system

Abstract

We provide the system. [Solution] A system including means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel who lack network knowledge.

Description

Technical Field

[0006]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is difficult for on-site staff without knowledge of network introduction situations or design to generate an appropriate network configuration diagram.

Means for Solving the Problems

[0005] Provided is a system that provides information based on the network introduction situation and uses interactive AI to generate a network configuration diagram for on-site staff without network knowledge.

Brief Description of the Drawings

[0006] [Figure 1] It 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 the data processing device and 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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 1 of Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 of Embodiment 2. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 of Form Example 2. [Figure 15] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 3 of Example 3. [Figure 16] This is a sequence diagram showing the processing flow of the data processing system in Application Example 3 of Form Example 3. [Figure 17]It is a sequence diagram showing the processing flow of the data processing system in Example 1 of Form Example 1 when the emotion engine is combined. [Figure 18] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1 of Form Example 1 when the emotion engine is combined. [Figure 19] It is a sequence diagram showing the processing flow of the data processing system in other embodiments.

Embodiments for Carrying Out the Invention

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the language used in the following description will be explained.

[0009] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one 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 (registered trademark)), etc.

[0010] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0011] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0012] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0013] 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.

[0014] [First Embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0019] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0021] 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.

[0022] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] The 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0026] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0027] "Example of form 1"

[0028] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0029] "Example of form 2"

[0030] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0031] The following describes the processing flow for each example of the form.

[0032] "Example of form 1"

[0033] Step 1: The field staff enters information about the network equipment to be installed into this system. Step 2: The entered information is saved in the database and managed as network installation status.

[0034] Step 3: The interactive AI generates the optimal network configuration diagram based on the information in the database.

[0035] Step 4: The generated network configuration diagram is provided to the field staff and used for network deployment and management.

[0036] (Example 1)

[0037] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0038] It is difficult for users without specialized knowledge of network deployment and configuration to create appropriate network diagrams. Furthermore, there is a lack of efficient means to propose optimal configurations based on the current network deployment situation. This can lead to inefficiencies in network design and deployment.

[0039] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0040] In this invention, the server includes means for generating a network configuration diagram, means for saving the network deployment status to a database, and means for creating prompt statements using a generation AI model based on the saved data. This makes it possible for users without network knowledge to efficiently and accurately create network configuration diagrams and achieve optimal network design.

[0041] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[0042] A "database" is a system for efficiently storing, managing, and retrieving information, and is used to accumulate data such as network deployment status.

[0043] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform specific tasks, and is used to generate network diagrams.

[0044] A "prompt statement" is an input statement used to give instructions to a generating AI model, and includes the content of the instructions used when generating a network diagram.

[0045] "Interactive artificial intelligence" refers to artificial intelligence that provides information and performs tasks through dialogue with users, and is used to support users who lack network knowledge.

[0046] This invention is a system that generates network configuration diagrams and provides information based on the network deployment status. The system aims to provide interactive artificial intelligence for users without network knowledge.

[0047] The server executes a program to generate a network configuration diagram. This program has the function of saving the network deployment status to a database and creating prompt statements using a generation AI model based on that information. Specifically, the server saves information about network devices entered by the user to the database and generates prompt statements based on the saved data. The generated prompt statements are input into the generation AI model, and the optimal network configuration diagram is generated.

[0048] Users can input information about network devices into the system via their terminal. For example, by inputting specific device information such as "We plan to install a router, switch, and firewall," the server will generate a network configuration diagram based on this information.

[0049] For example, if a user inputs "I want to install a router and switch in the new office," the server saves this information to its database and generates a prompt message saying, "Please generate the optimal network configuration diagram for the new office." By inputting this prompt message into the AI ​​generation model, the optimal network configuration diagram is generated and provided to the user.

[0050] This system enables even users without network knowledge to efficiently and accurately create network configuration diagrams and achieve optimal network design.

[0051] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0052] Step 1:

[0053] The user inputs information about network equipment through a terminal. Specifically, the user inputs information such as "We plan to install a router, switch, and firewall." This input information serves as the basic data for generating the network configuration diagram.

[0054] Step 2:

[0055] The server stores network device information entered by the user in a database. This entered information is stored in the database and used in subsequent processing. This storage process manages the network deployment status.

[0056] Step 3:

[0057] The server generates prompt messages using a generative AI model based on information stored in the database. Specifically, it analyzes the stored network device information and generates prompt messages such as, "Generate the optimal network configuration diagram for the new office." These prompt messages are then used as input to the generative AI model.

[0058] Step 4:

[0059] The server inputs prompt messages into a generation AI model, which then generates the optimal network configuration diagram. The generation AI model analyzes the prompt messages and creates the network configuration diagram based on historical data and best practices. This process generates a configuration diagram that meets the user's requirements.

[0060] Step 5:

[0061] The server provides the user with a generated network configuration diagram. The user can view this diagram through their terminal and use it as a reference for network deployment. This output allows the user to obtain information necessary to achieve an appropriate network design.

[0062] (Application Example 1)

[0063] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0064] There is a need to optimize communication between machinery and equipment within a factory to improve production efficiency. However, it is difficult for workers without network knowledge to design an appropriate network configuration. Furthermore, if the implemented network does not provide the optimal communication path, the operation of the machinery and equipment may become inefficient.

[0065] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0066] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for operators without network knowledge, and means for proposing communication paths to optimize communication between machine devices. This makes it possible to generate an optimal network configuration diagram and efficiently communicate between machine devices even if the operator does not have network knowledge.

[0067] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network.

[0068] "Network deployment status" refers to information indicating how the network is installed and operated.

[0069] A "worker" is a field staff member who is involved in the design and operation of a network but does not possess specialized knowledge.

[0070] "Conversational artificial intelligence" refers to artificial intelligence that communicates with users using natural language and assists in providing information and solving problems.

[0071] A "communication path" is the network route through which data is sent and received.

[0072] "Mechanical equipment" refers to robots and other automated devices used within a factory.

[0073] The system for implementing this invention is designed to optimize communication between machinery and equipment within a factory. The server runs a program to generate a network configuration diagram and provides information based on the network deployment status. Specifically, the server generates the network configuration diagram using a generative AI model implemented in Python. This allows operators to obtain the optimal network configuration even without network knowledge.

[0074] The server collects data from sensors installed in machinery within the factory and uses this data to optimize communication paths. The hardware used consists of sensors and communication modules installed in the factory machinery. Data processing involves collecting data from sensors and converting it into the format necessary to generate a network diagram. Data calculation uses a generative AI model to calculate the optimal communication path.

[0075] As a concrete example, when introducing new machinery in a factory, workers input information about the machinery into the system. The server then uses AI to generate an optimal network configuration diagram and optimizes communication between the machinery. This results in smoother operation of the machinery and improved production efficiency.

[0076] An example of a prompt to input into a generating AI model is: "We have installed new machinery. Please generate a network configuration diagram and suggest the optimal communication path."

[0077] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0078] Step 1:

[0079] The user enters information about the new machine into the terminal. This information includes the machine's identification and the network it will connect to. The terminal then sends this information to the server.

[0080] Step 2:

[0081] The server processes the received information about the machinery and equipment to generate a network configuration diagram. Specifically, it converts the received data into a format that the AI ​​model can process. This converted data then becomes the input for the AI ​​model.

[0082] Step 3:

[0083] The server uses a generative AI model to generate the optimal network configuration diagram. Based on the input data, the generative AI model calculates the optimal connection method between mechanical devices and outputs the network configuration diagram.

[0084] Step 4:

[0085] The server uses the generated network diagram to perform calculations to optimize communication paths between mechanical devices. Specifically, it performs data calculations related to communication path selection to determine the optimal communication path. This result is output as the communication path.

[0086] Step 5:

[0087] The server sends an optimized network configuration diagram and communication paths to the terminal. The user then uses the terminal to review the generated network configuration diagram and communication paths and configure the machinery and equipment within the factory.

[0088] Step 6:

[0089] Users use terminals to configure machinery within the factory and initiate communication between machines based on an optimized network configuration. This results in smoother machine operation and improved production efficiency.

[0090] (Example 2)

[0091] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0092] Designing and creating configuration diagrams for communication networks requires specialized knowledge, making it particularly difficult for users with limited knowledge of communication networks. Furthermore, creating appropriate configuration diagrams requires accurately understanding the current deployment situation and designing the network based on that understanding. This presents a challenge for users in efficiently and accurately constructing communication networks.

[0093] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0094] In this invention, the server includes means for generating a communication network configuration diagram using an information processing device, means for providing information based on the status of communication network deployment, and means for providing interactive artificial intelligence for users who do not have specialized knowledge of communication networks. As a result, users can efficiently and accurately create a communication network configuration diagram and construct an optimal communication network, even without specialized knowledge.

[0095] An "information processing device" is a device that has the functions of inputting, processing, storing, and outputting data, and is used to generate a diagram of the configuration of a communication network.

[0096] A "communication network" is a network system in which multiple information devices are interconnected to send and receive data.

[0097] A "configuration diagram" is a diagram that visually shows the arrangement and connection relationships of equipment in a communication network, and it forms the basis of network design.

[0098] "Deployment status" refers to information indicating the installation and operational status of equipment in a communication network, and is fundamental data for designing an optimal network.

[0099] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information through dialogue with users and generates responses that meet the user's requests.

[0100] A "generative AI model" is an artificial intelligence model that generates new information or diagrams based on input data, and is particularly used in natural language processing and data generation.

[0101] A "user" is an individual or organization that uses this system to design communication networks or create configuration diagrams.

[0102] This invention is a system that generates and provides a diagram of a communication network configuration to users. The system is implemented using an information processing device, a database, and a generation AI model.

[0103] The server functions as an information processing device and generates a diagram of the communication network configuration. Specifically, the server uses a database to store the communication network deployment status entered by users. This database can use relational database management systems such as MySQL® or PostgreSQL.

[0104] The user inputs information about the communication network through a terminal. The terminal uses a web browser to access the system interface and enters information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This information is passed to the generating AI model as prompt messages.

[0105] The server uses a generative AI model to generate an optimal network configuration diagram based on the input information. This generative AI model can use a model specialized for natural language processing, such as OpenAI's GPT model. The generated configuration diagram is provided to the user, who can view it through a web browser and make modifications as needed.

[0106] As a concrete example, a user inputs a prompt message into the AI ​​model such as, "The communication network equipment we plan to install in our new office will be a router from a specific manufacturer and a switch from a specific manufacturer." Based on this information, the AI ​​generates an optimal communication network configuration diagram and provides it to the user.

[0107] In this way, even users without specialized knowledge can efficiently and accurately create network configuration diagrams and construct optimal network infrastructure.

[0108] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0109] Step 1:

[0110] The user inputs the information necessary for the deployment of the communication network via a terminal. Specifically, they access the system interface using a web browser and enter information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This input information is then prepared to be passed to the AI ​​model as prompt messages.

[0111] Step 2:

[0112] The terminal sends information entered by the user to the server. The entered data is converted into a common data format such as JSON and sent to the server over the network. This data transmission allows the server to receive the user's request.

[0113] Step 3:

[0114] The server stores the received data in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to store information about communication network equipment in tables. This storage process ensures that the data necessary for subsequent AI processing is secured.

[0115] Step 4:

[0116] The server invokes a generating AI model based on the stored data. Specifically, it inputs the stored information as prompts into the generating AI model and performs data processing to generate the optimal network configuration diagram. The generating AI model performs data calculations based on the input prompts and generates the optimal configuration diagram.

[0117] Step 5:

[0118] The server receives the network configuration diagram generated from the AI ​​model and provides it to the user. Specifically, it renders the generated diagram as a web page, making it accessible to the user. The user can view the diagram through a web browser and make modifications as needed. This output enables the user to create network configuration diagrams efficiently and accurately.

[0119] (Application Example 2)

[0120] Next, we will describe Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0121] Optimizing the network configuration of automated equipment within a factory is crucial for improving operational efficiency. However, creating an appropriate network diagram is difficult for on-site personnel without network knowledge. Therefore, there is a need for a system that allows on-site personnel to easily achieve the optimal network configuration.

[0122] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0123] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for on-site personnel without network knowledge, and means for generating an optimal network configuration diagram for automated equipment in the factory and improving operational efficiency. As a result, on-site personnel can optimize the network configuration of automated equipment in the factory and improve operational efficiency even without specialized knowledge.

[0124] A "network diagram" is a diagram that visually shows the arrangement of network equipment and connections, and is used to understand the overall structure of the network.

[0125] "Network deployment status" refers to information indicating the current placement, connection status, and usage of network equipment, and is useful as a reference when optimizing or expanding the network.

[0126] "Conversational artificial intelligence" is artificial intelligence that provides information and assists in problem-solving through dialogue with users, and responds to user questions using natural language processing technology.

[0127] "Automated equipment" refers to robots and mechanical devices used in factories that are designed to perform specific tasks automatically.

[0128] "Operational efficiency" is an indicator that shows how effectively a system or piece of equipment functions to achieve its purpose, and efficient operation contributes to cost reduction and productivity improvement.

[0129] To implement this invention, it is necessary to build a system in which a server generates a network configuration diagram and provides information based on the network deployment status. The server executes a program developed using Python and provides an interactive web interface using Flask. Users can input information about network devices through this interface.

[0130] The input information is sent as a prompt to a generative AI model running on the server, specifically to OpenAI's GPT. The GPT model returns instructions to generate the optimal network configuration diagram based on the input information. Upon receiving these instructions, the server uses Graphviz to draw the network configuration diagram.

[0131] The generated network configuration diagram is provided to the user via a web interface. This allows users to create the optimal network configuration for their factory's automated equipment without requiring specialized network knowledge.

[0132] For example, when a user is inputting information about a newly introduced robotic arm and existing network equipment, the following prompt message can be used.

[0133] Prompt example:

[0134] "The new robotic arm to be introduced is model RA-2023, and the existing network equipment includes a switch SW-100 and a router RT-200. Please generate a network configuration diagram that optimally connects these components."

[0135] This prompt allows the server to generate and provide the user with an optimal network configuration diagram.

[0136] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0137] Step 1:

[0138] The user uses a terminal to access the web interface and enter information about the network equipment. This information includes the model number of the newly installed equipment and details of existing network equipment. This information is then sent to the server.

[0139] Step 2:

[0140] The server converts the received network device information into prompt statements suitable for the generating AI model. Specifically, it creates prompt statements containing instructions for generating the optimal network configuration diagram based on the input information. These prompt statements are then sent to the OpenAI GPT model.

[0141] Step 3:

[0142] The server receives a response from the GPT model and obtains instructions for generating the optimal network configuration diagram. Based on the input prompt, the GPT model provides the information necessary to generate the network configuration diagram.

[0143] Step 4:

[0144] The server uses Graphviz to draw a network configuration diagram based on instructions obtained from the GPT model. Graphviz visually represents the placement and connections of network devices based on these instructions.

[0145] Step 5:

[0146] The generated network configuration diagram is sent from the server to the user's terminal and provided to the user via a web interface. The user can review the generated configuration diagram and use it to optimize the network configuration of automated equipment within the factory.

[0147] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0148] "Example of form 1"

[0149] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0150] "Example of form 2"

[0151] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0152] The following describes the processing flow for each example of the form.

[0153] "Example of form 1"

[0154] Step 1: Gain an understanding of the user's network knowledge and needs through dialogue with the user.

[0155] Step 2: The emotion engine recognizes the user's emotions and adjusts how the network diagram is generated accordingly. For example, if the user feels stressed, the emotion engine adjusts the network diagram generation process to be simpler or easier for the user to understand.

[0156] Step 3: The emotion engine recognizes the user's emotions and adjusts how network deployment information is provided accordingly. For example, if the user is perceived as feeling happy, the emotion engine generates a more detailed network configuration diagram, providing optimal information based on the user's emotions.

[0157] Step 4: Provide the generated network diagram and information to the user.

[0158] (Example 1)

[0159] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0160] In generating network configuration diagrams, there is a challenge in that field personnel without network knowledge find it difficult to create appropriate diagrams. Furthermore, the lack of information tailored to the user's emotional needs may lead to decreased user understanding and satisfaction.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0162] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions, and means for storing information entered by the user in a database and generating prompt sentences using a generation AI model. This makes it possible to generate an appropriate network configuration diagram even without network knowledge and to provide information according to the user's emotions.

[0163] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[0164] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[0165] "Conversational artificial intelligence" refers to artificial intelligence that can provide information or solve problems through dialogue with users, and responds to user input using natural language processing technology.

[0166] An "emotion engine" is an engine that recognizes the user's emotions and adjusts the system's operation accordingly, and is used to improve the user experience.

[0167] A "generative AI model" is a model that uses artificial intelligence technology to generate new information and content from data, and is applied to areas such as natural language generation and image generation.

[0168] A "prompt" is an instruction given to artificial intelligence to perform a specific task, and it forms the basis for the AI ​​to make appropriate responses and generations.

[0169] A description of embodiments for carrying out this invention will be given.

[0170] The server runs a program to generate a network diagram. This program is developed using Python and uses MySQL as its database. The server receives information about network devices sent by the user and stores it in the database. Based on the stored information, the server generates prompts using a generative AI model and inputs them into an interactive artificial intelligence. This AI uses OpenAI's GPT model and generates the optimal network diagram based on the prompts.

[0171] Users input network device information via a terminal, enabling them to generate network diagrams even without network knowledge. For example, they can input information such as router model numbers, switch port counts, and access point coverage ranges. The information entered by the user is then sent from the terminal to the server.

[0172] The terminal is responsible for sending information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. It also displays the network configuration diagram sent from the server to the user.

[0173] As a concrete example, when a company sets up a network in a new office, the user inputs information such as "Router model: XYZ123, Number of switch ports: 24, Access point coverage: 50m". Based on this information, the server generates a prompt message, "Generate a network configuration diagram using router XYZ123 and a 24-port switch," and inputs it into the AI. The AI ​​generates a configuration diagram based on the prompt, and the server uses an emotion engine to adjust the diagram according to the user's emotions. Finally, the terminal displays the adjusted configuration diagram to the user.

[0174] In this way, even users without network knowledge can generate appropriate network diagrams and provide information tailored to their needs.

[0175] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0176] Step 1:

[0177] The user enters information about network devices into the terminal. Specifically, they enter information such as the router model number, the number of ports on the switch, and the coverage range of the access point. The entered information becomes the basic data for generating the network configuration diagram.

[0178] Step 2:

[0179] The terminal sends the information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. At this time, the terminal verifies the integrity of the data and adjusts the data format as needed.

[0180] Step 3:

[0181] The server saves the received data to a MySQL database. The server uses the Python SQLAlchemy library to connect to the database and insert the data. The saved data is then used for subsequent processing.

[0182] Step 4:

[0183] The server generates prompt messages using a generation AI model based on stored data. Specifically, it retrieves the necessary information from the database and uses it to create prompt messages such as "Generate a network configuration diagram using router XYZ123 and a 24-port switch."

[0184] Step 5:

[0185] The server inputs the generated prompt text into the interactive artificial intelligence. The AI ​​uses OpenAI's GPT model to generate the optimal network configuration diagram based on the prompt. The AI ​​analyzes the input prompt and outputs the appropriate configuration diagram.

[0186] Step 6:

[0187] The server uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's past input history and current input to determine whether the user is stressed or happy. For example, if the server determines that the user is stressed, it simplifies the generated configuration diagram.

[0188] Step 7:

[0189] The server sends the adjusted network configuration diagram to the terminal. The terminal displays the received configuration diagram to the user. The user can review the displayed configuration diagram and make corrections or additional inputs as needed.

[0190] (Application Example 1)

[0191] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0192] If on-site personnel lack network knowledge, understanding complex network configurations and implementing them properly becomes difficult. Furthermore, the lack of information tailored to user needs can lead to stress. Additionally, proposing the optimal network configuration for the layout and operation of machinery within the factory presents a challenge.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0194] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for proposing the optimal network for the placement and operation of machinery and equipment within the factory. As a result, field personnel can understand and implement an appropriate network configuration even without network knowledge, information can be provided in accordance with the user's emotions, and the optimal network for the placement and operation of machinery and equipment within the factory can be proposed.

[0195] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used to understand the overall structure of the network.

[0196] "Network deployment status" refers to information that shows the current state of how the network is installed and operated.

[0197] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with the user.

[0198] "Emotion recognition means" refers to technology that analyzes a user's emotions and takes appropriate action based on those emotions.

[0199] "An optimal network for the placement and operation of machinery and equipment" refers to the network configuration that is best suited for the efficient operation of machinery and equipment within a factory.

[0200] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server executes a program for generating a network configuration diagram and provides information based on the network deployment status. Specifically, the server uses interactive artificial intelligence to generate an optimal network configuration diagram based on information about network devices entered by the user. Furthermore, it analyzes the user's emotions using emotion recognition means and adjusts the method of providing information according to those emotions.

[0201] The terminal provides an interface for users to input information about network devices. Users can use terminals such as smartphones and tablets to input information about network devices and receive network configuration diagrams provided by the server.

[0202] As a concrete example, when introducing new machinery in a factory, the user inputs the layout information of the machinery using a terminal. Based on this information, the server uses a generative AI model to generate an optimal network configuration diagram and provides it to the user. If the user is experiencing stress, the server simplifies the information provided using emotion recognition, and provides detailed information if the user is understanding the situation.

[0203] Examples of prompt messages include, "Generate the optimal network configuration diagram for introducing new machinery and equipment," and "Simplify information provision if the user is experiencing stress."

[0204] In this way, even if field personnel do not possess network knowledge, they can understand and implement the appropriate network configuration.

[0205] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0206] Step 1:

[0207] The user uses a terminal to enter information about the network device. This information includes the type of device, its location, and the connection method. This information is then sent to the server.

[0208] Step 2:

[0209] The server stores information about received network devices in a database. Based on the stored information, it uses a generative AI model to generate an optimal network configuration diagram. In this process, it calculates network connection patterns and communication paths between devices to create a visual configuration diagram.

[0210] Step 3:

[0211] The server analyzes the user's emotions using emotion recognition technology. It analyzes the user's facial expressions and voice data during input to identify emotions such as stress and joy. This emotion information is used to adjust the way information is provided.

[0212] Step 4:

[0213] The server provides the user with the most relevant information based on the generated network diagram and sentiment analysis results. If the user is feeling stressed, the information is simplified and presented in an easy-to-understand format. Conversely, if the user is feeling happy, detailed information is provided.

[0214] Step 5:

[0215] The user reviews the network configuration diagram provided through the terminal and enters any necessary corrections or additional information. This determines the final network configuration.

[0216] (Example 2)

[0217] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0218] In network implementation and management, it is difficult for users without specialized knowledge to create appropriate network configuration diagrams. Furthermore, there is a need for flexible information provision that responds to user needs, but conventional systems are unable to accommodate this.

[0219] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0220] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, and means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions. As a result, users can obtain an appropriate network configuration diagram even without specialized knowledge, and flexible information provision tailored to the user's emotions becomes possible.

[0221] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[0222] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[0223] "Interactive artificial intelligence" is a system that provides information to users through natural language dialogue and generates responses that meet the user's requests.

[0224] An "emotion engine" is a system that recognizes the user's emotions and adjusts the way information is provided based on those emotions.

[0225] A "generative AI model" is an artificial intelligence model that generates optimal results based on input data, and provides highly accurate output for specific tasks.

[0226] One embodiment of this invention is a system that generates a network configuration diagram and provides information based on the network deployment status. The system provides interactive artificial intelligence for users without network knowledge and further includes an emotion engine that recognizes the user's emotions and adjusts the information delivery method accordingly.

[0227] The server stores network device information in a database and generates a network configuration diagram using a generative AI model. The generative AI model analyzes the input data and proposes the optimal network configuration. In doing so, the server takes into account historical data and general network design best practices.

[0228] The terminal provides an interface for users to input information about network devices. Users can use the terminal to input device names and specifications such as "routers," "switches," and "access points." The entered information is sent to the server and used to generate a network configuration diagram.

[0229] The emotion engine recognizes the user's emotions and adjusts the generated network diagram accordingly. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information.

[0230] As a concrete example, when a company implements a new network, the user uses a terminal to input information about network devices such as "routers, switches, and access points" into the system. An example of a prompt to the generating AI model is, "Please generate the optimal network configuration diagram for the company's network implementation." Based on this prompt, the system generates the optimal network configuration diagram and provides it to the user.

[0231] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0232] Step 1:

[0233] The user uses a terminal to input information about network devices. Specifically, the user inputs device names such as "routers," "switches," and "access points," as well as detailed information such as the specifications and location of each device. This information serves as the basic data for generating a network configuration diagram. The entered data is sent from the terminal to the server.

[0234] Step 2:

[0235] The server stores information about network devices received from terminals in a database. The stored data needs to be managed accurately and efficiently because it will be used in subsequent processing. Checks are performed to ensure data integrity when saving to the database.

[0236] Step 3:

[0237] The server generates a network configuration diagram using a generative AI model based on stored data. The generative AI model analyzes the input network device information and proposes the optimal network configuration. In this process, the AI ​​takes historical data and general network design best practices into consideration. An initial network configuration diagram is generated as output.

[0238] Step 4:

[0239] The server uses an emotion engine to recognize the user's emotions. Based on the user's emotions, it adjusts the generated network diagram. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information. The adjusted network diagram is then generated as the final output.

[0240] Step 5:

[0241] The server then sends the finalized network configuration diagram to the terminal, providing it to the user. The user can review the diagram on the terminal and make corrections or provide feedback as needed. This process allows users to easily obtain an appropriate network configuration diagram, even without network knowledge.

[0242] (Application Example 2)

[0243] Next, we will describe Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal."

[0244] In factory environments where on-site personnel lack network knowledge, a system is needed that can easily generate and optimize network diagrams, enabling the rapid implementation of appropriate network configurations. Furthermore, it is essential to adjust information delivery based on the user's emotional state to aid user understanding.

[0245] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0246] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for optimizing the network environment as an application installed on machinery in the factory. This makes it possible to generate and optimize an appropriate network configuration diagram even if field personnel do not have network knowledge.

[0247] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[0248] "Network deployment status" refers to information indicating how the network is installed and operated.

[0249] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with users using natural language.

[0250] "Means of recognizing user emotions" refers to technology that analyzes a user's emotions from their facial expressions, tone of voice, etc., and determines their emotional state.

[0251] "Means of adjusting the method of information provision" refers to technologies that change the content and format of the information provided according to the user's emotions and circumstances.

[0252] "Applications installed on machinery within a factory" refers to software that is integrated into factory machinery and equipment to perform specific functions.

[0253] "Methods for optimizing network environments" refer to techniques for adjusting configurations and settings to maximize network performance and efficiency.

[0254] The system for implementing this invention is configured as an application installed on machinery within a factory. This application utilizes interactive artificial intelligence to generate network configuration diagrams and provide information based on the network deployment status. Furthermore, it has a function to recognize the user's emotions and adjust the method of information provision accordingly.

[0255] The server receives network device information entered by the user to generate a network configuration diagram, and uses a generation AI model to create the optimal configuration diagram. The network deployment status is stored in a database and updated as needed. To recognize the user's emotions, the system analyzes facial expressions and voice tone using a camera and microphone, and an emotion engine determines the user's state.

[0256] The terminal uses interactive artificial intelligence to generate a network configuration diagram based on information entered by the user, and displays it on a projector or screen. If the user is experiencing stress, the diagram is simplified and presented in an easy-to-understand format. Conversely, if the user is experiencing pleasure, a more detailed diagram is provided to facilitate a deeper understanding.

[0257] As a concrete example, when introducing a network to a new production line in a factory, the system generates an optimal network configuration diagram and displays it on a projector once the on-site staff input information about the network equipment. If the staff are feeling stressed, a simplified configuration diagram can be displayed to aid their understanding.

[0258] An example of a prompt message is: "Please enter the information for the network equipment required for the new production line. We will generate the optimal network configuration diagram, taking into account the current emotional state."

[0259] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0260] Step 1:

[0261] The user enters network device information into the terminal. This information includes details such as the type of network device, connection port, and IP address. The terminal then sends this information to the server.

[0262] Step 2:

[0263] The server generates an optimal network configuration diagram using a generation AI model based on the received network device information. It analyzes the input information and processes the data to design the network topology. The generated configuration diagram is stored digitally on the server.

[0264] Step 3:

[0265] The server retrieves network deployment status from the database and updates the configuration diagram based on the latest information. The database includes information on the existing network environment and past deployment history. This ensures that the generated configuration diagram reflects the current situation.

[0266] Step 4:

[0267] The device uses its camera and microphone to capture the user's facial expressions and voice tone in order to recognize their emotions. This data is input into an emotion engine, which analyzes the user's emotional state. The analysis results are then sent to a server.

[0268] Step 5:

[0269] The server adjusts how information is delivered based on the user's emotional state. If it determines that the user is stressed, it simplifies the diagram and sends it to the terminal in an easy-to-understand format. If the user is happy, it provides a detailed diagram.

[0270] Step 6:

[0271] The terminal displays the network configuration diagram received from the server on a projector or display. The user can review the displayed configuration diagram and input any necessary corrections or additional information.

[0272] Step 7:

[0273] When a user enters corrections or additional information, the terminal sends that information back to the server, and the server updates the configuration diagram. This results in an optimal network configuration diagram that reflects the user's feedback.

[0274] (Other examples)

[0275] Next, other embodiments will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0276] In the generation of a network configuration diagram, there is a problem that it is difficult for users without specialized knowledge to efficiently obtain an optimal configuration. Also, in the understanding and utilization of the generated configuration diagram, users may not be able to obtain sufficient information.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in other embodiments is realized by the following means.

[0278] In this invention, the server includes means for storing the network introduction status in a database, means for creating a prompt sentence for instructing requirements necessary for generating a network configuration diagram using a generated AI model based on the stored data, and means for inputting the prompt sentence into the generated AI model to generate an optimal network configuration diagram. Thereby, it becomes possible to efficiently generate an optimal network configuration diagram without specialized knowledge and provide it in a form that is easy for users to understand.

[0279] A "network configuration diagram" is a diagram that visually shows the arrangement of devices and connections within a network and plays an important role in the design and management of the network.

[0280] A "generated AI model" is a model trained to execute a specific task using artificial intelligence technology and has the ability to generate an appropriate output based on input data.

[0281] A "prompt sentence" is an input sentence for instructing a generated AI model to execute a specific task and provides guidelines for the model to generate an expected output.

[0282] An "interactive artificial intelligence" is an artificial intelligence system that has the ability to provide information and answer questions through natural language conversations with users.

[0283] A "database" is a system for efficiently storing, managing, and retrieving data, ensuring the integrity of information and the efficiency of access.

[0284] The following shows the "Mode for Carrying Out the Invention".

[0285] ---

[0286] This invention is a system that efficiently generates a network configuration diagram and provides it to users. The system mainly consists of three elements: a server, a terminal, and a user.

[0287] The server collects data on the network introduction status and stores it in a MySQL database. The data includes the types of network devices, connection status, traffic patterns, etc. The server generates a prompt sentence for inputting into the generated AI model based on the stored data. This prompt sentence indicates the requirements necessary for generating the network configuration diagram.

[0288] As the generated AI model, GPT-3 (registered trademark) of OpenAI is used. The server inputs the generated prompt sentence into this model to generate an optimal network configuration diagram. The generated AI model proposes a network topology according to the prompt sentence based on pre-trained data.

[0289] The generated network configuration diagram is transmitted from the server to the user's terminal. The user's terminal uses a web browser such as GOOGLE CHROME (registered trademark) to receive data from the server and visually display the configuration diagram. The user can view the configuration diagram on the browser and check the details as needed.

[0290] Furthermore, the server uses interactive artificial intelligence to explain network configuration diagrams to users without network knowledge. When a user enters a question through their terminal, the server analyzes the question and provides information on network address design and routing. For example, if a user asks, "What are the advantages of this network configuration?", the server will generate an answer such as, "This configuration improves data transfer efficiency and enhances security."

[0291] Example prompt: Generate a configuration diagram for optimizing the enterprise network.

[0292] In this way, the system can efficiently generate optimal network configuration diagrams even without specialized knowledge and provide them in a format that is easy for users to understand.

[0293] The flow of a specific process in another embodiment will be explained using Figure 19.

[0294] Step 1:

[0295] The server collects data on network deployment status. It receives information such as network device types, connectivity status, and traffic patterns as input. This data is stored in a MySQL database to ensure data integrity and efficient access. Specifically, the server uses network monitoring tools to acquire data in real time and write it to the database.

[0296] Step 2:

[0297] The server generates prompts for input to the AI ​​model based on the stored data. Network information stored in the database is used as input. Using Python and the NLTK natural language processing library, prompts are created to specify the requirements necessary for generating a network diagram. The output is a prompt such as, "Generate a network diagram for optimizing the corporate network."

[0298] Step 3:

[0299] The server inputs prompt text into a generative AI model (e.g., OpenAI's GPT-3). The prompt text generated in step 2 is used as input. The generative AI model analyzes the prompt text based on pre-trained data and generates an optimal network configuration diagram. The output is a configuration diagram showing the placement and connections of network devices.

[0300] Step 4:

[0301] The server sends the generated network diagram to the user's terminal. The diagram data obtained in step 3 is used as input. The user's terminal receives the data from the server using a web browser (e.g., Google Chrome) and visually displays the diagram. Specifically, the server sends data using the HTTP protocol, and the terminal renders the diagram using JavaScript.

[0302] Step 5:

[0303] The server uses interactive artificial intelligence to explain network configuration diagrams to users without network knowledge. It receives questions from the user as input. The server analyzes the questions and provides information on network address design and routing. As output, it generates appropriate answers to the user's questions. Specifically, the server uses natural language processing techniques to understand the questions and extracts appropriate information from a pre-prepared answer database.

[0304] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input with respect to the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0305] The data generation model 58 is a so-called generative AI (Artificial Intelligence). Examples of the data generation model 58 include generative AIs such as ChatGPT (registered trademark) (Internet search <URL: https: / / openai.com / blog / chatgpt>). The data generation model 58 is obtained by causing a neural network to perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization, etc.

[0306] As another example of the generative AI, Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0307] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0308] [Second Embodiment]

[0309] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0310] 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.

[0311] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0312] 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.

[0313] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0314] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0315] 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.

[0316] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0317] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0318] The 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.

[0319] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0320] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0321] "Example of form 1"

[0322] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0323] "Example of form 2"

[0324] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0325] The following describes the processing flow for each example of the form.

[0326] "Example of form 1"

[0327] Step 1: The field staff enters information about the network equipment to be installed into this system. Step 2: The entered information is saved in the database and managed as network installation status.

[0328] Step 3: The interactive AI generates the optimal network configuration diagram based on the information in the database.

[0329] Step 4: The generated network configuration diagram is provided to the field staff and used for network deployment and management.

[0330] (Example 1)

[0331] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0332] It is difficult for users without specialized knowledge of network deployment and configuration to create appropriate network diagrams. Furthermore, there is a lack of efficient means to propose optimal configurations based on the current network deployment situation. This can lead to inefficiencies in network design and deployment.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0334] In this invention, the server includes means for generating a network configuration diagram, means for saving the network deployment status to a database, and means for creating prompt statements using a generation AI model based on the saved data. This makes it possible for users without network knowledge to efficiently and accurately create network configuration diagrams and achieve optimal network design.

[0335] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[0336] A "database" is a system for efficiently storing, managing, and retrieving information, and is used to accumulate data such as network deployment status.

[0337] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform specific tasks, and is used to generate network diagrams.

[0338] A "prompt statement" is an input statement used to give instructions to a generating AI model, and includes the instructions given when generating a network diagram.

[0339] "Interactive artificial intelligence" refers to artificial intelligence that provides information and performs tasks through dialogue with users, and is used to support users who lack network knowledge.

[0340] This invention is a system that generates network configuration diagrams and provides information based on the network deployment status. The system aims to provide interactive artificial intelligence for users without network knowledge.

[0341] The server executes a program to generate a network configuration diagram. This program has the function of saving the network deployment status to a database and creating prompt statements using a generation AI model based on that information. Specifically, the server saves information about network devices entered by the user to the database and generates prompt statements based on the saved data. The generated prompt statements are input into the generation AI model, and the optimal network configuration diagram is generated.

[0342] Users can input information about network devices into the system via their terminal. For example, by inputting specific device information such as "We plan to install a router, switch, and firewall," the server will generate a network configuration diagram based on this information.

[0343] For example, if a user inputs "I want to install a router and switch in the new office," the server saves this information to its database and generates a prompt message saying, "Please generate the optimal network configuration diagram for the new office." By inputting this prompt message into the AI ​​generation model, the optimal network configuration diagram is generated and provided to the user.

[0344] This system enables users without network knowledge to efficiently and accurately create network diagrams and achieve optimal network designs.

[0345] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0346] Step 1:

[0347] The user inputs information about network equipment through a terminal. Specifically, the user inputs information such as "We plan to install a router, switch, and firewall." This input information serves as the basic data for generating the network configuration diagram.

[0348] Step 2:

[0349] The server stores network device information entered by the user in a database. This entered information is stored in the database and used in subsequent processing. This storage process manages the network deployment status.

[0350] Step 3:

[0351] The server generates prompt messages using a generative AI model based on information stored in the database. Specifically, it analyzes the stored network device information and generates prompt messages such as, "Generate the optimal network configuration diagram for the new office." These prompt messages are then used as input to the generative AI model.

[0352] Step 4:

[0353] The server inputs prompt messages into the generation AI model, which then generates the optimal network configuration diagram. The generation AI model analyzes the prompt messages and creates the network configuration diagram based on historical data and best practices. This process generates a configuration diagram that meets the user's requirements.

[0354] Step 5:

[0355] The server provides the user with a generated network configuration diagram. The user can view this diagram through their terminal and use it as a reference for network deployment. This output allows the user to obtain information necessary to achieve an appropriate network design.

[0356] (Application Example 1)

[0357] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0358] There is a need to optimize communication between machinery and equipment within a factory to improve production efficiency. However, it is difficult for workers without network knowledge to design an appropriate network configuration. Furthermore, if the implemented network does not provide the optimal communication path, the operation of the machinery and equipment may become inefficient.

[0359] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0360] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for operators without network knowledge, and means for proposing communication paths to optimize communication between machine devices. This makes it possible to generate an optimal network configuration diagram and efficiently communicate between machine devices even if the operator does not have network knowledge.

[0361] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network.

[0362] "Network deployment status" refers to information indicating how the network is installed and operated.

[0363] A "worker" is a field staff member who is involved in the design and operation of a network but does not possess specialized knowledge.

[0364] "Conversational artificial intelligence" refers to artificial intelligence that communicates with users using natural language and assists in providing information and solving problems.

[0365] A "communication path" is the network route through which data is sent and received.

[0366] "Mechanical equipment" refers to robots and other automated devices used within a factory.

[0367] The system for implementing this invention is designed to optimize communication between machinery and equipment within a factory. The server runs a program to generate a network configuration diagram and provides information based on the network deployment status. Specifically, the server generates the network configuration diagram using a generative AI model implemented in Python. This allows operators to obtain the optimal network configuration even without network knowledge.

[0368] The server collects data from sensors installed in machinery within the factory and uses this data to optimize communication paths. The hardware used consists of sensors and communication modules installed in the factory machinery. Data processing involves collecting data from sensors and converting it into the format necessary to generate a network diagram. Data calculation uses a generative AI model to calculate the optimal communication path.

[0369] As a concrete example, when introducing new machinery in a factory, workers input information about the machinery into the system. The server then uses AI to generate an optimal network configuration diagram and optimizes communication between the machinery. This results in smoother operation of the machinery and improved production efficiency.

[0370] An example of a prompt message to input into the generating AI model is: "We have installed new machinery. Please generate a network configuration diagram and suggest the optimal communication path."

[0371] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0372] Step 1:

[0373] The user enters information about the new machine into the terminal. This information includes the machine's identification and the network it will connect to. The terminal then sends this information to the server.

[0374] Step 2:

[0375] The server processes the received information about the machinery and equipment to generate a network configuration diagram. Specifically, it converts the received data into a format that the AI ​​model can process. This converted data then becomes the input for the AI ​​model.

[0376] Step 3:

[0377] The server uses a generative AI model to generate the optimal network configuration diagram. Based on the input data, the generative AI model calculates the optimal connection method between mechanical devices and outputs the network configuration diagram.

[0378] Step 4:

[0379] The server uses the generated network diagram to perform calculations to optimize communication paths between mechanical devices. Specifically, it performs data calculations related to communication path selection to determine the optimal communication path. This result is output as the communication path.

[0380] Step 5:

[0381] The server sends an optimized network configuration diagram and communication paths to the terminal. The user then uses the terminal to review the generated network configuration diagram and communication paths and configure the machinery and equipment within the factory.

[0382] Step 6:

[0383] Users use terminals to configure machinery within the factory and initiate communication between machines based on an optimized network configuration. This results in smoother machine operation and improved production efficiency.

[0384] (Example 2)

[0385] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0386] Designing and creating configuration diagrams for communication networks requires specialized knowledge, making it particularly difficult for users with limited knowledge of communication networks. Furthermore, creating appropriate configuration diagrams requires accurately understanding the current deployment situation and designing the network based on that understanding. This presents a challenge for users in efficiently and accurately constructing communication networks.

[0387] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0388] In this invention, the server includes means for generating a communication network configuration diagram using an information processing device, means for providing information based on the status of communication network deployment, and means for providing interactive artificial intelligence for users who do not have specialized knowledge of communication networks. As a result, users can efficiently and accurately create a communication network configuration diagram and construct an optimal communication network, even without specialized knowledge.

[0389] An "information processing device" is a device that has the functions of inputting, processing, storing, and outputting data, and is used to generate a diagram of the configuration of a communication network.

[0390] A "communication network" is a network system in which multiple information devices are interconnected to send and receive data.

[0391] A "configuration diagram" is a diagram that visually shows the arrangement and connection relationships of equipment in a communication network, and it forms the basis of network design.

[0392] "Deployment status" refers to information indicating the installation and operational status of equipment in a communication network, and is fundamental data for designing an optimal network.

[0393] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information through dialogue with users and generates responses that meet the user's requests.

[0394] A "generative AI model" is an artificial intelligence model that generates new information or diagrams based on input data, and is particularly used in natural language processing and data generation.

[0395] A "user" is an individual or organization that uses this system to design communication networks or create configuration diagrams.

[0396] This invention is a system that generates and provides a diagram of a communication network configuration to users. The system is implemented using an information processing device, a database, and a generation AI model.

[0397] The server functions as an information processing device and generates a diagram of the communication network configuration. Specifically, the server uses a database to store the communication network deployment status entered by users. This database can use a relational database management system such as MySQL or PostgreSQL.

[0398] The user inputs information about the communication network through a terminal. The terminal uses a web browser to access the system interface and enters information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This information is passed to the generating AI model as prompt messages.

[0399] The server uses a generative AI model to generate an optimal network configuration diagram based on the input information. This generative AI model can use a model specialized for natural language processing, such as OpenAI's GPT model. The generated configuration diagram is provided to the user, who can view it through a web browser and make modifications as needed.

[0400] As a concrete example, a user inputs a prompt message into the AI ​​model such as, "The communication network equipment we plan to install in our new office will be a router from a specific manufacturer and a switch from a specific manufacturer." Based on this information, the AI ​​generates an optimal communication network configuration diagram and provides it to the user.

[0401] In this way, even users without specialized knowledge can efficiently and accurately create network configuration diagrams and construct optimal network infrastructure.

[0402] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0403] Step 1:

[0404] The user inputs the information necessary for the deployment of the communication network via a terminal. Specifically, they access the system interface using a web browser and enter information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This input information is then prepared to be passed to the AI ​​model as prompt messages.

[0405] Step 2:

[0406] The terminal sends information entered by the user to the server. The entered data is converted into a common data format such as JSON and sent to the server over the network. This data transmission allows the server to receive the user's request.

[0407] Step 3:

[0408] The server stores the received data in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to store information about communication network equipment in tables. This storage process ensures that the data necessary for subsequent AI processing is secured.

[0409] Step 4:

[0410] The server invokes a generating AI model based on the stored data. Specifically, it inputs the stored information as prompts into the generating AI model and performs data processing to generate the optimal network configuration diagram. The generating AI model performs data calculations based on the input prompts and generates the optimal configuration diagram.

[0411] Step 5:

[0412] The server receives the network configuration diagram generated from the AI ​​model and provides it to the user. Specifically, it renders the generated diagram as a web page, making it accessible to the user. The user can view the diagram through a web browser and make modifications as needed. This output enables the user to create network configuration diagrams efficiently and accurately.

[0413] (Application Example 2)

[0414] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0415] Optimizing the network configuration of automated equipment within a factory is crucial for improving operational efficiency. However, creating an appropriate network diagram is difficult for on-site personnel without network knowledge. Therefore, there is a need for a system that allows on-site personnel to easily achieve the optimal network configuration.

[0416] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0417] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for on-site personnel without network knowledge, and means for generating an optimal network configuration diagram for automated equipment in the factory and improving operational efficiency. As a result, on-site personnel can optimize the network configuration of automated equipment in the factory and improve operational efficiency even without specialized knowledge.

[0418] A "network diagram" is a diagram that visually shows the arrangement of network equipment and connections, and is used to understand the overall structure of the network.

[0419] "Network deployment status" refers to information indicating the current placement, connection status, and usage of network equipment, and is useful as a reference when optimizing or expanding the network.

[0420] "Conversational artificial intelligence" is artificial intelligence that provides information and assists in problem-solving through dialogue with users, and responds to user questions using natural language processing technology.

[0421] "Automated equipment" refers to robots and mechanical devices used in factories that are designed to perform specific tasks automatically.

[0422] "Operational efficiency" is an indicator that shows how effectively a system or piece of equipment functions to achieve its purpose, and efficient operation contributes to cost reduction and productivity improvement.

[0423] To implement this invention, it is necessary to build a system in which a server generates a network configuration diagram and provides information based on the network deployment status. The server executes a program developed using Python and provides an interactive web interface using Flask. Users can input information about network devices through this interface.

[0424] The input information is sent as a prompt to a generative AI model running on the server, specifically to OpenAI's GPT. The GPT model returns instructions to generate the optimal network configuration diagram based on the input information. Upon receiving these instructions, the server uses Graphviz to draw the network configuration diagram.

[0425] The generated network configuration diagram is provided to the user via a web interface. This allows users to create the optimal network configuration for their factory's automated equipment without requiring specialized network knowledge.

[0426] For example, when a user is inputting information about a newly introduced robotic arm and existing network equipment, the following prompt message can be used.

[0427] Prompt example:

[0428] "The new robotic arm to be introduced is model RA-2023, and the existing network equipment includes a switch SW-100 and a router RT-200. Please generate a network configuration diagram that optimally connects these components."

[0429] This prompt allows the server to generate and provide the user with an optimal network configuration diagram.

[0430] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0431] Step 1:

[0432] The user uses a terminal to access the web interface and enter information about the network equipment. This information includes the model number of the newly installed equipment and details of existing network equipment. This information is then sent to the server.

[0433] Step 2:

[0434] The server converts the received network device information into prompt statements suitable for the generating AI model. Specifically, it creates prompt statements containing instructions for generating the optimal network configuration diagram based on the input information. These prompt statements are then sent to the OpenAI GPT model.

[0435] Step 3:

[0436] The server receives a response from the GPT model and obtains instructions for generating the optimal network diagram. Based on the input prompt, the GPT model provides the information necessary to generate the network diagram.

[0437] Step 4:

[0438] The server uses Graphviz to draw a network configuration diagram based on instructions obtained from the GPT model. Graphviz visually represents the placement and connections of network devices based on these instructions.

[0439] Step 5:

[0440] The generated network configuration diagram is sent from the server to the user's terminal and provided to the user via a web interface. The user can review the generated configuration diagram and use it to optimize the network configuration of automated equipment within the factory.

[0441] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0442] "Example of form 1"

[0443] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0444] "Example of form 2"

[0445] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0446] The following describes the processing flow for each example of the form.

[0447] "Example of form 1"

[0448] Step 1: Gain an understanding of the user's network knowledge and needs through dialogue with the user.

[0449] Step 2: The emotion engine recognizes the user's emotions and adjusts how the network diagram is generated accordingly. For example, if the user feels stressed, the emotion engine adjusts the network diagram generation process to be simpler or easier for the user to understand.

[0450] Step 3: The emotion engine recognizes the user's emotions and adjusts how network deployment information is provided accordingly. For example, if the user is perceived as feeling happy, the emotion engine generates a more detailed network configuration diagram, providing optimal information based on the user's emotions.

[0451] Step 4: Provide the generated network diagram and information to the user.

[0452] (Example 1)

[0453] Next, we will describe Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0454] In generating network configuration diagrams, there is a challenge in that field personnel without network knowledge find it difficult to create appropriate diagrams. Furthermore, the lack of information tailored to the user's emotional needs may lead to decreased user understanding and satisfaction.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0456] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions, and means for storing information entered by the user in a database and generating prompt sentences using a generation AI model. This makes it possible to generate an appropriate network configuration diagram even without network knowledge and to provide information according to the user's emotions.

[0457] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[0458] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[0459] "Interactive artificial intelligence" refers to artificial intelligence that can provide information and solve problems through dialogue with users, and responds to user input using natural language processing technology.

[0460] An "emotion engine" is an engine that recognizes a user's emotions and adjusts the system's operation accordingly, and is used to improve the user experience.

[0461] A "generative AI model" is a model that uses artificial intelligence technology to generate new information and content from data, and is applied to areas such as natural language generation and image generation.

[0462] A "prompt" is an instruction given to artificial intelligence to perform a specific task, and it forms the basis for the AI ​​to make appropriate responses and generations.

[0463] A description of embodiments for carrying out this invention will be given.

[0464] The server runs a program to generate a network diagram. This program is developed using Python and uses MySQL as its database. The server receives information about network devices sent by the user and stores it in the database. Based on the stored information, the server generates prompts using a generative AI model and inputs them into an interactive artificial intelligence. This AI uses OpenAI's GPT model and generates the optimal network diagram based on the prompts.

[0465] Users input network device information via a terminal, enabling them to generate network diagrams even without network knowledge. For example, they can input information such as router model numbers, switch port counts, and access point coverage ranges. The information entered by the user is then sent from the terminal to the server.

[0466] The terminal is responsible for sending information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. It also displays the network configuration diagram sent from the server to the user.

[0467] As a concrete example, when a company sets up a network in a new office, the user inputs information such as "Router model: XYZ123, Number of switch ports: 24, Access point coverage: 50m". Based on this information, the server generates a prompt message, "Generate a network configuration diagram using router XYZ123 and a 24-port switch," and inputs it into the AI. The AI ​​generates a configuration diagram based on the prompt, and the server uses an emotion engine to adjust the diagram according to the user's emotions. Finally, the terminal displays the adjusted configuration diagram to the user.

[0468] In this way, even users without network knowledge can generate appropriate network diagrams and provide information tailored to their needs.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0470] Step 1:

[0471] The user enters information about network devices into the terminal. Specifically, they enter information such as the router model number, the number of ports on the switch, and the coverage range of the access point. The entered information becomes the basic data for generating the network configuration diagram.

[0472] Step 2:

[0473] The terminal sends the information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. At this time, the terminal verifies the integrity of the data and adjusts the data format as needed.

[0474] Step 3:

[0475] The server saves the received data to a MySQL database. The server uses the Python SQLAlchemy library to connect to the database and insert the data. The saved data is then used for subsequent processing.

[0476] Step 4:

[0477] The server generates prompt messages using a generation AI model based on the stored data. Specifically, it retrieves the necessary information from the database and uses it to create prompt messages such as "Generate a network configuration diagram using router XYZ123 and a 24-port switch."

[0478] Step 5:

[0479] The server inputs the generated prompt text into the interactive artificial intelligence. The AI ​​uses OpenAI's GPT model to generate the optimal network configuration diagram based on the prompt. The AI ​​analyzes the input prompt and outputs the appropriate configuration diagram.

[0480] Step 6:

[0481] The server uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's past input history and current input to determine whether the user is stressed or happy. For example, if the server determines that the user is stressed, it simplifies the generated configuration diagram.

[0482] Step 7:

[0483] The server sends the adjusted network configuration diagram to the terminal. The terminal displays the received configuration diagram to the user. The user can review the displayed configuration diagram and make corrections or additional inputs as needed.

[0484] (Application Example 1)

[0485] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0486] If on-site personnel lack network knowledge, understanding complex network configurations and implementing them properly becomes difficult. Furthermore, the lack of information tailored to user needs can lead to stress. Additionally, proposing the optimal network configuration for the layout and operation of machinery within the factory presents a challenge.

[0487] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0488] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for proposing the optimal network for the placement and operation of machinery and equipment within the factory. As a result, field personnel can understand and implement an appropriate network configuration even without network knowledge, information can be provided in accordance with the user's emotions, and the optimal network for the placement and operation of machinery and equipment within the factory can be proposed.

[0489] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used to understand the overall structure of the network.

[0490] "Network deployment status" refers to information that shows the current state of how the network is installed and operated.

[0491] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with the user.

[0492] "Emotion recognition means" refers to technology that analyzes a user's emotions and takes appropriate action based on those emotions.

[0493] "An optimal network for the placement and operation of machinery and equipment" refers to the network configuration that is best suited for the efficient operation of machinery and equipment within a factory.

[0494] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server executes a program for generating a network configuration diagram and provides information based on the network deployment status. Specifically, the server uses interactive artificial intelligence to generate an optimal network configuration diagram based on information about network devices entered by the user. Furthermore, it analyzes the user's emotions using emotion recognition means and adjusts the method of providing information according to those emotions.

[0495] The terminal provides an interface for users to input information about network devices. Users can use terminals such as smartphones and tablets to input information about network devices and receive network configuration diagrams provided by the server.

[0496] As a concrete example, when introducing new machinery in a factory, the user inputs the layout information of the machinery using a terminal. Based on this information, the server uses a generative AI model to generate an optimal network configuration diagram and provides it to the user. If the user is experiencing stress, the server simplifies the information provided using emotion recognition, and provides detailed information if the user is understanding the situation.

[0497] Examples of prompt messages include, "Generate the optimal network configuration diagram for introducing new machinery and equipment," and "Simplify information provision if the user is experiencing stress."

[0498] In this way, even if field personnel do not possess network knowledge, they can understand and implement the appropriate network configuration.

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0500] Step 1:

[0501] The user uses a terminal to enter information about the network device. This information includes the type of device, its location, and the connection method. This information is then sent to the server.

[0502] Step 2:

[0503] The server stores information about received network devices in a database. Based on the stored information, it uses a generative AI model to generate an optimal network configuration diagram. In this process, it calculates network connection patterns and communication paths between devices to create a visual configuration diagram.

[0504] Step 3:

[0505] The server analyzes the user's emotions using emotion recognition technology. It analyzes the user's facial expressions and voice data during input to identify emotions such as stress and joy. This emotion information is used to adjust the way information is provided.

[0506] Step 4:

[0507] The server provides the user with the most relevant information based on the generated network diagram and sentiment analysis results. If the user is feeling stressed, the information is simplified and presented in an easy-to-understand format. Conversely, if the user is feeling happy, detailed information is provided.

[0508] Step 5:

[0509] The user reviews the network configuration diagram provided through the terminal and enters any necessary corrections or additional information. This determines the final network configuration.

[0510] (Example 2)

[0511] Next, we will describe Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0512] In network deployment and management, it is difficult for users without specialized knowledge to create appropriate network configuration diagrams. Furthermore, there is a need for flexible information provision that responds to user needs, but conventional systems are unable to accommodate this.

[0513] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0514] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, and means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions. As a result, users can obtain an appropriate network configuration diagram even without specialized knowledge, and flexible information provision tailored to the user's emotions becomes possible.

[0515] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[0516] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[0517] "Interactive artificial intelligence" is a system that provides information to users through natural language dialogue and generates responses that meet the user's requests.

[0518] An "emotion engine" is a system that recognizes the user's emotions and adjusts the way information is provided based on those emotions.

[0519] A "generative AI model" is an artificial intelligence model that generates optimal results based on input data, and provides highly accurate output for specific tasks.

[0520] One embodiment of this invention is a system that generates a network configuration diagram and provides information based on the network deployment status. The system provides interactive artificial intelligence for users without network knowledge and further includes an emotion engine that recognizes the user's emotions and adjusts the information delivery method accordingly.

[0521] The server stores network device information in a database and generates a network configuration diagram using a generative AI model. The generative AI model analyzes the input data and proposes the optimal network configuration. In doing so, the server takes into account historical data and general network design best practices.

[0522] The terminal provides an interface for users to input information about network devices. Users can use the terminal to input device names and specifications such as "routers," "switches," and "access points." The entered information is sent to the server and used to generate a network configuration diagram.

[0523] The emotion engine recognizes the user's emotions and adjusts the generated network diagram accordingly. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information.

[0524] As a concrete example, when a company implements a new network, the user uses a terminal to input information about network devices such as "routers, switches, and access points" into the system. An example of a prompt to the generating AI model is, "Please generate the optimal network configuration diagram for the company's network implementation." Based on this prompt, the system generates the optimal network configuration diagram and provides it to the user.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0526] Step 1:

[0527] The user uses a terminal to input information about network devices. Specifically, the user inputs device names such as "routers," "switches," and "access points," as well as detailed information such as the specifications and location of each device. This information serves as the basic data for generating a network configuration diagram. The entered data is sent from the terminal to the server.

[0528] Step 2:

[0529] The server stores information about network devices received from terminals in a database. The stored data needs to be managed accurately and efficiently because it will be used in subsequent processing. Checks are performed to ensure data integrity when saving data to the database.

[0530] Step 3:

[0531] The server generates a network configuration diagram using a generative AI model based on stored data. The generative AI model analyzes the input network device information and proposes the optimal network configuration. In this process, the AI ​​takes historical data and general network design best practices into consideration. An initial network configuration diagram is generated as output.

[0532] Step 4:

[0533] The server uses an emotion engine to recognize the user's emotions. Based on the user's emotions, it adjusts the generated network diagram. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information. The adjusted network diagram is then generated as the final output.

[0534] Step 5:

[0535] The server then sends the finalized network configuration diagram to the terminal, providing it to the user. The user can review the diagram on the terminal and make corrections or provide feedback as needed. This process allows users to easily obtain an appropriate network configuration diagram, even without network knowledge.

[0536] (Application Example 2)

[0537] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0538] In factory environments where on-site personnel lack network knowledge, a system is needed that can easily generate and optimize network diagrams, enabling the rapid implementation of appropriate network configurations. Furthermore, it is essential to adjust information delivery based on the user's emotional state to aid user understanding.

[0539] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0540] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for optimizing the network environment as an application installed on machinery in the factory. This makes it possible to generate and optimize an appropriate network configuration diagram even if field personnel do not have network knowledge.

[0541] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[0542] "Network deployment status" refers to information indicating how the network is installed and operated.

[0543] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with users using natural language.

[0544] "Means of recognizing user emotions" refers to technology that analyzes a user's emotions from their facial expressions, tone of voice, etc., and determines their emotional state.

[0545] "Means of adjusting the method of information provision" refers to technologies that change the content and format of the information provided according to the user's emotions and circumstances.

[0546] "Applications installed on machinery within a factory" refers to software that is integrated into factory machinery and equipment to perform specific functions.

[0547] "Methods for optimizing network environments" refer to techniques for adjusting configurations and settings to maximize network performance and efficiency.

[0548] The system for implementing this invention is configured as an application installed on machinery within a factory. This application utilizes interactive artificial intelligence to generate network configuration diagrams and provide information based on the network deployment status. Furthermore, it has a function to recognize the user's emotions and adjust the method of information provision accordingly.

[0549] The server receives network device information entered by the user to generate a network configuration diagram, and uses a generation AI model to create the optimal configuration diagram. The network deployment status is stored in a database and updated as needed. To recognize the user's emotions, the system analyzes facial expressions and voice tone using a camera and microphone, and an emotion engine determines the user's state.

[0550] The terminal uses interactive artificial intelligence to generate a network configuration diagram based on information entered by the user, and displays it on a projector or screen. If the user is experiencing stress, the diagram is simplified and presented in an easy-to-understand format. Conversely, if the user is experiencing pleasure, a more detailed diagram is provided to facilitate a deeper understanding.

[0551] As a concrete example, when introducing a network to a new production line in a factory, the system generates an optimal network configuration diagram and displays it on a projector once the on-site staff input information about the network equipment. If the staff are feeling stressed, a simplified configuration diagram can be displayed to aid their understanding.

[0552] An example of a prompt message is: "Please enter the information for the network equipment required for the new production line. We will generate the optimal network configuration diagram, taking into account the current emotional state."

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0554] Step 1:

[0555] The user enters network device information into the terminal. This information includes details such as the type of network device, connection port, and IP address. The terminal then sends this information to the server.

[0556] Step 2:

[0557] The server generates an optimal network configuration diagram using a generation AI model based on the received network device information. It analyzes the input information and processes the data to design the network topology. The generated configuration diagram is stored digitally on the server.

[0558] Step 3:

[0559] The server retrieves network deployment status from the database and updates the configuration diagram based on the latest information. The database includes information on the existing network environment and past deployment history. This ensures that the generated configuration diagram reflects the current situation.

[0560] Step 4:

[0561] The device uses its camera and microphone to capture the user's facial expressions and voice tone in order to recognize their emotions. This data is input into an emotion engine, which analyzes the user's emotional state. The analysis results are then sent to a server.

[0562] Step 5:

[0563] The server adjusts how information is delivered based on the user's emotional state. If it determines that the user is stressed, it simplifies the diagram and sends it to the terminal in an easy-to-understand format. If the user is happy, it provides a detailed diagram.

[0564] Step 6:

[0565] The terminal displays the network configuration diagram received from the server on a projector or display. The user can review the displayed configuration diagram and input any necessary modifications or additional information.

[0566] Step 7:

[0567] When a user enters corrections or additional information, the terminal sends that information back to the server, and the server updates the configuration diagram. This results in an optimal network configuration diagram that reflects the user's feedback.

[0568] (Other examples)

[0569] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0570] 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.

[0571] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0572] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0573] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0574] [Third Embodiment]

[0575] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0576] 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.

[0577] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0578] 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.

[0579] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0580] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0581] 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.

[0582] 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.

[0583] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0584] The 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.

[0585] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0586] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0587] "Example of form 1"

[0588] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0589] "Example of form 2"

[0590] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0591] The following describes the processing flow for each example of the form.

[0592] "Example of form 1"

[0593] Step 1: The field staff enters information about the network equipment to be installed into this system. Step 2: The entered information is saved in the database and managed as network installation status.

[0594] Step 3: The interactive AI generates the optimal network configuration diagram based on the information in the database.

[0595] Step 4: The generated network configuration diagram is provided to the field staff and used for network deployment and management.

[0596] (Example 1)

[0597] Next, we will describe Embodiment 1 of Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0598] It is difficult for users without specialized knowledge of network deployment and configuration to create appropriate network diagrams. Furthermore, there is a lack of efficient means to propose optimal configurations based on the current network deployment situation. This can lead to inefficiencies in network design and deployment.

[0599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0600] In this invention, the server includes means for generating a network configuration diagram, means for saving the network deployment status to a database, and means for creating prompt statements using a generation AI model based on the saved data. This makes it possible for users without network knowledge to efficiently and accurately create network configuration diagrams and achieve optimal network design.

[0601] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[0602] A "database" is a system for efficiently storing, managing, and retrieving information, and is used to accumulate data such as network deployment status.

[0603] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform specific tasks, and is used to generate network diagrams.

[0604] A "prompt statement" is an input statement used to give instructions to a generating AI model, and includes the instructions given when generating a network diagram.

[0605] "Interactive artificial intelligence" refers to artificial intelligence that provides information and performs tasks through dialogue with users, and is used to support users who lack network knowledge.

[0606] This invention is a system that generates network configuration diagrams and provides information based on the network deployment status. The system aims to provide interactive artificial intelligence for users without network knowledge.

[0607] The server executes a program to generate a network configuration diagram. This program has the function of saving the network deployment status to a database and creating prompt statements using a generation AI model based on that information. Specifically, the server saves information about network devices entered by the user to the database and generates prompt statements based on the saved data. The generated prompt statements are input into the generation AI model, and the optimal network configuration diagram is generated.

[0608] Users can input information about network devices into the system via their terminal. For example, by inputting specific device information such as "We plan to install a router, switch, and firewall," the server will generate a network configuration diagram based on this information.

[0609] For example, if a user inputs "I want to install a router and switch in the new office," the server saves this information to its database and generates a prompt message saying, "Please generate the optimal network configuration diagram for the new office." By inputting this prompt message into the AI ​​generation model, the optimal network configuration diagram is generated and provided to the user.

[0610] This system enables users without network knowledge to efficiently and accurately create network diagrams and achieve optimal network designs.

[0611] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0612] Step 1:

[0613] The user inputs information about network equipment through a terminal. Specifically, the user inputs information such as "We plan to install a router, switch, and firewall." This input information serves as the basic data for generating the network configuration diagram.

[0614] Step 2:

[0615] The server stores network device information entered by the user in a database. This entered information is stored in the database and used in subsequent processing. This storage process manages the network deployment status.

[0616] Step 3:

[0617] The server generates prompt messages using a generative AI model based on information stored in the database. Specifically, it analyzes the stored network device information and generates prompt messages such as, "Generate the optimal network configuration diagram for the new office." These prompt messages are then used as input to the generative AI model.

[0618] Step 4:

[0619] The server inputs prompt messages into the generation AI model, which then generates the optimal network configuration diagram. The generation AI model analyzes the prompt messages and creates the network configuration diagram based on historical data and best practices. This process generates a configuration diagram that meets the user's requirements.

[0620] Step 5:

[0621] The server provides the user with a generated network configuration diagram. The user can view this diagram through their terminal and use it as a reference for network deployment. This output allows the user to obtain information necessary to achieve an appropriate network design.

[0622] (Application Example 1)

[0623] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0624] There is a need to optimize communication between machinery and equipment within a factory to improve production efficiency. However, it is difficult for workers without network knowledge to design an appropriate network configuration. Furthermore, if the implemented network does not provide the optimal communication path, the operation of the machinery and equipment may become inefficient.

[0625] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0626] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for operators without network knowledge, and means for proposing communication paths to optimize communication between machine devices. This makes it possible to generate an optimal network configuration diagram and efficiently communicate between machine devices even if the operator does not have network knowledge.

[0627] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network.

[0628] "Network deployment status" refers to information indicating how the network is installed and operated.

[0629] A "worker" is a field staff member who is involved in the design and operation of a network but does not possess specialized knowledge.

[0630] "Conversational artificial intelligence" refers to artificial intelligence that communicates with users using natural language and assists in providing information and solving problems.

[0631] A "communication path" is the network route through which data is sent and received.

[0632] "Mechanical equipment" refers to robots and other automated devices used within a factory.

[0633] The system for implementing this invention is designed to optimize communication between machinery and equipment within a factory. The server runs a program to generate a network configuration diagram and provides information based on the network deployment status. Specifically, the server generates the network configuration diagram using a generative AI model implemented in Python. This allows operators to obtain the optimal network configuration even without network knowledge.

[0634] The server collects data from sensors installed in machinery within the factory and uses this data to optimize communication paths. The hardware used consists of sensors and communication modules installed in the factory machinery. Data processing involves collecting data from sensors and converting it into the format necessary to generate a network diagram. Data calculation uses a generative AI model to calculate the optimal communication path.

[0635] As a concrete example, when introducing new machinery in a factory, workers input information about the machinery into the system. The server then uses AI to generate an optimal network configuration diagram and optimizes communication between the machinery. This results in smoother operation of the machinery and improved production efficiency.

[0636] An example of a prompt message to input into the generating AI model is: "We have installed new machinery. Please generate a network configuration diagram and suggest the optimal communication path."

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The user enters information about the new machine into the terminal. This information includes machine identification and network connection details. The terminal then sends this information to the server.

[0640] Step 2:

[0641] The server processes the received information about the machinery and equipment to generate a network configuration diagram. Specifically, it converts the received data into a format that the AI ​​model can process. This converted data then becomes the input for the AI ​​model.

[0642] Step 3:

[0643] The server uses a generative AI model to generate the optimal network configuration diagram. Based on the input data, the generative AI model calculates the optimal connection method between mechanical devices and outputs the network configuration diagram.

[0644] Step 4:

[0645] The server uses the generated network diagram to perform calculations to optimize communication paths between mechanical devices. Specifically, it performs data calculations related to communication path selection to determine the optimal communication path. This result is output as the communication path.

[0646] Step 5:

[0647] The server sends an optimized network configuration diagram and communication paths to the terminal. The user then uses the terminal to review the generated network configuration diagram and communication paths and configure the machinery and equipment within the factory.

[0648] Step 6:

[0649] Users use terminals to configure machinery within the factory and initiate communication between machines based on an optimized network configuration. This results in smoother machine operation and improved production efficiency.

[0650] (Example 2)

[0651] Next, we will describe Example 2 of the Form Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0652] Designing and creating configuration diagrams for communication networks requires specialized knowledge, making it particularly difficult for users with limited knowledge of communication networks. Furthermore, creating appropriate configuration diagrams requires accurately understanding the current deployment situation and designing the network based on that understanding. This presents a challenge for users in efficiently and accurately constructing communication networks.

[0653] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0654] In this invention, the server includes means for generating a communication network configuration diagram using an information processing device, means for providing information based on the status of communication network deployment, and means for providing interactive artificial intelligence for users who do not have specialized knowledge of communication networks. As a result, users can efficiently and accurately create a communication network configuration diagram and construct an optimal communication network, even without specialized knowledge.

[0655] An "information processing device" is a device that has the functions of inputting, processing, storing, and outputting data, and is used to generate a diagram of the configuration of a communication network.

[0656] A "communication network" is a network system in which multiple information devices are interconnected to send and receive data.

[0657] A "configuration diagram" is a diagram that visually shows the arrangement and connection relationships of equipment in a communication network, and it forms the basis of network design.

[0658] "Deployment status" refers to information indicating the installation and operational status of equipment in a communication network, and is fundamental data for designing an optimal network.

[0659] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information through dialogue with users and generates responses that meet the user's requests.

[0660] A "generative AI model" is an artificial intelligence model that generates new information or diagrams based on input data, and is particularly used in natural language processing and data generation.

[0661] A "user" is an individual or organization that uses this system to design communication networks or create configuration diagrams.

[0662] This invention is a system that generates and provides a diagram of a communication network configuration to users. The system is implemented using an information processing device, a database, and a generation AI model.

[0663] The server functions as an information processing device and generates a diagram of the communication network configuration. Specifically, the server uses a database to store the communication network deployment status entered by users. This database can use a relational database management system such as MySQL or PostgreSQL.

[0664] The user inputs information about the communication network through a terminal. The terminal uses a web browser to access the system interface and enters information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This information is passed to the generating AI model as prompt messages.

[0665] The server uses a generative AI model to generate an optimal network configuration diagram based on the input information. This generative AI model can use a model specialized for natural language processing, such as OpenAI's GPT model. The generated configuration diagram is provided to the user, who can view it through a web browser and make modifications as needed.

[0666] As a concrete example, a user inputs a prompt message into the AI ​​model such as, "The communication network equipment we plan to install in our new office will be a router from a specific manufacturer and a switch from a specific manufacturer." Based on this information, the AI ​​generates an optimal communication network configuration diagram and provides it to the user.

[0667] In this way, even users without specialized knowledge can efficiently and accurately create network configuration diagrams and construct optimal network infrastructure.

[0668] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0669] Step 1:

[0670] The user inputs the information necessary for the deployment of the communication network via a terminal. Specifically, they access the system interface using a web browser and enter information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This input information is then prepared to be passed to the AI ​​model as prompt messages.

[0671] Step 2:

[0672] The terminal sends information entered by the user to the server. The entered data is converted into a common data format such as JSON and sent to the server over the network. This data transmission allows the server to receive the user's request.

[0673] Step 3:

[0674] The server stores the received data in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to store information about communication network equipment in tables. This storage process ensures that the data necessary for subsequent AI processing is secured.

[0675] Step 4:

[0676] The server invokes a generating AI model based on the stored data. Specifically, it inputs the stored information as prompts into the generating AI model and performs data processing to generate the optimal network configuration diagram. The generating AI model performs data calculations based on the input prompts and generates the optimal configuration diagram.

[0677] Step 5:

[0678] The server receives the network configuration diagram generated from the AI ​​model and provides it to the user. Specifically, it renders the generated diagram as a web page, making it accessible to the user. The user can view the diagram through a web browser and make modifications as needed. This output enables the user to create network configuration diagrams efficiently and accurately.

[0679] (Application Example 2)

[0680] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[0681] Optimizing the network configuration of automated equipment within a factory is crucial for improving operational efficiency. However, creating an appropriate network diagram is difficult for on-site personnel without network knowledge. Therefore, there is a need for a system that allows on-site personnel to easily achieve the optimal network configuration.

[0682] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0683] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for on-site personnel without network knowledge, and means for generating an optimal network configuration diagram for automated equipment in the factory and improving operational efficiency. As a result, on-site personnel can optimize the network configuration of automated equipment in the factory and improve operational efficiency even without specialized knowledge.

[0684] A "network diagram" is a diagram that visually shows the arrangement of network equipment and connections, and is used to understand the overall structure of the network.

[0685] "Network deployment status" refers to information indicating the current placement, connection status, and usage of network equipment, and is useful as a reference when optimizing or expanding the network.

[0686] "Conversational artificial intelligence" is artificial intelligence that provides information and assists in problem-solving through dialogue with users, and responds to user questions using natural language processing technology.

[0687] "Automated equipment" refers to robots and mechanical devices used in factories that are designed to perform specific tasks automatically.

[0688] "Operational efficiency" is an indicator that shows how effectively a system or piece of equipment functions to achieve its purpose, and efficient operation contributes to cost reduction and productivity improvement.

[0689] To implement this invention, it is necessary to build a system in which a server generates a network configuration diagram and provides information based on the network deployment status. The server executes a program developed using Python and provides an interactive web interface using Flask. Users can input information about network devices through this interface.

[0690] The input information is sent as a prompt to a generative AI model running on the server, specifically to OpenAI's GPT. The GPT model returns instructions to generate the optimal network configuration diagram based on the input information. Upon receiving these instructions, the server uses Graphviz to draw the network configuration diagram.

[0691] The generated network configuration diagram is provided to the user via a web interface. This allows users to create the optimal network configuration for their factory's automated equipment without requiring specialized network knowledge.

[0692] For example, when a user is inputting information about a newly introduced robotic arm and existing network equipment, the following prompt message can be used.

[0693] Prompt example:

[0694] "The new robotic arm to be introduced is model RA-2023, and the existing network equipment includes a switch SW-100 and a router RT-200. Please generate a network configuration diagram that optimally connects these components."

[0695] This prompt allows the server to generate and provide the user with an optimal network configuration diagram.

[0696] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0697] Step 1:

[0698] The user uses a terminal to access the web interface and enter information about the network equipment. This information includes the model number of the newly installed equipment and details of existing network equipment. This information is then sent to the server.

[0699] Step 2:

[0700] The server converts the received network device information into prompt statements suitable for the generating AI model. Specifically, it creates prompt statements containing instructions for generating the optimal network configuration diagram based on the input information. These prompt statements are then sent to the OpenAI GPT model.

[0701] Step 3:

[0702] The server receives a response from the GPT model and obtains instructions for generating the optimal network diagram. Based on the input prompt, the GPT model provides the information necessary to generate the network diagram.

[0703] Step 4:

[0704] The server uses Graphviz to draw a network configuration diagram based on instructions obtained from the GPT model. Graphviz visually represents the placement and connections of network devices based on these instructions.

[0705] Step 5:

[0706] The generated network configuration diagram is sent from the server to the user's terminal and provided to the user via a web interface. The user can review the generated configuration diagram and use it to optimize the network configuration of automated equipment within the factory.

[0707] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0708] "Example of form 1"

[0709] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0710] "Example of form 2"

[0711] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0712] The following describes the processing flow for each example of the form.

[0713] "Example of form 1"

[0714] Step 1: Gain an understanding of the user's network knowledge and needs through dialogue with the user.

[0715] Step 2: The emotion engine recognizes the user's emotions and adjusts how the network diagram is generated accordingly. For example, if the user feels stressed, the emotion engine adjusts the network diagram generation process to be simpler or easier for the user to understand.

[0716] Step 3: The emotion engine recognizes the user's emotions and adjusts how network deployment information is provided accordingly. For example, if the user is perceived as feeling happy, the emotion engine generates a more detailed network configuration diagram, providing optimal information based on the user's emotions.

[0717] Step 4: Provide the generated network diagram and information to the user.

[0718] (Example 1)

[0719] Next, we will describe Embodiment 1 of Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0720] In generating network configuration diagrams, there is a challenge in that field personnel without network knowledge find it difficult to create appropriate diagrams. Furthermore, the lack of information tailored to the user's emotional needs may lead to decreased user understanding and satisfaction.

[0721] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0722] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions, and means for storing information entered by the user in a database and generating prompt sentences using a generation AI model. This makes it possible to generate an appropriate network configuration diagram even without network knowledge and to provide information according to the user's emotions.

[0723] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[0724] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[0725] "Conversational artificial intelligence" refers to artificial intelligence that can provide information or solve problems through dialogue with users, and responds to user input using natural language processing technology.

[0726] An "emotion engine" is an engine that recognizes the user's emotions and adjusts the system's operation accordingly, and is used to improve the user experience.

[0727] A "generative AI model" is a model that uses artificial intelligence technology to generate new information and content from data, and is applied to areas such as natural language generation and image generation.

[0728] A "prompt" is an instruction given to artificial intelligence to perform a specific task, and it forms the basis for the AI ​​to make appropriate responses and generations.

[0729] A description of embodiments for carrying out this invention will be given.

[0730] The server runs a program to generate a network diagram. This program is developed using Python and uses MySQL as its database. The server receives information about network devices sent by the user and stores it in the database. Based on the stored information, the server generates prompts using a generative AI model and inputs them into an interactive artificial intelligence. This AI uses OpenAI's GPT model and generates the optimal network diagram based on the prompts.

[0731] Users input network device information via a terminal, enabling them to generate network diagrams even without network knowledge. For example, they can input information such as router model numbers, switch port counts, and access point coverage ranges. The information entered by the user is then sent from the terminal to the server.

[0732] The terminal is responsible for sending information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. It also displays the network configuration diagram sent from the server to the user.

[0733] As a concrete example, when a company sets up a network in a new office, the user inputs information such as "Router model: XYZ123, Number of switch ports: 24, Access point coverage: 50m". Based on this information, the server generates a prompt message, "Generate a network configuration diagram using router XYZ123 and a 24-port switch," and inputs it into the AI. The AI ​​generates a configuration diagram based on the prompt, and the server uses an emotion engine to adjust the diagram according to the user's emotions. Finally, the terminal displays the adjusted configuration diagram to the user.

[0734] In this way, even users without network knowledge can generate appropriate network diagrams and provide information tailored to their needs.

[0735] The flow of the specific processing in Example 1 will be explained using Figure 15.

[0736] Step 1:

[0737] The user enters information about network devices into the terminal. Specifically, they enter information such as the router model number, the number of ports on the switch, and the coverage range of the access point. The entered information becomes the basic data for generating the network configuration diagram.

[0738] Step 2:

[0739] The terminal sends the information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. At this time, the terminal verifies the integrity of the data and adjusts the data format as needed.

[0740] Step 3:

[0741] The server saves the received data to a MySQL database. The server uses the Python SQLAlchemy library to connect to the database and insert the data. The saved data is then used for subsequent processing.

[0742] Step 4:

[0743] The server generates prompt messages using a generation AI model based on the stored data. Specifically, it retrieves the necessary information from the database and uses it to create prompt messages such as "Generate a network configuration diagram using router XYZ123 and a 24-port switch."

[0744] Step 5:

[0745] The server inputs the generated prompt text into the interactive artificial intelligence. The AI ​​uses OpenAI's GPT model to generate the optimal network configuration diagram based on the prompt. The AI ​​analyzes the input prompt and outputs the appropriate configuration diagram.

[0746] Step 6:

[0747] The server uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's past input history and current input to determine whether the user is stressed or happy. For example, if the server determines that the user is stressed, it simplifies the generated configuration diagram.

[0748] Step 7:

[0749] The server sends the adjusted network configuration diagram to the terminal. The terminal displays the received configuration diagram to the user. The user can review the displayed configuration diagram and make corrections or additional inputs as needed.

[0750] (Application Example 1)

[0751] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0752] If on-site personnel lack network knowledge, understanding complex network configurations and implementing them properly becomes difficult. Furthermore, the lack of information tailored to user needs can lead to stress. Additionally, proposing the optimal network configuration for the layout and operation of machinery within the factory presents a challenge.

[0753] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0754] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for proposing the optimal network for the placement and operation of machinery and equipment within the factory. As a result, field personnel can understand and implement an appropriate network configuration even without network knowledge, information can be provided in accordance with the user's emotions, and the optimal network for the placement and operation of machinery and equipment within the factory can be proposed.

[0755] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used to understand the overall structure of the network.

[0756] "Network deployment status" refers to information that shows the current state of how the network is installed and operated.

[0757] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with the user.

[0758] "Emotion recognition means" refers to technology that analyzes a user's emotions and takes appropriate action based on those emotions.

[0759] "An optimal network for the placement and operation of machinery and equipment" refers to the network configuration that is best suited for the efficient operation of machinery and equipment within a factory.

[0760] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server executes a program for generating a network configuration diagram and provides information based on the network deployment status. Specifically, the server uses interactive artificial intelligence to generate an optimal network configuration diagram based on information about network devices entered by the user. Furthermore, it analyzes the user's emotions using emotion recognition means and adjusts the method of providing information according to those emotions.

[0761] The terminal provides an interface for users to input information about network devices. Users can use terminals such as smartphones and tablets to input information about network devices and receive network configuration diagrams provided by the server.

[0762] As a concrete example, when introducing new machinery in a factory, the user inputs the layout information of the machinery using a terminal. Based on this information, the server uses a generative AI model to generate an optimal network configuration diagram and provides it to the user. If the user is experiencing stress, the server simplifies the information provided using emotion recognition, and provides detailed information if the user is understanding the situation.

[0763] Examples of prompt messages include, "Generate the optimal network configuration diagram for introducing new machinery and equipment," and "Simplify information provision if the user is experiencing stress."

[0764] In this way, even if field personnel do not possess network knowledge, they can understand and implement the appropriate network configuration.

[0765] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[0766] Step 1:

[0767] The user uses a terminal to enter information about the network device. This information includes the type of device, its location, and the connection method. This information is then sent to the server.

[0768] Step 2:

[0769] The server stores information about received network devices in a database. Based on the stored information, it uses a generative AI model to generate an optimal network configuration diagram. In this process, it calculates network connection patterns and communication paths between devices to create a visual configuration diagram.

[0770] Step 3:

[0771] The server analyzes the user's emotions using emotion recognition technology. It analyzes the user's facial expressions and voice data during input to identify emotions such as stress and joy. This emotion information is used to adjust the way information is provided.

[0772] Step 4:

[0773] The server provides the user with the most relevant information based on the generated network diagram and sentiment analysis results. If the user is feeling stressed, the information is simplified and presented in an easy-to-understand format. Conversely, if the user is feeling happy, detailed information is provided.

[0774] Step 5:

[0775] The user reviews the network configuration diagram provided through the terminal and enters any necessary corrections or additional information. This determines the final network configuration.

[0776] (Example 2)

[0777] Next, we will describe Example 2 of the Form Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0778] In network implementation and management, it is difficult for users without specialized knowledge to create appropriate network configuration diagrams. Furthermore, there is a need for flexible information provision that responds to user needs, but conventional systems are unable to accommodate this.

[0779] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0780] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, and means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions. As a result, users can obtain an appropriate network configuration diagram even without specialized knowledge, and flexible information provision tailored to the user's emotions becomes possible.

[0781] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[0782] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[0783] "Interactive artificial intelligence" is a system that provides information to users through natural language dialogue and generates responses that meet the user's requests.

[0784] An "emotion engine" is a system that recognizes the user's emotions and adjusts the way information is provided based on those emotions.

[0785] A "generative AI model" is an artificial intelligence model that generates optimal results based on input data, and provides highly accurate output for specific tasks.

[0786] One embodiment of this invention is a system that generates a network configuration diagram and provides information based on the network deployment status. The system provides interactive artificial intelligence for users without network knowledge and further includes an emotion engine that recognizes the user's emotions and adjusts the information delivery method accordingly.

[0787] The server stores network device information in a database and generates a network configuration diagram using a generative AI model. The generative AI model analyzes the input data and proposes the optimal network configuration. In doing so, the server takes into account historical data and general network design best practices.

[0788] The terminal provides an interface for users to input information about network devices. Users can use the terminal to input device names and specifications such as "routers," "switches," and "access points." The entered information is sent to the server and used to generate a network configuration diagram.

[0789] The emotion engine recognizes the user's emotions and adjusts the generated network diagram accordingly. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information.

[0790] As a concrete example, when a company implements a new network, the user uses a terminal to input information about network devices such as "routers, switches, and access points" into the system. An example of a prompt to the generating AI model is, "Please generate the optimal network configuration diagram for the company's network implementation." Based on this prompt, the system generates the optimal network configuration diagram and provides it to the user.

[0791] The flow of the specific processing in Example 2 will be explained using Figure 17.

[0792] Step 1:

[0793] The user uses a terminal to input information about network devices. Specifically, the user inputs device names such as "routers," "switches," and "access points," as well as detailed information such as the specifications and location of each device. This information serves as the basic data for generating a network configuration diagram. The entered data is sent from the terminal to the server.

[0794] Step 2:

[0795] The server stores information about network devices received from terminals in a database. The stored data needs to be managed accurately and efficiently because it will be used in subsequent processing. Checks are performed to ensure data integrity when saving data to the database.

[0796] Step 3:

[0797] The server generates a network configuration diagram using a generative AI model based on stored data. The generative AI model analyzes the input network device information and proposes the optimal network configuration. In this process, the AI ​​takes historical data and general network design best practices into consideration. An initial network configuration diagram is generated as output.

[0798] Step 4:

[0799] The server uses an emotion engine to recognize the user's emotions. Based on the user's emotions, it adjusts the generated network diagram. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information. The adjusted network diagram is then generated as the final output.

[0800] Step 5:

[0801] The server then sends the finalized network configuration diagram to the terminal, providing it to the user. The user can review the diagram on the terminal and make corrections or provide feedback as needed. This process allows users to easily obtain an appropriate network configuration diagram, even without network knowledge.

[0802] (Application Example 2)

[0803] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as a "server," and the headset-type terminal 314 will be referred to as a "terminal."

[0804] In factory environments where on-site personnel lack network knowledge, a system is needed that can easily generate and optimize network diagrams, enabling the rapid implementation of appropriate network configurations. Furthermore, it is essential to adjust information delivery based on the user's emotional state to aid user understanding.

[0805] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0806] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for optimizing the network environment as an application installed on machinery in the factory. This makes it possible to generate and optimize an appropriate network configuration diagram even if field personnel do not have network knowledge.

[0807] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[0808] "Network deployment status" refers to information indicating how the network is installed and operated.

[0809] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with users using natural language.

[0810] "Means of recognizing user emotions" refers to technology that analyzes a user's emotions from their facial expressions, tone of voice, etc., and determines their emotional state.

[0811] "Means of adjusting the method of information provision" refers to technologies that change the content and format of the information provided according to the user's emotions and circumstances.

[0812] "Applications installed on machinery within a factory" refers to software that is integrated into factory machinery and equipment to perform specific functions.

[0813] "Methods for optimizing network environments" refer to techniques for adjusting configurations and settings to maximize network performance and efficiency.

[0814] The system for implementing this invention is configured as an application installed on machinery within a factory. This application utilizes interactive artificial intelligence to generate network configuration diagrams and provide information based on the network deployment status. Furthermore, it has a function to recognize the user's emotions and adjust the method of information provision accordingly.

[0815] The server receives network device information entered by the user to generate a network configuration diagram, and uses a generation AI model to create the optimal configuration diagram. The network deployment status is stored in a database and updated as needed. To recognize the user's emotions, the system analyzes facial expressions and voice tone using a camera and microphone, and an emotion engine determines the user's state.

[0816] The terminal uses interactive artificial intelligence to generate a network configuration diagram based on information entered by the user, and displays it on a projector or screen. If the user is experiencing stress, the diagram is simplified and presented in an easy-to-understand format. Conversely, if the user is experiencing pleasure, a more detailed diagram is provided to facilitate a deeper understanding.

[0817] As a concrete example, when introducing a network to a new production line in a factory, the system generates an optimal network configuration diagram and displays it on a projector once the on-site staff input information about the network equipment. If the staff are feeling stressed, a simplified configuration diagram can be displayed to aid their understanding.

[0818] An example of a prompt message is: "Please enter the information for the network equipment required for the new production line. We will generate the optimal network configuration diagram, taking into account the current emotional state."

[0819] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[0820] Step 1:

[0821] The user enters network device information into the terminal. This information includes details such as the type of network device, connection port, and IP address. The terminal then sends this information to the server.

[0822] Step 2:

[0823] The server generates an optimal network configuration diagram using a generation AI model based on the received network device information. It analyzes the input information and processes the data to design the network topology. The generated configuration diagram is stored digitally on the server.

[0824] Step 3:

[0825] The server retrieves network deployment status from the database and updates the configuration diagram based on the latest information. The database includes information on the existing network environment and past deployment history. This ensures that the generated configuration diagram reflects the current situation.

[0826] Step 4:

[0827] The device uses its camera and microphone to capture the user's facial expressions and voice tone in order to recognize their emotions. This data is input into an emotion engine, which analyzes the user's emotional state. The analysis results are then sent to a server.

[0828] Step 5:

[0829] The server adjusts how information is delivered based on the user's emotional state. If it determines that the user is stressed, it simplifies the diagram and sends it to the terminal in an easy-to-understand format. If the user is happy, it provides a detailed diagram.

[0830] Step 6:

[0831] The terminal displays the network configuration diagram received from the server on a projector or display. The user can review the displayed configuration diagram and input any necessary modifications or additional information.

[0832] Step 7:

[0833] When a user enters corrections or additional information, the terminal sends that information back to the server, and the server updates the configuration diagram. This results in an optimal network configuration diagram that reflects the user's feedback.

[0834] (Other examples)

[0835] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[0836] 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.

[0837] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0838] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[0839] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0840] [Fourth Embodiment]

[0841] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0842] 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.

[0843] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0844] 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.

[0845] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0846] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0847] 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.

[0848] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0849] 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.

[0850] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0851] The 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.

[0852] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0853] Next, the identification process performed by the identification processing unit 290 of the data processing device 12 will be described.

[0854] "Example of form 1"

[0855] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0856] "Example of form 2"

[0857] Embodiments of the present invention include a system comprising means for generating a network configuration diagram, means for providing information based on the network deployment status, and means for providing an interactive AI for field personnel without network knowledge. Specifically, the network deployment status is stored in a database, and the interactive AI generates a network configuration diagram based on that information. For example, when company A newly deploys a network, the field personnel input information on the network equipment to be deployed into this system. The interactive AI then generates an optimal network configuration diagram based on that information and provides it to the field personnel. This makes it possible for field personnel without network knowledge to generate an appropriate network configuration diagram.

[0858] The following describes the processing flow for each example of the form.

[0859] "Example of form 1"

[0860] Step 1: The field staff enters information about the network equipment to be installed into this system. Step 2: The entered information is saved in the database and managed as network installation status.

[0861] Step 3: The interactive AI generates the optimal network configuration diagram based on the information in the database.

[0862] Step 4: The generated network configuration diagram is provided to the field staff and used for network deployment and management.

[0863] (Example 1)

[0864] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0865] It is difficult for users without specialized knowledge of network deployment and configuration to create appropriate network diagrams. Furthermore, there is a lack of efficient means to propose optimal configurations based on the current network deployment situation. This can lead to inefficiencies in network design and deployment.

[0866] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0867] In this invention, the server includes means for generating a network configuration diagram, means for saving the network deployment status to a database, and means for creating prompt statements using a generation AI model based on the saved data. This makes it possible for users without network knowledge to efficiently and accurately create network configuration diagrams and achieve optimal network design.

[0868] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[0869] A "database" is a system for efficiently storing, managing, and retrieving information, and is used to accumulate data such as network deployment status.

[0870] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and perform specific tasks, and is used to generate network diagrams.

[0871] A "prompt statement" is an input statement used to give instructions to a generating AI model, and includes the instructions given when generating a network diagram.

[0872] "Interactive artificial intelligence" refers to artificial intelligence that provides information and performs tasks through dialogue with users, and is used to support users who lack network knowledge.

[0873] This invention is a system that generates network configuration diagrams and provides information based on the network deployment status. The system aims to provide interactive artificial intelligence for users without network knowledge.

[0874] The server executes a program to generate a network configuration diagram. This program has the function of saving the network deployment status to a database and creating prompt statements using a generation AI model based on that information. Specifically, the server saves information about network devices entered by the user to the database and generates prompt statements based on the saved data. The generated prompt statements are input into the generation AI model, and the optimal network configuration diagram is generated.

[0875] Users can input information about network devices into the system via their terminal. For example, by inputting specific device information such as "We plan to install a router, switch, and firewall," the server will generate a network configuration diagram based on this information.

[0876] For example, if a user inputs "I want to install a router and switch in the new office," the server saves this information to its database and generates a prompt message saying, "Please generate the optimal network configuration diagram for the new office." By inputting this prompt message into the AI ​​generation model, the optimal network configuration diagram is generated and provided to the user.

[0877] This system enables users without network knowledge to efficiently and accurately create network diagrams and achieve optimal network designs.

[0878] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0879] Step 1:

[0880] The user inputs information about network equipment through a terminal. Specifically, the user inputs information such as "We plan to install a router, switch, and firewall." This input information serves as the basic data for generating the network configuration diagram.

[0881] Step 2:

[0882] The server stores network device information entered by the user in a database. This entered information is stored in the database and used in subsequent processing. This storage process manages the network deployment status.

[0883] Step 3:

[0884] The server generates prompt messages using a generative AI model based on information stored in the database. Specifically, it analyzes the stored network device information and generates prompt messages such as, "Generate the optimal network configuration diagram for the new office." These prompt messages are then used as input to the generative AI model.

[0885] Step 4:

[0886] The server inputs prompt messages into the generation AI model, which then generates the optimal network configuration diagram. The generation AI model analyzes the prompt messages and creates the network configuration diagram based on historical data and best practices. This process generates a configuration diagram that meets the user's requirements.

[0887] Step 5:

[0888] The server provides the user with a generated network configuration diagram. The user can view this diagram through their terminal and use it as a reference for network deployment. This output allows the user to obtain information necessary to achieve an appropriate network design.

[0889] (Application Example 1)

[0890] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0891] There is a need to optimize communication between machinery and equipment within a factory to improve production efficiency. However, it is difficult for workers without network knowledge to design an appropriate network configuration. Furthermore, if the implemented network does not provide the optimal communication path, the operation of the machinery and equipment may become inefficient.

[0892] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0893] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for operators without network knowledge, and means for proposing communication paths to optimize communication between machine devices. This makes it possible to generate an optimal network configuration diagram and efficiently communicate between machine devices even if the operator does not have network knowledge.

[0894] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network.

[0895] "Network deployment status" refers to information indicating how the network is installed and operated.

[0896] A "worker" is a field staff member who is involved in the design and operation of a network but does not possess specialized knowledge.

[0897] "Conversational artificial intelligence" refers to artificial intelligence that communicates with users using natural language and assists in providing information and solving problems.

[0898] A "communication path" is the network route through which data is sent and received.

[0899] "Mechanical equipment" refers to robots and other automated devices used within a factory.

[0900] The system for implementing this invention is designed to optimize communication between machinery and equipment within a factory. The server runs a program to generate a network configuration diagram and provides information based on the network deployment status. Specifically, the server generates the network configuration diagram using a generative AI model implemented in Python. This allows operators to obtain the optimal network configuration even without network knowledge.

[0901] The server collects data from sensors installed in machinery within the factory and uses this data to optimize communication paths. The hardware used consists of sensors and communication modules installed in the factory machinery. Data processing involves collecting data from sensors and converting it into the format necessary to generate a network diagram. Data calculation uses a generative AI model to calculate the optimal communication path.

[0902] As a concrete example, when introducing new machinery in a factory, workers input information about the machinery into the system. The server then uses AI to generate an optimal network configuration diagram and optimizes communication between the machinery. This results in smoother operation of the machinery and improved production efficiency.

[0903] An example of a prompt message to input into the generating AI model is: "We have installed new machinery. Please generate a network configuration diagram and suggest the optimal communication path."

[0904] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0905] Step 1:

[0906] The user enters information about the new machine into the terminal. This information includes machine identification and network connection details. The terminal then sends this information to the server.

[0907] Step 2:

[0908] The server processes the received information about the machinery and equipment to generate a network configuration diagram. Specifically, it converts the received data into a format that the AI ​​model can process. This converted data then becomes the input for the AI ​​model.

[0909] Step 3:

[0910] The server uses a generative AI model to generate the optimal network configuration diagram. Based on the input data, the generative AI model calculates the optimal connection method between mechanical devices and outputs the network configuration diagram.

[0911] Step 4:

[0912] The server uses the generated network diagram to perform calculations to optimize communication paths between mechanical devices. Specifically, it performs data calculations related to communication path selection to determine the optimal communication path. This result is output as the communication path.

[0913] Step 5:

[0914] The server sends an optimized network configuration diagram and communication paths to the terminal. The user then uses the terminal to review the generated network configuration diagram and communication paths and configure the machinery and equipment within the factory.

[0915] Step 6:

[0916] Users use terminals to configure machinery within the factory and initiate communication between machines based on an optimized network configuration. This results in smoother machine operation and improved production efficiency.

[0917] (Example 2)

[0918] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0919] Designing and creating configuration diagrams for communication networks requires specialized knowledge, making it particularly difficult for users with limited knowledge of communication networks. Furthermore, creating appropriate configuration diagrams requires accurately understanding the current deployment situation and designing the network based on that understanding. This presents a challenge for users in efficiently and accurately constructing communication networks.

[0920] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0921] In this invention, the server includes means for generating a communication network configuration diagram using an information processing device, means for providing information based on the status of communication network deployment, and means for providing interactive artificial intelligence for users who do not have specialized knowledge of communication networks. As a result, users can efficiently and accurately create a communication network configuration diagram and construct an optimal communication network, even without specialized knowledge.

[0922] An "information processing device" is a device that has the functions of inputting, processing, storing, and outputting data, and is used to generate a diagram of the configuration of a communication network.

[0923] A "communication network" is a network system in which multiple information devices are interconnected to send and receive data.

[0924] A "configuration diagram" is a diagram that visually shows the arrangement and connection relationships of equipment in a communication network, and it forms the basis of network design.

[0925] "Deployment status" refers to information indicating the installation and operational status of equipment in a communication network, and is fundamental data for designing an optimal network.

[0926] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information through dialogue with users and generates responses that meet the user's requests.

[0927] A "generative AI model" is an artificial intelligence model that generates new information or diagrams based on input data, and is particularly used in natural language processing and data generation.

[0928] A "user" is an individual or organization that uses this system to design communication networks or create configuration diagrams.

[0929] This invention is a system that generates and provides a diagram of a communication network configuration to users. The system is implemented using an information processing device, a database, and a generation AI model.

[0930] The server functions as an information processing device and generates a diagram of the communication network configuration. Specifically, the server uses a database to store the communication network deployment status entered by users. This database can use a relational database management system such as MySQL or PostgreSQL.

[0931] The user inputs information about the communication network through a terminal. The terminal uses a web browser to access the system interface and enters information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This information is passed to the generating AI model as prompt messages.

[0932] The server uses a generative AI model to generate an optimal network configuration diagram based on the input information. This generative AI model can use a model specialized for natural language processing, such as OpenAI's GPT model. The generated configuration diagram is provided to the user, who can view it through a web browser and make modifications as needed.

[0933] As a concrete example, a user inputs a prompt message into the AI ​​model such as, "The communication network equipment we plan to install in our new office will be a router from a specific manufacturer and a switch from a specific manufacturer." Based on this information, the AI ​​generates an optimal communication network configuration diagram and provides it to the user.

[0934] In this way, even users without specialized knowledge can efficiently and accurately create network configuration diagrams and construct optimal network infrastructure.

[0935] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0936] Step 1:

[0937] The user inputs the information necessary for the deployment of the communication network via a terminal. Specifically, they access the system interface using a web browser and enter information such as the model number, manufacturer, quantity, and installation location of the communication network equipment into a form. This input information is then prepared to be passed to the AI ​​model as prompt messages.

[0938] Step 2:

[0939] The terminal sends information entered by the user to the server. The entered data is converted into a common data format such as JSON and sent to the server over the network. This data transmission allows the server to receive the user's request.

[0940] Step 3:

[0941] The server stores the received data in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to store information about communication network equipment in tables. This storage process ensures that the data necessary for subsequent AI processing is secured.

[0942] Step 4:

[0943] The server invokes a generating AI model based on the stored data. Specifically, it inputs the stored information as prompts into the generating AI model and performs data processing to generate the optimal network configuration diagram. The generating AI model performs data calculations based on the input prompts and generates the optimal configuration diagram.

[0944] Step 5:

[0945] The server receives the network configuration diagram generated from the AI ​​model and provides it to the user. Specifically, it renders the generated diagram as a web page, making it accessible to the user. The user can view the diagram through a web browser and make modifications as needed. This output enables the user to create network configuration diagrams efficiently and accurately.

[0946] (Application Example 2)

[0947] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0948] Optimizing the network configuration of automated equipment within a factory is crucial for improving operational efficiency. However, creating an appropriate network diagram is difficult for on-site personnel without network knowledge. Therefore, there is a need for a system that allows on-site personnel to easily achieve the optimal network configuration.

[0949] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0950] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for on-site personnel without network knowledge, and means for generating an optimal network configuration diagram for automated equipment in the factory and improving operational efficiency. As a result, on-site personnel can optimize the network configuration of automated equipment in the factory and improve operational efficiency even without specialized knowledge.

[0951] A "network diagram" is a diagram that visually shows the arrangement of network equipment and connections, and is used to understand the overall structure of the network.

[0952] "Network deployment status" refers to information indicating the current placement, connection status, and usage of network equipment, and is useful as a reference when optimizing or expanding the network.

[0953] "Conversational artificial intelligence" is artificial intelligence that provides information and assists in problem-solving through dialogue with users, and responds to user questions using natural language processing technology.

[0954] "Automated equipment" refers to robots and mechanical devices used in factories that are designed to perform specific tasks automatically.

[0955] "Operational efficiency" is an indicator that shows how effectively a system or piece of equipment functions to achieve its purpose, and efficient operation contributes to cost reduction and productivity improvement.

[0956] To implement this invention, it is necessary to build a system in which a server generates a network configuration diagram and provides information based on the network deployment status. The server executes a program developed using Python and provides an interactive web interface using Flask. Users can input information about network devices through this interface.

[0957] The input information is sent as a prompt to a generative AI model running on the server, specifically to OpenAI's GPT. The GPT model returns instructions to generate the optimal network configuration diagram based on the input information. Upon receiving these instructions, the server uses Graphviz to draw the network configuration diagram.

[0958] The generated network configuration diagram is provided to the user via a web interface. This allows users to create the optimal network configuration for their factory's automated equipment without requiring specialized network knowledge.

[0959] For example, when a user is inputting information about a newly introduced robotic arm and existing network equipment, the following prompt message can be used.

[0960] Prompt example:

[0961] "The new robotic arm to be introduced is model RA-2023, and the existing network equipment includes a switch SW-100 and a router RT-200. Please generate a network configuration diagram that optimally connects these components."

[0962] This prompt allows the server to generate and provide the user with an optimal network configuration diagram.

[0963] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0964] Step 1:

[0965] The user uses a terminal to access the web interface and enter information about the network equipment. This information includes the model number of the newly installed equipment and details of existing network equipment. This information is then sent to the server.

[0966] Step 2:

[0967] The server converts the received network device information into prompt statements suitable for the generating AI model. Specifically, it creates prompt statements containing instructions for generating the optimal network configuration diagram based on the input information. These prompt statements are then sent to the OpenAI GPT model.

[0968] Step 3:

[0969] The server receives a response from the GPT model and obtains instructions for generating the optimal network diagram. Based on the input prompt, the GPT model provides the information necessary to generate the network diagram.

[0970] Step 4:

[0971] The server uses Graphviz to draw a network configuration diagram based on instructions obtained from the GPT model. Graphviz visually represents the placement and connections of network devices based on these instructions.

[0972] Step 5:

[0973] The generated network configuration diagram is sent from the server to the user's terminal and provided to the user via a web interface. The user can review the generated configuration diagram and use it to optimize the network configuration of automated equipment within the factory.

[0974] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0975] "Example of form 1"

[0976] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0977] "Example of form 2"

[0978] One embodiment of the present invention provides a system that includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing an interactive AI for field personnel without network knowledge, and an emotion engine that recognizes the user's emotions. This emotion engine recognizes the user's emotions and adjusts the method of generating the network configuration diagram and the method of providing information on the network deployment status according to those emotions. For example, if the user feels stressed, the emotion engine adjusts the generation of the network configuration diagram to be simpler or in a form that is easier for the user to understand. Also, if the user feels happy, the emotion engine generates a more detailed network configuration diagram, and so on, providing optimal information according to the user's emotions.

[0979] The following describes the processing flow for each example of the form.

[0980] "Example of form 1"

[0981] Step 1: Gain an understanding of the user's network knowledge and needs through dialogue with the user.

[0982] Step 2: The emotion engine recognizes the user's emotions and adjusts how the network diagram is generated accordingly. For example, if the user feels stressed, the emotion engine adjusts the network diagram generation process to be simpler or easier for the user to understand.

[0983] Step 3: The emotion engine recognizes the user's emotions and adjusts how network deployment information is provided accordingly. For example, if the user is perceived as feeling happy, the emotion engine generates a more detailed network configuration diagram, providing optimal information based on the user's emotions.

[0984] Step 4: Provide the generated network diagram and information to the user.

[0985] (Example 1)

[0986] Next, we will describe Embodiment 1 of Example Form 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0987] In generating network configuration diagrams, there is a challenge in that field personnel without network knowledge find it difficult to create appropriate diagrams. Furthermore, the lack of information tailored to the user's emotional needs may lead to decreased user understanding and satisfaction.

[0988] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0989] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions, and means for storing information entered by the user in a database and generating prompt sentences using a generation AI model. This makes it possible to generate an appropriate network configuration diagram even without network knowledge and to provide information according to the user's emotions.

[0990] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[0991] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[0992] "Interactive artificial intelligence" refers to artificial intelligence that can provide information and solve problems through dialogue with users, and responds to user input using natural language processing technology.

[0993] An "emotion engine" is an engine that recognizes a user's emotions and adjusts the system's operation accordingly, and is used to improve the user experience.

[0994] A "generative AI model" is a model that uses artificial intelligence technology to generate new information and content from data, and is applied to areas such as natural language generation and image generation.

[0995] A "prompt" is an instruction given to artificial intelligence to perform a specific task, and it forms the basis for the AI ​​to make appropriate responses and generations.

[0996] A description of embodiments for carrying out this invention will be given.

[0997] The server runs a program to generate a network diagram. This program is developed using Python and uses MySQL as its database. The server receives information about network devices sent by the user and stores it in the database. Based on the stored information, the server generates prompts using a generative AI model and inputs them into an interactive artificial intelligence. This AI uses OpenAI's GPT model and generates the optimal network diagram based on the prompts.

[0998] Users input network device information via a terminal, enabling them to generate network diagrams even without network knowledge. For example, they can input information such as router model numbers, switch port counts, and access point coverage ranges. The information entered by the user is then sent from the terminal to the server.

[0999] The terminal is responsible for sending information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. It also displays the network configuration diagram sent from the server to the user.

[1000] As a concrete example, when a company sets up a network in a new office, the user inputs information such as "Router model: XYZ123, Number of switch ports: 24, Access point coverage: 50m". Based on this information, the server generates a prompt message, "Generate a network configuration diagram using router XYZ123 and a 24-port switch," and inputs it into the AI. The AI ​​generates a configuration diagram based on the prompt, and the server uses an emotion engine to adjust the diagram according to the user's emotions. Finally, the terminal displays the adjusted configuration diagram to the user.

[1001] In this way, even users without network knowledge can generate appropriate network diagrams and provide information tailored to their needs.

[1002] The flow of the specific processing in Example 1 will be explained using Figure 15.

[1003] Step 1:

[1004] The user enters information about network devices into the terminal. Specifically, they enter information such as the router model number, the number of ports on the switch, and the coverage range of the access point. The entered information becomes the basic data for generating the network configuration diagram.

[1005] Step 2:

[1006] The terminal sends the information entered by the user to the server. The terminal converts the input data into JSON format and sends it to the server using an HTTP request. At this time, the terminal verifies the integrity of the data and adjusts the data format as needed.

[1007] Step 3:

[1008] The server saves the received data to a MySQL database. The server uses the Python SQLAlchemy library to connect to the database and insert the data. The saved data is then used for subsequent processing.

[1009] Step 4:

[1010] The server generates prompt messages using a generation AI model based on the stored data. Specifically, it retrieves the necessary information from the database and uses it to create prompt messages such as "Generate a network configuration diagram using router XYZ123 and a 24-port switch."

[1011] Step 5:

[1012] The server inputs the generated prompt text into the interactive artificial intelligence. The AI ​​uses OpenAI's GPT model to generate the optimal network configuration diagram based on the prompt. The AI ​​analyzes the input prompt and outputs the appropriate configuration diagram.

[1013] Step 6:

[1014] The server uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's past input history and current input to determine whether the user is stressed or happy. For example, if the server determines that the user is stressed, it simplifies the generated configuration diagram.

[1015] Step 7:

[1016] The server sends the adjusted network configuration diagram to the terminal. The terminal displays the received configuration diagram to the user. The user can review the displayed configuration diagram and make corrections or additional inputs as needed.

[1017] (Application Example 1)

[1018] Next, we will describe Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1019] If on-site personnel lack network knowledge, understanding complex network configurations and implementing them properly becomes difficult. Furthermore, the lack of information tailored to user needs can lead to stress. Additionally, proposing the optimal network configuration for the layout and operation of machinery within the factory presents a challenge.

[1020] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1021] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for proposing the optimal network for the placement and operation of machinery and equipment within the factory. As a result, field personnel can understand and implement an appropriate network configuration even without network knowledge, information can be provided in accordance with the user's emotions, and the optimal network for the placement and operation of machinery and equipment within the factory can be proposed.

[1022] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used to understand the overall structure of the network.

[1023] "Network deployment status" refers to information that shows the current state of how the network is installed and operated.

[1024] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with the user.

[1025] "Emotion recognition means" refers to technology that analyzes a user's emotions and takes appropriate action based on those emotions.

[1026] "An optimal network for the placement and operation of machinery and equipment" refers to the network configuration that is best suited for the efficient operation of machinery and equipment within a factory.

[1027] The system for implementing this invention consists of three elements: a server, a terminal, and a user. The server executes a program for generating a network configuration diagram and provides information based on the network deployment status. Specifically, the server uses interactive artificial intelligence to generate an optimal network configuration diagram based on information about network devices entered by the user. Furthermore, it analyzes the user's emotions using emotion recognition means and adjusts the method of providing information according to those emotions.

[1028] The terminal provides an interface for users to input information about network devices. Users can use terminals such as smartphones and tablets to input information about network devices and receive network configuration diagrams provided by the server.

[1029] As a concrete example, when introducing new machinery in a factory, the user inputs the layout information of the machinery using a terminal. Based on this information, the server uses a generative AI model to generate an optimal network configuration diagram and provides it to the user. If the user is experiencing stress, the server simplifies the information provided using emotion recognition, and provides detailed information if the user is understanding the situation.

[1030] Examples of prompt messages include, "Generate the optimal network configuration diagram for introducing new machinery and equipment," and "Simplify information provision if the user is experiencing stress."

[1031] In this way, even if field personnel do not possess network knowledge, they can understand and implement the appropriate network configuration.

[1032] The flow of a specific process in Application Example 1 will be explained using Figure 16.

[1033] Step 1:

[1034] The user uses a terminal to enter information about the network device. This information includes the type of device, its location, and the connection method. This information is then sent to the server.

[1035] Step 2:

[1036] The server stores information about received network devices in a database. Based on the stored information, it uses a generative AI model to generate an optimal network configuration diagram. In this process, it calculates network connection patterns and communication paths between devices to create a visual configuration diagram.

[1037] Step 3:

[1038] The server analyzes the user's emotions using emotion recognition technology. It analyzes the user's facial expressions and voice data during input to identify emotions such as stress and joy. This emotion information is used to adjust the way information is provided.

[1039] Step 4:

[1040] The server provides the user with the most relevant information based on the generated network diagram and sentiment analysis results. If the user is feeling stressed, the information is simplified and presented in an easy-to-understand format. Conversely, if the user is feeling happy, detailed information is provided.

[1041] Step 5:

[1042] The user reviews the network configuration diagram provided through the terminal and enters any necessary corrections or additional information. This determines the final network configuration.

[1043] (Example 2)

[1044] Next, we will describe Example 2 of the morphological example. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1045] In network deployment and management, it is difficult for users without specialized knowledge to create appropriate network configuration diagrams. Furthermore, there is a need for flexible information provision that responds to user needs, but conventional systems are unable to accommodate this.

[1046] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1047] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, and means including an emotion engine that recognizes the user's emotions and adjusts the information provision method according to those emotions. As a result, users can obtain an appropriate network configuration diagram even without specialized knowledge, and flexible information provision tailored to the user's emotions becomes possible.

[1048] A "network diagram" is a diagram that visually shows the arrangement of devices and connections within a network, and is used for network design and management.

[1049] "Network deployment status" refers to information that indicates how the network is installed and operated, and is useful for network optimization and troubleshooting.

[1050] "Interactive artificial intelligence" is a system that provides information to users through natural language dialogue and generates responses that meet the user's requests.

[1051] An "emotion engine" is a system that recognizes the user's emotions and adjusts the way information is provided based on those emotions.

[1052] A "generative AI model" is an artificial intelligence model that generates optimal results based on input data, and provides highly accurate output for specific tasks.

[1053] One embodiment of this invention is a system that generates a network configuration diagram and provides information based on the network deployment status. The system provides interactive artificial intelligence for users without network knowledge and further includes an emotion engine that recognizes the user's emotions and adjusts the information delivery method accordingly.

[1054] The server stores network device information in a database and generates a network configuration diagram using a generative AI model. The generative AI model analyzes the input data and proposes the optimal network configuration. In doing so, the server takes into account historical data and general network design best practices.

[1055] The terminal provides an interface for users to input information about network devices. Users can use the terminal to input device names and specifications such as "routers," "switches," and "access points." The entered information is sent to the server and used to generate a network configuration diagram.

[1056] The emotion engine recognizes the user's emotions and adjusts the generated network diagram accordingly. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information.

[1057] As a concrete example, when a company implements a new network, the user uses a terminal to input information about network devices such as "routers, switches, and access points" into the system. An example of a prompt to the generating AI model is, "Please generate the optimal network configuration diagram for the company's network implementation." Based on this prompt, the system generates the optimal network configuration diagram and provides it to the user.

[1058] The flow of the specific processing in Example 2 will be explained using Figure 17.

[1059] Step 1:

[1060] The user uses a terminal to input information about network devices. Specifically, the user inputs device names such as "routers," "switches," and "access points," as well as detailed information such as the specifications and location of each device. This information serves as the basic data for generating a network configuration diagram. The entered data is sent from the terminal to the server.

[1061] Step 2:

[1062] The server stores information about network devices received from terminals in a database. The stored data needs to be managed accurately and efficiently because it will be used in subsequent processing. Checks are performed to ensure data integrity when saving data to the database.

[1063] Step 3:

[1064] The server generates a network configuration diagram using a generative AI model based on stored data. The generative AI model analyzes the input network device information and proposes the optimal network configuration. In this process, the AI ​​takes historical data and general network design best practices into consideration. An initial network configuration diagram is generated as output.

[1065] Step 4:

[1066] The server uses an emotion engine to recognize the user's emotions. Based on the user's emotions, it adjusts the generated network diagram. For example, if the user is stressed, the emotion engine simplifies the diagram to make it easier to understand. Conversely, if the user is happy, it provides a diagram with more detailed information. The adjusted network diagram is then generated as the final output.

[1067] Step 5:

[1068] The server then sends the finalized network configuration diagram to the terminal, providing it to the user. The user can review the diagram on the terminal and make corrections or provide feedback as needed. This process allows users to easily obtain an appropriate network configuration diagram, even without network knowledge.

[1069] (Application Example 2)

[1070] Next, we will describe application example 2 of form example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1071] In factory environments where on-site personnel lack network knowledge, a system is needed that can easily generate and optimize network diagrams, enabling the rapid implementation of appropriate network configurations. Furthermore, it is essential to adjust information delivery based on the user's emotional state to aid user understanding.

[1072] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1073] In this invention, the server includes means for generating a network configuration diagram, means for providing information based on the network deployment status, means for providing interactive artificial intelligence for field personnel without network knowledge, means for recognizing the user's emotions and adjusting the information provision method accordingly, and means for optimizing the network environment as an application installed on machinery in the factory. This makes it possible to generate and optimize an appropriate network configuration diagram even if field personnel do not have network knowledge.

[1074] A "network diagram" is a diagram that visually represents the arrangement of devices and connections within a network, and is used for network design and management.

[1075] "Network deployment status" refers to information indicating how the network is installed and operated.

[1076] "Conversational artificial intelligence" refers to an artificial intelligence system that provides information and assists in problem-solving through dialogue with users using natural language.

[1077] "Means of recognizing user emotions" refers to technology that analyzes a user's emotions from their facial expressions, tone of voice, etc., and determines their emotional state.

[1078] "Means of adjusting the method of information provision" refers to technologies that change the content and format of the information provided according to the user's emotions and circumstances.

[1079] "Applications installed on machinery within a factory" refers to software that is integrated into factory machinery and equipment to perform specific functions.

[1080] "Methods for optimizing network environments" refer to techniques for adjusting configurations and settings to maximize network performance and efficiency.

[1081] The system for implementing this invention is configured as an application installed on machinery within a factory. This application utilizes interactive artificial intelligence to generate network configuration diagrams and provide information based on the network deployment status. Furthermore, it has a function to recognize the user's emotions and adjust the method of information provision accordingly.

[1082] The server receives network device information entered by the user to generate a network configuration diagram, and uses a generation AI model to create the optimal configuration diagram. The network deployment status is stored in a database and updated as needed. To recognize the user's emotions, the system analyzes facial expressions and voice tone using a camera and microphone, and an emotion engine determines the user's state.

[1083] The terminal uses interactive artificial intelligence to generate a network configuration diagram based on information entered by the user, and displays it on a projector or screen. If the user is experiencing stress, the diagram is simplified and presented in an easy-to-understand format. Conversely, if the user is experiencing pleasure, a more detailed diagram is provided to facilitate a deeper understanding.

[1084] As a concrete example, when introducing a network to a new production line in a factory, the system generates an optimal network configuration diagram and displays it on a projector once the on-site staff input information about the network equipment. If the staff are feeling stressed, a simplified configuration diagram can be displayed to aid their understanding.

[1085] An example of a prompt message is: "Please enter the information for the network equipment required for the new production line. We will generate the optimal network configuration diagram, taking into account the current emotional state."

[1086] The flow of a specific process in Application Example 2 will be explained using Figure 18.

[1087] Step 1:

[1088] The user enters network device information into the terminal. This information includes details such as the type of network device, connection port, and IP address. The terminal then sends this information to the server.

[1089] Step 2:

[1090] The server generates an optimal network configuration diagram using a generation AI model based on the received network device information. It analyzes the input information and processes the data to design the network topology. The generated configuration diagram is stored digitally on the server.

[1091] Step 3:

[1092] The server retrieves network deployment status from the database and updates the configuration diagram based on the latest information. The database includes information on the existing network environment and past deployment history. This ensures that the generated configuration diagram reflects the current situation.

[1093] Step 4:

[1094] The device uses its camera and microphone to capture the user's facial expressions and voice tone in order to recognize their emotions. This data is input into an emotion engine, which analyzes the user's emotional state. The analysis results are then sent to a server.

[1095] Step 5:

[1096] The server adjusts how information is delivered based on the user's emotional state. If it determines that the user is stressed, it simplifies the diagram and sends it to the terminal in an easy-to-understand format. If the user is happy, it provides a detailed diagram.

[1097] Step 6:

[1098] The terminal displays the network configuration diagram received from the server on a projector or display. The user can review the displayed configuration diagram and input any necessary modifications or additional information.

[1099] Step 7:

[1100] When a user enters corrections or additional information, the terminal sends that information back to the server, and the server updates the configuration diagram. This results in an optimal network configuration diagram that reflects the user's feedback.

[1101] (Other examples)

[1102] Since this is the same as the specific processing described in the other embodiments of the first embodiment above, the explanation will be omitted.

[1103] 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.

[1104] The data generation model 58 is a form of so-called generative AI (Artificial Intelligence). One example of the data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1105] Other examples of generative AI include Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are some examples.

[1106] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1107] 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.

[1108] Figure 9 shows an 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.

[1109] 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.

[1110] 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.

[1111] 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, motorcycles, etc., 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, for example, based 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.

[1112] 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."

[1113] 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.

[1114] 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.

[1115] 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.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] 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.

[1120] 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.

[1121] 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.

[1122] 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 the like 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.

[1123] 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 as being incorporated by reference.

[1124] The following is further disclosed regarding the embodiments described above.

[1125] (Claim 1)

[1126] A system that includes means for generating network configuration diagrams, means for providing information based on the network deployment status, and means for providing interactive AI for field personnel without network knowledge.

[1127] (Claim 2)

[1128] The system according to claim 1, wherein the conversational AI provides information regarding IP address design and routing.

[1129] (Claim 3)

[1130] The system according to claim 1, wherein the conversational AI generates an optimal network configuration diagram based on the network deployment status.

[1131] (Claim 4)

[1132] The system according to claim 1, wherein the conversational AI includes an emotion engine that recognizes the user's emotions.

[1133] (Claim 5)

[1134] The system according to claim 4, wherein the emotion engine adjusts the method for generating the network configuration diagram according to the user's emotions.

[1135] (Claim 6)

[1136] The system according to claim 4, wherein the emotion engine adjusts the method of providing information on network deployment status according to the user's emotions.

[1137] "Example 1"

[1138] (Claim 1)

[1139] A means for generating a network configuration diagram,

[1140] A means of saving network deployment status to a database,

[1141] A means of generating prompt sentences using a generative AI model based on saved data,

[1142] A means of generating an optimal network configuration diagram by inputting prompt sentences into a generation AI model,

[1143] Means for providing the generated network configuration diagram,

[1144] A means of providing interactive artificial intelligence for users without network knowledge,

[1145] A system that includes this.

[1146] (Claim 2)

[1147] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding network address design and routing.

[1148] (Claim 3)

[1149] The system according to claim 1, wherein the generating AI model generates an optimal network configuration diagram based on the network deployment status.

[1150] "Application Example 1"

[1151] (Claim 1)

[1152] A means for generating a network configuration diagram,

[1153] A means of providing information based on the network deployment status,

[1154] A means of providing interactive artificial intelligence for workers without network knowledge,

[1155] A means for proposing a communication path to optimize communication between mechanical devices,

[1156] A system that includes this.

[1157] (Claim 2)

[1158] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding identification information design and route selection.

[1159] (Claim 3)

[1160] The system according to claim 1, wherein the interactive artificial intelligence generates an optimal network configuration diagram based on the network deployment status and proposes a communication path that optimizes communication between mechanical devices.

[1161] Example 2

[1162] (Claim 1)

[1163] A means for generating a diagram of the communication network using an information processing device,

[1164] A means of providing information based on the status of communication network deployment,

[1165] A means of providing interactive artificial intelligence to users who do not have expertise in communication networks,

[1166] A means of receiving user input information and saving it to a database,

[1167] A means for generating an optimal communication network configuration diagram using an AI model based on stored information,

[1168] A means of providing the generated configuration diagram to the user,

[1169] A system that includes this.

[1170] (Claim 2)

[1171] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding address design and routing control of a communication network.

[1172] (Claim 3)

[1173] The system according to claim 1, wherein the generating AI model generates an optimal communication network configuration diagram based on input information from the user.

[1174] "Application Example 2"

[1175] (Claim 1)

[1176] A means for generating a network configuration diagram,

[1177] A means of providing information based on the network deployment status,

[1178] A means of providing interactive artificial intelligence to field personnel who lack network knowledge,

[1179] A means to generate an optimal network configuration diagram for automated equipment within a factory and improve operational efficiency,

[1180] A system that includes this.

[1181] (Claim 2)

[1182] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding Internet Protocol address design and routing.

[1183] (Claim 3)

[1184] The system according to claim 1, wherein the interactive artificial intelligence generates an optimal network configuration diagram based on the network deployment status and improves the operational efficiency of automated equipment in the factory.

[1185] "Example 1 of combining an emotion engine"

[1186] (Claim 1)

[1187] A means for generating a network configuration diagram,

[1188] A means of providing information based on the network deployment status,

[1189] A means of providing interactive artificial intelligence to field personnel who lack network knowledge,

[1190] A means including an emotion engine that recognizes the user's emotions and adjusts the method of providing information according to those emotions,

[1191] A means of saving user-inputted information to a database and generating prompt text using a generative AI model,

[1192] A system that includes this.

[1193] (Claim 2)

[1194] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding Internet Protocol address design and route selection.

[1195] (Claim 3)

[1196] The system according to claim 1, wherein the conversational artificial intelligence generates an optimal network configuration diagram based on the network deployment status, and the emotion engine adjusts the configuration diagram according to the user's emotions.

[1197] "Application example 1 of combining emotional engines"

[1198] (Claim 1)

[1199] A means for generating a network configuration diagram,

[1200] A means of providing information based on the network deployment status,

[1201] A means of providing interactive artificial intelligence to field personnel who lack network knowledge,

[1202] An emotion recognition means that recognizes the user's emotions and adjusts the method of providing information according to those emotions,

[1203] A means of proposing the optimal network for the placement and operation of machinery and equipment within a factory,

[1204] A system that includes this.

[1205] (Claim 2)

[1206] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding Internet Protocol address design and route selection.

[1207] (Claim 3)

[1208] The system according to claim 1, wherein the conversational artificial intelligence generates an optimal network configuration diagram based on the network deployment status and further adjusts the information provided according to the user's emotions.

[1209] "Example 2 of combining an emotion engine"

[1210] (Claim 1)

[1211] A means for generating a network configuration diagram,

[1212] A means of providing information based on the network deployment status,

[1213] A means of providing interactive artificial intelligence for users without network knowledge,

[1214] A means including an emotion engine that recognizes the user's emotions and adjusts the method of providing information according to those emotions,

[1215] A means of providing the generated network configuration diagram to the user,

[1216] A system that includes this.

[1217] (Claim 2)

[1218] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding Internet Protocol address design and routing.

[1219] (Claim 3)

[1220] The system according to claim 1, wherein the conversational artificial intelligence generates an optimal network configuration diagram based on the network deployment status and provides information adjusted according to the user's emotions by an emotion engine.

[1221] "Application example 2 when combining with an emotional engine"

[1222] (Claim 1)

[1223] A means for generating a network configuration diagram,

[1224] A means of providing information based on the network deployment status,

[1225] A means of providing interactive artificial intelligence to field personnel who lack network knowledge,

[1226] A means of recognizing user emotions and adjusting the method of providing information accordingly,

[1227] As an application installed on machinery within a factory, it provides a means to optimize the network environment,

[1228] A system that includes this.

[1229] (Claim 2)

[1230] The system according to claim 1, wherein the interactive artificial intelligence provides information regarding Internet Protocol address design and route selection.

[1231] (Claim 3)

[1232] The system according to claim 1, wherein the conversational artificial intelligence generates an optimal network configuration diagram based on the network deployment status and adjusts the level of detail of the configuration diagram according to the user's emotional state. [Explanation of Symbols]

[1233] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Equipped with a processor, The aforementioned processor, The network deployment status, including network device types, connectivity status, and traffic patterns, is stored in a database using network monitoring tools in real time. A prompt statement is created that includes instructions for generating a network configuration diagram using an AI model based on the network deployment status stored in the database. The AI ​​model generates a network configuration diagram by inputting prompt text and calculating the communication paths between devices. Information including the existing network environment and past deployment history is retrieved from the database, and the generated network configuration diagram is updated based on the retrieved information and displayed on the user's terminal. If the user submits a correction or additional information regarding the displayed network configuration diagram, the network configuration diagram will be further updated based on the entered information. When a user submits a question regarding the updated network configuration diagram, the system uses interactive artificial intelligence to analyze the question and generate and output an answer explaining the advantages of routing in the network configuration diagram. system.

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