system

Abstract

We provide the system. [Solution] Means of collecting data from base stations, A means for analyzing the aforementioned data using a generating AI model and detecting traffic patterns, A means to activate an AI agent that autonomously optimizes the operation of base stations based on the analysis results, A feedback means for monitoring and adjusting the results of the optimization, 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 method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] It is to solve the problems of reduced efficiency in the operation of mobile phone base stations, increased power consumption, and unstable communication quality due to unexpected failures, and to prevent an increase in operating costs and deterioration of the user experience. Also, it aims to provide a more stable communication service by predicting future traffic demands and effectively allocating resources.

Means for Solving the Problems

[0005] This invention includes means for collecting data from base stations and analyzing it using a generative AI model to detect traffic patterns. It also includes means for activating an AI agent that autonomously optimizes base station operations based on the analysis results, and includes feedback means for monitoring and adjusting the optimization results, thereby improving operational efficiency and reducing power consumption. Furthermore, it includes learning means for predicting future traffic demand using the generative AI model and allocating resources in advance, enabling it to respond to future fluctuations in traffic demand. In addition, it provides means for notifying users of traffic conditions and fault occurrences via alerts, thereby improving the user experience.

[0006] A "base station" is a wireless facility used for communication with mobile devices within a mobile phone network, and is responsible for managing the communication area and transmitting and receiving signals.

[0007] A "generative AI model" is a type of artificial intelligence technology used to analyze data and perform pattern recognition and prediction, and it specifically includes models that incorporate generative adversarial networks and deep learning technologies.

[0008] "Traffic patterns" refer to the flow of data within a communication network, as well as trends and time-series fluctuations in data usage.

[0009] An "AI agent" is a part of artificial intelligence software designed to operate autonomously according to a specific purpose and take optimal action in response to changes in the environment.

[0010] A "feedback mechanism" is a system that monitors the output results of a system and adjusts the system's operation based on those results.

[0011] "Resource allocation" refers to the appropriate distribution of limited resources to various economic activities and operational processes in order to utilize them efficiently.

[0012] An "alert" is a warning message or signal that quickly notifies information when an event deviates from the normal state occurs. [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

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

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

[0016] In the following embodiments, a processor with a reference numeral (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 RAM (Random Access Memory) with a reference numeral 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 storage with a reference numeral 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, etc.

[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] In an embodiment of this invention, the program for the next-generation smart base station management system efficiently manages, analyzes, optimizes, predicts, and notifies information from multiple communication base stations. This system has different roles for the server, terminal, and user, and as a whole, it improves the efficiency of the communication network.

[0035] The server periodically collects traffic data, power usage data, and fault data from each base station. It then uses a generative AI model to analyze this data in detail, enabling it to detect traffic patterns and anomalies. For example, the server can detect a sudden surge in data traffic during specific time periods and analyze its cause.

[0036] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This process allows for adjustment of resource allocation to base stations with high traffic volumes and the application of energy-saving modes to reduce power consumption. This ensures both stable communication and efficient power consumption.

[0037] Furthermore, the server continuously learns from data accumulation and generative AI models to predict future traffic demand. Based on this prediction, it reserves the necessary resources in advance and prepares to improve service quality.

[0038] The terminal notifies the user of traffic conditions and outage status in real time. This notification provides the user with information to take necessary actions quickly.

[0039] For example, if a large-scale event is scheduled in a certain area, the server predicts the increase in traffic in that area based on past data and prepares to increase the necessary resources in advance of the event. Furthermore, it enables users to take countermeasures against fluctuations in communication quality through notifications to their terminals.

[0040] In this way, the next-generation smart base station management system aims to improve the efficiency and reliability of communication networks and provide users with higher quality services.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects information from base stations, including traffic data, power usage data, and fault occurrence data. This data is stored in a database on the server and prepared for later analysis.

[0044] Step 2:

[0045] The server preprocesses the collected data. Specifically, it improves data quality by removing noise and filling in missing data. This enables reliable analysis.

[0046] Step 3:

[0047] The server inputs pre-processed data into a generating AI model, which analyzes traffic patterns and anomalies. During this process, the analysis engine analyzes the data flow and makes decisions based on the situation.

[0048] Step 4:

[0049] The server activates an AI agent based on the analysis results. The AI ​​agent dynamically optimizes the base station settings and operation methods, maximizing communication efficiency while simultaneously minimizing power consumption.

[0050] Step 5:

[0051] The server monitors the optimization results in real time and makes adjustments as needed if any inappropriate behavior occurs. This feedback is used to correct and improve operations.

[0052] Step 6:

[0053] The server utilizes a pre-trained generative AI model to predict future traffic demand. Based on the prediction results, it allocates the necessary resources in advance to improve communication quality.

[0054] Step 7:

[0055] The device notifies the user of traffic conditions and alerts about any outages. This allows the user to take appropriate action, ensuring a smooth communication experience.

[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] Modern communication networks require efficient management that can quickly respond to sudden fluctuations in traffic and the occurrence of failures. However, conventional systems have limitations in resource allocation and traffic forecasting, which leads to a decline in communication quality and an increase in energy consumption. This invention aims to solve these problems and improve the operational efficiency of each communication device.

[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] In this invention, the server includes means for collecting information from communication devices, means for analyzing and recognizing data patterns using a generative AI model, and means for activating an AI agent that autonomously improves the operation of communication devices based on the analysis results. This improves the accuracy of traffic prediction in communication networks, enables optimization of resource allocation, and speeds up fault response.

[0061] "Communication equipment" refers to hardware or software for receiving and transmitting data, including base stations and network nodes.

[0062] "Information" refers to a collection of data such as traffic data, power usage data, and fault occurrence data collected from communication devices.

[0063] A "generative AI model" refers to an artificial intelligence model used to analyze and predict traffic patterns and anomalies through machine learning.

[0064] "Means of recognizing data patterns" refers to the process of analyzing collected information to identify normal traffic patterns and abnormal activity.

[0065] An "AI agent" refers to a software component that operates to autonomously improve the operation of communication devices based on analysis results.

[0066] "Autonomous improvement" refers to a process in which the system itself makes decisions and optimizes its operations without waiting for external instructions.

[0067] "Resource allocation" refers to the process of managing the allocation of computing resources and communication channels within a communication network.

[0068] "Accelerating" refers to the process of information processing and countermeasures being implemented faster than before.

[0069] In this invention, a server plays a central role in configuring a next-generation smart communication management system. The server periodically collects information from communication devices and centrally manages traffic data, power usage data, and fault data. For handling this information, a database management system is used, for example, to organize large amounts of data and enable efficient searching and updating.

[0070] The server utilizes a generative AI model to analyze the collected information. This AI model leverages machine learning techniques to recognize data patterns and perform anomaly detection and traffic prediction. For example, if data traffic surges during a specific time period, the server sends a prompt to the AI ​​model such as "Detect anomaly peaks from the traffic data of the past 72 hours." This allows the AI ​​model to perform appropriate analysis based on the data.

[0071] Furthermore, the server runs an AI agent that autonomously improves the operation of communication equipment based on the analysis results. This AI agent has the ability to dynamically adjust resource allocation and apply energy-saving modes. As a result, the overall energy efficiency of the system is improved and communication stability is maintained.

[0072] The terminal notifies the user in real time of traffic conditions and any outages. For example, if an anomaly is detected within the system, the terminal immediately sends a notification to the user's device, providing information to take appropriate action. The user receives this notification and can adjust their communication environment or use services accordingly.

[0073] In this way, the embodiment of this invention aims to contribute to improving the efficiency and reliability of communication networks and to provide high-quality services to users.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server collects information from communication devices. Inputs include traffic data, power usage data, and fault data. The server stores this data in a database for centralized management. This allows for the integrated handling of information from multiple data sources, enabling the rapid and accurate use of data.

[0077] Step 2:

[0078] The server inputs the collected information into a generating AI model to recognize data patterns. The input is centrally managed data. The server instructs the AI ​​model to analyze the data using prompts such as, "Detect abnormal peaks from the traffic data of the past 72 hours." The output is the predicted traffic patterns and the results of the anomaly detection. Through this process, the server can understand traffic trends and quickly detect early signs of anomalies.

[0079] Step 3:

[0080] The server operates the AI ​​agent based on the analysis results obtained from the AI ​​model. The input is the analyzed pattern and anomaly data. The server uses the AI ​​agent to autonomously improve the operation of the communication equipment, specifically by dynamically allocating resources and applying energy-saving modes. The output is improved communication efficiency and reduced energy consumption due to optimized resource allocation.

[0081] Step 4:

[0082] The terminal sends real-time traffic status and outage notifications to the user. Input is the latest traffic data and outage information sent from the server. The terminal sends this information to the user's device via a push notification system, enabling the user to respond quickly to the situation. Output is specific action guidelines and countermeasures provided to the user. The user can use this information to maintain communication quality and mitigate risks.

[0083] (Application Example 1)

[0084] 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."

[0085] Current communication infrastructure faces challenges in providing stable communication services to users because it struggles to respond quickly to sudden spikes in communication traffic in specific areas or time zones. Furthermore, efficient energy consumption is required in the operation of the communication infrastructure. Overcoming these challenges and providing efficient and reliable communication services, especially in smart cities, is essential.

[0086] 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.

[0087] In this invention, the server includes means for acquiring information from a communication infrastructure, means for analyzing the information using a generation AI model to understand trends, and means for activating an intelligent agent that autonomously optimizes the operation of the communication infrastructure based on the analysis results. This enables the provision of real-time communication trends in a specific area, efficient resource allocation based on predictions, and the provision of stable communication services.

[0088] "Communication infrastructure" refers to the basic infrastructure necessary to provide communication services.

[0089] "Means of acquiring information" refers to methods and mechanisms for collecting necessary data from communication infrastructure.

[0090] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze data and detect patterns or anomalies.

[0091] "Means of understanding trends" refers to methods of analyzing communication flows and trends based on acquired information.

[0092] An "intelligent agent" refers to a program that autonomously selects and executes the optimal action based on information within the system.

[0093] "Optimization" refers to adjustments made to maximize the use of limited resources and provide efficient and effective services.

[0094] "Providing information in real time" means providing information such as communication trends immediately and without delay.

[0095] "Efficient resource allocation" refers to the effective distribution of resources in communication infrastructure according to demand.

[0096] A description of the embodiment for carrying out the invention will be provided.

[0097] The server in this invention acquires necessary information from the communication infrastructure and performs data analysis by inputting it into a generating AI model. The analysis includes acquired traffic data and power usage data, and the AI ​​model performs pattern recognition to detect anomalies. Specifically, it can grasp the flow of communication in real time and immediately detect increasing traffic.

[0098] Based on the analysis results, the server automatically activates an intelligent agent to optimize the operation of the communication infrastructure. This optimization includes dynamic reallocation of resources and adjustment of power consumption. This ensures the stability of communication services while enabling efficient use of energy.

[0099] On the device, this optimized information is used to quickly notify the user. These notifications include real-time feedback on communication status and detection of potential problems, allowing the user to take necessary adjustments early.

[0100] A concrete example is when a server predicts a surge in traffic due to a large-scale event and allocates resources accordingly. This ensures that communication is not interrupted during the event, allowing users to enjoy a comfortable communication environment. Further detailed analysis can be performed using prompt messages such as, "Detect abnormal communication patterns based on the following base data."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server acquires traffic data and power usage data from the communication infrastructure. The input for this step is various communication data, and the output is the acquired raw data. The server periodically collects this data using an API.

[0104] Step 2:

[0105] The server inputs the acquired data into a generating AI model for analysis. The input consists of traffic data and power usage data obtained in step 1, and the output is the analysis results. The server uses the AI ​​model to perform pattern recognition on the data and detect anomalies and trends.

[0106] Step 3:

[0107] The server activates an intelligent agent based on the analysis results to optimize the operation of the communication infrastructure. The input is the analysis results from step 2, and the output is the optimized operation instructions. The server then performs actions such as automatically reallocating resources and adjusting energy-saving settings.

[0108] Step 4:

[0109] The server sends the optimization results to the terminal and provides necessary notification information. The input is the operation instructions created in step 3, and the output is the notification content sent to the terminal. The server transmits information in real time and operates so that the user can check it on the terminal.

[0110] Step 5:

[0111] Users receive notifications through their devices and take timely actions regarding communication status and failures. Input is the notification content from the server, and output is the user's actions. The device displays alerts to the user and provides prompts for necessary actions.

[0112] 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.

[0113] The next-generation smart base station management system, incorporating an emotion engine, provides a powerful framework for recognizing user emotions in real time and optimizing network operations based on that information. First, the server utilizes emotion data collected from user terminals, in addition to conventional traffic data, power usage data, and fault data. This emotion data is extracted from the user's voice, facial expressions, and input content, and then analyzed by the emotion engine.

[0114] The server uses a generative AI model to analyze all received data, identifying not only common traffic patterns and anomalies, but also the impact of changes in user emotions on communication quality and resource demands. For example, if the server detects that a user is dissatisfied, it uses that information to perform optimization processes more quickly in order to improve communication quality.

[0115] Based on the analysis results, an AI agent automatically optimizes base station operations. Emotional data, in particular, supports rapid response during communication problems. For example, when a user's emotions are negative, traffic prioritization is reviewed and problems are resolved quickly, improving the user experience.

[0116] Furthermore, the server evolves its generative AI model based on changes in user emotions to predict future traffic demand. In this prediction, if user emotions foreshadow significant traffic fluctuations, this information is incorporated into the prediction model to prepare the network.

[0117] The device displays customized alerts that take into account the user's emotional state at the time when notifying them of traffic conditions or outages. This enables emotionally sensitive communication.

[0118] This system aims to further improve the quality of communication services by evolving network operations from simple data analysis to incorporating emotions as an element closely related to the user experience.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The server collects traffic data, power usage data, fault data, and user sentiment data from base stations and user terminals. Sentiment data is obtained from user terminals using voice analysis and facial recognition technology.

[0122] Step 2:

[0123] The server preprocesses the collected sentiment data, removing noise and imputing data. This preprocessing increases the reliability of the sentiment data and improves the accuracy of subsequent analysis.

[0124] Step 3:

[0125] The server inputs pre-processed emotional data into the emotion engine and analyzes the user's emotional state. The analyzed emotional state is then categorized, for example, into categories such as "satisfied," "dissatisfied," and "stressed."

[0126] Step 4:

[0127] The server uses a generative AI model to analyze traffic and sentiment data, evaluating the impact of user sentiment changes on communications, in addition to identifying traffic patterns and detecting anomalies. This makes it possible to identify patterns such as when user dissatisfaction is high at peak times.

[0128] Step 5:

[0129] The server activates an AI agent based on the analysis results. This agent dynamically adjusts the base station settings, optimizing resource allocation, especially when the user's emotions are negative, in order to improve communication quality.

[0130] Step 6:

[0131] The server monitors the optimization results and the user's emotional state. This allows it to understand whether the user is satisfied, and if further adjustments are needed, the AI ​​agent automatically takes action.

[0132] Step 7:

[0133] The device notifies the user with customized alerts. For example, if it detects that the user is stressed, it can provide alerts using a softer tone and encouraging messages.

[0134] (Example 2)

[0135] 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".

[0136] Existing communication systems suffer from a lack of network management that takes into account users' emotional states, resulting in insufficient improvement in the quality of the user experience. Furthermore, network optimization is often based solely on normal traffic and failure conditions, leading to delays in improving communication quality. This can result in decreased user satisfaction and potentially undermine the competitiveness of telecommunications carriers.

[0137] 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.

[0138] In this invention, the server includes means for generating emotional information from a user terminal, means for analyzing various data including the emotional information using a generation AI model to identify the impact on traffic patterns and communication quality, and means for autonomously optimizing the operation of the base station based on the analysis results and activating an AI agent that performs rapid optimization processing that takes emotional data into consideration. This enables rapid network optimization and improvement of communication quality that takes user emotions into consideration.

[0139] A "user terminal" is a device that a user directly operates and is equipped with various sensors and input devices for generating emotional information.

[0140] "Emotional information" refers to information extracted from the user's voice, facial expressions, and input content, and is data used to identify the user's emotional state.

[0141] A "generative AI model" is a model that uses artificial intelligence technology to analyze data collected from user terminals, and its role is to identify the impact on traffic patterns and communication quality.

[0142] "Traffic patterns" are information that represents the flow and trends of data in a network, and are used to understand the communication status.

[0143] "Communication quality" is an indicator used to evaluate the performance of communication services provided through a network, and it is a factor that directly affects the user experience.

[0144] An "AI agent" is an artificial intelligence program that functions to automatically optimize the operation of base stations, and it operates autonomously based on the analysis results.

[0145] A "feedback mechanism" is a system for monitoring the results of optimization and making adjustments as needed, thereby ensuring the quality of system operation.

[0146] An "alert" is a message used to notify users of traffic conditions or outages, and its purpose is to convey information while taking into account the user's emotional state.

[0147] This invention aims to achieve more advanced communication optimization by utilizing user emotional information in a network management system.

[0148] First, the user's device is equipped with a microphone and camera to capture voice and facial expressions. Through this hardware, the device acquires emotional information from the user's voice and facial expressions, and sends all the data, including the input content, to the server. At this time, the data is processed in real time. For example, when the user types "the connection is slow," the device identifies the emotion of dissatisfaction.

[0149] The server is equipped with an emotion engine that analyzes voice and facial expression data received from user terminals. This analysis uses specific software tools such as voice tone analysis algorithms and facial expression recognition APIs. The analyzed emotion information, along with conventional traffic data and fault data, is input into a generative AI model within the server. The generative AI model uses this information to identify the impact of traffic patterns and changes in user emotions on communication quality.

[0150] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This ensures that measures are taken to immediately improve communication quality, especially when users express dissatisfaction. For example, when a user's mood deteriorates, the system re-evaluates the priority of communication resources and instantly implements necessary corrective measures. This system achieves improvements in the quality of communication services and the user experience.

[0151] Furthermore, the server uses a generative AI model to predict future traffic demand. This prediction reflects user sentiment data and is made using prompt messages such as: "The user expressed dissatisfaction with the connection speed. Prioritization is required."

[0152] Furthermore, when the device notifies the user of traffic conditions or outages, it generates customized alerts that reflect the user's emotions. This provides users with a sense of security and a better communication experience.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The device collects emotional data such as voice, facial expressions, and text input. Specifically, it records voice using the device's microphone, captures facial expressions with its camera, and obtains input from the keyboard. This emotional data is then sent to the server as input.

[0156] Step 2:

[0157] The server analyzes the emotional data received from the terminal using an emotion engine. Voice data is processed by a voice tone analysis algorithm, and facial expression data is recognized using a facial expression recognition API. The input text content is analyzed using natural language processing to identify emotions. As a result of this processing, detailed information about the user's emotional state is obtained.

[0158] Step 3:

[0159] The server integrates the analyzed sentiment data with other traffic and fault data and inputs it into a generative AI model. Based on this data, the generative AI model identifies the impact on traffic patterns and communication quality, and generates detailed analysis results as output. Specifically, it determines how user dissatisfaction is affecting communication requests.

[0160] Step 4:

[0161] The server activates an AI agent and automatically optimizes base station operations based on the analysis results. This process includes reprioritizing traffic and reallocating resources using sentiment data. The output is an optimized communication environment.

[0162] Step 5:

[0163] The server uses a generative AI model to predict future traffic demand. It creates prompt messages that reflect sentiment data (for example, "Users expressed dissatisfaction with connection speed. Prioritization is needed.") and analyzes future traffic trends. The output is predicted traffic demand information.

[0164] Step 6:

[0165] The device notifies the user based on information from the server. Specifically, it displays customized alerts that take the user's emotions into consideration. For example, it might say, "The network is currently congested, but the situation is improving," providing a sense of reassurance.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] Modern communication systems face the challenge of communication delays and interruptions that negatively impact user satisfaction and directly lead to a decline in service quality. Furthermore, while there is a need to resolve these communication problems quickly, traditional methods have not adequately considered the emotional state of the user.

[0169] 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.

[0170] In this invention, the server includes means for acquiring information from a base station, means for analyzing the information using a generation AI model and detecting data trends, and means for collecting user emotion data and analyzing it using an emotion analysis engine. This makes it possible to optimize communication quality while understanding the user's emotional state in real time. Furthermore, based on the analysis results, it becomes possible to autonomously perform optimal operations using an artificial intelligence agent and improve the user experience.

[0171] "Means of acquiring information" refers to a device or process that has the function of collecting necessary data and information from a base station.

[0172] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict future data trends and demand.

[0173] "Means for detecting data trends" refers to analytical devices or software used to identify certain patterns or changes within collected information.

[0174] An "emotion analysis engine" is a system or algorithm that reads and analyzes a user's emotions from their voice, facial expressions, and other data.

[0175] An "artificial intelligence agent" is software or a system that has the function of making autonomous decisions based on analysis results and optimizing the operation of a base station.

[0176] A "feedback mechanism for monitoring and adjusting" refers to a device or software process that continuously checks the communication status after optimization and revises the settings as needed.

[0177] A "means of providing customized notifications" refers to a system that delivers individually tailored information notifications based on the user's emotional data and circumstances.

[0178] The system implementing this invention is realized through the interaction of a server, a terminal, and a user. The server acquires data from a communication base station and analyzes the information using its own generative AI model. Not only are traffic trends detected from the analyzed data, but emotional data collected from the user terminal is also analyzed. An emotional analysis engine is activated to identify changes in emotion based on voice and facial expressions.

[0179] Based on analysis results and sentiment data, the server uses an artificial intelligence agent to optimize base station communication operations. Specifically, it autonomously determines communication priorities and reallocates resources as needed. This significantly improves the user experience and minimizes communication delays and service interruptions.

[0180] The user's smartphone or smart glasses collect voice capture and facial expression data and send it to the server in real time. The emotion analysis engine used here is expected to include Google® Cloud Emotion API or Microsoft® Azure® Emotion Recognition API.

[0181] The server continuously monitors the optimization results, provides feedback based on the monitoring results, and makes adjustments as needed to enhance responsiveness. Furthermore, it incorporates a mechanism to provide customized notifications that take into account changes in the user's emotions. For example, when a user expresses dissatisfaction, a prompt message is sent to the server to resolve the delay, enabling a quick response. This message might read something like, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and resolve the delay."

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The device collects the user's voice and facial expression data. Input is real-time audio and images captured by a smartphone or smart glasses. Output is this data ready to be sent to a server in digital format.

[0185] Step 2:

[0186] The server processes the received audio and facial expression data using an emotion analysis engine to identify the user's emotions. The input is digital data transmitted from the terminal. Data processing is performed using emotion analysis with the Google Cloud Emotion API or Microsoft Azure Emotion Recognition API. The output is the analysis result indicating the user's emotional state.

[0187] Step 3:

[0188] The server uses a generative AI model to analyze traffic data acquired from base stations. The input consists of communication data from base stations and the results of sentiment analysis. Data processing is performed by using the generative AI model to detect future traffic trends and their impact. The output is the analysis results, showing traffic patterns and anomalies.

[0189] Step 4:

[0190] The server uses an artificial intelligence agent to instruct the optimization of communication operations based on analysis results and sentiment data. Inputs are traffic analysis and decision-making data based on sentiment. Specific actions include resetting priorities and reviewing resource allocation. Output is the optimized operational instructions.

[0191] Step 5:

[0192] The server monitors the communication status after optimization. The input is real-time communication quality data. Adjustments are made as needed through feedback mechanisms. The output is updated operational instructions.

[0193] Step 6:

[0194] The server sends customized notifications to the terminal, taking into account the user's emotional state. The input is information based on the emotional state and optimized operation. The output is a customized notification displayed to the user. For example, a prompt message such as, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and eliminate the delay," is generated.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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".

[0211] In an embodiment of this invention, the program for the next-generation smart base station management system efficiently manages, analyzes, optimizes, predicts, and notifies information from multiple communication base stations. This system has different roles for the server, terminal, and user, and as a whole, it improves the efficiency of the communication network.

[0212] The server periodically collects traffic data, power usage data, and fault data from each base station. It then uses a generative AI model to analyze this data in detail, enabling it to detect traffic patterns and anomalies. For example, the server can detect a sudden surge in data traffic during specific time periods and analyze its cause.

[0213] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This process allows for adjustment of resource allocation to base stations with high traffic volumes and the application of energy-saving modes to reduce power consumption. This ensures both stable communication and efficient power consumption.

[0214] Furthermore, the server continuously learns from data accumulation and generative AI models to predict future traffic demand. Based on this prediction, it reserves the necessary resources in advance and prepares to improve service quality.

[0215] The terminal notifies the user of traffic conditions and outage status in real time. This notification provides the user with information to take necessary actions quickly.

[0216] For example, if a large-scale event is scheduled in a certain area, the server predicts the increase in traffic in that area based on past data and prepares to increase the necessary resources in advance of the event. Furthermore, it enables users to take countermeasures against fluctuations in communication quality through notifications to their terminals.

[0217] In this way, the next-generation smart base station management system aims to improve the efficiency and reliability of communication networks and provide users with higher quality services.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server collects information from base stations, including traffic data, power usage data, and fault occurrence data. This data is stored in a database on the server and prepared for later analysis.

[0221] Step 2:

[0222] The server preprocesses the collected data. Specifically, it improves data quality by removing noise and filling in missing data. This enables reliable analysis.

[0223] Step 3:

[0224] The server inputs pre-processed data into a generating AI model, which analyzes traffic patterns and anomalies. During this process, the analysis engine analyzes the data flow and makes decisions based on the situation.

[0225] Step 4:

[0226] The server activates an AI agent based on the analysis results. The AI ​​agent dynamically optimizes the base station settings and operation methods, maximizing communication efficiency while simultaneously minimizing power consumption.

[0227] Step 5:

[0228] The server monitors the optimization results in real time and makes adjustments as needed if any inappropriate behavior occurs. This feedback is used to correct and improve operations.

[0229] Step 6:

[0230] The server utilizes a pre-trained generative AI model to predict future traffic demand. Based on the prediction results, it allocates the necessary resources in advance to improve communication quality.

[0231] Step 7:

[0232] The device notifies the user of traffic conditions and alerts about any outages. This allows the user to take appropriate action, ensuring a smooth communication experience.

[0233] (Example 1)

[0234] 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."

[0235] Modern communication networks require efficient management that can quickly respond to sudden fluctuations in traffic and the occurrence of failures. However, conventional systems have limitations in resource allocation and traffic forecasting, which leads to a decline in communication quality and an increase in energy consumption. This invention aims to solve these problems and improve the operational efficiency of each communication device.

[0236] 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.

[0237] In this invention, the server includes means for collecting information from communication devices, means for analyzing and recognizing data patterns using a generative AI model, and means for activating an AI agent that autonomously improves the operation of communication devices based on the analysis results. This improves the accuracy of traffic prediction in communication networks, enables optimization of resource allocation, and speeds up fault response.

[0238] "Communication equipment" refers to hardware or software for receiving and transmitting data, including base stations and network nodes.

[0239] "Information" refers to a collection of data such as traffic data, power usage data, and fault occurrence data collected from communication devices.

[0240] A "generative AI model" refers to an artificial intelligence model used to analyze and predict traffic patterns and anomalies through machine learning.

[0241] "Means of recognizing data patterns" refers to the process of analyzing collected information to identify normal traffic patterns and abnormal activity.

[0242] An "AI agent" refers to a software component that operates to autonomously improve the operation of communication devices based on analysis results.

[0243] "Autonomous improvement" refers to a process in which the system itself makes decisions and optimizes its operations without waiting for external instructions.

[0244] "Resource allocation" refers to the process of managing the allocation of computing resources and communication channels within a communication network.

[0245] "Accelerating" refers to the process of information processing and countermeasures being implemented faster than before.

[0246] In this invention, a server plays a central role in configuring a next-generation smart communication management system. The server periodically collects information from communication devices and centrally manages traffic data, power usage data, and fault data. For handling this information, a database management system is used, for example, to organize large amounts of data and enable efficient searching and updating.

[0247] The server utilizes a generative AI model to analyze the collected information. This AI model leverages machine learning techniques to recognize data patterns and perform anomaly detection and traffic prediction. For example, if data traffic surges during a specific time period, the server sends a prompt to the AI ​​model such as "Detect anomaly peaks from the traffic data of the past 72 hours." This allows the AI ​​model to perform appropriate analysis based on the data.

[0248] Furthermore, the server runs an AI agent that autonomously improves the operation of communication equipment based on the analysis results. This AI agent has the ability to dynamically adjust resource allocation and apply energy-saving modes. As a result, the overall energy efficiency of the system is improved and communication stability is maintained.

[0249] The terminal notifies the user in real time of traffic conditions and any outages. For example, if an anomaly is detected within the system, the terminal immediately sends a notification to the user's device, providing information to take appropriate action. The user receives this notification and can adjust their communication environment or use services accordingly.

[0250] In this way, the embodiment of this invention aims to contribute to improving the efficiency and reliability of communication networks and to provide high-quality services to users.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The server collects information from communication devices. Inputs include traffic data, power usage data, and fault data. The server stores this data in a database for centralized management. This allows for the integrated handling of information from multiple data sources, enabling the rapid and accurate use of data.

[0254] Step 2:

[0255] The server inputs the collected information into a generating AI model to recognize data patterns. The input is centrally managed data. The server instructs the AI ​​model to analyze the data using prompts such as, "Detect abnormal peaks from the traffic data of the past 72 hours." The output is the predicted traffic patterns and the results of the anomaly detection. Through this process, the server can understand traffic trends and quickly detect early signs of anomalies.

[0256] Step 3:

[0257] The server operates the AI ​​agent based on the analysis results obtained from the AI ​​model. The input is the analyzed pattern and anomaly data. The server uses the AI ​​agent to autonomously improve the operation of the communication equipment, specifically by dynamically allocating resources and applying energy-saving modes. The output is improved communication efficiency and reduced energy consumption due to optimized resource allocation.

[0258] Step 4:

[0259] The terminal sends real-time traffic status and outage notifications to the user. Input is the latest traffic data and outage information sent from the server. The terminal sends this information to the user's device via a push notification system, enabling the user to respond quickly to the situation. Output is specific action guidelines and countermeasures provided to the user. The user can use this information to maintain communication quality and mitigate risks.

[0260] (Application Example 1)

[0261] 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."

[0262] Current communication infrastructure faces challenges in providing stable communication services to users because it struggles to respond quickly to sudden spikes in communication traffic in specific areas or time zones. Furthermore, efficient energy consumption is required in the operation of the communication infrastructure. Overcoming these challenges and providing efficient and reliable communication services, especially in smart cities, is essential.

[0263] 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.

[0264] In this invention, the server includes means for acquiring information from a communication infrastructure, means for analyzing the information using a generation AI model to understand trends, and means for activating an intelligent agent that autonomously optimizes the operation of the communication infrastructure based on the analysis results. This enables the provision of real-time communication trends in a specific area, efficient resource allocation based on predictions, and the provision of stable communication services.

[0265] "Communication infrastructure" refers to the basic infrastructure necessary to provide communication services.

[0266] "Means of acquiring information" refers to methods and mechanisms for collecting necessary data from communication infrastructure.

[0267] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze data and detect patterns or anomalies.

[0268] "Means of understanding trends" refers to methods of analyzing communication flows and trends based on acquired information.

[0269] An "intelligent agent" refers to a program that autonomously selects and executes the optimal action based on information within the system.

[0270] "Optimization" refers to adjustments made to maximize the use of limited resources and provide efficient and effective services.

[0271] "Providing information in real time" means providing information such as communication trends immediately and without delay.

[0272] "Efficient resource allocation" refers to the effective distribution of resources in communication infrastructure according to demand.

[0273] A description of the embodiment for carrying out the invention will be provided.

[0274] The server in this invention acquires necessary information from the communication infrastructure and performs data analysis by inputting it into a generating AI model. The analysis includes acquired traffic data and power usage data, and the AI ​​model performs pattern recognition to detect anomalies. Specifically, it can grasp the flow of communication in real time and immediately detect increasing traffic.

[0275] Based on the analysis results, the server automatically activates an intelligent agent to optimize the operation of the communication infrastructure. This optimization includes dynamic reallocation of resources and adjustment of power consumption. This ensures the stability of communication services while enabling efficient use of energy.

[0276] On the device, this optimized information is used to quickly notify the user. These notifications include real-time feedback on communication status and detection of potential problems, allowing the user to take necessary adjustments early.

[0277] A concrete example is when a server predicts a surge in traffic due to a large-scale event and allocates resources accordingly. This ensures that communication is not interrupted during the event, allowing users to enjoy a comfortable communication environment. Further detailed analysis can be performed using prompt messages such as, "Detect abnormal communication patterns based on the following base data."

[0278] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0279] Step 1:

[0280] The server acquires traffic data and power consumption data from the communication infrastructure. The input for this step is various communication data, and the output is the raw data obtained. The server performs an operation of periodically collecting these data using an API.

[0281] Step 2:

[0282] The server inputs the acquired data into the generated AI model for analysis. The input is the traffic data and power consumption data obtained in Step 1, and the output is the analysis result. The server performs an operation of pattern recognition of the data by the AI model to detect anomalies and trends.

[0283] Step 3:

[0284] The server activates the intelligent agent based on the analysis result to optimize the operation of the communication infrastructure. The input is the analysis result of Step 2, and the output is the optimized operation instruction. The server performs an operation of automatically adjusting the redistribution of resources and energy-saving settings.

[0285] Step 4:

[0286] The server transmits the optimization result to the terminal and provides the necessary notification information. The input is the operation instruction created in Step 3, and the output is the notification content to the terminal. The server performs an operation of transmitting the information in real time so that the user can confirm it on the terminal.

[0287] Step 5:

[0288] Users receive notifications through their devices and take timely actions regarding communication status and failures. Input is the notification content from the server, and output is the user's actions. The device displays alerts to the user and provides prompts for necessary actions.

[0289] 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.

[0290] The next-generation smart base station management system, incorporating an emotion engine, provides a powerful framework for recognizing user emotions in real time and optimizing network operations based on that information. First, the server utilizes emotion data collected from user terminals, in addition to conventional traffic data, power usage data, and fault data. This emotion data is extracted from the user's voice, facial expressions, and input content, and then analyzed by the emotion engine.

[0291] The server uses a generative AI model to analyze all received data, identifying not only common traffic patterns and anomalies, but also the impact of changes in user emotions on communication quality and resource demands. For example, if the server detects that a user is dissatisfied, it uses that information to perform optimization processes more quickly in order to improve communication quality.

[0292] Based on the analysis results, an AI agent automatically optimizes base station operations. Emotional data, in particular, supports rapid response during communication problems. For example, when a user's emotions are negative, traffic prioritization is reviewed and problems are resolved quickly, improving the user experience.

[0293] Furthermore, the server evolves its generative AI model based on changes in user emotions to predict future traffic demand. In this prediction, if user emotions foreshadow significant traffic fluctuations, this information is incorporated into the prediction model to prepare the network.

[0294] The device displays customized alerts that take into account the user's emotional state at the time when notifying them of traffic conditions or outages. This enables emotionally sensitive communication.

[0295] This system aims to further improve the quality of communication services by evolving network operations from simple data analysis to incorporating emotions as an element closely related to the user experience.

[0296] The following describes the processing flow.

[0297] Step 1:

[0298] The server collects traffic data, power usage data, fault data, and user sentiment data from base stations and user terminals. Sentiment data is obtained from user terminals using voice analysis and facial recognition technology.

[0299] Step 2:

[0300] The server preprocesses the collected sentiment data, removing noise and imputing data. This preprocessing increases the reliability of the sentiment data and improves the accuracy of subsequent analysis.

[0301] Step 3:

[0302] The server inputs pre-processed emotional data into the emotion engine and analyzes the user's emotional state. The analyzed emotional state is then categorized, for example, into categories such as "satisfied," "dissatisfied," and "stressed."

[0303] Step 4:

[0304] The server uses a generative AI model to analyze traffic data and sentiment data, and in addition to traffic patterns and anomaly detection, it evaluates the impact of users' emotional changes on communication. This enables the identification of patterns such as when user dissatisfaction is high during peak times.

[0305] Step 5:

[0306] The server operates an AI agent based on the analysis results. This agent dynamically adjusts the settings of the base station, and particularly when the user's sentiment is deteriorating, it optimizes the resource allocation to improve communication quality.

[0307] Step 6:

[0308] The server monitors the optimization results and the user's emotional state. This enables it to determine whether the user is satisfied, and if further adjustment is necessary, the AI agent automatically takes action.

[0309] Step 7:

[0310] The terminal notifies the user of customized alerts. For example, if it is detected that the user is feeling stressed, an alert using a softer tone or an encouraging message can be provided.

[0311] (Example 2)

[0312] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] Existing communication systems suffer from a lack of network management that takes into account users' emotional states, resulting in insufficient improvement in the quality of the user experience. Furthermore, network optimization is often based solely on normal traffic and failure conditions, leading to delays in improving communication quality. This can result in decreased user satisfaction and potentially undermine the competitiveness of telecommunications carriers.

[0314] 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.

[0315] In this invention, the server includes means for generating emotional information from a user terminal, means for analyzing various data including the emotional information using a generation AI model to identify the impact on traffic patterns and communication quality, and means for autonomously optimizing the operation of the base station based on the analysis results and activating an AI agent that performs rapid optimization processing that takes emotional data into consideration. This enables rapid network optimization and improvement of communication quality that takes user emotions into consideration.

[0316] A "user terminal" is a device that a user directly operates and is equipped with various sensors and input devices for generating emotional information.

[0317] "Emotional information" refers to information extracted from the user's voice, facial expressions, and input content, and is data used to identify the user's emotional state.

[0318] A "generative AI model" is a model that uses artificial intelligence technology to analyze data collected from user terminals, and its role is to identify the impact on traffic patterns and communication quality.

[0319] "Traffic patterns" are information that represents the flow and trends of data in a network, and are used to understand the communication status.

[0320] "Communication quality" is an indicator used to evaluate the performance of communication services provided through a network, and it is a factor that directly affects the user experience.

[0321] An "AI agent" is an artificial intelligence program that functions to automatically optimize the operation of base stations, and it operates autonomously based on the analysis results.

[0322] A "feedback mechanism" is a system for monitoring the results of optimization and making adjustments as needed, thereby ensuring the quality of system operation.

[0323] An "alert" is a message used to notify users of traffic conditions or outages, and its purpose is to convey information while taking into account the user's emotional state.

[0324] This invention aims to achieve more advanced communication optimization by utilizing user emotional information in a network management system.

[0325] First, the user's device is equipped with a microphone and camera to capture voice and facial expressions. Through this hardware, the device acquires emotional information from the user's voice and facial expressions, and sends all the data, including the input content, to the server. At this time, the data is processed in real time. For example, when the user types "the connection is slow," the device identifies the emotion of dissatisfaction.

[0326] The server is equipped with an emotion engine that analyzes voice and facial expression data received from user terminals. This analysis uses specific software tools such as voice tone analysis algorithms and facial expression recognition APIs. The analyzed emotion information, along with conventional traffic data and fault data, is input into a generative AI model within the server. The generative AI model uses this information to identify the impact of traffic patterns and changes in user emotions on communication quality.

[0327] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This ensures that measures are taken to immediately improve communication quality, especially when users express dissatisfaction. For example, when a user's mood deteriorates, the system re-evaluates the priority of communication resources and instantly implements necessary corrective measures. This system achieves improvements in the quality of communication services and the user experience.

[0328] Furthermore, the server uses a generative AI model to predict future traffic demand. This prediction reflects user sentiment data and is made using prompt messages such as: "The user expressed dissatisfaction with the connection speed. Prioritization is required."

[0329] Furthermore, when the device notifies the user of traffic conditions or outages, it generates customized alerts that reflect the user's emotions. This provides users with a sense of security and a better communication experience.

[0330] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0331] Step 1:

[0332] The device collects emotional data such as voice, facial expressions, and text input. Specifically, it records voice using the device's microphone, captures facial expressions with its camera, and obtains input from the keyboard. This emotional data is then sent to the server as input.

[0333] Step 2:

[0334] The server analyzes the emotional data received from the terminal using an emotion engine. Voice data is processed by a voice tone analysis algorithm, and facial expression data is recognized using a facial expression recognition API. The input text content is analyzed using natural language processing to identify emotions. As a result of this processing, detailed information about the user's emotional state is obtained.

[0335] Step 3:

[0336] The server integrates the analyzed sentiment data with other traffic and fault data and inputs it into a generative AI model. Based on this data, the generative AI model identifies the impact on traffic patterns and communication quality, and generates detailed analysis results as output. Specifically, it determines how user dissatisfaction is affecting communication requests.

[0337] Step 4:

[0338] The server activates an AI agent and automatically optimizes base station operations based on the analysis results. This process includes reprioritizing traffic and reallocating resources using sentiment data. The output is an optimized communication environment.

[0339] Step 5:

[0340] The server uses a generative AI model to predict future traffic demand. It creates prompt messages that reflect sentiment data (for example, "Users expressed dissatisfaction with connection speed. Prioritization is needed.") and analyzes future traffic trends. The output is predicted traffic demand information.

[0341] Step 6:

[0342] The device notifies the user based on information from the server. Specifically, it displays customized alerts that take the user's emotions into consideration. For example, it might say, "The network is currently congested, but the situation is improving," providing a sense of reassurance.

[0343] (Application Example 2)

[0344] 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 will be referred to as the "terminal."

[0345] Modern communication systems face the challenge of communication delays and interruptions that negatively impact user satisfaction and directly lead to a decline in service quality. Furthermore, while there is a need to resolve these communication problems quickly, traditional methods have not adequately considered the emotional state of the user.

[0346] 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.

[0347] In this invention, the server includes means for acquiring information from a base station, means for analyzing the information using a generation AI model and detecting data trends, and means for collecting user emotion data and analyzing it using an emotion analysis engine. This makes it possible to optimize communication quality while understanding the user's emotional state in real time. Furthermore, based on the analysis results, it becomes possible to autonomously perform optimal operations using an artificial intelligence agent and improve the user experience.

[0348] "Means of acquiring information" refers to a device or process that has the function of collecting necessary data and information from a base station.

[0349] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict future data trends and demand.

[0350] "Means for detecting data trends" refers to analytical devices or software used to identify certain patterns or changes within collected information.

[0351] An "emotion analysis engine" is a system or algorithm that reads and analyzes a user's emotions from their voice, facial expressions, and other data.

[0352] An "artificial intelligence agent" is software or a system that has the function of making autonomous decisions based on analysis results and optimizing the operation of a base station.

[0353] A "feedback mechanism for monitoring and adjusting" refers to a device or software process that continuously checks the communication status after optimization and revises the settings as needed.

[0354] A "means of providing customized notifications" refers to a system that delivers individually tailored information notifications based on the user's emotional data and circumstances.

[0355] The system implementing this invention is realized through the interaction of a server, a terminal, and a user. The server acquires data from a communication base station and analyzes the information using its own generative AI model. Not only are traffic trends detected from the analyzed data, but emotional data collected from the user terminal is also analyzed. An emotional analysis engine is activated to identify changes in emotion based on voice and facial expressions.

[0356] Based on analysis results and sentiment data, the server uses an artificial intelligence agent to optimize base station communication operations. Specifically, it autonomously determines communication priorities and reallocates resources as needed. This significantly improves the user experience and minimizes communication delays and service interruptions.

[0357] The user's smartphone or smart glasses collect voice capture and facial expression data, and send it to the server in real time. The emotion analysis engine used here is expected to include the Google Cloud Emotion API or the Microsoft Azure Emotion Recognition API.

[0358] The server continuously monitors the optimization results, provides feedback based on the monitoring results, and makes adjustments as needed to enhance responsiveness. Furthermore, it incorporates a mechanism to provide customized notifications that take into account changes in the user's emotions. For example, when a user expresses dissatisfaction, a prompt message is sent to the server to resolve the delay, enabling a quick response. This message might read something like, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and resolve the delay."

[0359] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0360] Step 1:

[0361] The device collects the user's voice and facial expression data. Input is real-time audio and images captured by a smartphone or smart glasses. Output is this data ready to be sent to a server in digital format.

[0362] Step 2:

[0363] The server processes the received audio and facial expression data using an emotion analysis engine to identify the user's emotions. The input is digital data transmitted from the terminal. Data processing is performed using emotion analysis with the Google Cloud Emotion API or Microsoft Azure Emotion Recognition API. The output is the analysis result indicating the user's emotional state.

[0364] Step 3:

[0365] The server uses a generative AI model to analyze traffic data acquired from base stations. The input consists of communication data from base stations and the results of sentiment analysis. Data processing is performed by using the generative AI model to detect future traffic trends and their impact. The output is the analysis results, showing traffic patterns and anomalies.

[0366] Step 4:

[0367] The server uses an artificial intelligence agent to instruct the optimization of communication operations based on analysis results and sentiment data. Inputs are traffic analysis and decision-making data based on sentiment. Specific actions include resetting priorities and reviewing resource allocation. Output is the optimized operational instructions.

[0368] Step 5:

[0369] The server monitors the communication status after optimization. The input is real-time communication quality data. Adjustments are made as needed through feedback mechanisms. The output is updated operational instructions.

[0370] Step 6:

[0371] The server sends customized notifications to the terminal, taking into account the user's emotional state. The input is information based on the emotional state and optimized operation. The output is a customized notification displayed to the user. For example, a prompt message such as, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and eliminate the delay," is generated.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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".

[0388] In an embodiment of this invention, the program for the next-generation smart base station management system efficiently manages, analyzes, optimizes, predicts, and notifies information from multiple communication base stations. This system has different roles for the server, terminal, and user, and as a whole, it improves the efficiency of the communication network.

[0389] The server periodically collects traffic data, power usage data, and fault data from each base station. It then uses a generative AI model to analyze this data in detail, enabling it to detect traffic patterns and anomalies. For example, the server can detect a sudden surge in data traffic during specific time periods and analyze its cause.

[0390] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This process allows for adjustment of resource allocation to base stations with high traffic volumes and the application of energy-saving modes to reduce power consumption. This ensures both stable communication and efficient power consumption.

[0391] Furthermore, the server continuously learns from data accumulation and generative AI models to predict future traffic demand. Based on this prediction, it reserves the necessary resources in advance and prepares to improve service quality.

[0392] The terminal notifies the user of traffic conditions and outage status in real time. This notification provides the user with information to take necessary actions quickly.

[0393] For example, if a large-scale event is scheduled in a certain area, the server predicts the increase in traffic in that area based on past data and prepares to increase the necessary resources in advance of the event. Furthermore, it enables users to take countermeasures against fluctuations in communication quality through notifications to their terminals.

[0394] In this way, the next-generation smart base station management system aims to improve the efficiency and reliability of communication networks and provide users with higher quality services.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The server collects information from base stations, including traffic data, power usage data, and fault occurrence data. This data is stored in a database on the server and prepared for later analysis.

[0398] Step 2:

[0399] The server preprocesses the collected data. Specifically, it improves data quality by removing noise and filling in missing data. This enables reliable analysis.

[0400] Step 3:

[0401] The server inputs pre-processed data into a generating AI model, which analyzes traffic patterns and anomalies. During this process, the analysis engine analyzes the data flow and makes decisions based on the situation.

[0402] Step 4:

[0403] The server activates an AI agent based on the analysis results. The AI ​​agent dynamically optimizes the base station settings and operation methods, maximizing communication efficiency while simultaneously minimizing power consumption.

[0404] Step 5:

[0405] The server monitors the optimization results in real time and makes adjustments as needed if any inappropriate behavior occurs. This feedback is used to correct and improve operations.

[0406] Step 6:

[0407] The server utilizes a pre-trained generative AI model to predict future traffic demand. Based on the prediction results, it allocates the necessary resources in advance to improve communication quality.

[0408] Step 7:

[0409] The device notifies the user of traffic conditions and alerts about any outages. This allows the user to take appropriate action, ensuring a smooth communication experience.

[0410] (Example 1)

[0411] 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."

[0412] Modern communication networks require efficient management that can quickly respond to sudden fluctuations in traffic and the occurrence of failures. However, conventional systems have limitations in resource allocation and traffic forecasting, which leads to a decline in communication quality and an increase in energy consumption. This invention aims to solve these problems and improve the operational efficiency of each communication device.

[0413] 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.

[0414] In this invention, the server includes means for collecting information from communication devices, means for analyzing and recognizing data patterns using a generative AI model, and means for activating an AI agent that autonomously improves the operation of communication devices based on the analysis results. This improves the accuracy of traffic prediction in communication networks, enables optimization of resource allocation, and speeds up fault response.

[0415] "Communication equipment" refers to hardware or software for receiving and transmitting data, including base stations and network nodes.

[0416] "Information" refers to a collection of data such as traffic data, power usage data, and fault occurrence data collected from communication devices.

[0417] A "generative AI model" refers to an artificial intelligence model used to analyze and predict traffic patterns and anomalies through machine learning.

[0418] "Means of recognizing data patterns" refers to the process of analyzing collected information to identify normal traffic patterns and abnormal activity.

[0419] An "AI agent" refers to a software component that operates to autonomously improve the operation of communication devices based on analysis results.

[0420] "Autonomous improvement" refers to a process in which the system itself makes decisions and optimizes its operations without waiting for external instructions.

[0421] "Resource allocation" refers to the process of managing the allocation of computing resources and communication channels within a communication network.

[0422] "Accelerating" refers to the process of information processing and countermeasures being implemented faster than before.

[0423] In this invention, a server plays a central role in configuring a next-generation smart communication management system. The server periodically collects information from communication devices and centrally manages traffic data, power usage data, and fault data. For handling this information, a database management system is used, for example, to organize large amounts of data and enable efficient searching and updating.

[0424] The server utilizes a generative AI model to analyze the collected information. This AI model leverages machine learning techniques to recognize data patterns and perform anomaly detection and traffic prediction. For example, if data traffic surges during a specific time period, the server sends a prompt to the AI ​​model such as "Detect anomaly peaks from the traffic data of the past 72 hours." This allows the AI ​​model to perform appropriate analysis based on the data.

[0425] Furthermore, the server runs an AI agent that autonomously improves the operation of communication equipment based on the analysis results. This AI agent has the ability to dynamically adjust resource allocation and apply energy-saving modes. As a result, the overall energy efficiency of the system is improved and communication stability is maintained.

[0426] The terminal notifies the user in real time of traffic conditions and any outages. For example, if an anomaly is detected within the system, the terminal immediately sends a notification to the user's device, providing information to take appropriate action. The user receives this notification and can adjust their communication environment or use services accordingly.

[0427] In this way, the embodiment of this invention aims to contribute to improving the efficiency and reliability of communication networks and to provide high-quality services to users.

[0428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0429] Step 1:

[0430] The server collects information from communication devices. Inputs include traffic data, power usage data, and fault data. The server stores this data in a database for centralized management. This allows for the integrated handling of information from multiple data sources, enabling the rapid and accurate use of data.

[0431] Step 2:

[0432] The server inputs the collected information into a generating AI model to recognize data patterns. The input is centrally managed data. The server instructs the AI ​​model to analyze the data using prompts such as, "Detect abnormal peaks from the traffic data of the past 72 hours." The output is the predicted traffic patterns and the results of the anomaly detection. Through this process, the server can understand traffic trends and quickly detect early signs of anomalies.

[0433] Step 3:

[0434] The server operates the AI ​​agent based on the analysis results obtained from the AI ​​model. The input is the analyzed pattern and anomaly data. The server uses the AI ​​agent to autonomously improve the operation of the communication equipment, specifically by dynamically allocating resources and applying energy-saving modes. The output is improved communication efficiency and reduced energy consumption due to optimized resource allocation.

[0435] Step 4:

[0436] The terminal sends real-time traffic status and outage notifications to the user. Input is the latest traffic data and outage information sent from the server. The terminal sends this information to the user's device via a push notification system, enabling the user to respond quickly to the situation. Output is specific action guidelines and countermeasures provided to the user. The user can use this information to maintain communication quality and mitigate risks.

[0437] (Application Example 1)

[0438] 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."

[0439] Current communication infrastructure faces challenges in providing stable communication services to users because it struggles to respond quickly to sudden spikes in communication traffic in specific areas or time zones. Furthermore, efficient energy consumption is required in the operation of the communication infrastructure. Overcoming these challenges and providing efficient and reliable communication services, especially in smart cities, is essential.

[0440] 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.

[0441] In this invention, the server includes means for acquiring information from a communication infrastructure, means for analyzing the information using a generation AI model to understand trends, and means for activating an intelligent agent that autonomously optimizes the operation of the communication infrastructure based on the analysis results. This enables the provision of real-time communication trends in a specific area, efficient resource allocation based on predictions, and the provision of stable communication services.

[0442] "Communication infrastructure" refers to the basic infrastructure necessary to provide communication services.

[0443] "Means of acquiring information" refers to methods and mechanisms for collecting necessary data from communication infrastructure.

[0444] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze data and detect patterns or anomalies.

[0445] "Means of understanding trends" refers to methods of analyzing communication flows and trends based on acquired information.

[0446] An "intelligent agent" refers to a program that autonomously selects and executes the optimal action based on information within the system.

[0447] "Optimization" refers to adjustments made to maximize the use of limited resources and provide efficient and effective services.

[0448] "Providing information in real time" means providing information such as communication trends immediately and without delay.

[0449] "Efficient resource allocation" refers to the effective distribution of resources in communication infrastructure according to demand.

[0450] A description of the embodiment for carrying out the invention will be provided.

[0451] The server in this invention acquires necessary information from the communication infrastructure and performs data analysis by inputting it into a generating AI model. The analysis includes acquired traffic data and power usage data, and the AI ​​model performs pattern recognition to detect anomalies. Specifically, it can grasp the flow of communication in real time and immediately detect increasing traffic.

[0452] Based on the analysis results, the server automatically activates an intelligent agent to optimize the operation of the communication infrastructure. This optimization includes dynamic reallocation of resources and adjustment of power consumption. This ensures the stability of communication services while enabling efficient use of energy.

[0453] On the device, this optimized information is used to quickly notify the user. These notifications include real-time feedback on communication status and detection of potential problems, allowing the user to take necessary adjustments early.

[0454] A concrete example is when a server predicts a surge in traffic due to a large-scale event and allocates resources accordingly. This ensures that communication is not interrupted during the event, allowing users to enjoy a comfortable communication environment. Further detailed analysis can be performed using prompt messages such as, "Detect abnormal communication patterns based on the following base data."

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The server acquires traffic data and power usage data from the communication infrastructure. The input for this step is various communication data, and the output is the acquired raw data. The server periodically collects this data using an API.

[0458] Step 2:

[0459] The server inputs the acquired data into a generating AI model for analysis. The input consists of traffic data and power usage data obtained in step 1, and the output is the analysis results. The server uses the AI ​​model to perform pattern recognition on the data and detect anomalies and trends.

[0460] Step 3:

[0461] The server activates an intelligent agent based on the analysis results to optimize the operation of the communication infrastructure. The input is the analysis results from step 2, and the output is the optimized operation instructions. The server then performs actions such as automatically reallocating resources and adjusting energy-saving settings.

[0462] Step 4:

[0463] The server sends the optimization results to the terminal and provides necessary notification information. The input is the operation instructions created in step 3, and the output is the notification content sent to the terminal. The server transmits information in real time and operates so that the user can check it on the terminal.

[0464] Step 5:

[0465] Users receive notifications through their devices and take timely actions regarding communication status and failures. Input is the notification content from the server, and output is the user's actions. The device displays alerts to the user and provides prompts for necessary actions.

[0466] 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.

[0467] The next-generation smart base station management system, incorporating an emotion engine, provides a powerful framework for recognizing user emotions in real time and optimizing network operations based on that information. First, the server utilizes emotion data collected from user terminals, in addition to conventional traffic data, power usage data, and fault data. This emotion data is extracted from the user's voice, facial expressions, and input content, and then analyzed by the emotion engine.

[0468] The server uses a generative AI model to analyze all received data, identifying not only common traffic patterns and anomalies, but also the impact of changes in user emotions on communication quality and resource demands. For example, if the server detects that a user is dissatisfied, it uses that information to perform optimization processes more quickly in order to improve communication quality.

[0469] Based on the analysis results, an AI agent automatically optimizes base station operations. Emotional data, in particular, supports rapid response during communication problems. For example, when a user's emotions are negative, traffic prioritization is reviewed and problems are resolved quickly, improving the user experience.

[0470] Furthermore, the server evolves its generative AI model based on changes in user emotions to predict future traffic demand. In this prediction, if user emotions foreshadow significant traffic fluctuations, this information is incorporated into the prediction model to prepare the network.

[0471] The device displays customized alerts that take into account the user's emotional state at the time when notifying them of traffic conditions or outages. This enables emotionally sensitive communication.

[0472] This system aims to further improve the quality of communication services by evolving network operations from simple data analysis to incorporating emotions as an element closely related to the user experience.

[0473] The following describes the processing flow.

[0474] Step 1:

[0475] The server collects traffic data, power usage data, fault data, and user sentiment data from base stations and user terminals. Sentiment data is obtained from user terminals using voice analysis and facial recognition technology.

[0476] Step 2:

[0477] The server preprocesses the collected sentiment data, removing noise and imputing data. This preprocessing increases the reliability of the sentiment data and improves the accuracy of subsequent analysis.

[0478] Step 3:

[0479] The server inputs pre-processed emotional data into the emotion engine and analyzes the user's emotional state. The analyzed emotional state is then categorized, for example, into categories such as "satisfied," "dissatisfied," and "stressed."

[0480] Step 4:

[0481] The server uses a generative AI model to analyze traffic and sentiment data, evaluating the impact of user sentiment changes on communications, in addition to identifying traffic patterns and detecting anomalies. This makes it possible to identify patterns such as when user dissatisfaction is high at peak times.

[0482] Step 5:

[0483] The server activates an AI agent based on the analysis results. This agent dynamically adjusts the base station settings, optimizing resource allocation, especially when the user's emotions are negative, in order to improve communication quality.

[0484] Step 6:

[0485] The server monitors the optimization results and the user's emotional state. This allows it to understand whether the user is satisfied, and if further adjustments are needed, the AI ​​agent automatically takes action.

[0486] Step 7:

[0487] The device notifies the user with customized alerts. For example, if it detects that the user is stressed, it can provide alerts using a softer tone and encouraging messages.

[0488] (Example 2)

[0489] 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."

[0490] Existing communication systems suffer from a lack of network management that takes into account users' emotional states, resulting in insufficient improvement in the quality of the user experience. Furthermore, network optimization is often based solely on normal traffic and failure conditions, leading to delays in improving communication quality. This can result in decreased user satisfaction and potentially undermine the competitiveness of telecommunications carriers.

[0491] 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.

[0492] In this invention, the server includes means for generating emotional information from a user terminal, means for analyzing various data including the emotional information using a generation AI model to identify the impact on traffic patterns and communication quality, and means for autonomously optimizing the operation of the base station based on the analysis results and activating an AI agent that performs rapid optimization processing that takes emotional data into consideration. This enables rapid network optimization and improvement of communication quality that takes user emotions into consideration.

[0493] A "user terminal" is a device that a user directly operates and is equipped with various sensors and input devices for generating emotional information.

[0494] "Emotional information" refers to information extracted from the user's voice, facial expressions, and input content, and is data used to identify the user's emotional state.

[0495] A "generative AI model" is a model that uses artificial intelligence technology to analyze data collected from user terminals, and its role is to identify the impact on traffic patterns and communication quality.

[0496] "Traffic patterns" are information that represents the flow and trends of data in a network, and are used to understand the communication status.

[0497] "Communication quality" is an indicator used to evaluate the performance of communication services provided through a network, and it is a factor that directly affects the user experience.

[0498] An "AI agent" is an artificial intelligence program that functions to automatically optimize the operation of base stations, and it operates autonomously based on the analysis results.

[0499] A "feedback mechanism" is a system for monitoring the results of optimization and making adjustments as needed, thereby ensuring the quality of system operation.

[0500] An "alert" is a message used to notify users of traffic conditions or outages, and its purpose is to convey information while taking into account the user's emotional state.

[0501] This invention aims to achieve more advanced communication optimization by utilizing user emotional information in a network management system.

[0502] First, the user's device is equipped with a microphone and camera to capture voice and facial expressions. Through this hardware, the device acquires emotional information from the user's voice and facial expressions, and sends all the data, including the input content, to the server. At this time, the data is processed in real time. For example, when the user types "the connection is slow," the device identifies the emotion of dissatisfaction.

[0503] The server is equipped with an emotion engine that analyzes voice and facial expression data received from user terminals. This analysis uses specific software tools such as voice tone analysis algorithms and facial expression recognition APIs. The analyzed emotion information, along with conventional traffic data and fault data, is input into a generative AI model within the server. The generative AI model uses this information to identify the impact of traffic patterns and changes in user emotions on communication quality.

[0504] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This ensures that measures are taken to immediately improve communication quality, especially when users express dissatisfaction. For example, when a user's mood deteriorates, the system re-evaluates the priority of communication resources and instantly implements necessary corrective measures. This system achieves improvements in the quality of communication services and the user experience.

[0505] Furthermore, the server uses a generative AI model to predict future traffic demand. This prediction reflects user sentiment data and is made using prompt messages such as: "The user expressed dissatisfaction with the connection speed. Prioritization is required."

[0506] Furthermore, when the device notifies the user of traffic conditions or outages, it generates customized alerts that reflect the user's emotions. This provides users with a sense of security and a better communication experience.

[0507] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0508] Step 1:

[0509] The device collects emotional data such as voice, facial expressions, and text input. Specifically, it records voice using the device's microphone, captures facial expressions with its camera, and obtains input from the keyboard. This emotional data is then sent to the server as input.

[0510] Step 2:

[0511] The server analyzes the emotional data received from the terminal using an emotion engine. Voice data is processed by a voice tone analysis algorithm, and facial expression data is recognized using a facial expression recognition API. The input text content is analyzed using natural language processing to identify emotions. As a result of this processing, detailed information about the user's emotional state is obtained.

[0512] Step 3:

[0513] The server integrates the analyzed sentiment data with other traffic and fault data and inputs it into a generative AI model. Based on this data, the generative AI model identifies the impact on traffic patterns and communication quality, and generates detailed analysis results as output. Specifically, it determines how user dissatisfaction is affecting communication requests.

[0514] Step 4:

[0515] The server activates an AI agent and automatically optimizes base station operations based on the analysis results. This process includes reprioritizing traffic and reallocating resources using sentiment data. The output is an optimized communication environment.

[0516] Step 5:

[0517] The server uses a generative AI model to predict future traffic demand. It creates prompt messages that reflect sentiment data (for example, "Users expressed dissatisfaction with connection speed. Prioritization is needed.") and analyzes future traffic trends. The output is predicted traffic demand information.

[0518] Step 6:

[0519] The device notifies the user based on information from the server. Specifically, it displays customized alerts that take the user's emotions into consideration. For example, it might say, "The network is currently congested, but the situation is improving," providing a sense of reassurance.

[0520] (Application Example 2)

[0521] 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."

[0522] Modern communication systems face the challenge of communication delays and interruptions that negatively impact user satisfaction and directly lead to a decline in service quality. Furthermore, while there is a need to resolve these communication problems quickly, traditional methods have not adequately considered the emotional state of the user.

[0523] 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.

[0524] In this invention, the server includes means for acquiring information from a base station, means for analyzing the information using a generation AI model and detecting data trends, and means for collecting user emotion data and analyzing it using an emotion analysis engine. This makes it possible to optimize communication quality while understanding the user's emotional state in real time. Furthermore, based on the analysis results, it becomes possible to autonomously perform optimal operations using an artificial intelligence agent and improve the user experience.

[0525] "Means of acquiring information" refers to a device or process that has the function of collecting necessary data and information from a base station.

[0526] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict future data trends and demand.

[0527] "Means for detecting data trends" refers to analytical devices or software used to identify certain patterns or changes within collected information.

[0528] An "emotion analysis engine" is a system or algorithm that reads and analyzes a user's emotions from their voice, facial expressions, and other data.

[0529] An "artificial intelligence agent" is software or a system that has the function of making autonomous decisions based on analysis results and optimizing the operation of a base station.

[0530] A "feedback mechanism for monitoring and adjusting" refers to a device or software process that continuously checks the communication status after optimization and revises the settings as needed.

[0531] A "means of providing customized notifications" refers to a system that delivers individually tailored information notifications based on the user's emotional data and circumstances.

[0532] The system implementing this invention is realized through the interaction of a server, a terminal, and a user. The server acquires data from a communication base station and analyzes the information using its own generative AI model. Not only are traffic trends detected from the analyzed data, but emotional data collected from the user terminal is also analyzed. An emotional analysis engine is activated to identify changes in emotion based on voice and facial expressions.

[0533] Based on analysis results and sentiment data, the server uses an artificial intelligence agent to optimize base station communication operations. Specifically, it autonomously determines communication priorities and reallocates resources as needed. This significantly improves the user experience and minimizes communication delays and service interruptions.

[0534] The user's smartphone or smart glasses collect voice capture and facial expression data, and send it to the server in real time. The emotion analysis engine used here is expected to include the Google Cloud Emotion API or the Microsoft Azure Emotion Recognition API.

[0535] The server continuously monitors the optimization results, provides feedback based on the monitoring results, and makes adjustments as needed to enhance responsiveness. Furthermore, it incorporates a mechanism to provide customized notifications that take into account changes in the user's emotions. For example, when a user expresses dissatisfaction, a prompt message is sent to the server to resolve the delay, enabling a quick response. This message might read something like, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and resolve the delay."

[0536] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0537] Step 1:

[0538] The device collects the user's voice and facial expression data. Input is real-time audio and images captured by a smartphone or smart glasses. Output is this data ready to be sent to a server in digital format.

[0539] Step 2:

[0540] The server processes the received audio and facial expression data using an emotion analysis engine to identify the user's emotions. The input is digital data transmitted from the terminal. Data processing is performed using emotion analysis with the Google Cloud Emotion API or Microsoft Azure Emotion Recognition API. The output is the analysis result indicating the user's emotional state.

[0541] Step 3:

[0542] The server uses a generative AI model to analyze traffic data acquired from base stations. The input consists of communication data from base stations and the results of sentiment analysis. Data processing is performed by using the generative AI model to detect future traffic trends and their impact. The output is the analysis results, showing traffic patterns and anomalies.

[0543] Step 4:

[0544] The server uses an artificial intelligence agent to instruct the optimization of communication operations based on analysis results and sentiment data. Inputs are traffic analysis and decision-making data based on sentiment. Specific actions include resetting priorities and reviewing resource allocation. Output is the optimized operational instructions.

[0545] Step 5:

[0546] The server monitors the communication status after optimization. The input is real-time communication quality data. Adjustments are made as needed through feedback mechanisms. The output is updated operational instructions.

[0547] Step 6:

[0548] The server sends customized notifications to the terminal, taking into account the user's emotional state. The input is information based on the emotional state and optimized operation. The output is a customized notification displayed to the user. For example, a prompt message such as, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and eliminate the delay," is generated.

[0549] 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.

[0550] 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.

[0551] 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.

[0552] [Fourth Embodiment]

[0553] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0554] 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.

[0555] 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).

[0556] 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.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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".

[0566] In an embodiment of this invention, the program for the next-generation smart base station management system efficiently manages, analyzes, optimizes, predicts, and notifies information from multiple communication base stations. This system has different roles for the server, terminal, and user, and as a whole, it improves the efficiency of the communication network.

[0567] The server periodically collects traffic data, power usage data, and fault data from each base station. It then uses a generative AI model to analyze this data in detail, enabling it to detect traffic patterns and anomalies. For example, the server can detect a sudden surge in data traffic during specific time periods and analyze its cause.

[0568] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This process allows for adjustment of resource allocation to base stations with high traffic volumes and the application of energy-saving modes to reduce power consumption. This ensures both stable communication and efficient power consumption.

[0569] Furthermore, the server continuously learns from data accumulation and generative AI models to predict future traffic demand. Based on this prediction, it reserves the necessary resources in advance and prepares to improve service quality.

[0570] The terminal notifies the user of traffic conditions and outage status in real time. This notification provides the user with information to take necessary actions quickly.

[0571] For example, if a large-scale event is scheduled in a certain area, the server predicts the increase in traffic in that area based on past data and prepares to increase the necessary resources in advance of the event. Furthermore, it enables users to take countermeasures against fluctuations in communication quality through notifications to their terminals.

[0572] In this way, the next-generation smart base station management system aims to improve the efficiency and reliability of communication networks and provide users with higher quality services.

[0573] The following describes the processing flow.

[0574] Step 1:

[0575] The server collects information from base stations, including traffic data, power usage data, and fault occurrence data. This data is stored in a database on the server and prepared for later analysis.

[0576] Step 2:

[0577] The server preprocesses the collected data. Specifically, it improves data quality by removing noise and filling in missing data. This enables reliable analysis.

[0578] Step 3:

[0579] The server inputs pre-processed data into a generating AI model, which analyzes traffic patterns and anomalies. During this process, the analysis engine analyzes the data flow and makes decisions based on the situation.

[0580] Step 4:

[0581] The server activates an AI agent based on the analysis results. The AI ​​agent dynamically optimizes the base station settings and operation methods, maximizing communication efficiency while simultaneously minimizing power consumption.

[0582] Step 5:

[0583] The server monitors the optimization results in real time and makes adjustments as needed if any inappropriate behavior occurs. This feedback is used to correct and improve operations.

[0584] Step 6:

[0585] The server utilizes a pre-trained generative AI model to predict future traffic demand. Based on the prediction results, it allocates the necessary resources in advance to improve communication quality.

[0586] Step 7:

[0587] The device notifies the user of traffic conditions and alerts about any outages. This allows the user to take appropriate action, ensuring a smooth communication experience.

[0588] (Example 1)

[0589] 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".

[0590] Modern communication networks require efficient management that can quickly respond to sudden fluctuations in traffic and the occurrence of failures. However, conventional systems have limitations in resource allocation and traffic forecasting, which leads to a decline in communication quality and an increase in energy consumption. This invention aims to solve these problems and improve the operational efficiency of each communication device.

[0591] 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.

[0592] In this invention, the server includes means for collecting information from communication devices, means for analyzing and recognizing data patterns using a generative AI model, and means for activating an AI agent that autonomously improves the operation of communication devices based on the analysis results. This improves the accuracy of traffic prediction in communication networks, enables optimization of resource allocation, and speeds up fault response.

[0593] "Communication equipment" refers to hardware or software for receiving and transmitting data, including base stations and network nodes.

[0594] "Information" refers to a collection of data such as traffic data, power usage data, and fault occurrence data collected from communication devices.

[0595] A "generative AI model" refers to an artificial intelligence model used to analyze and predict traffic patterns and anomalies through machine learning.

[0596] "Means of recognizing data patterns" refers to the process of analyzing collected information to identify normal traffic patterns and abnormal activity.

[0597] An "AI agent" refers to a software component that operates to autonomously improve the operation of communication devices based on analysis results.

[0598] "Autonomous improvement" refers to a process in which the system itself makes decisions and optimizes its operations without waiting for external instructions.

[0599] "Resource allocation" refers to the process of managing the allocation of computing resources and communication channels within a communication network.

[0600] "Accelerating" refers to the process of information processing and countermeasures being implemented faster than before.

[0601] In this invention, a server plays a central role in configuring a next-generation smart communication management system. The server periodically collects information from communication devices and centrally manages traffic data, power usage data, and fault data. For handling this information, a database management system is used, for example, to organize large amounts of data and enable efficient searching and updating.

[0602] The server utilizes a generative AI model to analyze the collected information. This AI model leverages machine learning techniques to recognize data patterns and perform anomaly detection and traffic prediction. For example, if data traffic surges during a specific time period, the server sends a prompt to the AI ​​model such as "Detect anomaly peaks from the traffic data of the past 72 hours." This allows the AI ​​model to perform appropriate analysis based on the data.

[0603] Furthermore, the server runs an AI agent that autonomously improves the operation of communication equipment based on the analysis results. This AI agent has the ability to dynamically adjust resource allocation and apply energy-saving modes. As a result, the overall energy efficiency of the system is improved and communication stability is maintained.

[0604] The terminal notifies the user in real time of traffic conditions and any outages. For example, if an anomaly is detected within the system, the terminal immediately sends a notification to the user's device, providing information to take appropriate action. The user receives this notification and can adjust their communication environment or use services accordingly.

[0605] In this way, the embodiment of this invention aims to contribute to improving the efficiency and reliability of communication networks and to provide high-quality services to users.

[0606] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0607] Step 1:

[0608] The server collects information from communication devices. Inputs include traffic data, power usage data, and fault data. The server stores this data in a database for centralized management. This allows for the integrated handling of information from multiple data sources, enabling the rapid and accurate use of data.

[0609] Step 2:

[0610] The server inputs the collected information into a generating AI model to recognize data patterns. The input is centrally managed data. The server instructs the AI ​​model to analyze the data using prompts such as, "Detect abnormal peaks from the traffic data of the past 72 hours." The output is the predicted traffic patterns and the results of the anomaly detection. Through this process, the server can understand traffic trends and quickly detect early signs of anomalies.

[0611] Step 3:

[0612] The server operates the AI ​​agent based on the analysis results obtained from the AI ​​model. The input is the analyzed pattern and anomaly data. The server uses the AI ​​agent to autonomously improve the operation of the communication equipment, specifically by dynamically allocating resources and applying energy-saving modes. The output is improved communication efficiency and reduced energy consumption due to optimized resource allocation.

[0613] Step 4:

[0614] The terminal sends real-time traffic status and outage notifications to the user. Input is the latest traffic data and outage information sent from the server. The terminal sends this information to the user's device via a push notification system, enabling the user to respond quickly to the situation. Output is specific action guidelines and countermeasures provided to the user. The user can use this information to maintain communication quality and mitigate risks.

[0615] (Application Example 1)

[0616] 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".

[0617] Current communication infrastructure faces challenges in providing stable communication services to users because it struggles to respond quickly to sudden spikes in communication traffic in specific areas or time zones. Furthermore, efficient energy consumption is required in the operation of the communication infrastructure. Overcoming these challenges and providing efficient and reliable communication services, especially in smart cities, is essential.

[0618] 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.

[0619] In this invention, the server includes means for acquiring information from a communication infrastructure, means for analyzing the information using a generation AI model to understand trends, and means for activating an intelligent agent that autonomously optimizes the operation of the communication infrastructure based on the analysis results. This enables the provision of real-time communication trends in a specific area, efficient resource allocation based on predictions, and the provision of stable communication services.

[0620] "Communication infrastructure" refers to the basic infrastructure necessary to provide communication services.

[0621] "Means of acquiring information" refers to methods and mechanisms for collecting necessary data from communication infrastructure.

[0622] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze data and detect patterns or anomalies.

[0623] "Means of understanding trends" refers to methods of analyzing communication flows and trends based on acquired information.

[0624] An "intelligent agent" refers to a program that autonomously selects and executes the optimal action based on information within the system.

[0625] "Optimization" refers to adjustments made to maximize the use of limited resources and provide efficient and effective services.

[0626] "Providing information in real time" means providing information such as communication trends immediately and without delay.

[0627] "Efficient resource allocation" refers to the effective distribution of resources in communication infrastructure according to demand.

[0628] A description of the embodiment for carrying out the invention will be provided.

[0629] The server in this invention acquires necessary information from the communication infrastructure and performs data analysis by inputting it into a generating AI model. The analysis includes acquired traffic data and power usage data, and the AI ​​model performs pattern recognition to detect anomalies. Specifically, it can grasp the flow of communication in real time and immediately detect increasing traffic.

[0630] Based on the analysis results, the server automatically activates an intelligent agent to optimize the operation of the communication infrastructure. This optimization includes dynamic reallocation of resources and adjustment of power consumption. This ensures the stability of communication services while enabling efficient use of energy.

[0631] On the device, this optimized information is used to quickly notify the user. These notifications include real-time feedback on communication status and detection of potential problems, allowing the user to take necessary adjustments early.

[0632] A concrete example is when a server predicts a surge in traffic due to a large-scale event and allocates resources accordingly. This ensures that communication is not interrupted during the event, allowing users to enjoy a comfortable communication environment. Further detailed analysis can be performed using prompt messages such as, "Detect abnormal communication patterns based on the following base data."

[0633] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0634] Step 1:

[0635] The server acquires traffic data and power usage data from the communication infrastructure. The input for this step is various communication data, and the output is the acquired raw data. The server periodically collects this data using an API.

[0636] Step 2:

[0637] The server inputs the acquired data into a generating AI model for analysis. The input consists of traffic data and power usage data obtained in step 1, and the output is the analysis results. The server uses the AI ​​model to perform pattern recognition on the data and detect anomalies and trends.

[0638] Step 3:

[0639] The server activates an intelligent agent based on the analysis results to optimize the operation of the communication infrastructure. The input is the analysis results from step 2, and the output is the optimized operation instructions. The server then performs actions such as automatically reallocating resources and adjusting energy-saving settings.

[0640] Step 4:

[0641] The server sends the optimization results to the terminal and provides necessary notification information. The input is the operation instructions created in step 3, and the output is the notification content sent to the terminal. The server transmits information in real time and operates so that the user can check it on the terminal.

[0642] Step 5:

[0643] Users receive notifications through their devices and take timely actions regarding communication status and failures. Input is the notification content from the server, and output is the user's actions. The device displays alerts to the user and provides prompts for necessary actions.

[0644] 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.

[0645] The next-generation smart base station management system, incorporating an emotion engine, provides a powerful framework for recognizing user emotions in real time and optimizing network operations based on that information. First, the server utilizes emotion data collected from user terminals, in addition to conventional traffic data, power usage data, and fault data. This emotion data is extracted from the user's voice, facial expressions, and input content, and then analyzed by the emotion engine.

[0646] The server uses a generative AI model to analyze all received data, identifying not only common traffic patterns and anomalies, but also the impact of changes in user emotions on communication quality and resource demands. For example, if the server detects that a user is dissatisfied, it uses that information to perform optimization processes more quickly in order to improve communication quality.

[0647] Based on the analysis results, an AI agent automatically optimizes base station operations. Emotional data, in particular, supports rapid response during communication problems. For example, when a user's emotions are negative, traffic prioritization is reviewed and problems are resolved quickly, improving the user experience.

[0648] Furthermore, the server evolves its generative AI model based on changes in user emotions to predict future traffic demand. In this prediction, if user emotions foreshadow significant traffic fluctuations, this information is incorporated into the prediction model to prepare the network.

[0649] The device displays customized alerts that take into account the user's emotional state at the time when notifying them of traffic conditions or outages. This enables emotionally sensitive communication.

[0650] This system aims to further improve the quality of communication services by evolving network operations from simple data analysis to incorporating emotions as an element closely related to the user experience.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The server collects traffic data, power usage data, fault data, and user sentiment data from base stations and user terminals. Sentiment data is obtained from user terminals using voice analysis and facial recognition technology.

[0654] Step 2:

[0655] The server preprocesses the collected sentiment data, removing noise and imputing data. This preprocessing increases the reliability of the sentiment data and improves the accuracy of subsequent analysis.

[0656] Step 3:

[0657] The server inputs pre-processed emotional data into the emotion engine and analyzes the user's emotional state. The analyzed emotional state is then categorized, for example, into categories such as "satisfied," "dissatisfied," and "stressed."

[0658] Step 4:

[0659] The server uses a generative AI model to analyze traffic and sentiment data, evaluating the impact of user sentiment changes on communications, in addition to identifying traffic patterns and detecting anomalies. This makes it possible to identify patterns such as when user dissatisfaction is high at peak times.

[0660] Step 5:

[0661] The server activates an AI agent based on the analysis results. This agent dynamically adjusts the base station settings, optimizing resource allocation, especially when the user's emotions are negative, in order to improve communication quality.

[0662] Step 6:

[0663] The server monitors the optimization results and the user's emotional state. This allows it to understand whether the user is satisfied, and if further adjustments are needed, the AI ​​agent automatically takes action.

[0664] Step 7:

[0665] The device notifies the user with customized alerts. For example, if it detects that the user is stressed, it can provide alerts using a softer tone and encouraging messages.

[0666] (Example 2)

[0667] 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".

[0668] Existing communication systems suffer from a lack of network management that takes into account users' emotional states, resulting in insufficient improvement in the quality of the user experience. Furthermore, network optimization is often based solely on normal traffic and failure conditions, leading to delays in improving communication quality. This can result in decreased user satisfaction and potentially undermine the competitiveness of telecommunications carriers.

[0669] 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.

[0670] In this invention, the server includes means for generating emotional information from a user terminal, means for analyzing various data including the emotional information using a generation AI model to identify the impact on traffic patterns and communication quality, and means for autonomously optimizing the operation of the base station based on the analysis results and activating an AI agent that performs rapid optimization processing that takes emotional data into consideration. This enables rapid network optimization and improvement of communication quality that takes user emotions into consideration.

[0671] A "user terminal" is a device that a user directly operates and is equipped with various sensors and input devices for generating emotional information.

[0672] "Emotional information" refers to information extracted from the user's voice, facial expressions, and input content, and is data used to identify the user's emotional state.

[0673] A "generative AI model" is a model that uses artificial intelligence technology to analyze data collected from user terminals, and its role is to identify the impact on traffic patterns and communication quality.

[0674] "Traffic patterns" are information that represents the flow and trends of data in a network, and are used to understand the communication status.

[0675] "Communication quality" is an indicator used to evaluate the performance of communication services provided through a network, and it is a factor that directly affects the user experience.

[0676] An "AI agent" is an artificial intelligence program that functions to automatically optimize the operation of base stations, and it operates autonomously based on the analysis results.

[0677] A "feedback mechanism" is a system for monitoring the results of optimization and making adjustments as needed, thereby ensuring the quality of system operation.

[0678] An "alert" is a message used to notify users of traffic conditions or outages, and its purpose is to convey information while taking into account the user's emotional state.

[0679] This invention aims to achieve more advanced communication optimization by utilizing user emotional information in a network management system.

[0680] First, the user's device is equipped with a microphone and camera to capture voice and facial expressions. Through this hardware, the device acquires emotional information from the user's voice and facial expressions, and sends all the data, including the input content, to the server. At this time, the data is processed in real time. For example, when the user types "the connection is slow," the device identifies the emotion of dissatisfaction.

[0681] The server is equipped with an emotion engine that analyzes voice and facial expression data received from user terminals. This analysis uses specific software tools such as voice tone analysis algorithms and facial expression recognition APIs. The analyzed emotion information, along with conventional traffic data and fault data, is input into a generative AI model within the server. The generative AI model uses this information to identify the impact of traffic patterns and changes in user emotions on communication quality.

[0682] Based on the analysis results, the server activates an AI agent to automatically optimize base station operations. This ensures that measures are taken to immediately improve communication quality, especially when users express dissatisfaction. For example, when a user's mood deteriorates, the system re-evaluates the priority of communication resources and instantly implements necessary corrective measures. This system achieves improvements in the quality of communication services and the user experience.

[0683] Furthermore, the server uses a generative AI model to predict future traffic demand. This prediction reflects user sentiment data and is made using prompt messages such as: "The user expressed dissatisfaction with the connection speed. Prioritization is required."

[0684] Furthermore, when the device notifies the user of traffic conditions or outages, it generates customized alerts that reflect the user's emotions. This provides users with a sense of security and a better communication experience.

[0685] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0686] Step 1:

[0687] The device collects emotional data such as voice, facial expressions, and text input. Specifically, it records voice using the device's microphone, captures facial expressions with its camera, and obtains input from the keyboard. This emotional data is then sent to the server as input.

[0688] Step 2:

[0689] The server analyzes the emotional data received from the terminal using an emotion engine. Voice data is processed by a voice tone analysis algorithm, and facial expression data is recognized using a facial expression recognition API. The input text content is analyzed using natural language processing to identify emotions. As a result of this processing, detailed information about the user's emotional state is obtained.

[0690] Step 3:

[0691] The server integrates the analyzed sentiment data with other traffic and fault data and inputs it into a generative AI model. Based on this data, the generative AI model identifies the impact on traffic patterns and communication quality, and generates detailed analysis results as output. Specifically, it determines how user dissatisfaction is affecting communication requests.

[0692] Step 4:

[0693] The server activates an AI agent and automatically optimizes base station operations based on the analysis results. This process includes reprioritizing traffic and reallocating resources using sentiment data. The output is an optimized communication environment.

[0694] Step 5:

[0695] The server uses a generative AI model to predict future traffic demand. It creates prompt messages that reflect sentiment data (for example, "Users expressed dissatisfaction with connection speed. Prioritization is needed.") and analyzes future traffic trends. The output is predicted traffic demand information.

[0696] Step 6:

[0697] The device notifies the user based on information from the server. Specifically, it displays customized alerts that take the user's emotions into consideration. For example, it might say, "The network is currently congested, but the situation is improving," providing a sense of reassurance.

[0698] (Application Example 2)

[0699] 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".

[0700] Modern communication systems face the challenge of communication delays and interruptions that negatively impact user satisfaction and directly lead to a decline in service quality. Furthermore, while there is a need to resolve these communication problems quickly, traditional methods have not adequately considered the emotional state of the user.

[0701] 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.

[0702] In this invention, the server includes means for acquiring information from a base station, means for analyzing the information using a generation AI model and detecting data trends, and means for collecting user emotion data and analyzing it using an emotion analysis engine. This makes it possible to optimize communication quality while understanding the user's emotional state in real time. Furthermore, based on the analysis results, it becomes possible to autonomously perform optimal operations using an artificial intelligence agent and improve the user experience.

[0703] "Means of acquiring information" refers to a device or process that has the function of collecting necessary data and information from a base station.

[0704] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to analyze data and predict future data trends and demand.

[0705] "Means for detecting data trends" refers to analytical devices or software used to identify certain patterns or changes within collected information.

[0706] An "emotion analysis engine" is a system or algorithm that reads and analyzes a user's emotions from their voice, facial expressions, and other data.

[0707] An "artificial intelligence agent" is software or a system that has the function of making autonomous decisions based on analysis results and optimizing the operation of a base station.

[0708] A "feedback mechanism for monitoring and adjusting" refers to a device or software process that continuously checks the communication status after optimization and revises the settings as needed.

[0709] A "means of providing customized notifications" refers to a system that delivers individually tailored information notifications based on the user's emotional data and circumstances.

[0710] The system implementing this invention is realized through the interaction of a server, a terminal, and a user. The server acquires data from a communication base station and analyzes the information using its own generative AI model. Not only are traffic trends detected from the analyzed data, but emotional data collected from the user terminal is also analyzed. An emotional analysis engine is activated to identify changes in emotion based on voice and facial expressions.

[0711] Based on analysis results and sentiment data, the server uses an artificial intelligence agent to optimize base station communication operations. Specifically, it autonomously determines communication priorities and reallocates resources as needed. This significantly improves the user experience and minimizes communication delays and service interruptions.

[0712] The user's smartphone or smart glasses collect voice capture and facial expression data, and send it to the server in real time. The emotion analysis engine used here is expected to include the Google Cloud Emotion API or the Microsoft Azure Emotion Recognition API.

[0713] The server continuously monitors the optimization results, provides feedback based on the monitoring results, and makes adjustments as needed to enhance responsiveness. Furthermore, it incorporates a mechanism to provide customized notifications that take into account changes in the user's emotions. For example, when a user expresses dissatisfaction, a prompt message is sent to the server to resolve the delay, enabling a quick response. This message might read something like, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and resolve the delay."

[0714] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0715] Step 1:

[0716] The device collects the user's voice and facial expression data. Input is real-time audio and images captured by a smartphone or smart glasses. Output is this data ready to be sent to a server in digital format.

[0717] Step 2:

[0718] The server processes the received audio and facial expression data using an emotion analysis engine to identify the user's emotions. The input is digital data transmitted from the terminal. Data processing is performed using emotion analysis with the Google Cloud Emotion API or Microsoft Azure Emotion Recognition API. The output is the analysis result indicating the user's emotional state.

[0719] Step 3:

[0720] The server uses a generative AI model to analyze traffic data acquired from base stations. The input consists of communication data from base stations and the results of sentiment analysis. Data processing is performed by using the generative AI model to detect future traffic trends and their impact. The output is the analysis results, showing traffic patterns and anomalies.

[0721] Step 4:

[0722] The server uses an artificial intelligence agent to instruct the optimization of communication operations based on analysis results and sentiment data. Inputs are traffic analysis and decision-making data based on sentiment. Specific actions include resetting priorities and reviewing resource allocation. Output is the optimized operational instructions.

[0723] Step 5:

[0724] The server monitors the communication status after optimization. The input is real-time communication quality data. Adjustments are made as needed through feedback mechanisms. The output is updated operational instructions.

[0725] Step 6:

[0726] The server sends customized notifications to the terminal, taking into account the user's emotional state. The input is information based on the emotional state and optimized operation. The output is a customized notification displayed to the user. For example, a prompt message such as, "During payment, we detected that the user's facial expression indicated dissatisfaction. Immediately optimize the communication path and eliminate the delay," is generated.

[0727] 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.

[0728] 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.

[0729] In the above embodiment, an example was given in which the 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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."

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0748] The following is further disclosed regarding the embodiments described above.

[0749] (Claim 1)

[0750] Means of collecting data from base stations,

[0751] A means for analyzing the aforementioned data using a generating AI model and detecting traffic patterns,

[0752] A means to activate an AI agent that autonomously optimizes the operation of base stations based on the analysis results,

[0753] A feedback means for monitoring and adjusting the results of the optimization,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, characterized in that it includes a learning means for predicting future traffic demand using a generative AI model and allocating resources in advance.

[0757] (Claim 3)

[0758] The system according to claim 1, characterized in that it includes means for notifying users of traffic conditions and failure status via alerts.

[0759] "Example 1"

[0760] (Claim 1)

[0761] Means for collecting information from communication devices,

[0762] A means for analyzing the aforementioned information using a generating AI model and recognizing data patterns,

[0763] A means to activate an AI agent that autonomously improves the operation of communication devices based on the analysis results,

[0764] A feedback means for monitoring and adjusting the results of the aforementioned improvements,

[0765] A means of predicting future data demand using a generative AI model and securing additional communication resources in advance,

[0766] A means of notifying users of data status and the occurrence of failures,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, characterized by utilizing a generative AI model to learn data structures and providing an automatic update function.

[0770] (Claim 3)

[0771] The system according to claim 1, characterized in that it includes a support function that enables the provision of information to users in real time and the implementation of countermeasures.

[0772] "Application Example 1"

[0773] (Claim 1)

[0774] Means of obtaining information from the communication infrastructure,

[0775] A means of analyzing the aforementioned information using a generating AI model to understand trends,

[0776] A means for activating an intelligent agent that autonomously optimizes the operation of the communication infrastructure based on the analysis results,

[0777] A feedback means for observing and fitting the results of the optimization,

[0778] A means of providing real-time information on communication trends in a specific area,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, characterized in that it includes a learning means for estimating future communication demand using a generative AI model and allocating resources in advance.

[0782] (Claim 3)

[0783] The system according to claim 1, characterized in that it includes means for notifying the user of communication status and failure status by warning.

[0784] "Example 2 of combining an emotion engine"

[0785] (Claim 1)

[0786] A means of generating emotional information from a user terminal,

[0787] A means for analyzing various data, including the aforementioned emotional information, using a generating AI model to identify the impact on traffic patterns and communication quality,

[0788] A means for operating an AI agent that autonomously optimizes base station operations based on analysis results and performs rapid optimization processing that takes emotional data into consideration,

[0789] A feedback means for monitoring and adjusting the results of the optimization,

[0790] A means of generating and notifying alerts that take into account the user's emotional state,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, characterized in that it includes a learning means for predicting future traffic demand that takes into account changes in user emotions using a generative AI model, and for allocating resources in advance.

[0794] (Claim 3)

[0795] The system according to claim 1, characterized by including means for notifying users of customized traffic conditions and fault conditions based on their emotional state.

[0796] "Application example 2 when combining with an emotional engine"

[0797] (Claim 1)

[0798] Means of obtaining information from base stations,

[0799] The means for analyzing the aforementioned information using a generation AI model and detecting data trends,

[0800] A means of collecting user emotion data and analyzing it using an emotion analysis engine,

[0801] A means for activating an artificial intelligence agent that autonomously optimizes the operation of base stations based on analysis results and emotional data,

[0802] A feedback means for monitoring and adjusting the results of the optimization,

[0803] A means of providing customized notifications based on the user's emotional state,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, characterized in that it uses a generative AI model to predict future data demand, has a learning mechanism for allocating resources in advance, and improves prediction accuracy by taking user sentiment data into consideration.

[0807] (Claim 3)

[0808] The system according to claim 1, characterized in that it notifies the user of the current communication status and the status of any failures through alerts that take the user's feelings into consideration. [Explanation of Symbols]

[0809] 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. Means of collecting data from base stations, A means for analyzing the aforementioned data using a generating AI model and detecting traffic patterns, A means to activate an AI agent that autonomously optimizes the operation of base stations based on the analysis results, A feedback means for monitoring and adjusting the results of the optimization, A system that includes this.

2. The system according to claim 1, characterized in that it includes a learning means for predicting future traffic demand using a generative AI model and allocating resources in advance.

3. The system according to claim 1, characterized in that it includes means for notifying users of traffic conditions and failure status via alerts.

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