important system information. This allows for efficient information input by prioritizing input information according to the user's emotions.

[0091] The generation unit can estimate the user's emotions and adjust the length of the generated system diagram based on those emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise system diagram. If the user is relaxed, it can generate a longer system diagram with more detailed explanations. Furthermore, if the user is excited, it can generate a system diagram with visually stimulating effects. By adjusting the length of the diagram according to the user's emotions, it is possible to provide a system diagram of an appropriate length.

[0092] The input unit can analyze the user's past input history and select the optimal input method. For example, it can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). Furthermore, the input unit can predict and suggest input methods to be used during specific time periods based on the user's past input history. In addition, the input unit can provide input assistance functions by referring to information the user has previously entered. This allows the system to provide the user with the most suitable input method by analyzing their past input history.

[0093] The generation unit can adjust the level of detail in the generated diagram based on the importance of the system information. For example, it can display highly important system information in detail and simplify less important information. The generation unit can also highlight highly important system information and place less important information in the background. Furthermore, it can place highly important system information in the center and less important information around it. By adjusting the level of detail based on the importance of the system information, it can provide a diagram that highlights important information.

[0094] The generation unit can apply different generation algorithms depending on the system category during generation. For example, in the case of a network configuration diagram, a generation algorithm that emphasizes communication paths can be applied. Similarly, for a diagram representing the entire system, a generation algorithm that emphasizes the system's role can be applied. Furthermore, for system configuration diagrams used in design documents, a generation algorithm that emphasizes detailed technical information can be applied. This allows for the generation of appropriate configuration diagrams by applying the appropriate generation algorithm according to the system category.

[0095] The generation unit can determine the priority of the configuration diagrams to be generated based on the submission timing of the system information. For example, it can prioritize the generation of system information with an approaching submission deadline. It can also postpone the generation of system information with a distant submission deadline. Furthermore, it can automatically adjust the priority of system information according to the submission deadline. As a result, by determining the priority based on the submission timing of the system information, it can provide configuration diagrams that are appropriate for the submission deadline.

[0096] The generation unit can adjust the order of the generated configuration diagrams based on the relationships between system information during generation. For example, it can prioritize the display of highly relevant system information. It can also postpone the display of less relevant system information. Furthermore, it can automatically adjust the display order of system information according to its relevance. This allows for the priority display of highly relevant information by adjusting the order based on the relationships between system information.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The input unit receives system information. This information includes, for example, IP address, hostname, system name, and role. The input unit can receive system information through methods such as text input, voice input, and file upload. Step 2: The generation unit generates a system configuration diagram based on the information input by the input unit. The generation unit generates system configuration diagrams using generation AI and has learned from many system configuration diagrams created in the past. The generation unit analyzes requirements such as the intended use and the desired level of detail in the configuration diagram, and generates a system configuration diagram that meets the user's needs. For example, if a document that provides an overview of the entire business is needed, it generates a configuration diagram that shows the general connections of the systems, and if a network configuration diagram is needed, it generates a configuration diagram that includes detailed information such as equipment, IP addresses, and hostnames at the unit level. Step 3: The output unit outputs the system configuration diagram generated by the generation unit. The output unit can output the generated system configuration diagram in formats such as PowerPoint, CAD, and image. For example, the generated system configuration diagram can be output in PDF format, as an image file, or printed.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the input unit, generation unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit can input system information using the receiving device 38 of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a system configuration diagram using generation AI. The output unit can output the generated system configuration diagram using the output device 40 of the smart device 14. The output unit can also transmit the generated system configuration diagram to other devices via the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the input unit, generation unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit can input system information via voice input using the microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a system configuration diagram using generation AI. The output unit can output the contents of the generated system configuration diagram as voice using the speaker 240 of the smart glasses 214. The output unit can also transmit the generated system configuration diagram to other devices via the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the input unit, generation unit, and output unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the input unit can input system information via voice input using the microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a system configuration diagram using generation AI. The output unit can display the generated system configuration diagram using the display 343 of the headset terminal 314. The output unit can also transmit the generated system configuration diagram to other devices via the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the input unit, generation unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the input unit can input system information via voice input using the microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a system configuration diagram using generation AI. The output unit can output the contents of the generated system configuration diagram as voice using the speaker 240 of the robot 414. The output unit can also transmit the generated system configuration diagram to other devices via the communication I / F 26 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) An input section for inputting system information, A generation unit that generates a system configuration diagram based on the information input by the input unit, The system comprises an output unit that outputs the system configuration diagram generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The generation AI learns from many system configuration diagrams created in the past. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The generation AI analyzes requirements such as intended use and the desired level of detail in the system configuration diagram, and generates an appropriate system configuration diagram. The system described in Appendix 1, characterized by the features described herein. (Note 4) The output unit is, Output the generated system configuration diagram in formats such as PowerPoint, CAD, and image. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It generates various types of system configuration diagrams, such as diagrams showing the entire system for use in proposal documents, system configuration diagrams for use in design documents, and network configuration diagrams showing communication paths. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system generates an appropriate system configuration diagram based on the information the user enters. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is, The system estimates the user's emotions and adjusts the timing of system information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is, Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is, When entering system information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is, It estimates the user's emotions and determines the priority of system information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is, When entering system information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is, When entering system information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate user emotions and adjust the representation of the system configuration diagram generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail in the generated configuration diagram is adjusted based on the importance of the system information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the system category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the system configuration diagram generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the priority of the configuration diagrams to be generated is determined based on the timing of system information submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, adjust the order of the generated configuration diagrams based on the relationships between system information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, This process involves estimating user emotions and adjusting the format of the system configuration diagram output based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, During output, the system selects the optimal output method by referring to the user's past output history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, During output, the output format is customized based on the user's current project and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, The system estimates user emotions and determines the priority of the system configuration diagrams to be output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, When outputting data, the system selects the optimal output method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The output unit is, When outputting data, the system analyzes the user's social media activity and suggests an output format. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for inputting system information, A generation unit that generates a system configuration diagram based on the information input by the input unit, The system comprises an output unit that outputs the system configuration diagram generated by the generation unit. A system characterized by the following features.

2. The generating unit is The generation AI learns from many system configuration diagrams created in the past. The system according to feature 1.

3. The generating unit is The AI ​​generates a system configuration diagram by analyzing requirements such as intended use and the desired level of detail in the diagram. The system according to feature 1.

4. The output unit is, Output the generated system configuration diagram in formats such as PowerPoint, CAD, and image. The system according to feature 1.

5. The generating unit is It generates various types of system configuration diagrams, such as diagrams showing the entire system for use in proposal documents, system configuration diagrams for use in design documents, and network configuration diagrams showing communication paths. The system according to feature 1.

6. The generating unit is The system generates an appropriate system configuration diagram based on the information the user enters. The system according to feature 1.

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

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

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