system

Abstract

We provide the system. [Solution] A means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, A means of maintaining the latest network state by dynamically updating equipment information within the communication network, Means for optimizing equipment placement to minimize power loss, A means of predicting future communication demand and optimizing resource allocation, A means of proposing resource allocation to minimize overall costs, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the design and construction of an optical communication network, there are problems that information on transmission lines and facilities obtained from a plurality of information sources cannot be effectively integrated, it is difficult to select an optimal communication path, and furthermore, suppression of power loss and response to future communication demands are insufficient. In the conventional technology, due to the delay in updating new equipment information, the efficiency and cost performance of the entire network are reduced, and as a result, the communication quality is impaired.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that integrates routing information obtained from multiple information sources in an optical communication network and identifies the optimal communication path using AI. This system maintains the latest network state at all times by dynamically updating equipment information within the network and optimizes equipment placement to minimize power loss. Furthermore, it builds a system that minimizes overall costs by predicting future communication demand using AI and optimizing resource allocation based on that prediction.

[0006] An "optical communication network" is a communication infrastructure that uses optical fibers to transmit data, enabling high-speed and high-capacity data communication.

[0007] "Routing information" refers to information about the routes used for data transmission within a communication network, and is fundamental data for selecting the optimal route.

[0008] "Equipment information" refers to information about hardware and devices in a communication network, including details of the equipment such as its location, function, and performance.

[0009] "Power loss" refers to the attenuation or leakage of energy that occurs during communication transmission, and minimizing this leads to improved network efficiency.

[0010] "Prediction" is the act of making plans, such as those for communication demand, by inferring future events and situations based on past and present data.

[0011] Resource allocation is the method of efficiently allocating available resources within a system to maximize its overall performance and cost efficiency.

[0012] "Cost minimization" means reducing overall operating costs by optimizing the selection of necessary equipment and the allocation of resources. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to a system for automating the design and construction of optical communication networks, aiming to streamline the network design process and minimize costs. Embodiments of this system are described below.

[0035] First, the server collects routing and equipment information from multiple sources within the network. This includes communication channel information from both the company and other companies. The server integrates this information and interacts with a database to understand the latest status of the entire network.

[0036] Next, the server uses AI to identify the optimal communication path from the collected data. In this process, conditions are set according to user requests, such as the shortest path or the fastest path, and an optimized circuit design is created.

[0037] Furthermore, the terminals play a role in updating local equipment information in real time. For example, if a new fiber optic cable is laid, the terminal immediately sends that information to the server, and the database is updated. This ensures that the network status is always kept up-to-date.

[0038] Users forecast future communication demand and input it into the system. The server uses this forecast data to optimize resource allocation and plan the placement of necessary equipment. For example, if a surge in communication demand is predicted in a certain area, the server automatically plans the necessary network capacity and equipment upgrades for that area.

[0039] Finally, the server analyzes resource allocation to minimize overall network costs and provides the most suitable design for the budget. This process makes it possible to build a sophisticated network while reducing operational costs.

[0040] In this way, the system centrally manages the design, construction, and operation of optical communication networks, supporting the realization of efficient and economical networks. For example, when designing a network for a new corporate office, the server can quickly formulate the optimal equipment and wiring plan, significantly reducing installation costs.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects routing and equipment information from multiple databases within the network. This includes fiber optic cable routing information, equipment connectivity status, and associated properties.

[0044] Step 2:

[0045] The terminal monitors the addition of new equipment and modifications to existing equipment on-site in real time and transmits this information to the server. By instantly reflecting the updated equipment information, the terminal improves the overall accuracy of the system.

[0046] Step 3:

[0047] The server integrates the collected routing and equipment information and updates a central database built on the cloud. Based on this updated set of information, it creates a network map of the entire system.

[0048] Step 4:

[0049] The server uses an AI algorithm to calculate the optimal communication path. Based on user requirements, it decides whether to select the shortest path or a redundant path, for example.

[0050] Step 5:

[0051] Users input their projected future communication demands into the system. This allows the server to plan network adjustments based on that demand.

[0052] Step 6:

[0053] The server compares the input forecast data with the current network status and automatically generates plans for necessary resource allocation and equipment additions. This includes suggesting expansion of line capacity and new equipment.

[0054] Step 7:

[0055] The server performs a cost analysis of the entire network and provides the user with the most cost-effective design proposal. This process aims to reduce wasted resources and operational costs.

[0056] (Example 1)

[0057] Next, we will describe 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."

[0058] In designing and constructing optical communication networks, it is essential to effectively manage routing and equipment information and to respond quickly to changing demands. However, conventional methods have made it difficult to collect various types of information, dynamically update the network, and allocate resources appropriately based on predictions, hindering the minimization of operating costs.

[0059] 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.

[0060] This invention includes a server that automatically collects routing information and equipment information obtained from multiple sources within an optical communication network and integrates it into a database; a server that identifies the optimal communication path using a generated AI model based on the integrated information; and a server that transmits local equipment information to the server in real time and updates the database to maintain the latest network status. This enables efficient management of information across the entire network and allows for immediate adjustments and resource allocation based on highly accurate predictions.

[0061] An "optical communication network" refers to network technology that transmits data at high speed and with high capacity via optical fibers.

[0062] "Information source" refers to the systems or devices that provide data such as route information and equipment information.

[0063] "Routing information" refers to information about the paths used to send and receive data within a communication network.

[0064] "Equipment information" refers to information about equipment and infrastructure in a communication network.

[0065] A "server" refers to a computer system used to collect, process, and manage data on a network.

[0066] A "database" refers to a system for efficiently storing, searching, and managing data.

[0067] A "generative AI model" refers to an algorithmic model that uses artificial intelligence technology to generate new data and suggestions.

[0068] "Local equipment information" refers to the latest information on equipment and infrastructure based on the physical location of the network.

[0069] "Predictive data" refers to data formulated based on future demand and circumstances.

[0070] "Resource allocation" refers to methods and plans for effectively and efficiently allocating resources.

[0071] This invention relates to a system for automating the design and construction of optical communication networks. The embodiments for carrying out the invention are described below in detail.

[0072] The server first automatically collects routing and equipment information from multiple sources within the optical communication network. This process retrieves data from multiple suppliers via APIs and integrates it into an SQL database. This streamlines information management across the entire network.

[0073] Next, the server uses a generative AI model to identify the optimal communication path from the collected information. The AI ​​model performs optimization calculations using Python algorithms and proposes the best path based on user-specified conditions (e.g., shortest path or fastest path). This AI model utilizes generative AI technology, enabling flexible design to meet diverse communication requirements.

[0074] The terminal manages local equipment information in real time. The terminal transmits information about newly installed communication equipment to the server via Wi-Fi, Bluetooth, etc., instantly updating the database. This update process ensures that the network status is always up-to-date, enabling efficient operation.

[0075] Users input data into the system to predict future communication demand. This prediction data can be registered with the server via a web interface. The server then uses this data to optimize resource allocation using a generated AI model. The resulting plan enables proactive responses regarding required line capacity and infrastructure enhancements.

[0076] For example, when a surge in communication demand is predicted in a certain area, the server can automatically plan the optimal equipment deployment to cope with this and derive cost-effective solutions. Furthermore, an example of a prompt message for the generated AI model would be, "Based on the communication demand forecast data for a specific area in the next fiscal year, propose the optimal equipment upgrade plan."

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server collects routing and equipment information from multiple sources within the network. This includes accessing other companies' network information using APIs and retrieving data from its own database. The input consists of routing and equipment information obtained from multiple sources, and based on this data, the server outputs a unified information list integrated into its database.

[0080] Step 2:

[0081] The server uses a generated AI model to identify the optimal communication path based on integrated route and infrastructure information. Specifically, it takes user-specified conditions (e.g., shortest path or fastest path) as input and calculates optimized route suggestions using the AI ​​model. The output is the optimal communication path information that meets the conditions.

[0082] Step 3:

[0083] The terminal transmits information about newly installed communication equipment on-site to the server in real time, updating the network status. Input from the terminal includes the latest equipment information on-site, and this information is transmitted to the server via Wi-Fi or Bluetooth, instantly updating the network database. The output is always a constantly updated network status.

[0084] Step 4:

[0085] Users predict future communication demand and input relevant data into the system. Specifically, users send prediction data to the server via a web interface. The input is predicted communication demand data, and based on this, the server uses a generation AI model to output an optimized resource allocation plan. The output is an optimal resource allocation plan according to future demand.

[0086] Step 5:

[0087] The server proposes resource allocation to minimize overall network costs based on predictive data and AI analysis results. Specifically, it takes existing resources and predictive data as input, plans a cost-effective equipment allocation, and performs simulations. The output is an efficient equipment allocation plan that reduces operating costs.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In the design and operation of optical communication networks, there is a need for efficient network path optimization, dynamic updating of equipment information, minimization of power loss, and optimization of resources based on forecasts of future communication demand. Furthermore, a challenge lies in the lack of means to visualize the complex state of communication networks and respond quickly to these changes.

[0091] 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.

[0092] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for providing network visualization information in a specific environment to a terminal. This enables more efficient network design and operation, as well as rapid response through visualization.

[0093] An "optical communication network" is a communication infrastructure that uses optical fiber cables to transmit data at high speed.

[0094] "Information source" refers to a data source that provides routing information or equipment information within a communication network.

[0095] "Routing information" refers to information about the optimal route for data transmission within a communication network.

[0096] "Communication path" refers to the route selected when data travels through a communication network.

[0097] "Equipment information" refers to information about the physical and logical components within a communication network.

[0098] "Dynamic updating" means changing the current state in real time to provide the latest information.

[0099] "Network status" refers to information about the current performance and structure of a communication network.

[0100] "Power loss" refers to unnecessary power consumed during data transmission in optical communication networks.

[0101] "Equipment layout" refers to the geographical or logical arrangement of devices and components within a communication network.

[0102] "Future communication demand" refers to estimated information regarding the volume and types of data communications expected in the future.

[0103] "Resource allocation" refers to the effective distribution of personnel, equipment, and other resources within a communications network.

[0104] "Resource allocation to minimize costs" refers to the optimal allocation of resources in order to reduce the expenses associated with operating a communication network.

[0105] "Visualized information" refers to data that visually represents the status and performance of a communication network.

[0106] A "terminal" is a device or equipment connected to a communication network that sends and receives data.

[0107] A "server" refers to a computer system that processes and stores data over a network.

[0108] This invention relates to a system for efficiently designing and operating optical communication networks. Specific embodiments for realizing this system are described below.

[0109] The server first collects routing information from multiple sources within the optical communication network. This information is used to provide data necessary for understanding the current state of the communication network and predicting its future performance. The primary hardware used is a server computer for data processing, and the software includes a database management system and AI libraries (e.g., MySQL® and TENSORFLOW®).

[0110] The terminal plays a role in dynamically updating equipment information within the communication network. Specifically, it uses IoT devices to monitor the operating status and changes of each piece of equipment in real time and transmits that information to the server.

[0111] The server uses AI to analyze collected information and design the optimal communication network. This includes generating network visualizations and providing them to terminals and specific devices (e.g., smart glasses). This allows users to intuitively understand the network's performance and status and make necessary adjustments.

[0112] For example, if a sudden increase in communication demand is anticipated within a logistics center, the server will immediately recommend the optimal equipment placement and configuration. These suggestions will also be visually displayed via smart glasses, enabling staff to respond immediately.

[0113] An example of a prompt to input into the generated AI model would be: "Monitor the communication network status within the logistics center in real time and suggest the optimal equipment placement using smart glasses."

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The server collects routing information from multiple sources within the optical communication network. The input data obtained from these sources concerns the latest status of communication routes and equipment. This data is stored in a database and prepared as foundational data for analysis by an AI library. The output is an analyzable, integrated dataset.

[0117] Step 2:

[0118] The terminal acquires real-time status information from each piece of equipment within the communication network. This input information includes the operating status and performance metrics of the equipment. The terminal sends this information to the server, which dynamically updates the equipment information. The output is updated information that reflects the latest state of the network.

[0119] Step 3:

[0120] The server uses AI with collected data to calculate the optimal communication path. The input is a dataset that integrates route information and equipment information. The server uses an algorithm to process the data and identify efficient communication paths. This result is output as the optimal route configuration.

[0121] Step 4:

[0122] The user predicts future communication demand and inputs this data into the server. The server then plans the optimal resource allocation based on this predicted data. The input is the predicted communication demand data, and the output is the resource allocation plan. This allows the system to respond to future demand.

[0123] Step 5:

[0124] The server generates network visualization information for a specific environment based on the information obtained. The input is the aforementioned optimal routing and resource allocation plan. As output, data visualizing the network status is provided to terminals and smart glasses. Specifically, an intuitive visualization is performed using a generated AI model.

[0125] 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.

[0126] This invention is a system for designing and managing optical communication networks that recognizes user emotions and provides optimal services based on those emotions. The following describes a specific embodiment of this system.

[0127] First, the server collects routing and equipment information for the optical communication network and uses this information to keep the network status constantly up-to-date. This makes it possible to identify the optimal communication path.

[0128] Next, an emotion engine built into the system recognizes the user's emotions in real time. This emotion recognition is determined from the feedback the user provides while using the network, the options they select, and their behavioral patterns when interacting.

[0129] The emotion engine obtains user emotion data, which is then sent to the server. The server uses this data to dynamically adjust the quality of service to meet the individual user's needs. For example, if a user is feeling stressed, the server can offer a simpler interface or faster support.

[0130] Furthermore, the system has a function that automatically optimizes network resource allocation according to the user's emotional state. Based on emotional data, it can allocate additional resources to specific users if necessary, providing a more comfortable service environment.

[0131] For example, if a user experiences network lag while playing an online game and expresses frustration through the emotion engine, the server can use this information to temporarily prioritize communication for that user. This can improve the user experience.

[0132] In this way, this system enables flexible network management that takes user emotions into consideration, and facilitates the efficient and personalized delivery of services.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The server collects routing and equipment information within the optical communication network from multiple sources and creates a network map of the entire system based on an existing database.

[0136] Step 2:

[0137] The device monitors user interactions and feedback from the user's device and uses an emotion engine to analyze the user's emotional state in real time. This includes the frequency of choices and interactions during operation.

[0138] Step 3:

[0139] Users provide feedback when they experience dissatisfaction or stress while using the network, and the device sends this information to the emotion engine.

[0140] Step 4:

[0141] The server receives emotion data sent from the terminal and updates the database based on the user's emotional state. If a specific emotion is detected, the system automatically adjusts the quality of service and settings.

[0142] Step 5:

[0143] The server optimizes the allocation of network resources based on the user's emotions. For example, for a user who is feeling frustrated, it increases bandwidth and improves response speed.

[0144] Step 6:

[0145] The device presents the user with optimized network settings and simplifies access to options and support as needed, thereby improving the user experience.

[0146] Step 7:

[0147] The server performs an overall cost analysis, develops the most efficient resource allocation plan that takes user sentiment data into account, and ensures sustainable service delivery.

[0148] (Example 2)

[0149] Next, we will describe 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".

[0150] In optical communication networks, conventional technologies have made it difficult to provide dynamic services based on user emotions and individual needs, resulting in limited improvements in service quality and user experience. Furthermore, efficient allocation of network resources was challenging, requiring flexible responses to instantaneous load fluctuations and emotional stress. Therefore, a new solution was needed to improve user satisfaction and optimize resource utilization.

[0151] 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.

[0152] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for recognizing the user's emotions and dynamically adjusting the quality of service based on that emotion data. This makes it possible to optimize resource allocation and service quality according to the individual emotional state of the user.

[0153] An "optical communication network" is a communication infrastructure that uses optical fibers to transmit data, and is a network system that enables high-speed and high-capacity data transfer.

[0154] "Routing information" refers to information about the network path used when transmitting data, and is fundamental data for determining the optimal communication path.

[0155] "Equipment information" refers to information about the placement and status of various hardware and devices within a communication network, and is data necessary to enable the efficient operation of the network.

[0156] An "emotion engine" is a system designed to analyze a user's emotional state in real time, and is software or hardware that recognizes emotions using data acquired from the user.

[0157] "Resource allocation" is the process of appropriately allocating available resources within a network, and is essential for efficient network operation and improved user experience.

[0158] "Dynamic adjustment" is the process of changing system parameters and settings in response to real-time state changes, and is a means of maintaining optimal service quality.

[0159] This invention is a system aimed at improving the efficiency of optical communication networks and enhancing the user experience. The system mainly consists of a server, a user terminal, and an emotion engine.

[0160] The server uses numerous sensors and monitoring tools to collect routing and equipment information within the optical communication network. This ensures that the latest network status is always maintained and that the optimal communication path can be instantly identified. Furthermore, the server has the capability to dynamically adjust the quality of service based on user sentiment data.

[0161] The user's device sends data about feedback, options used, and behavioral patterns obtained during use to the emotion engine. The emotion engine uses this data to analyze the user's emotional state in real time and determine the user's satisfaction level and stress level.

[0162] The data analyzed by the emotion engine is sent to the server, which then uses this information to reallocate network resources to individual users. This allows for the provision of an optimal network experience tailored to the user's emotional state, thereby improving the quality of service.

[0163] For example, if the emotion engine determines that a user is experiencing stress during an online game, it can temporarily increase the communication priority of that user to provide a smoother gaming experience. An example of a prompt for the generative AI model would be a specific inquiry such as, "If it is detected that the user is experiencing stress, what resource adjustments would be optimal?"

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server collects routing and equipment information from sensors and monitoring tools installed within the optical communication network. Input data includes status information for each network device and real-time traffic conditions. The server integrates and analyzes this data and outputs a specific communication path to maintain the optimal network state. This operation maximizes communication efficiency and allows for rapid response to dynamic network state changes.

[0167] Step 2:

[0168] The user terminal sends user actions, selected options, and feedback to the emotion engine. Input data includes user behavior patterns and interaction logs. The emotion engine uses this data to analyze the user's emotional state and outputs the results indicating the user's emotions. This process identifies the user's satisfaction level and current emotional state.

[0169] Step 3:

[0170] User emotion data is sent to the server. The server receives the user's emotional state as input data and uses this data to dynamically adjust the quality of service. Specifically, it presents a simple interface to users who are feeling stressed and provides prompt support to users who are dissatisfied. As output, a service environment optimized for each individual user is generated.

[0171] Step 4:

[0172] The server reallocates network resources based on the user's emotional state. Inputs include emotional data and the current network resource status. The server analyzes this data and prioritizes allocating the necessary bandwidth and resources to specific users. The output is resource allocation tailored to the user's needs and optimized network performance. A concrete example of its operation is increasing bandwidth for users experiencing lag during online games, providing a more comfortable gaming experience.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0175] In recent years, communication networks and online platforms have been required to provide services that meet the diverse needs of users. However, the current situation of providing uniform services to users with different emotional states can lead to a decline in the quality of the user experience. In particular, when users feel confused, stressed, or frustrated, it becomes difficult to provide a stress-free user environment.

[0176] 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.

[0177] In this invention, the server includes means for recognizing the user's emotions and dynamically adjusting the quality of communication services based on these emotions, means for dynamically changing the site user interface based on the user's emotion data, and means for integrating routing information obtained from multiple sources within the optical communication network to identify the optimal communication path. This enables personalized user service through emotion recognition, thereby improving the user experience.

[0178] An "optical communication network" is a type of network that uses optical fibers to transmit data, enabling high-speed and large-volume data transmission.

[0179] "Routing information" refers to information about the path data takes within a communication network and is used to identify the optimal communication route.

[0180] "Equipment information" refers to information about the physical and technical equipment within a communication network, and forms the basis for understanding and managing the network's state.

[0181] "Emotion recognition" is a technology that estimates a user's emotional state by analyzing their facial expressions and behavioral patterns.

[0182] A "user interface" refers to the interface, including screens and control panels, that a user uses to interact with a system, and is an element that directly impacts the user experience.

[0183] "Communication service quality" refers to the quality of communication provided to users, including speed, stability, and responsiveness, and has a significant impact on the user experience.

[0184] "Dynamic adjustment" refers to a function that appropriately modifies the system's operation and settings in response to real-time changes in circumstances.

[0185] In order to implement this invention, it is necessary to realize a system that combines and operates a server, user terminal, and emotion recognition engine in a network communication system.

[0186] The server collects and integrates routing and equipment information obtained from multiple sources within the optical communication network. This allows the server to maintain the network's optimal communication path and up-to-date status. Furthermore, the server receives user sentiment data and dynamically adjusts the quality of communication services and the user interface.

[0187] The user terminal serves as an interface for recognizing the user's emotions. For example, a smartphone or smart glasses run an emotion recognition engine that analyzes the user's facial expressions and behavior. This allows the user's emotional state to be collected in real time and transmitted to a server.

[0188] The emotion recognition engine analyzes user emotions using services such as Microsoft® Azure® Face API. Based on the real-time emotion analysis results performed on the device, it estimates what emotions the user is experiencing. For example, if the user is confused, this information is sent to the server for analysis, and a simplified interface is provided to improve the user experience.

[0189] As one specific example, if a user browsing an online shopping site is struggling to decide on a particular product, the emotion recognition engine analyzes that emotion, and the server uses this information to suggest other related products. This allows the user to make a purchase decision quickly.

[0190] An example of a prompt to input into the generating AI model is: "The user has a troubled expression; please recommend the best way to suggest products to address this."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user operates the device to access the online platform. The device inputs the user's behavior and facial expression data in real time to the emotion recognition engine through its interface. The input data obtained here includes, for example, camera images and touch operation data, which is analyzed to estimate the user's emotional state. As a result of the analysis, emotion data is generated.

[0194] Step 2:

[0195] The device uses an emotion recognition engine to process input behavior and facial expression data and identify the user's emotions (e.g., stress, confusion, frustration). Specifically, it uses Microsoft Azure's Face API to analyze facial expressions and generates data indicating the emotional state. The output emotional data is sent to the server.

[0196] Step 3:

[0197] The server receives user sentiment data sent from the terminal and dynamically adjusts the quality of communication services and the site user interface based on that sentiment. For example, if the server determines that the user is confused, it simplifies the user interface and changes the suggestions for relevant content using a generative AI model. The generated output, including the new interface design and content recommendations, is sent to the user's terminal.

[0198] Step 4:

[0199] The user receives the adjusted interface and content provided by the server through their device, and resumes comfortable use. The device then displays the new interface and plays the content, thereby improving the user experience.

[0200] Step 5:

[0201] Based on continuous user sentiment data, the server continuously adjusts the interface and optimizes communication services, generating prompts and providing new information feedback to the network as needed. Through this process, the user experience is further improved and the system becomes more efficient.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0218] This invention relates to a system for automating the design and construction of optical communication networks, aiming to streamline the network design process and minimize costs. Embodiments of this system are described below.

[0219] First, the server collects routing and equipment information from multiple sources within the network. This includes communication channel information from both the company and other companies. The server integrates this information and interacts with a database to understand the latest status of the entire network.

[0220] Next, the server uses AI to identify the optimal communication path from the collected data. In this process, conditions are set according to user requests, such as the shortest path or the fastest path, and an optimized circuit design is created.

[0221] Furthermore, the terminals play a role in updating local equipment information in real time. For example, if a new fiber optic cable is laid, the terminal immediately sends that information to the server, and the database is updated. This ensures that the network status is always kept up-to-date.

[0222] Users forecast future communication demand and input it into the system. The server uses this forecast data to optimize resource allocation and plan the placement of necessary equipment. For example, if a surge in communication demand is predicted in a certain area, the server automatically plans the necessary network capacity and equipment upgrades for that area.

[0223] Finally, the server analyzes resource allocation to minimize overall network costs and provides the most suitable design for the budget. This process makes it possible to build a sophisticated network while reducing operational costs.

[0224] In this way, the system centrally manages the design, construction, and operation of optical communication networks, supporting the realization of efficient and economical networks. For example, when designing a network for a new corporate office, the server can quickly formulate the optimal equipment and wiring plan, significantly reducing installation costs.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The server collects routing and equipment information from multiple databases within the network. This includes fiber optic cable routing information, equipment connectivity status, and associated properties.

[0228] Step 2:

[0229] The terminal monitors the addition of new equipment and modifications to existing equipment on-site in real time and transmits this information to the server. By instantly reflecting the updated equipment information, the terminal improves the overall accuracy of the system.

[0230] Step 3:

[0231] The server integrates the collected routing and equipment information and updates a central database built on the cloud. Based on this updated set of information, it creates a network map of the entire system.

[0232] Step 4:

[0233] The server uses an AI algorithm to calculate the optimal communication path. Based on user requirements, it decides whether to select the shortest path or a redundant path, for example.

[0234] Step 5:

[0235] Users input their projected future communication demands into the system. This allows the server to plan network adjustments based on that demand.

[0236] Step 6:

[0237] The server compares the input forecast data with the current network status and automatically generates plans for necessary resource allocation and equipment additions. This includes suggesting expansion of line capacity and new equipment.

[0238] Step 7:

[0239] The server performs a cost analysis of the entire network and provides the user with the most cost-effective design proposal. This process aims to reduce wasted resources and operational costs.

[0240] (Example 1)

[0241] Next, we will describe 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."

[0242] In designing and constructing optical communication networks, it is essential to effectively manage routing and equipment information and to respond quickly to changing demands. However, conventional methods have made it difficult to collect various types of information, dynamically update the network, and allocate resources appropriately based on predictions, hindering the minimization of operating costs.

[0243] 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.

[0244] This invention includes a server that automatically collects routing information and equipment information obtained from multiple sources within an optical communication network and integrates it into a database; a server that identifies the optimal communication path using a generated AI model based on the integrated information; and a server that transmits local equipment information to the server in real time and updates the database to maintain the latest network status. This enables efficient management of information across the entire network and allows for immediate adjustments and resource allocation based on highly accurate predictions.

[0245] An "optical communication network" refers to network technology that transmits data at high speed and with high capacity via optical fibers.

[0246] "Information source" refers to the systems or devices that provide data such as route information and equipment information.

[0247] "Routing information" refers to information about the paths used to send and receive data within a communication network.

[0248] "Equipment information" refers to information about equipment and infrastructure in a communication network.

[0249] A "server" refers to a computer system used to collect, process, and manage data on a network.

[0250] A "database" refers to a system for efficiently storing, searching, and managing data.

[0251] A "generative AI model" refers to an algorithmic model that uses artificial intelligence technology to generate new data and suggestions.

[0252] "Local equipment information" refers to the latest information on equipment and infrastructure based on the physical location of the network.

[0253] "Predictive data" refers to data formulated based on future demand and circumstances.

[0254] "Resource allocation" refers to methods and plans for effectively and efficiently allocating resources.

[0255] This invention relates to a system for automating the design and construction of optical communication networks. The embodiments for carrying out the invention are described below in detail.

[0256] The server first automatically collects routing and equipment information from multiple sources within the optical communication network. This process retrieves data from multiple suppliers via APIs and integrates it into an SQL database. This streamlines information management across the entire network.

[0257] Next, the server uses a generative AI model to identify the optimal communication path from the collected information. The AI ​​model performs optimization calculations using Python algorithms and proposes the best path based on user-specified conditions (e.g., shortest path or fastest path). This AI model utilizes generative AI technology, enabling flexible design to meet diverse communication requirements.

[0258] The terminal manages local equipment information in real time. The terminal transmits information about newly installed communication equipment to the server via Wi-Fi, Bluetooth, etc., instantly updating the database. This update process ensures that the network status is always up-to-date, enabling efficient operation.

[0259] Users input data into the system to predict future communication demand. This prediction data can be registered with the server via a web interface. The server then uses this data to optimize resource allocation using a generated AI model. The resulting plan enables proactive responses regarding required line capacity and infrastructure enhancements.

[0260] For example, when a surge in communication demand is predicted in a certain area, the server can automatically plan the optimal equipment deployment to cope with this and derive cost-effective solutions. Furthermore, an example of a prompt message for the generated AI model would be, "Based on the communication demand forecast data for a specific area in the next fiscal year, propose the optimal equipment upgrade plan."

[0261] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0262] Step 1:

[0263] The server collects routing and equipment information from multiple sources within the network. This includes accessing other companies' network information using APIs and retrieving data from its own database. The input consists of routing and equipment information obtained from multiple sources, and based on this data, the server outputs a unified information list integrated into its database.

[0264] Step 2:

[0265] The server uses a generated AI model to identify the optimal communication path based on integrated route and infrastructure information. Specifically, it takes user-specified conditions (e.g., shortest path or fastest path) as input and calculates optimized route suggestions using the AI ​​model. The output is the optimal communication path information that meets the conditions.

[0266] Step 3:

[0267] The terminal transmits information about newly installed communication equipment on-site to the server in real time, updating the network status. Input from the terminal includes the latest equipment information on-site, and this information is transmitted to the server via Wi-Fi or Bluetooth, instantly updating the network database. The output is always a constantly updated network status.

[0268] Step 4:

[0269] Users predict future communication demand and input relevant data into the system. Specifically, users send prediction data to the server via a web interface. The input is predicted communication demand data, and based on this, the server uses a generation AI model to output an optimized resource allocation plan. The output is an optimal resource allocation plan according to future demand.

[0270] Step 5:

[0271] The server proposes resource allocation to minimize overall network costs based on predictive data and AI analysis results. Specifically, it takes existing resources and predictive data as input, plans a cost-effective equipment allocation, and performs simulations. The output is an efficient equipment allocation plan that reduces operating costs.

[0272] (Application Example 1)

[0273] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] In the design and operation of optical communication networks, there is a need for efficient network path optimization, dynamic updating of equipment information, minimization of power loss, and optimization of resources based on forecasts of future communication demand. Furthermore, a challenge lies in the lack of means to visualize the complex state of communication networks and respond quickly to these changes.

[0275] 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.

[0276] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for providing network visualization information in a specific environment to a terminal. This enables more efficient network design and operation, as well as rapid response through visualization.

[0277] An "optical communication network" is a communication infrastructure that uses optical fiber cables to transmit data at high speed.

[0278] "Information source" refers to a data source that provides routing information or equipment information within a communication network.

[0279] "Routing information" refers to information about the optimal route for data transmission within a communication network.

[0280] "Communication path" refers to the route selected when data travels through a communication network.

[0281] "Equipment information" refers to information about the physical and logical components within a communication network.

[0282] "Dynamically update" means changing the current state in real time to provide the latest information.

[0283] "Network state" refers to information regarding the current performance and structure of a communication network.

[0284] "Power loss" refers to the unnecessary power consumed during data transmission in an optical communication network.

[0285] "Equipment placement" refers to the geographical or logical placement of devices and components within a communication network.

[0286] "Future communication demand" is estimated information regarding the volume and type of data communication predicted in the future.

[0287] "Resource allocation" refers to the effective distribution of personnel, equipment, and other resources in a communication network.

[0288] "Resource placement for minimizing cost" is to optimally place resources in order to reduce the costs associated with the operation of a communication network.

[0289] "Visualization information" refers to data that visually shows the state and performance of a communication network.

[0290] "Terminal" refers to a device or apparatus that is connected to a communication network and transmits and receives data.

[0291] "Server" refers to a computer system that performs data processing and storage on a network.

[0292] This invention is related to a system for efficiently designing and operating an optical communication network. Specific embodiments for realizing this system will be described below.

[0293] The server first collects routing information from multiple sources within the optical communication network. This information is used to provide data necessary for understanding the current state of the communication network and predicting its future performance. The primary hardware used is a server computer for processing the data, while the software includes a database management system and AI libraries (e.g., MySQL and TensorFlow).

[0294] The terminal plays a role in dynamically updating equipment information within the communication network. Specifically, it uses IoT devices to monitor the operating status and changes of each piece of equipment in real time and transmits that information to the server.

[0295] The server uses AI to analyze collected information and design the optimal communication network. This includes generating network visualizations and providing them to terminals and specific devices (e.g., smart glasses). This allows users to intuitively understand the network's performance and status and make necessary adjustments.

[0296] For example, if a sudden increase in communication demand is anticipated within a logistics center, the server will immediately recommend the optimal equipment placement and configuration. These suggestions will also be visually displayed via smart glasses, enabling staff to respond immediately.

[0297] An example of a prompt to input into the generated AI model would be: "Monitor the communication network status within the logistics center in real time and suggest the optimal equipment placement using smart glasses."

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects routing information from multiple information sources within the optical communication network. The input data obtained from the information sources is data related to the latest status of communication routes and facilities. These data are stored in a database and prepared as basic data for analysis by the AI library. The output is an integrated dataset that can be analyzed.

[0301] Step 2:

[0302] The terminal acquires real-time status information from each facility within the communication network. This input information includes the operating status and performance metrics of the facilities. The terminal transmits this information to the server to dynamically update the facility information. The output is updated information reflecting the latest status of the network.

[0303] Step 3:

[0304] The server utilizes AI with the collected data to calculate the optimal communication route. The input is a dataset that integrates routing information and facility information. The server performs calculations on the data using an algorithm to identify an efficient communication route. This result is output as the optimal route configuration.

[0305] Step 4:

[0306] The user predicts future communication demands and inputs that data into the server. The server plans the optimal allocation of resources according to this predicted data. The input is the predicted communication demand data, and the output is the resource allocation plan. This enables it to respond to future demands.

[0307] Step 5:

[0308] Based on the information obtained, the server generates visualization information of the network in a specific environment. The input is the aforementioned optimal route configuration and resource allocation plan. As output, data visualizing the network situation is provided to the terminal and smart glasses. As a specific operation, intuitive visualization is performed using a generated AI model.

[0309] 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.

[0310] This invention is a system for designing and managing optical communication networks that recognizes user emotions and provides optimal services based on those emotions. The following describes a specific embodiment of this system.

[0311] First, the server collects routing and equipment information for the optical communication network and uses this information to keep the network status constantly up-to-date. This makes it possible to identify the optimal communication path.

[0312] Next, an emotion engine built into the system recognizes the user's emotions in real time. This emotion recognition is determined from the feedback the user provides while using the network, the options they select, and their behavioral patterns when interacting.

[0313] The emotion engine obtains user emotion data, which is then sent to the server. The server uses this data to dynamically adjust the quality of service to meet the individual user's needs. For example, if a user is feeling stressed, the server can offer a simpler interface or faster support.

[0314] Furthermore, the system has a function that automatically optimizes network resource allocation according to the user's emotional state. Based on emotional data, it can allocate additional resources to specific users if necessary, providing a more comfortable service environment.

[0315] For example, if a user experiences network lag while playing an online game and expresses frustration through the emotion engine, the server can use this information to temporarily prioritize communication for that user. This can improve the user experience.

[0316] In this way, this system enables flexible network management that takes user emotions into consideration, and facilitates the efficient and personalized delivery of services.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server collects routing and equipment information within the optical communication network from multiple sources and creates a network map of the entire system based on an existing database.

[0320] Step 2:

[0321] The device monitors user interactions and feedback from the user's device and uses an emotion engine to analyze the user's emotional state in real time. This includes the frequency of choices and interactions during operation.

[0322] Step 3:

[0323] Users provide feedback when they experience dissatisfaction or stress while using the network, and the device sends this information to the emotion engine.

[0324] Step 4:

[0325] The server receives emotion data sent from the terminal and updates the database based on the user's emotional state. If a specific emotion is detected, the system automatically adjusts the quality of service and settings.

[0326] Step 5:

[0327] The server optimizes the allocation of network resources based on the user's emotions. For example, for a user who is feeling frustrated, it increases bandwidth and improves response speed.

[0328] Step 6:

[0329] The device presents the user with optimized network settings and simplifies access to options and support as needed, thereby improving the user experience.

[0330] Step 7:

[0331] The server performs an overall cost analysis, develops the most efficient resource allocation plan that takes user sentiment data into account, and ensures sustainable service delivery.

[0332] (Example 2)

[0333] Next, we will describe 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".

[0334] In optical communication networks, conventional technologies have made it difficult to provide dynamic services based on user emotions and individual needs, resulting in limited improvements in service quality and user experience. Furthermore, efficient allocation of network resources was challenging, requiring flexible responses to instantaneous load fluctuations and emotional stress. Therefore, a new solution was needed to improve user satisfaction and optimize resource utilization.

[0335] 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.

[0336] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for recognizing the user's emotions and dynamically adjusting the quality of service based on that emotion data. This makes it possible to optimize resource allocation and service quality according to the individual emotional state of the user.

[0337] An "optical communication network" is a communication infrastructure that uses optical fibers to transmit data, and is a network system that enables high-speed and high-capacity data transfer.

[0338] "Routing information" refers to information about the network path used when transmitting data, and is fundamental data for determining the optimal communication path.

[0339] "Equipment information" refers to information about the placement and status of various hardware and devices within a communication network, and is data necessary to enable the efficient operation of the network.

[0340] An "emotion engine" is a system designed to analyze a user's emotional state in real time, and is software or hardware that recognizes emotions using data acquired from the user.

[0341] "Resource allocation" is the process of appropriately allocating available resources within a network, and is essential for efficient network operation and improved user experience.

[0342] "Dynamic adjustment" is the process of changing system parameters and settings in response to real-time state changes, and is a means of maintaining optimal service quality.

[0343] This invention is a system aimed at improving the efficiency of optical communication networks and enhancing the user experience. The system mainly consists of a server, a user terminal, and an emotion engine.

[0344] The server uses numerous sensors and monitoring tools to collect routing and equipment information within the optical communication network. This ensures that the latest network status is always maintained and that the optimal communication path can be instantly identified. Furthermore, the server has the capability to dynamically adjust the quality of service based on user sentiment data.

[0345] The user's device sends data about feedback, options used, and behavioral patterns obtained during use to the emotion engine. The emotion engine uses this data to analyze the user's emotional state in real time and determine the user's satisfaction level and stress level.

[0346] The data analyzed by the emotion engine is sent to the server, which then uses this information to reallocate network resources to individual users. This allows for the provision of an optimal network experience tailored to the user's emotional state, thereby improving the quality of service.

[0347] For example, if the emotion engine determines that a user is experiencing stress during an online game, it can temporarily increase the communication priority of that user to provide a smoother gaming experience. An example of a prompt for the generative AI model would be a specific inquiry such as, "If it is detected that the user is experiencing stress, what resource adjustments would be optimal?"

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The server collects routing and equipment information from sensors and monitoring tools installed within the optical communication network. Input data includes status information for each network device and real-time traffic conditions. The server integrates and analyzes this data and outputs a specific communication path to maintain the optimal network state. This operation maximizes communication efficiency and allows for rapid response to dynamic network state changes.

[0351] Step 2:

[0352] The user terminal sends user actions, selected options, and feedback to the emotion engine. Input data includes user behavior patterns and interaction logs. The emotion engine uses this data to analyze the user's emotional state and outputs the results indicating the user's emotions. This process identifies the user's satisfaction level and current emotional state.

[0353] Step 3:

[0354] User emotion data is sent to the server. The server receives the user's emotional state as input data and uses this data to dynamically adjust the quality of service. Specifically, it presents a simple interface to users who are feeling stressed and provides prompt support to users who are dissatisfied. As output, a service environment optimized for each individual user is generated.

[0355] Step 4:

[0356] The server reallocates network resources based on the user's emotional state. Inputs include emotional data and the current network resource status. The server analyzes this data and prioritizes allocating the necessary bandwidth and resources to specific users. The output is resource allocation tailored to the user's needs and optimized network performance. A concrete example of its operation is increasing bandwidth for users experiencing lag during online games, providing a more comfortable gaming experience.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0359] In recent years, communication networks and online platforms have been required to provide services that meet the diverse needs of users. However, the current situation of providing uniform services to users with different emotional states can lead to a decline in the quality of the user experience. In particular, when users feel confused, stressed, or frustrated, it becomes difficult to provide a stress-free user environment.

[0360] 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.

[0361] In this invention, the server includes means for recognizing the user's emotions and dynamically adjusting the quality of communication services based on these emotions, means for dynamically changing the site user interface based on the user's emotion data, and means for integrating routing information obtained from multiple sources within the optical communication network to identify the optimal communication path. This enables personalized user service through emotion recognition, thereby improving the user experience.

[0362] An "optical communication network" is a type of network that uses optical fibers to transmit data, enabling high-speed and large-volume data transmission.

[0363] "Routing information" refers to information about the path data takes within a communication network and is used to identify the optimal communication route.

[0364] "Equipment information" refers to information about the physical and technical equipment within a communication network, and forms the basis for understanding and managing the network's state.

[0365] "Emotion recognition" is a technology that estimates a user's emotional state by analyzing their facial expressions and behavioral patterns.

[0366] A "user interface" refers to the interface, including screens and control panels, that a user uses to interact with a system, and is an element that directly impacts the user experience.

[0367] "Communication service quality" refers to the quality of communication provided to users, including speed, stability, and responsiveness, and has a significant impact on the user experience.

[0368] "Dynamic adjustment" refers to a function that appropriately modifies the system's operation and settings in response to real-time changes in circumstances.

[0369] In order to implement this invention, it is necessary to realize a system that combines and operates a server, user terminal, and emotion recognition engine in a network communication system.

[0370] The server collects and integrates routing and equipment information obtained from multiple sources within the optical communication network. This allows the server to maintain the network's optimal communication path and up-to-date status. Furthermore, the server receives user sentiment data and dynamically adjusts the quality of communication services and the user interface.

[0371] The user terminal serves as an interface for recognizing the user's emotions. For example, a smartphone or smart glasses run an emotion recognition engine that analyzes the user's facial expressions and behavior. This allows the user's emotional state to be collected in real time and transmitted to a server.

[0372] The emotion recognition engine analyzes user emotions using services such as Microsoft Azure's Face API. Based on the real-time emotion analysis results performed on the device, it estimates what emotions the user is experiencing. For example, if the user is confused, that information is sent to the server for analysis, and a simplified interface is provided to improve the user experience.

[0373] As one specific example, if a user browsing an online shopping site is struggling to decide on a particular product, the emotion recognition engine analyzes that emotion, and the server uses this information to suggest other related products. This allows the user to make a purchase decision quickly.

[0374] An example of a prompt to input into the generating AI model is: "The user has a troubled expression; please recommend the best way to suggest products to address this."

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The user operates the device to access the online platform. The device inputs the user's behavior and facial expression data in real time to the emotion recognition engine through its interface. The input data obtained here includes, for example, camera images and touch operation data, which is analyzed to estimate the user's emotional state. As a result of the analysis, emotion data is generated.

[0378] Step 2:

[0379] The device uses an emotion recognition engine to process input behavior and facial expression data and identify the user's emotions (e.g., stress, confusion, frustration). Specifically, it uses Microsoft Azure's Face API to analyze facial expressions and generates data indicating the emotional state. The output emotional data is sent to the server.

[0380] Step 3:

[0381] The server receives user sentiment data sent from the terminal and dynamically adjusts the quality of communication services and the site user interface based on that sentiment. For example, if the server determines that the user is confused, it simplifies the user interface and changes the suggestions for relevant content using a generative AI model. The generated output, including the new interface design and content recommendations, is sent to the user's terminal.

[0382] Step 4:

[0383] The user receives the adjusted interface and content provided by the server through their device, and resumes comfortable use. The device then displays the new interface and plays the content, thereby improving the user experience.

[0384] Step 5:

[0385] Based on continuous user sentiment data, the server continuously adjusts the interface and optimizes communication services, generating prompts and providing new information feedback to the network as needed. Through this process, the user experience is further improved and the system becomes more efficient.

[0386] 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.

[0387] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0402] This invention relates to a system for automating the design and construction of optical communication networks, aiming to streamline the network design process and minimize costs. Embodiments of this system are described below.

[0403] First, the server collects routing and equipment information from multiple sources within the network. This includes communication channel information from both the company and other companies. The server integrates this information and interacts with a database to understand the latest status of the entire network.

[0404] Next, the server uses AI to identify the optimal communication path from the collected data. In this process, conditions are set according to user requests, such as the shortest path or the fastest path, and an optimized circuit design is created.

[0405] Furthermore, the terminals play a role in updating local equipment information in real time. For example, if a new fiber optic cable is laid, the terminal immediately sends that information to the server, and the database is updated. This ensures that the network status is always kept up-to-date.

[0406] Users forecast future communication demand and input it into the system. The server uses this forecast data to optimize resource allocation and plan the placement of necessary equipment. For example, if a surge in communication demand is predicted in a certain area, the server automatically plans the necessary network capacity and equipment upgrades for that area.

[0407] Finally, the server analyzes resource allocation to minimize overall network costs and provides the most suitable design for the budget. This process makes it possible to build a sophisticated network while reducing operational costs.

[0408] In this way, the system centrally manages the design, construction, and operation of optical communication networks, supporting the realization of efficient and economical networks. For example, when designing a network for a new corporate office, the server can quickly formulate the optimal equipment and wiring plan, significantly reducing installation costs.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The server collects routing and equipment information from multiple databases within the network. This includes fiber optic cable routing information, equipment connectivity status, and associated properties.

[0412] Step 2:

[0413] The terminal monitors the addition of new equipment and modifications to existing equipment on-site in real time and transmits this information to the server. By instantly reflecting the updated equipment information, the terminal improves the overall accuracy of the system.

[0414] Step 3:

[0415] The server integrates the collected routing and equipment information and updates a central database built on the cloud. Based on this updated set of information, it creates a network map of the entire system.

[0416] Step 4:

[0417] The server uses an AI algorithm to calculate the optimal communication path. Based on user requirements, it decides whether to select the shortest path or a redundant path, for example.

[0418] Step 5:

[0419] Users input their projected future communication demands into the system. This allows the server to plan network adjustments based on that demand.

[0420] Step 6:

[0421] The server compares the input forecast data with the current network status and automatically generates plans for necessary resource allocation and equipment additions. This includes suggesting expansion of line capacity and new equipment.

[0422] Step 7:

[0423] The server performs a cost analysis of the entire network and provides the user with the most cost-effective design proposal. This process aims to reduce wasted resources and operational costs.

[0424] (Example 1)

[0425] Next, we will describe 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."

[0426] In designing and constructing optical communication networks, it is essential to effectively manage routing and equipment information and to respond quickly to changing demands. However, conventional methods have made it difficult to collect various types of information, dynamically update the network, and allocate resources appropriately based on predictions, hindering the minimization of operating costs.

[0427] 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.

[0428] This invention includes a server that automatically collects routing information and equipment information obtained from multiple sources within an optical communication network and integrates it into a database; a server that identifies the optimal communication path using a generated AI model based on the integrated information; and a server that transmits local equipment information to the server in real time and updates the database to maintain the latest network status. This enables efficient management of information across the entire network and allows for immediate adjustments and resource allocation based on highly accurate predictions.

[0429] An "optical communication network" refers to network technology that transmits data at high speed and with high capacity via optical fibers.

[0430] "Information source" refers to the systems or devices that provide data such as route information and equipment information.

[0431] "Routing information" refers to information about the paths used to send and receive data within a communication network.

[0432] "Equipment information" refers to information about equipment and infrastructure in a communication network.

[0433] A "server" refers to a computer system used to collect, process, and manage data on a network.

[0434] A "database" refers to a system for efficiently storing, searching, and managing data.

[0435] A "generative AI model" refers to an algorithmic model that uses artificial intelligence technology to generate new data and suggestions.

[0436] "Local equipment information" refers to the latest information on equipment and infrastructure based on the physical location of the network.

[0437] "Predictive data" refers to data formulated based on future demand and circumstances.

[0438] "Resource allocation" refers to methods and plans for effectively and efficiently allocating resources.

[0439] This invention relates to a system for automating the design and construction of optical communication networks. The embodiments for carrying out the invention are described below in detail.

[0440] The server first automatically collects routing and equipment information from multiple sources within the optical communication network. This process retrieves data from multiple suppliers via APIs and integrates it into an SQL database. This streamlines information management across the entire network.

[0441] Next, the server uses a generative AI model to identify the optimal communication path from the collected information. The AI ​​model performs optimization calculations using Python algorithms and proposes the best path based on user-specified conditions (e.g., shortest path or fastest path). This AI model utilizes generative AI technology, enabling flexible design to meet diverse communication requirements.

[0442] The terminal manages local equipment information in real time. The terminal transmits information about newly installed communication equipment to the server via Wi-Fi, Bluetooth, etc., instantly updating the database. This update process ensures that the network status is always up-to-date, enabling efficient operation.

[0443] Users input data into the system to predict future communication demand. This prediction data can be registered with the server via a web interface. The server then uses this data to optimize resource allocation using a generated AI model. The resulting plan enables proactive responses regarding required line capacity and infrastructure enhancements.

[0444] For example, when a surge in communication demand is predicted in a certain area, the server can automatically plan the optimal equipment deployment to cope with this and derive cost-effective solutions. Furthermore, an example of a prompt message for the generated AI model would be, "Based on the communication demand forecast data for a specific area in the next fiscal year, propose the optimal equipment upgrade plan."

[0445] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0446] Step 1:

[0447] The server collects routing and equipment information from multiple sources within the network. This includes accessing other companies' network information using APIs and retrieving data from its own database. The input consists of routing and equipment information obtained from multiple sources, and based on this data, the server outputs a unified information list integrated into its database.

[0448] Step 2:

[0449] The server uses a generated AI model to identify the optimal communication path based on integrated route and infrastructure information. Specifically, it takes user-specified conditions (e.g., shortest path or fastest path) as input and calculates optimized route suggestions using the AI ​​model. The output is the optimal communication path information that meets the conditions.

[0450] Step 3:

[0451] The terminal transmits information about newly installed communication equipment on-site to the server in real time, updating the network status. Input from the terminal includes the latest equipment information on-site, and this information is transmitted to the server via Wi-Fi or Bluetooth, instantly updating the network database. The output is always a constantly updated network status.

[0452] Step 4:

[0453] Users predict future communication demand and input relevant data into the system. Specifically, users send prediction data to the server via a web interface. The input is predicted communication demand data, and based on this, the server uses a generation AI model to output an optimized resource allocation plan. The output is an optimal resource allocation plan according to future demand.

[0454] Step 5:

[0455] The server proposes resource allocation to minimize overall network costs based on predictive data and AI analysis results. Specifically, it takes existing resources and predictive data as input, plans a cost-effective equipment allocation, and performs simulations. The output is an efficient equipment allocation plan that reduces operating costs.

[0456] (Application Example 1)

[0457] Next, we will explain Application Example 1. In the following explanation, 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."

[0458] In the design and operation of optical communication networks, there is a need for efficient network path optimization, dynamic updating of equipment information, minimization of power loss, and optimization of resources based on forecasts of future communication demand. Furthermore, a challenge lies in the lack of means to visualize the complex state of communication networks and respond quickly to these changes.

[0459] 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.

[0460] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for providing network visualization information in a specific environment to a terminal. This enables more efficient network design and operation, as well as rapid response through visualization.

[0461] An "optical communication network" is a communication infrastructure that uses optical fiber cables to transmit data at high speed.

[0462] "Information source" refers to a data source that provides routing information or equipment information within a communication network.

[0463] "Routing information" refers to information about the optimal route for data transmission within a communication network.

[0464] "Communication path" refers to the route selected when data travels through a communication network.

[0465] "Equipment information" refers to information about the physical and logical components within a communication network.

[0466] "Dynamic updating" means changing the current state in real time to provide the latest information.

[0467] "Network status" refers to information about the current performance and structure of a communication network.

[0468] "Power loss" refers to unnecessary power consumed during data transmission in optical communication networks.

[0469] "Equipment layout" refers to the geographical or logical arrangement of devices and components within a communication network.

[0470] "Future communication demand" refers to estimated information regarding the volume and types of data communications expected in the future.

[0471] "Resource allocation" refers to the effective distribution of personnel, equipment, and other resources within a communications network.

[0472] "Resource allocation to minimize costs" refers to the optimal allocation of resources in order to reduce the expenses associated with operating a communication network.

[0473] "Visualized information" refers to data that visually represents the status and performance of a communication network.

[0474] A "terminal" is a device or equipment connected to a communication network that sends and receives data.

[0475] A "server" refers to a computer system that processes and stores data over a network.

[0476] This invention relates to a system for efficiently designing and operating optical communication networks. Specific embodiments for realizing this system are described below.

[0477] The server first collects routing information from multiple sources within the optical communication network. This information is used to provide data necessary for understanding the current state of the communication network and predicting its future performance. The primary hardware used is a server computer for processing the data, while the software includes a database management system and AI libraries (e.g., MySQL and TensorFlow).

[0478] The terminal plays a role in dynamically updating equipment information within the communication network. Specifically, it uses IoT devices to monitor the operating status and changes of each piece of equipment in real time and transmits that information to the server.

[0479] The server uses AI to analyze collected information and design the optimal communication network. This includes generating network visualizations and providing them to terminals and specific devices (e.g., smart glasses). This allows users to intuitively understand the network's performance and status and make necessary adjustments.

[0480] For example, if a sudden increase in communication demand is anticipated within a logistics center, the server will immediately recommend the optimal equipment placement and configuration. These suggestions will also be visually displayed via smart glasses, enabling staff to respond immediately.

[0481] An example of a prompt to input into the generated AI model would be: "Monitor the communication network status within the logistics center in real time and suggest the optimal equipment placement using smart glasses."

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The server collects routing information from multiple sources within the optical communication network. The input data obtained from these sources concerns the latest status of communication routes and equipment. This data is stored in a database and prepared as foundational data for analysis by an AI library. The output is an analyzable, integrated dataset.

[0485] Step 2:

[0486] The terminal acquires real-time status information from each piece of equipment within the communication network. This input information includes the operating status and performance metrics of the equipment. The terminal sends this information to the server, which dynamically updates the equipment information. The output is updated information that reflects the latest state of the network.

[0487] Step 3:

[0488] The server uses AI with collected data to calculate the optimal communication path. The input is a dataset that integrates route information and equipment information. The server uses an algorithm to process the data and identify efficient communication paths. This result is output as the optimal route configuration.

[0489] Step 4:

[0490] The user predicts future communication demand and inputs this data into the server. The server then plans the optimal resource allocation based on this predicted data. The input is the predicted communication demand data, and the output is the resource allocation plan. This allows the system to respond to future demand.

[0491] Step 5:

[0492] The server generates network visualization information for a specific environment based on the information obtained. The input is the aforementioned optimal routing and resource allocation plan. As output, data visualizing the network status is provided to terminals and smart glasses. Specifically, an intuitive visualization is performed using a generated AI model.

[0493] 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.

[0494] This invention is a system for designing and managing optical communication networks that recognizes user emotions and provides optimal services based on those emotions. The following describes a specific embodiment of this system.

[0495] First, the server collects routing and equipment information for the optical communication network and uses this information to keep the network status constantly up-to-date. This makes it possible to identify the optimal communication path.

[0496] Next, an emotion engine built into the system recognizes the user's emotions in real time. This emotion recognition is determined from the feedback the user provides while using the network, the options they select, and their behavioral patterns when interacting.

[0497] The emotion engine obtains user emotion data, which is then sent to the server. The server uses this data to dynamically adjust the quality of service to meet the individual user's needs. For example, if a user is feeling stressed, the server can offer a simpler interface or faster support.

[0498] Furthermore, the system has a function that automatically optimizes network resource allocation according to the user's emotional state. Based on emotional data, it can allocate additional resources to specific users if necessary, providing a more comfortable service environment.

[0499] For example, if a user experiences network lag while playing an online game and expresses frustration through the emotion engine, the server can use this information to temporarily prioritize communication for that user. This can improve the user experience.

[0500] In this way, this system enables flexible network management that takes user emotions into consideration, and facilitates the efficient and personalized delivery of services.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] The server collects routing and equipment information within the optical communication network from multiple sources and creates a network map of the entire system based on an existing database.

[0504] Step 2:

[0505] The device monitors user interactions and feedback from the user's device and uses an emotion engine to analyze the user's emotional state in real time. This includes the frequency of choices and interactions during operation.

[0506] Step 3:

[0507] Users provide feedback when they experience dissatisfaction or stress while using the network, and the device sends this information to the emotion engine.

[0508] Step 4:

[0509] The server receives emotion data sent from the terminal and updates the database based on the user's emotional state. If a specific emotion is detected, the system automatically adjusts the quality of service and settings.

[0510] Step 5:

[0511] The server optimizes the allocation of network resources based on the user's emotions. For example, for a user who is feeling frustrated, it increases bandwidth and improves response speed.

[0512] Step 6:

[0513] The device presents the user with optimized network settings and simplifies access to options and support as needed, thereby improving the user experience.

[0514] Step 7:

[0515] The server performs an overall cost analysis, develops the most efficient resource allocation plan that takes user sentiment data into account, and ensures sustainable service delivery.

[0516] (Example 2)

[0517] Next, we will describe 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."

[0518] In optical communication networks, conventional technologies have made it difficult to provide dynamic services based on user emotions and individual needs, resulting in limited improvements in service quality and user experience. Furthermore, efficient allocation of network resources was challenging, requiring flexible responses to instantaneous load fluctuations and emotional stress. Therefore, a new solution was needed to improve user satisfaction and optimize resource utilization.

[0519] 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.

[0520] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for recognizing the user's emotions and dynamically adjusting the quality of service based on that emotion data. This makes it possible to optimize resource allocation and service quality according to the individual emotional state of the user.

[0521] An "optical communication network" is a communication infrastructure that uses optical fibers to transmit data, and is a network system that enables high-speed and high-capacity data transfer.

[0522] "Routing information" refers to information about the network path used when transmitting data, and is fundamental data for determining the optimal communication path.

[0523] "Equipment information" refers to information about the placement and status of various hardware and devices within a communication network, and is data necessary to enable the efficient operation of the network.

[0524] An "emotion engine" is a system designed to analyze a user's emotional state in real time, and is software or hardware that recognizes emotions using data acquired from the user.

[0525] "Resource allocation" is the process of appropriately allocating available resources within a network, and is essential for efficient network operation and improved user experience.

[0526] "Dynamic adjustment" is the process of changing system parameters and settings in response to real-time state changes, and is a means of maintaining optimal service quality.

[0527] This invention is a system aimed at improving the efficiency of optical communication networks and enhancing the user experience. The system mainly consists of a server, a user terminal, and an emotion engine.

[0528] The server uses numerous sensors and monitoring tools to collect routing and equipment information within the optical communication network. This ensures that the latest network status is always maintained and that the optimal communication path can be instantly identified. Furthermore, the server has the capability to dynamically adjust the quality of service based on user sentiment data.

[0529] The user's device sends data about feedback, options used, and behavioral patterns obtained during use to the emotion engine. The emotion engine uses this data to analyze the user's emotional state in real time and determine the user's satisfaction level and stress level.

[0530] The data analyzed by the emotion engine is sent to the server, which then uses this information to reallocate network resources to individual users. This allows for the provision of an optimal network experience tailored to the user's emotional state, thereby improving the quality of service.

[0531] For example, if the emotion engine determines that a user is experiencing stress during an online game, it can temporarily increase the communication priority of that user to provide a smoother gaming experience. An example of a prompt for the generative AI model would be a specific inquiry such as, "If it is detected that the user is experiencing stress, what resource adjustments would be optimal?"

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The server collects routing and equipment information from sensors and monitoring tools installed within the optical communication network. Input data includes status information for each network device and real-time traffic conditions. The server integrates and analyzes this data and outputs a specific communication path to maintain the optimal network state. This operation maximizes communication efficiency and allows for rapid response to dynamic network state changes.

[0535] Step 2:

[0536] The user terminal sends user actions, selected options, and feedback to the emotion engine. Input data includes user behavior patterns and interaction logs. The emotion engine uses this data to analyze the user's emotional state and outputs the results indicating the user's emotions. This process identifies the user's satisfaction level and current emotional state.

[0537] Step 3:

[0538] User emotion data is sent to the server. The server receives the user's emotional state as input data and uses this data to dynamically adjust the quality of service. Specifically, it presents a simple interface to users who are feeling stressed and provides prompt support to users who are dissatisfied. As output, a service environment optimized for each individual user is generated.

[0539] Step 4:

[0540] The server reallocates network resources based on the user's emotional state. Inputs include emotional data and the current network resource status. The server analyzes this data and prioritizes allocating the necessary bandwidth and resources to specific users. The output is resource allocation tailored to the user's needs and optimized network performance. A concrete example of its operation is increasing bandwidth for users experiencing lag during online games, providing a more comfortable gaming experience.

[0541] (Application Example 2)

[0542] Next, we will explain application example 2. In the following explanation, 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."

[0543] In recent years, communication networks and online platforms have been required to provide services that meet the diverse needs of users. However, the current situation of providing uniform services to users with different emotional states can lead to a decline in the quality of the user experience. In particular, when users feel confused, stressed, or frustrated, it becomes difficult to provide a stress-free user environment.

[0544] 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.

[0545] In this invention, the server includes means for recognizing the user's emotions and dynamically adjusting the quality of communication services based on these emotions, means for dynamically changing the site user interface based on the user's emotion data, and means for integrating routing information obtained from multiple sources within the optical communication network to identify the optimal communication path. This enables personalized user service through emotion recognition, thereby improving the user experience.

[0546] An "optical communication network" is a type of network that uses optical fibers to transmit data, enabling high-speed and large-volume data transmission.

[0547] "Routing information" refers to information about the path data takes within a communication network and is used to identify the optimal communication route.

[0548] "Equipment information" refers to information about the physical and technical equipment within a communication network, and forms the basis for understanding and managing the network's state.

[0549] "Emotion recognition" is a technology that estimates a user's emotional state by analyzing their facial expressions and behavioral patterns.

[0550] A "user interface" refers to the interface, including screens and control panels, that a user uses to interact with a system, and is an element that directly impacts the user experience.

[0551] "Communication service quality" refers to the quality of communication provided to users, including speed, stability, and responsiveness, and has a significant impact on the user experience.

[0552] "Dynamic adjustment" refers to a function that appropriately modifies the system's operation and settings in response to real-time changes in circumstances.

[0553] In order to implement this invention, it is necessary to realize a system that combines and operates a server, user terminal, and emotion recognition engine in a network communication system.

[0554] The server collects and integrates routing and equipment information obtained from multiple sources within the optical communication network. This allows the server to maintain the network's optimal communication path and up-to-date status. Furthermore, the server receives user sentiment data and dynamically adjusts the quality of communication services and the user interface.

[0555] The user terminal serves as an interface for recognizing the user's emotions. For example, a smartphone or smart glasses run an emotion recognition engine that analyzes the user's facial expressions and behavior. This allows the user's emotional state to be collected in real time and transmitted to a server.

[0556] The emotion recognition engine analyzes user emotions using services such as Microsoft Azure's Face API. Based on the real-time emotion analysis results performed on the device, it estimates what emotions the user is experiencing. For example, if the user is confused, that information is sent to the server for analysis, and a simplified interface is provided to improve the user experience.

[0557] As one specific example, if a user browsing an online shopping site is struggling to decide on a particular product, the emotion recognition engine analyzes that emotion, and the server uses this information to suggest other related products. This allows the user to make a purchase decision quickly.

[0558] An example of a prompt to input into the generating AI model is: "The user has a troubled expression; please recommend the best way to suggest products to address this."

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The user operates the device to access the online platform. The device inputs the user's behavior and facial expression data in real time to the emotion recognition engine through its interface. The input data obtained here includes, for example, camera images and touch operation data, which is analyzed to estimate the user's emotional state. As a result of the analysis, emotion data is generated.

[0562] Step 2:

[0563] The device uses an emotion recognition engine to process input behavior and facial expression data and identify the user's emotions (e.g., stress, confusion, frustration). Specifically, it uses Microsoft Azure's Face API to analyze facial expressions and generates data indicating the emotional state. The output emotional data is sent to the server.

[0564] Step 3:

[0565] The server receives user sentiment data sent from the terminal and dynamically adjusts the quality of communication services and the site user interface based on that sentiment. For example, if the server determines that the user is confused, it simplifies the user interface and changes the suggestions for relevant content using a generative AI model. The generated output, including the new interface design and content recommendations, is sent to the user's terminal.

[0566] Step 4:

[0567] The user receives the adjusted interface and content provided by the server through their device, and resumes comfortable use. The device then displays the new interface and plays the content, thereby improving the user experience.

[0568] Step 5:

[0569] Based on continuous user sentiment data, the server continuously adjusts the interface and optimizes communication services, generating prompts and providing new information feedback to the network as needed. Through this process, the user experience is further improved and the system becomes more efficient.

[0570] 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.

[0571] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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] 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.

[0573] [Fourth Embodiment]

[0574] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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).

[0580] 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.

[0581] 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.

[0582] 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.

[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 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.

[0586] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0587] This invention relates to a system for automating the design and construction of optical communication networks, aiming to streamline the network design process and minimize costs. Embodiments of this system are described below.

[0588] First, the server collects routing and equipment information from multiple sources within the network. This includes communication channel information from both the company and other companies. The server integrates this information and interacts with a database to understand the latest status of the entire network.

[0589] Next, the server uses AI to identify the optimal communication path from the collected data. In this process, conditions are set according to user requests, such as the shortest path or the fastest path, and an optimized circuit design is created.

[0590] Furthermore, the terminals play a role in updating local equipment information in real time. For example, if a new fiber optic cable is laid, the terminal immediately sends that information to the server, and the database is updated. This ensures that the network status is always kept up-to-date.

[0591] Users forecast future communication demand and input it into the system. The server uses this forecast data to optimize resource allocation and plan the placement of necessary equipment. For example, if a surge in communication demand is predicted in a certain area, the server automatically plans the necessary network capacity and equipment upgrades for that area.

[0592] Finally, the server analyzes resource allocation to minimize overall network costs and provides the most suitable design for the budget. This process makes it possible to build a sophisticated network while reducing operational costs.

[0593] In this way, the system centrally manages the design, construction, and operation of optical communication networks, supporting the realization of efficient and economical networks. For example, when designing a network for a new corporate office, the server can quickly formulate the optimal equipment and wiring plan, significantly reducing installation costs.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The server collects routing and equipment information from multiple databases within the network. This includes fiber optic cable routing information, equipment connectivity status, and associated properties.

[0597] Step 2:

[0598] The terminal monitors the addition of new equipment and modifications to existing equipment on-site in real time and transmits this information to the server. By instantly reflecting the updated equipment information, the terminal improves the overall accuracy of the system.

[0599] Step 3:

[0600] The server integrates the collected routing and equipment information and updates a central database built on the cloud. Based on this updated set of information, it creates a network map of the entire system.

[0601] Step 4:

[0602] The server uses an AI algorithm to calculate the optimal communication path. Based on user requirements, it decides whether to select the shortest path or a redundant path, for example.

[0603] Step 5:

[0604] Users input their projected future communication demands into the system. This allows the server to plan network adjustments based on that demand.

[0605] Step 6:

[0606] The server compares the input forecast data with the current network status and automatically generates plans for necessary resource allocation and equipment additions. This includes suggesting expansion of line capacity and new equipment.

[0607] Step 7:

[0608] The server performs a cost analysis of the entire network and provides the user with the most cost-effective design proposal. This process aims to reduce wasted resources and operational costs.

[0609] (Example 1)

[0610] Next, we will describe 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".

[0611] In designing and constructing optical communication networks, it is essential to effectively manage routing and equipment information and to respond quickly to changing demands. However, conventional methods have made it difficult to collect various types of information, dynamically update the network, and allocate resources appropriately based on predictions, hindering the minimization of operating costs.

[0612] 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.

[0613] This invention includes a server that automatically collects routing information and equipment information obtained from multiple sources within an optical communication network and integrates it into a database; a server that identifies the optimal communication path using a generated AI model based on the integrated information; and a server that transmits local equipment information to the server in real time and updates the database to maintain the latest network status. This enables efficient management of information across the entire network and allows for immediate adjustments and resource allocation based on highly accurate predictions.

[0614] An "optical communication network" refers to network technology that transmits data at high speed and with high capacity via optical fibers.

[0615] "Information source" refers to the systems or devices that provide data such as route information and equipment information.

[0616] "Routing information" refers to information about the paths used to send and receive data within a communication network.

[0617] "Equipment information" refers to information about equipment and infrastructure in a communication network.

[0618] A "server" refers to a computer system used to collect, process, and manage data on a network.

[0619] A "database" refers to a system for efficiently storing, searching, and managing data.

[0620] A "generative AI model" refers to an algorithmic model that uses artificial intelligence technology to generate new data and suggestions.

[0621] "Local equipment information" refers to the latest information on equipment and infrastructure based on the physical location of the network.

[0622] "Predictive data" refers to data formulated based on future demand and circumstances.

[0623] "Resource allocation" refers to methods and plans for effectively and efficiently allocating resources.

[0624] This invention relates to a system for automating the design and construction of optical communication networks. The embodiments for carrying out the invention are described below in detail.

[0625] The server first automatically collects routing and equipment information from multiple sources within the optical communication network. This process retrieves data from multiple suppliers via APIs and integrates it into an SQL database. This streamlines information management across the entire network.

[0626] Next, the server uses a generative AI model to identify the optimal communication path from the collected information. The AI ​​model performs optimization calculations using Python algorithms and proposes the best path based on user-specified conditions (e.g., shortest path or fastest path). This AI model utilizes generative AI technology, enabling flexible design to meet diverse communication requirements.

[0627] The terminal manages local equipment information in real time. The terminal transmits information about newly installed communication equipment to the server via Wi-Fi, Bluetooth, etc., instantly updating the database. This update process ensures that the network status is always up-to-date, enabling efficient operation.

[0628] Users input data into the system to predict future communication demand. This prediction data can be registered with the server via a web interface. The server then uses this data to optimize resource allocation using a generated AI model. The resulting plan enables proactive responses regarding required line capacity and infrastructure enhancements.

[0629] For example, when a surge in communication demand is predicted in a certain area, the server can automatically plan the optimal equipment deployment to cope with this and derive cost-effective solutions. Furthermore, an example of a prompt message for the generated AI model would be, "Based on the communication demand forecast data for a specific area in the next fiscal year, propose the optimal equipment upgrade plan."

[0630] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0631] Step 1:

[0632] The server collects routing and equipment information from multiple sources within the network. This includes accessing other companies' network information using APIs and retrieving data from its own database. The input consists of routing and equipment information obtained from multiple sources, and based on this data, the server outputs a unified information list integrated into its database.

[0633] Step 2:

[0634] The server uses a generated AI model to identify the optimal communication path based on integrated route and infrastructure information. Specifically, it takes user-specified conditions (e.g., shortest path or fastest path) as input and calculates optimized route suggestions using the AI ​​model. The output is the optimal communication path information that meets the conditions.

[0635] Step 3:

[0636] The terminal transmits information about newly installed communication equipment on-site to the server in real time, updating the network status. Input from the terminal includes the latest equipment information on-site, and this information is transmitted to the server via Wi-Fi or Bluetooth, instantly updating the network database. The output is always a constantly updated network status.

[0637] Step 4:

[0638] Users predict future communication demand and input relevant data into the system. Specifically, users send prediction data to the server via a web interface. The input is predicted communication demand data, and based on this, the server uses a generation AI model to output an optimized resource allocation plan. The output is an optimal resource allocation plan according to future demand.

[0639] Step 5:

[0640] The server proposes resource allocation to minimize overall network costs based on predictive data and AI analysis results. Specifically, it takes existing resources and predictive data as input, plans a cost-effective equipment allocation, and performs simulations. The output is an efficient equipment allocation plan that reduces operating costs.

[0641] (Application Example 1)

[0642] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0643] In the design and operation of optical communication networks, there is a need for efficient network path optimization, dynamic updating of equipment information, minimization of power loss, and optimization of resources based on forecasts of future communication demand. Furthermore, a challenge lies in the lack of means to visualize the complex state of communication networks and respond quickly to these changes.

[0644] 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.

[0645] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for providing network visualization information in a specific environment to a terminal. This enables more efficient network design and operation, as well as rapid response through visualization.

[0646] An "optical communication network" is a communication infrastructure that uses optical fiber cables to transmit data at high speed.

[0647] "Information source" refers to a data source that provides routing information or equipment information within a communication network.

[0648] "Routing information" refers to information about the optimal route for data transmission within a communication network.

[0649] "Communication path" refers to the route selected when data travels through a communication network.

[0650] "Equipment information" refers to information about the physical and logical components within a communication network.

[0651] "Dynamic updating" means changing the current state in real time to provide the latest information.

[0652] "Network status" refers to information about the current performance and structure of a communication network.

[0653] "Power loss" refers to unnecessary power consumed during data transmission in optical communication networks.

[0654] "Equipment layout" refers to the geographical or logical arrangement of devices and components within a communication network.

[0655] "Future communication demand" refers to estimated information regarding the volume and types of data communications expected in the future.

[0656] "Resource allocation" refers to the effective distribution of personnel, equipment, and other resources within a communications network.

[0657] "Resource allocation to minimize costs" refers to the optimal allocation of resources in order to reduce the expenses associated with operating a communication network.

[0658] "Visualized information" refers to data that visually represents the status and performance of a communication network.

[0659] A "terminal" is a device or equipment connected to a communication network that sends and receives data.

[0660] A "server" refers to a computer system that processes and stores data over a network.

[0661] This invention relates to a system for efficiently designing and operating optical communication networks. Specific embodiments for realizing this system are described below.

[0662] The server first collects routing information from multiple sources within the optical communication network. This information is used to provide data necessary for understanding the current state of the communication network and predicting its future performance. The primary hardware used is a server computer for processing the data, while the software includes a database management system and AI libraries (e.g., MySQL and TensorFlow).

[0663] The terminal plays a role in dynamically updating equipment information within the communication network. Specifically, it uses IoT devices to monitor the operating status and changes of each piece of equipment in real time and transmits that information to the server.

[0664] The server uses AI to analyze collected information and design the optimal communication network. This includes generating network visualizations and providing them to terminals and specific devices (e.g., smart glasses). This allows users to intuitively understand the network's performance and status and make necessary adjustments.

[0665] For example, if a sudden increase in communication demand is anticipated within a logistics center, the server will immediately recommend the optimal equipment placement and configuration. These suggestions will also be visually displayed via smart glasses, enabling staff to respond immediately.

[0666] An example of a prompt to input into the generated AI model would be: "Monitor the communication network status within the logistics center in real time and suggest the optimal equipment placement using smart glasses."

[0667] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0668] Step 1:

[0669] The server collects routing information from multiple sources within the optical communication network. The input data obtained from these sources concerns the latest status of communication routes and equipment. This data is stored in a database and prepared as foundational data for analysis by an AI library. The output is an analyzable, integrated dataset.

[0670] Step 2:

[0671] The terminal acquires real-time status information from each piece of equipment within the communication network. This input information includes the operating status and performance metrics of the equipment. The terminal sends this information to the server, which dynamically updates the equipment information. The output is updated information that reflects the latest state of the network.

[0672] Step 3:

[0673] The server uses AI with collected data to calculate the optimal communication path. The input is a dataset that integrates route information and equipment information. The server uses an algorithm to process the data and identify efficient communication paths. This result is output as the optimal route configuration.

[0674] Step 4:

[0675] The user predicts future communication demand and inputs this data into the server. The server then plans the optimal resource allocation based on this predicted data. The input is the predicted communication demand data, and the output is the resource allocation plan. This allows the system to respond to future demand.

[0676] Step 5:

[0677] The server generates network visualization information for a specific environment based on the information obtained. The input is the aforementioned optimal routing and resource allocation plan. As output, data visualizing the network status is provided to terminals and smart glasses. Specifically, an intuitive visualization is performed using a generated AI model.

[0678] 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.

[0679] This invention is a system for designing and managing optical communication networks that recognizes user emotions and provides optimal services based on those emotions. The following describes a specific embodiment of this system.

[0680] First, the server collects routing and equipment information for the optical communication network and uses this information to keep the network status constantly up-to-date. This makes it possible to identify the optimal communication path.

[0681] Next, an emotion engine built into the system recognizes the user's emotions in real time. This emotion recognition is determined from the feedback the user provides while using the network, the options they select, and their behavioral patterns when interacting.

[0682] The emotion engine obtains user emotion data, which is then sent to the server. The server uses this data to dynamically adjust the quality of service to meet the individual user's needs. For example, if a user is feeling stressed, the server can offer a simpler interface or faster support.

[0683] Furthermore, the system has a function that automatically optimizes network resource allocation according to the user's emotional state. Based on emotional data, it can allocate additional resources to specific users if necessary, providing a more comfortable service environment.

[0684] For example, if a user experiences network lag while playing an online game and expresses frustration through the emotion engine, the server can use this information to temporarily prioritize communication for that user. This can improve the user experience.

[0685] In this way, this system enables flexible network management that takes user emotions into consideration, and facilitates the efficient and personalized delivery of services.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The server collects routing and equipment information within the optical communication network from multiple sources and creates a network map of the entire system based on an existing database.

[0689] Step 2:

[0690] The device monitors user interactions and feedback from the user's device and uses an emotion engine to analyze the user's emotional state in real time. This includes the frequency of choices and interactions during operation.

[0691] Step 3:

[0692] Users provide feedback when they experience dissatisfaction or stress while using the network, and the device sends this information to the emotion engine.

[0693] Step 4:

[0694] The server receives emotion data sent from the terminal and updates the database based on the user's emotional state. If a specific emotion is detected, the system automatically adjusts the quality of service and settings.

[0695] Step 5:

[0696] The server optimizes the allocation of network resources based on the user's emotions. For example, for a user who is feeling frustrated, it increases bandwidth and improves response speed.

[0697] Step 6:

[0698] The device presents the user with optimized network settings and simplifies access to options and support as needed, thereby improving the user experience.

[0699] Step 7:

[0700] The server performs an overall cost analysis, develops the most efficient resource allocation plan that takes user sentiment data into account, and ensures sustainable service delivery.

[0701] (Example 2)

[0702] Next, we will describe 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".

[0703] In optical communication networks, conventional technologies have made it difficult to provide dynamic services based on user emotions and individual needs, resulting in limited improvements in service quality and user experience. Furthermore, efficient allocation of network resources was challenging, requiring flexible responses to instantaneous load fluctuations and emotional stress. Therefore, a new solution was needed to improve user satisfaction and optimize resource utilization.

[0704] 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.

[0705] In this invention, the server includes means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, means for maintaining the latest network state by dynamically updating equipment information within the communication network, and means for recognizing the user's emotions and dynamically adjusting the quality of service based on that emotion data. This makes it possible to optimize resource allocation and service quality according to the individual emotional state of the user.

[0706] An "optical communication network" is a communication infrastructure that uses optical fibers to transmit data, and is a network system that enables high-speed and high-capacity data transfer.

[0707] "Routing information" refers to information about the network path used when transmitting data, and is fundamental data for determining the optimal communication path.

[0708] "Equipment information" refers to information about the placement and status of various hardware and devices within a communication network, and is data necessary to enable the efficient operation of the network.

[0709] An "emotion engine" is a system designed to analyze a user's emotional state in real time, and is software or hardware that recognizes emotions using data acquired from the user.

[0710] "Resource allocation" is the process of appropriately allocating available resources within a network, and is essential for efficient network operation and improved user experience.

[0711] "Dynamic adjustment" is the process of changing system parameters and settings in response to real-time state changes, and is a means of maintaining optimal service quality.

[0712] This invention is a system aimed at improving the efficiency of optical communication networks and enhancing the user experience. The system mainly consists of a server, a user terminal, and an emotion engine.

[0713] The server uses numerous sensors and monitoring tools to collect routing and equipment information within the optical communication network. This ensures that the latest network status is always maintained and that the optimal communication path can be instantly identified. Furthermore, the server has the capability to dynamically adjust the quality of service based on user sentiment data.

[0714] The user's device sends data about feedback, options used, and behavioral patterns obtained during use to the emotion engine. The emotion engine uses this data to analyze the user's emotional state in real time and determine the user's satisfaction level and stress level.

[0715] The data analyzed by the emotion engine is sent to the server, which then uses this information to reallocate network resources to individual users. This allows for the provision of an optimal network experience tailored to the user's emotional state, thereby improving the quality of service.

[0716] For example, if the emotion engine determines that a user is experiencing stress during an online game, it can temporarily increase the communication priority of that user to provide a smoother gaming experience. An example of a prompt for the generative AI model would be a specific inquiry such as, "If it is detected that the user is experiencing stress, what resource adjustments would be optimal?"

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] The server collects routing and equipment information from sensors and monitoring tools installed within the optical communication network. Input data includes status information for each network device and real-time traffic conditions. The server integrates and analyzes this data and outputs a specific communication path to maintain the optimal network state. This operation maximizes communication efficiency and allows for rapid response to dynamic network state changes.

[0720] Step 2:

[0721] The user terminal sends user actions, selected options, and feedback to the emotion engine. Input data includes user behavior patterns and interaction logs. The emotion engine uses this data to analyze the user's emotional state and outputs the results indicating the user's emotions. This process identifies the user's satisfaction level and current emotional state.

[0722] Step 3:

[0723] User emotion data is sent to the server. The server receives the user's emotional state as input data and uses this data to dynamically adjust the quality of service. Specifically, it presents a simple interface to users who are feeling stressed and provides prompt support to users who are dissatisfied. As output, a service environment optimized for each individual user is generated.

[0724] Step 4:

[0725] The server reallocates network resources based on the user's emotional state. Inputs include emotional data and the current network resource status. The server analyzes this data and prioritizes allocating the necessary bandwidth and resources to specific users. The output is resource allocation tailored to the user's needs and optimized network performance. A concrete example of its operation is increasing bandwidth for users experiencing lag during online games, providing a more comfortable gaming experience.

[0726] (Application Example 2)

[0727] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] In recent years, communication networks and online platforms have been required to provide services that meet the diverse needs of users. However, the current situation of providing uniform services to users with different emotional states can lead to a decline in the quality of the user experience. In particular, when users feel confused, stressed, or frustrated, it becomes difficult to provide a stress-free user environment.

[0729] 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.

[0730] In this invention, the server includes means for recognizing the user's emotions and dynamically adjusting the quality of communication services based on these emotions, means for dynamically changing the site user interface based on the user's emotion data, and means for integrating routing information obtained from multiple sources within the optical communication network to identify the optimal communication path. This enables personalized user service through emotion recognition, thereby improving the user experience.

[0731] An "optical communication network" is a type of network that uses optical fibers to transmit data, enabling high-speed and large-volume data transmission.

[0732] "Routing information" refers to information about the path data takes within a communication network and is used to identify the optimal communication route.

[0733] "Equipment information" refers to information about the physical and technical equipment within a communication network, and forms the basis for understanding and managing the network's state.

[0734] "Emotion recognition" is a technology that estimates a user's emotional state by analyzing their facial expressions and behavioral patterns.

[0735] A "user interface" refers to the interface, including screens and control panels, that a user uses to interact with a system, and is an element that directly impacts the user experience.

[0736] "Communication service quality" refers to the quality of communication provided to users, including speed, stability, and responsiveness, and has a significant impact on the user experience.

[0737] "Dynamic adjustment" refers to a function that appropriately modifies the system's operation and settings in response to real-time changes in circumstances.

[0738] In order to implement this invention, it is necessary to realize a system that combines and operates a server, user terminal, and emotion recognition engine in a network communication system.

[0739] The server collects and integrates routing and equipment information obtained from multiple sources within the optical communication network. This allows the server to maintain the network's optimal communication path and up-to-date status. Furthermore, the server receives user sentiment data and dynamically adjusts the quality of communication services and the user interface.

[0740] The user terminal serves as an interface for recognizing the user's emotions. For example, a smartphone or smart glasses run an emotion recognition engine that analyzes the user's facial expressions and behavior. This allows the user's emotional state to be collected in real time and transmitted to a server.

[0741] The emotion recognition engine analyzes user emotions using services such as Microsoft Azure's Face API. Based on the real-time emotion analysis results performed on the device, it estimates what emotions the user is experiencing. For example, if the user is confused, that information is sent to the server for analysis, and a simplified interface is provided to improve the user experience.

[0742] As one specific example, if a user browsing an online shopping site is struggling to decide on a particular product, the emotion recognition engine analyzes that emotion, and the server uses this information to suggest other related products. This allows the user to make a purchase decision quickly.

[0743] An example of a prompt to input into the generating AI model is: "The user has a troubled expression; please recommend the best way to suggest products to address this."

[0744] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0745] Step 1:

[0746] The user operates the device to access the online platform. The device inputs the user's behavior and facial expression data in real time to the emotion recognition engine through its interface. The input data obtained here includes, for example, camera images and touch operation data, which is analyzed to estimate the user's emotional state. As a result of the analysis, emotion data is generated.

[0747] Step 2:

[0748] The device uses an emotion recognition engine to process input behavior and facial expression data and identify the user's emotions (e.g., stress, confusion, frustration). Specifically, it uses Microsoft Azure's Face API to analyze facial expressions and generates data indicating the emotional state. The output emotional data is sent to the server.

[0749] Step 3:

[0750] The server receives user sentiment data sent from the terminal and dynamically adjusts the quality of communication services and the site user interface based on that sentiment. For example, if the server determines that the user is confused, it simplifies the user interface and changes the suggestions for relevant content using a generative AI model. The generated output, including the new interface design and content recommendations, is sent to the user's terminal.

[0751] Step 4:

[0752] The user receives the adjusted interface and content provided by the server through their device, and resumes comfortable use. The device then displays the new interface and plays the content, thereby improving the user experience.

[0753] Step 5:

[0754] Based on continuous user sentiment data, the server continuously adjusts the interface and optimizes communication services, generating prompts and providing new information feedback to the network as needed. Through this process, the user experience is further improved and the system becomes more efficient.

[0755] 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.

[0756] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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."

[0764] 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.

[0765] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0766] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] The following is further disclosed regarding the embodiments described above.

[0777] (Claim 1)

[0778] A means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path,

[0779] A means of maintaining the latest network state by dynamically updating equipment information within the communication network,

[0780] Means for optimizing equipment placement to minimize power loss,

[0781] A means of predicting future communication demand and optimizing resource allocation,

[0782] A means of proposing resource allocation to minimize overall costs,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, which integrates equipment information from multiple sources into a database to improve information accuracy in network design.

[0786] (Claim 3)

[0787] The system according to claim 1, which automatically adjusts the network capacity plan based on predicted future communication demand.

[0788] "Example 1"

[0789] (Claim 1)

[0790] A means for automatically collecting routing information and equipment information obtained from multiple sources within an optical communication network and integrating them into a database,

[0791] A means for identifying the optimal communication channel using a generated AI model based on integrated information,

[0792] A means of transmitting local equipment information to a server in real time, updating the database, and maintaining the latest network status,

[0793] A means of predicting future communication demand and optimizing resource allocation using a generated AI model based on the predicted data,

[0794] A means of analyzing resource allocation to minimize network operating costs and providing a design proposal that fits the budget,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, which integrates route information and equipment information from multiple sources into a database via a server to improve information accuracy and current situation understanding.

[0798] (Claim 3)

[0799] The system according to claim 1, which automatically adjusts network capacity planning using a generative AI model based on predicted future communication demand.

[0800] "Application Example 1"

[0801] (Claim 1)

[0802] A means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path,

[0803] A means of maintaining the latest network state by dynamically updating equipment information within the communication network,

[0804] Means for optimizing equipment placement to minimize power loss,

[0805] A means of predicting future communication demand and optimizing resource allocation,

[0806] A means of proposing resource allocation to minimize overall costs,

[0807] A means of providing network visualization information in a specific environment to a terminal,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, which integrates equipment information from multiple sources into an information store to improve information accuracy in network design.

[0811] (Claim 3)

[0812] The system according to claim 1, which automatically adjusts the network capacity plan based on predicted future communication demand and outputs it as visualization information.

[0813] "Example 2 of combining an emotion engine"

[0814] (Claim 1)

[0815] A means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path,

[0816] A means of maintaining the latest network state by dynamically updating equipment information within the communication network,

[0817] A means of recognizing user emotions and dynamically adjusting the quality of service based on that emotion data,

[0818] A means to automatically optimize the allocation of network resources according to the user's emotional state,

[0819] Means for optimizing equipment placement to minimize power loss,

[0820] A means of predicting future communication demand and optimizing resource allocation,

[0821] A means of proposing resource allocation to minimize overall costs,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which integrates equipment information from multiple sources into a database to improve information accuracy in network design.

[0825] (Claim 3)

[0826] The system according to claim 1, which automatically adjusts the network capacity plan based on predicted future communication demand.

[0827] "Application example 2 of combining emotional engines"

[0828] (Claim 1)

[0829] A means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path,

[0830] A means of maintaining the latest network state by dynamically updating equipment information within the communication network,

[0831] Means for optimizing equipment placement to minimize power loss,

[0832] A means of predicting future communication demand and optimizing resource allocation,

[0833] A means of proposing resource allocation to minimize overall costs,

[0834] A means of recognizing user emotions and dynamically adjusting the quality of communication services based on them,

[0835] A means of dynamically changing the site user interface based on user sentiment data,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, which integrates equipment information from multiple sources into a database to improve information accuracy in network design.

[0839] (Claim 3)

[0840] The system according to claim 1, which automatically adjusts the network capacity plan based on predicted future communication demand. [Explanation of Symbols]

[0841] 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

1. A means for integrating routing information obtained from multiple sources within an optical communication network to identify the optimal communication path, A means of maintaining the latest network state by dynamically updating equipment information within the communication network, Means for optimizing equipment placement to minimize power loss, A means of predicting future communication demand and optimizing resource allocation, A means of proposing resource allocation to minimize overall costs, A system that includes this.

2. The system according to claim 1, which integrates equipment information from multiple sources into a database to improve information accuracy in network design.

3. The system according to claim 1, which automatically adjusts the network capacity plan based on predicted future communication demand.

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