system

Abstract

We provide the system. [Solution] A data collection method for acquiring past communication data, A data analysis means that performs data analysis based on acquired past communication data, A priority determination means for determining the priority of communication devices based on analyzed data, A procedure generation means that automatically generates response procedures based on the determined priority, A system that includes a procedure output means for outputting generated procedures and design proposals.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Operation management in a wireless communication network requires advanced expertise and experience, so there is a problem that it takes a lot of time and resources to train new employees and handle troubles. In particular, in trouble countermeasures at a base station, it is difficult to judge priorities and analyze causes, and quick response is required. For this reason, means for improving operation efficiency while maintaining the stability of communication quality are needed.

Means for Solving the Problems

[0005] This invention provides data collection and data analysis means for collecting and analyzing past communication data to streamline the operation and management of wireless communication networks. Based on the analyzed data, a priority determination means is provided to automatically determine the priority of communication devices, and a procedure generation means is provided to automatically generate corresponding procedures based on that determination. Furthermore, by constructing a system that includes a procedure output means for outputting the generated procedures and design proposals, rapid and effective network management that does not rely on specialized knowledge is realized.

[0006] A "data collection means" is a component that has the function of periodically acquiring past communication data.

[0007] A "data analysis means" is a component that has the function of analyzing collected communication data to understand network conditions and trouble trends.

[0008] A "priority determination means" is a component that has the function of automatically determining the priority of communication devices based on the analyzed data.

[0009] A "procedure generation means" is a component that has the function of automatically generating response procedures to a problem based on the determined priority.

[0010] A "procedure output means" is a component that has the function of outputting generated procedures and design proposals and providing them to the user.

[0011] A "user inquiry handling mechanism" is a component that receives inquiries from users and generates the optimal response using a generated AI model.

[0012] A "feedback learning method" is a component that has the function of collecting feedback provided by users and using it as training data for a generated AI model. [Brief explanation of the drawing]

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

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[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] The system of the present invention is designed to streamline the operation of wireless communication networks and enable operation that does not rely on specialized knowledge. In this system, a server takes the lead in collecting historical communication data, and each component works in cooperation to perform data analysis, priority determination, procedure generation, and output.

[0035] The server stores traffic data acquired from the wireless network in a database. This data includes the number of users, signal strength, connection history, and troubleshooting status. The server periodically analyzes this data and uses machine learning models to detect potential trouble trends and performance changes within the network.

[0036] Based on the analyzed data, the server determines the priority of each base station. This process evaluates parameters such as user density, trouble history, and current network load, and automatically identifies base stations that require attention.

[0037] Next, the server utilizes a generation AI model to generate specific troubleshooting steps. These steps clearly outline the necessary items and procedures for troubleshooting and are provided in a format that even a novice engineer can understand.

[0038] Users can use their devices to submit inquiries about specific problems or operational issues. These inquiries are then processed on the server, where a generating AI model selects the most appropriate answer and provides it to the user.

[0039] As a concrete example, if a communication failure occurs at a base station, the server analyzes the collected data to identify the cause and prioritize it appropriately. Next, the generated response procedure is sent to the terminal, supporting the assigned engineer in responding quickly on-site.

[0040] Furthermore, user feedback is collected by the server and used as training data to improve the accuracy of the generated AI model. This iterative process is designed to continuously improve the overall operational efficiency of the system.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server retrieves historical traffic data from the communication network. This includes daily log data from each base station, signal strength, connection count, and troubleshooting information. The server periodically stores this data in the relevant database.

[0044] Step 2:

[0045] The server analyzes the accumulated data. Using machine learning models, it analyzes communication patterns and signs of anomalies to identify the location and time of potential problems.

[0046] Step 3:

[0047] The server determines the priority of communication devices within the network based on the results of data analysis. This process considers factors such as the trouble history of a particular base station, current user density, and signal quality, and lists the locations that require priority attention.

[0048] Step 4:

[0049] The server uses an AI model to generate specific action plans based on the determined priorities. These plans include detailed descriptions of necessary equipment, troubleshooting steps, and points to note.

[0050] Step 5:

[0051] Users make inquiries about problems or operational issues through their devices. The device sends the inquiry to the server, which uses a generative AI model to generate optimal solutions and advice, which are then provided to the user.

[0052] Step 6:

[0053] The server collects user feedback and uses it as training data for the generating AI model. The server analyzes the content of the feedback to improve the accuracy of future analyses and the quality of response procedures.

[0054] (Example 1)

[0055] 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."

[0056] In the operation of wireless communication networks, there is a need for systems that can detect potential communication failures and performance degradation early and enable efficient and appropriate responses even without specialized knowledge. Furthermore, a key challenge is to effectively utilize inquiries and feedback from users and engineers to continuously improve the overall operational efficiency and response accuracy of the system.

[0057] 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.

[0058] In this invention, the server includes information gathering means for acquiring communication information from a wireless communication network in real time, data analysis means for performing data preprocessing and data analysis using a machine learning model based on the collected communication information, priority determination means for evaluating the priority of devices used based on the analysis results and identifying devices of high importance, automatic procedure creation means for automatically creating corresponding procedures using a generated AI model based on the identified priorities, and procedure transmission means for transmitting the created procedures to a display device. This enables efficient and expertise-independent operation of the wireless communication network.

[0059] "Information gathering means" refers to a device or method that has the function of acquiring communication information in real time from a wireless communication network.

[0060] "Data analysis means" refers to a device or method that performs data preprocessing based on collected communication information and performs data analysis using a machine learning model.

[0061] A "priority determination means" is a device or method that utilizes analysis results to evaluate the priority of the devices used and identify the most important devices.

[0062] "Automatic procedure generation means" refers to a device or method that automatically generates corresponding procedures using a generation AI model based on identified priorities.

[0063] "Procedure transmission means" refers to a device or method for transmitting a created procedure to a display device.

[0064] A "generative AI model" is an artificial intelligence model that learns from past examples and data to generate appropriate responses and procedures for new data inputs.

[0065] "User" refers to someone who operates the system or provides inquiries or feedback.

[0066] This invention provides a system that streamlines the operation of wireless communication networks and enables rapid troubleshooting without requiring specialized knowledge. The central component of this system is a server, which collects and analyzes communication data from the wireless communication network in real time.

[0067] The server utilizes sensors and network monitoring tools during the data collection phase. The collected data is stored in databases such as MySQL® and PostgreSQL. Data preprocessing is performed using Python and its libraries Pandas and NumPy for data analysis. Machine learning models are also executed using TENSORFLOW® and PyTorch to analyze communication patterns and predict problems.

[0068] Based on the analysis results, the server executes an algorithm to determine the priority of the devices to be used. This is done by evaluating parameters such as the number of users, signal strength, connection history, and trouble occurrence status obtained from communication data. For devices of high importance, specific response procedures are automatically created using a generation AI model (for example, OpenAI®'s GPT series). The generated response procedures are sent to the terminal of the person in charge.

[0069] Users can contact the server via their device to inquire about specific problems or operational issues. The server receives the inquiry, uses a generative AI model to generate the most appropriate response, and provides it to the user.

[0070] For example, if a communication failure occurs at one of the base stations, the server immediately accesses the base station's historical data to identify the cause of the problem. Then, a generative AI model is used to create troubleshooting steps, which are sent to the engineer's terminal, enabling a rapid response on-site.

[0071] As an example of a prompt, by inputting an instruction such as "Please suggest a rapid response procedure for a communication failure at base station 123" into the generating AI model, an appropriate procedure will be suggested.

[0072] This system collects user feedback and uses it as training data for the generated AI model, thereby improving the model's accuracy. This design not only streamlines operations but also accelerates the system's own evolution.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The server collects communication data from the wireless communication network in real time. Inputs include the number of users, signal strength, connection history, and troubleshooting status. This data is collected using a stream processing module and stored in a MySQL database. Specifically, the server periodically polls the network for data and writes it to the database in real time.

[0076] Step 2:

[0077] The server periodically analyzes the accumulated communication data. This analysis involves preprocessing using Pandas to clean and format the data. The input is the raw data collected in step 1, from which features are extracted, and machine learning models are run using TensorFlow to analyze traffic trends and detect anomalies. The output consists of anomaly detection results and a traffic analysis report. Specifically, the server executes scheduled jobs and logs the analysis results.

[0078] Step 3:

[0079] The server evaluates the priority of each device based on the analysis results. The input is the output of step 2, which analyzes user density, trouble frequency, and current network load according to a specific algorithm. The output is a list of high-priority devices. Specifically, the server executes a priority determination algorithm internally and temporarily stores the results in memory.

[0080] Step 4:

[0081] The server automatically generates response procedures using a generative AI model based on the priority determination results. The input is the priority list from step 3, and the output is the specific response procedure. The generative AI model references past successes and best practices to create new procedures. In practice, the server invokes the AI ​​and saves the generated procedure in text format.

[0082] Step 5:

[0083] The server sends the generated procedure to the engineer's terminal. The input is the corresponding procedure created in step 4, and the output is the procedure notification to the terminal. Specifically, the server pushes the procedure to the engineer's terminal via the notification system and displays it.

[0084] Step 6:

[0085] The user uses an engineer's terminal to send a query to the server, seeking advice on a specific problem. The input is the query created by the user, and the server uses a generative AI model to find the best solution. The output is the response to the user. Specifically, the terminal sends the query to the server and displays the received response to the user.

[0086] Step 7:

[0087] The server collects feedback from users and engineers and uses it as training data for generative AI models. The input is feedback data, and the output is an improvement in the performance of the generative AI model. Specifically, the server stores the feedback in a database and incorporates it into training new models.

[0088] (Application Example 1)

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

[0090] The present invention aims to prevent potential communication failures and enable real-time anomaly detection and rapid problem resolution by achieving efficient operation of communication networks in factory environments. This will improve the operational efficiency of machinery within factories and solve the problem of enabling even novice engineers to easily manage the network.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes information gathering means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of communication devices based on the analyzed information, procedure generation means for automatically generating response procedures based on the determined priority, procedure output means for outputting the generated procedures and design proposals, and means for analyzing traffic information of a wireless communication network to improve the operation of machinery in a factory environment and detecting potential communication failures in real time. This enables the advance detection of communication failures within the factory and efficient operation through rapid response.

[0093] An "information gathering means" is an element that has the function of acquiring past communication information and storing or recording that information.

[0094] An "information analysis tool" is an element that can analyze and evaluate data trends and anomalies based on collected communication information.

[0095] A "priority determination method" is a component used to rank the importance of communication devices and problems using analyzed information and to determine the appropriate processing order.

[0096] A "procedure generation means" is an element that has the function of automatically generating necessary response procedures and solutions based on the priority ranking determined by the priority determination means.

[0097] A "procedure output means" is an element that has the function of presenting the generated procedures or design proposals to the user or operator.

[0098] "Communication failure" refers to a phenomenon in wireless communication networks where normal data transmission and reception become impossible or difficult.

[0099] "Factory environment" refers to the work area where machinery and equipment are operated within facilities where industrial production takes place or in their surrounding areas.

[0100] "Real-time problem solving" refers to responding quickly and without delay to problems that arise and implementing solutions immediately.

[0101] To implement this invention, the server first acquires historical and real-time communication information from a wireless communication network in a factory environment. The acquired communication information includes the number of users, signal strength, connection history, and trouble occurrence status, and is stored in a database.

[0102] Next, the server analyzes the collected information using data analysis tools to identify potential communication failures and changes in communication equipment performance. Here, AI algorithms and machine learning techniques are used to explore data trends and detect anomalies. Specifically, data analysis software such as Python and R may be used.

[0103] Subsequently, the server uses a priority determination mechanism to determine the priority of communication devices within the factory. This ensures that more critical communication devices are addressed first, enabling efficient responses when problems occur.

[0104] The generated data and response procedures are converted into specific steps by an automated generation system and sent to the assigned technician's terminal by a procedure output system. This process utilizes a generation AI model designed to provide the optimal steps for problem solving based on the original data.

[0105] Furthermore, users can submit inquiries to the system through their terminals. The inquiry response method, which uses a generative AI model, selects the most appropriate answer and presents it to the user. An example of a prompt used in this process would be, "Please generate a procedure for dealing with the situation when communication between robots is interrupted in a specific area."

[0106] This system allows for the proactive detection of communication network failures within the factory, enabling rapid resolution. Furthermore, by collecting user feedback and using it as training data for the generated AI model, continuous accuracy improvements can be achieved.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server acquires historical and real-time communication information from the factory's wireless communication network. This input data includes the number of users, signal strength, connection history, and trouble occurrences. Information is collected by storing this information in a database.

[0110] Step 2:

[0111] The server analyzes communication information stored in the database using information analysis tools. This analysis uses AI algorithms and machine learning techniques to identify trends in the input data and detect anomalies. For example, it includes processes to identify areas where signal strength is low.

[0112] Step 3:

[0113] The server uses a prioritization mechanism based on the analysis results to determine the priority of communication devices within the factory. User density and trouble history are input, and based on this, critical devices are identified. Prioritizing from high importance to low importance enables efficient response.

[0114] Step 4:

[0115] The server automatically generates specific response procedures using a generative AI model, according to the determined priorities. In this process, prompts are used to construct the optimal response method for the input trouble information and priorities, and the procedure is output.

[0116] Step 5:

[0117] The generated procedure is sent to the technician's terminal via the procedure output device. The terminal receives and displays the output of this procedure. The procedure is clearly presented so that the technician can quickly begin resolving the problem.

[0118] Step 6:

[0119] Users can use a terminal to make inquiries to the information system. The inquiry response method receives the user's inquiry as input, generates the optimal answer using a generation AI model, and presents it to the user as output. A specific example is the prompt message, "Generate the procedure for dealing with the situation when communication between robots is interrupted in a specific area."

[0120] Step 7:

[0121] User feedback is collected on the server and used as training data for the generated AI model. The input data from the feedback is used in calculations to improve the model's accuracy, contributing to improved troubleshooting quality in the future.

[0122] 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.

[0123] The system of the present invention incorporates an emotion engine that recognizes user emotions, in addition to conventional data collection, analysis, prioritization, response procedure generation, and output, in order to improve the operation of wireless communication networks and the user experience.

[0124] The server stores traffic data acquired from the wireless communication network in a database and analyzes this data using machine learning models to determine which base stations should be prioritized and which require troubleshooting. Subsequently, it uses a generative AI model to generate response procedures based on priorities and provides them to the user through a procedure output device.

[0125] Furthermore, when receiving inquiries from users, the device uses an emotion engine to analyze the user's emotions in real time. This allows it to assess the user's stress level and satisfaction level, and provide a more personalized service tailored to their emotions. Specifically, the emotion engine recognizes the user's emotions from voice and text data, and adjusts the advice and response procedures provided by the AI ​​model based on the results. This approach reduces user frustration and irritation, enabling a better user experience.

[0126] For example, if a user is dissatisfied with a base station problem, the server will use feedback from the emotion engine in addition to analysis results to offer a more considerate solution. Furthermore, if the server detects that the user is feeling anxious, it can respond flexibly by adding information to provide reassurance.

[0127] Furthermore, emotional data is incorporated into the learning process of the generative AI model, allowing the server to continuously optimize the model. As a result, the accuracy of responses and the quality of emotion-based personalized responses are improved, and the system is designed to make the management of wireless communication networks more efficient and effective.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server acquires traffic data from the wireless communication network and stores it in a database. This data includes signal quality for each base station, the number of users, connection errors, and past trouble history.

[0131] Step 2:

[0132] The server analyzes the accumulated data using a machine learning model. This analysis identifies potential problem areas and points requiring improvement within the network. The analysis results are used to prioritize specific base stations.

[0133] Step 3:

[0134] The server determines the priority of base stations based on the analysis results. This process evaluates the user utilization rate, past trouble frequency, and current load status of each base station and lists the order in which they should be addressed.

[0135] Step 4:

[0136] The server uses an AI model to automatically generate appropriate response procedures based on priority. These procedures include specific troubleshooting steps, necessary tools, and estimated response times. The procedures are generated in a format that is easy for the user to understand.

[0137] Step 5:

[0138] When a user makes an inquiry about a problem via their device, the device acquires the voice and text data and performs sentiment analysis using an emotion engine. The emotion engine evaluates dissatisfaction, stress, satisfaction, etc., and sends the results to the server.

[0139] Step 6:

[0140] The server receives information from the sentiment engine and adjusts the generated response procedures and answers. If the user expresses strong dissatisfaction, procedures including faster, more detailed explanations and a more benevolent response are provided.

[0141] Step 7:

[0142] The server collects user-provided feedback and sentiment data, which it then uses as training data for its AI model. The server continuously improves the model's accuracy based on this new data, resulting in better future responses.

[0143] (Example 2)

[0144] 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".

[0145] In recent years, information and communication networks have become increasingly complex and the users more diverse, making it difficult to troubleshoot and support users quickly and effectively using only traditional data analysis. Furthermore, users are increasingly seeking not only technical solutions but also emotional considerations to alleviate stress and anxiety. To address these challenges, there is a growing need for systems that offer the precision and flexibility that conventional technologies could not provide.

[0146] 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.

[0147] In this invention, the server includes data collection means for acquiring traffic data from a communication network, data analysis means for analyzing the acquired traffic data using a machine learning model, and an emotion analysis engine for analyzing user emotions. This enables rapid and highly accurate troubleshooting and allows for flexible responses that take user emotions into consideration.

[0148] A "data collection means for acquiring traffic data from a communication network" is a component that has the function of collecting various types of data from a wireless communication network, and is a system that plays the role of acquiring packet information and connection status in real time.

[0149] "Data analysis methods using machine learning models" refer to components that apply machine learning techniques to analyze collected data, with the aim of detecting network performance and anomalies.

[0150] "Priority determination means for determining the priority of communication management devices" refers to a component that executes methods or algorithms for determining which communication management device should be prioritized for processing based on the analyzed data.

[0151] A "procedure generation method that automatically generates using a generative AI model" is a system that utilizes generative AI technology to automatically generate response procedures and design proposals according to specific situations and priorities.

[0152] A "procedure output means" is a component that has an interface or mechanism for providing the user with the generated corresponding procedures or design proposals.

[0153] A "sentiment analysis engine that analyzes user emotions" is a technology that analyzes voice or text input from users and evaluates the user's emotional state in real time based on the content.

[0154] A "user inquiry response method that performs real-time sentiment analysis of user inquiries" refers to a set of components that include a process for quickly analyzing the sentiment of a user inquiry and deriving the optimal response.

[0155] A "feedback learning method" is a function that provides a way to collect user feedback and emotional data and use it as data to improve and optimize the generated AI model.

[0156] This invention is a system for achieving efficient and advanced communication network management. Servers, terminals, and users function as follows, providing a consistent service as a whole.

[0157] Server role:

[0158] The server collects traffic data from the communication network in real time. This data collection function is designed to monitor and analyze network performance and connection stability, and to store detailed data from each base station in a database. The server analyzes this data using machine learning models to detect anomalies and analyze network load. Based on the analysis results, it identifies communication management devices that require priority attention and passes that information to the next process.

[0159] Terminal role:

[0160] The device activates its sentiment analysis engine when it receives an inquiry from a user. This sentiment analysis evaluates the user's stress and satisfaction levels through voice or text data, and grasps their emotional state in real time. Based on this information, the device generates optimal response procedures and advice tailored to the user's state through a generative AI model.

[0161] System operation:

[0162] The system can automatically generate troubleshooting steps using a generative AI model based on data analyzed on the server. This enables rapid and highly accurate troubleshooting. For example, if a user complains of slow internet speed, the system uses historical data and real-time sentiment assessment to suggest appropriate solutions. In this process, the generative AI model receives prompts such as, "Analyze the emotions expressed by the user and generate the optimal troubleshooting steps corresponding to those emotions. For example, suggest solutions for a user who is dissatisfied with slow internet speed."

[0163] This system design simultaneously achieves flexible responses that take user emotions into consideration and streamlines network management.

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

[0165] Step 1:

[0166] The server continuously collects traffic data from the wireless communication network. Specifically, data such as packet information, connection status, and communication speed transmitted from each base station are stored in a database in real time. This process generates raw data for understanding the overall performance of the network using data collection methods.

[0167] Step 2:

[0168] The server analyzes the collected traffic data using a machine learning model. Calculations are performed to identify abnormal communication patterns and network load trends. It receives raw network data as input and outputs anomaly detection results and a list of degraded base stations. This output helps determine which items require priority attention.

[0169] Step 3:

[0170] When the device receives an inquiry from a user, it uses an emotion engine to analyze the user's emotions. Voice and text data are input, and based on this, data evaluating stress levels and satisfaction levels is output. This allows for support that takes into account the emotional factors behind what the user is dissatisfied with.

[0171] Step 4:

[0172] The server uses a generative AI model to generate specific troubleshooting steps based on the analyzed network data and feedback from the emotion engine. This process takes pre-configured prompts as input and outputs the most suitable troubleshooting steps for the user. For example, a prompt such as "Analyze the emotions the user is expressing and generate the most appropriate troubleshooting steps based on those emotions" might be used.

[0173] Step 5:

[0174] The server provides the generated response procedures to the user through a procedure output mechanism. For example, if the user is dissatisfied with slow communication speeds, the procedures will include suggestions for improvement and additional information that takes their feelings into consideration. Feedback from the user is also returned to the system and used to optimize the model in the next learning cycle. By forming this feedback loop, the overall accuracy and effectiveness of the system are improved.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0177] In the operation of communication networks, stress and dissatisfaction caused by responses that disregard user emotions are a major challenge. Traditional systems have a problem where improving the user experience is difficult because they do not understand user emotions and troubleshoot problems uniformly. Furthermore, in security services, it is necessary to provide appropriate reassuring information to users who are feeling anxious.

[0178] 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.

[0179] In this invention, the server includes data collection means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of wireless communication devices based on the analyzed information, procedure generation means for automatically generating response procedures based on the determined priority, emotion recognition means for recognizing the user's emotions from voice or text, and emotion response adjustment means for adjusting the response based on the recognized emotions. This enables detailed responses that respond to the user's emotions, thereby improving the efficiency of network operations and enhancing the user experience.

[0180] "Past communication information" refers to data and records that were previously sent and received via the network.

[0181] "Data collection means" refers to a device or system that has the function of acquiring and storing communication information.

[0182] "Information analysis means" refers to methods and tools used to recognize patterns, identify problems, or discover areas for improvement based on collected communication information.

[0183] A "prioritization decision mechanism" refers to a system that uses analyzed information to evaluate the importance and urgency of communication devices and processes, and to determine countermeasures.

[0184] A "procedure generation means" refers to a system that has the function of automatically constructing necessary processing procedures and countermeasures based on the results of priority determination.

[0185] "Emotion recognition means" refers to technology or devices that analyze audio data or text data to identify the emotional state of a user.

[0186] An "emotional response adjustment mechanism" refers to a system that has the function of adjusting the responses and notifications it provides in accordance with the recognized emotions.

[0187] The system of this invention aims to improve the effective management of communication networks and the user experience. The server acquires past communication information using data collection means and analyzes it using information analysis means. The importance of the analysis results is evaluated by priority determination means, and procedure generation means automatically creates corresponding procedures.

[0188] Furthermore, the device is equipped with emotion recognition means to identify emotions expressed by the user through voice or text. Emotion response adjustment means adjusts the response to the user accordingly, providing a flexible response that reduces stress.

[0189] This system utilizes hardware such as smartphones and computers, and software such as Google Cloud's TensorFlow and natural language processing toolkits. Generative AI models are used to learn user sentiment data and generate more accurate responses.

[0190] As a concrete example, if a user accesses the security system via their smartphone and asks about the status of their home, and the emotion recognition system detects anxiety, the server can immediately analyze past monitoring data and provide information indicating safety. An example of a prompt message for the generating AI model would be, "Please provide the most appropriate reassurance information to alleviate the user's anxiety."

[0191] This system will enable more human-centered and efficient monitoring and troubleshooting of communication networks.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server retrieves past communication information from a network database using data collection methods. This input data includes communication volume, connection time, and error rate. The collected data forms the basis for subsequent information analysis.

[0195] Step 2:

[0196] The server analyzes the collected communication information using information analysis tools. Specifically, it identifies traffic patterns using machine learning algorithms and identifies potential problems. The output of this step provides information to determine what needs to be improved.

[0197] Step 3:

[0198] The server uses a prioritization mechanism to evaluate the importance of wireless communication devices based on the analyzed information. This step prioritizes devices and issues that are likely to affect users. The output is a list of priorities.

[0199] Step 4:

[0200] The server uses a procedure generation mechanism to automatically generate response procedures based on priority. Prompt messages are input into a generation AI model to create appropriate responses and procedures. The output of this step is specific countermeasures or improvement suggestions.

[0201] Step 5:

[0202] The device uses emotion recognition to analyze the emotions in the user's voice and text. A dedicated emotion analysis engine is used to determine the user's emotional state. The input is the user's voice or text, and the output is the result of the emotion evaluation.

[0203] Step 6:

[0204] The device uses emotion response adjustment mechanisms to adjust its response based on recognized emotions. For example, if anxiety is detected, it adds reassuring information. The output is a customized notification or explanation for the user.

[0205] Step 7:

[0206] The user receives responses and notifications from the device and checks for specific instructions and situation-appropriate information. The input here is the tailored notification, and the output is the information provided to the user.

[0207] This series of processes makes communication network management more efficient and based on emotions, improving the user experience.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] The system of the present invention is designed to streamline the operation of wireless communication networks and enable operation that does not rely on specialized knowledge. In this system, a server takes the lead in collecting historical communication data, and each component works in cooperation to perform data analysis, priority determination, procedure generation, and output.

[0225] The server stores traffic data acquired from the wireless network in a database. This data includes the number of users, signal strength, connection history, and troubleshooting status. The server periodically analyzes this data and uses machine learning models to detect potential trouble trends and performance changes within the network.

[0226] Based on the analyzed data, the server determines the priority of each base station. This process evaluates parameters such as user density, trouble history, and current network load, and automatically identifies base stations that require attention.

[0227] Next, the server utilizes a generation AI model to generate specific troubleshooting steps. These steps clearly outline the necessary items and procedures for troubleshooting and are provided in a format that even a novice engineer can understand.

[0228] Users can use their devices to submit inquiries about specific problems or operational issues. These inquiries are then processed on the server, where a generating AI model selects the most appropriate answer and provides it to the user.

[0229] As a concrete example, if a communication failure occurs at a base station, the server analyzes the collected data to identify the cause and prioritize it appropriately. Next, the generated response procedure is sent to the terminal, supporting the assigned engineer in responding quickly on-site.

[0230] Furthermore, user feedback is collected by the server and used as training data to improve the accuracy of the generated AI model. This iterative process is designed to continuously improve the overall operational efficiency of the system.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The server retrieves historical traffic data from the communication network. This includes daily log data from each base station, signal strength, connection count, and troubleshooting information. The server periodically stores this data in the relevant database.

[0234] Step 2:

[0235] The server analyzes the accumulated data. Using machine learning models, it analyzes communication patterns and signs of anomalies to identify the location and time of potential problems.

[0236] Step 3:

[0237] The server determines the priority of communication devices within the network based on the results of data analysis. This process considers factors such as the trouble history of a particular base station, current user density, and signal quality, and lists the locations that require priority attention.

[0238] Step 4:

[0239] The server uses an AI model to generate specific action plans based on the determined priorities. These plans include detailed descriptions of necessary equipment, troubleshooting steps, and points to note.

[0240] Step 5:

[0241] Users make inquiries about problems or operational issues through their devices. The device sends the inquiry to the server, which uses a generative AI model to generate optimal solutions and advice, which are then provided to the user.

[0242] Step 6:

[0243] The server collects user feedback and uses it as training data for the generating AI model. The server analyzes the content of the feedback to improve the accuracy of future analyses and the quality of response procedures.

[0244] (Example 1)

[0245] 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."

[0246] In the operation of wireless communication networks, there is a need for systems that can detect potential communication failures and performance degradation early and enable efficient and appropriate responses even without specialized knowledge. Furthermore, a key challenge is to effectively utilize inquiries and feedback from users and engineers to continuously improve the overall operational efficiency and response accuracy of the system.

[0247] 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.

[0248] In this invention, the server includes information gathering means for acquiring communication information from a wireless communication network in real time, data analysis means for performing data preprocessing and data analysis using a machine learning model based on the collected communication information, priority determination means for evaluating the priority of devices used based on the analysis results and identifying devices of high importance, automatic procedure creation means for automatically creating corresponding procedures using a generated AI model based on the identified priorities, and procedure transmission means for transmitting the created procedures to a display device. This enables efficient and expertise-independent operation of the wireless communication network.

[0249] "Information gathering means" refers to a device or method that has the function of acquiring communication information in real time from a wireless communication network.

[0250] "Data analysis means" refers to a device or method that performs data preprocessing based on collected communication information and performs data analysis using a machine learning model.

[0251] A "priority determination means" is a device or method that utilizes analysis results to evaluate the priority of the devices used and identify the most important devices.

[0252] "Automatic procedure generation means" refers to a device or method that automatically generates corresponding procedures using a generation AI model based on identified priorities.

[0253] "Procedure transmission means" refers to a device or method for transmitting a created procedure to a display device.

[0254] A "generative AI model" is an artificial intelligence model that learns from past examples and data to generate appropriate responses and procedures for new data inputs.

[0255] "User" refers to someone who operates the system or provides inquiries or feedback.

[0256] This invention provides a system that streamlines the operation of wireless communication networks and enables rapid troubleshooting without requiring specialized knowledge. The central component of this system is a server, which collects and analyzes communication data from the wireless communication network in real time.

[0257] The server utilizes sensors and network monitoring tools during the data collection phase. The collected data is stored in databases such as MySQL and PostgreSQL. Data preprocessing is performed using Python and its libraries, Pandas and NumPy, for data analysis. Machine learning models are also executed using TensorFlow and PyTorch to analyze communication patterns and predict problems.

[0258] Based on the analysis results, the server executes an algorithm to determine the priority of the devices to be used. This is done by evaluating parameters such as the number of users, signal strength, connection history, and trouble occurrence status obtained from communication data. For devices of high importance, a specific response procedure is automatically created using a generation AI model (for example, OpenAI's GPT series). The generated response procedure is sent to the terminal of the person in charge.

[0259] Users can contact the server via their device to inquire about specific problems or operational issues. The server receives the inquiry, uses a generative AI model to generate the most appropriate response, and provides it to the user.

[0260] For example, if a communication failure occurs at one of the base stations, the server immediately accesses the base station's historical data to identify the cause of the problem. Then, a generative AI model is used to create troubleshooting steps, which are sent to the engineer's terminal, enabling a rapid response on-site.

[0261] As an example of a prompt, by inputting an instruction such as "Please suggest a rapid response procedure for a communication failure at base station 123" into the generating AI model, an appropriate procedure will be suggested.

[0262] This system collects user feedback and uses it as training data for the generated AI model, thereby improving the model's accuracy. This design not only streamlines operations but also accelerates the system's own evolution.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The server collects communication data from the wireless communication network in real time. Inputs include the number of users, signal strength, connection history, and troubleshooting status. This data is collected using a stream processing module and stored in a MySQL database. Specifically, the server periodically polls the network for data and writes it to the database in real time.

[0266] Step 2:

[0267] The server periodically analyzes the accumulated communication data. This analysis involves preprocessing using Pandas to clean and format the data. The input is the raw data collected in step 1, from which features are extracted, and machine learning models are run using TensorFlow to analyze traffic trends and detect anomalies. The output consists of anomaly detection results and a traffic analysis report. Specifically, the server executes scheduled jobs and logs the analysis results.

[0268] Step 3:

[0269] The server evaluates the priority of each device based on the analysis results. The input is the output of step 2, which analyzes user density, trouble frequency, and current network load according to a specific algorithm. The output is a list of high-priority devices. Specifically, the server executes a priority determination algorithm internally and temporarily stores the results in memory.

[0270] Step 4:

[0271] The server automatically generates response procedures using a generative AI model based on the priority determination results. The input is the priority list from step 3, and the output is the specific response procedure. The generative AI model references past successes and best practices to create new procedures. In practice, the server invokes the AI ​​and saves the generated procedure in text format.

[0272] Step 5:

[0273] The server sends the generated procedure to the engineer's terminal. The input is the corresponding procedure created in step 4, and the output is the procedure notification to the terminal. Specifically, the server pushes the procedure to the engineer's terminal via the notification system and displays it.

[0274] Step 6:

[0275] The user uses an engineer's terminal to send a query to the server, seeking advice on a specific problem. The input is the query created by the user, and the server uses a generative AI model to find the best solution. The output is the response to the user. Specifically, the terminal sends the query to the server and displays the received response to the user.

[0276] Step 7:

[0277] The server collects feedback from users and engineers and uses it as training data for generative AI models. The input is feedback data, and the output is an improvement in the performance of the generative AI model. Specifically, the server stores the feedback in a database and incorporates it into training new models.

[0278] (Application Example 1)

[0279] 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."

[0280] The present invention aims to prevent potential communication failures and enable real-time anomaly detection and rapid problem resolution by achieving efficient operation of communication networks in factory environments. This will improve the operational efficiency of machinery within factories and solve the problem of enabling even novice engineers to easily manage the network.

[0281] 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.

[0282] In this invention, the server includes information collection means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of communication devices based on the analyzed information, procedure generation means for automatically generating corresponding procedures based on the determined priority, procedure output means for outputting the generated procedures and design proposals, and means for analyzing traffic information of a wireless communication network to detect potential communication failures in real time in order to improve the operation of machines in a factory environment. Thereby, communication failures in the factory can be detected in advance, and efficient operation through prompt response becomes possible.

[0283] The "information collection means" is an element having the function of acquiring past communication information and accumulating or recording that information.

[0284] The "information analysis means" is an element capable of analyzing and evaluating data trends and anomalies based on the collected communication information.

[0285] The "priority determination means" is an element for ranking the importance of communication devices and problems using the analyzed information and determining an appropriate processing order.

[0286] The "procedure generation means" is an element having the function of automatically generating necessary corresponding procedures and solutions based on the ranking determined by the priority determination means.

[0287] The "procedure output means" is an element having the function of presenting the generated procedures and design proposals to users or operators.

[0288] "Communication failure" refers to a phenomenon in a wireless communication network where normal data transmission and reception become impossible or difficult.

[0289] The "factory environment" refers to the working area where machines and devices installed in and around the facilities where industrial production is carried out are operated. [[ID=,30]]

[0290] "Real-time problem solving" refers to responding quickly and without delay to problems that arise and implementing solutions immediately.

[0291] To implement this invention, the server first acquires historical and real-time communication information from a wireless communication network in a factory environment. The acquired communication information includes the number of users, signal strength, connection history, and trouble occurrence status, and is stored in a database.

[0292] Next, the server analyzes the collected information using data analysis tools to identify potential communication failures and changes in communication equipment performance. Here, AI algorithms and machine learning techniques are used to explore data trends and detect anomalies. Specifically, data analysis software such as Python and R may be used.

[0293] Subsequently, the server uses a priority determination mechanism to determine the priority of communication devices within the factory. This ensures that more critical communication devices are addressed first, enabling efficient responses when problems occur.

[0294] The generated data and response procedures are converted into specific steps by an automated generation system and sent to the assigned technician's terminal by a procedure output system. This process utilizes a generation AI model designed to provide the optimal steps for problem solving based on the original data.

[0295] Furthermore, users can submit inquiries to the system through their terminals. The inquiry response method, which uses a generative AI model, selects the most appropriate answer and presents it to the user. An example of a prompt used in this process would be, "Please generate a procedure for dealing with the situation when communication between robots is interrupted in a specific area."

[0296] This system allows for the proactive detection of communication network failures within the factory, enabling rapid resolution. Furthermore, by collecting user feedback and using it as training data for the generated AI model, continuous accuracy improvements can be achieved.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server acquires historical and real-time communication information from the factory's wireless communication network. This input data includes the number of users, signal strength, connection history, and trouble occurrences. Information is collected by storing this information in a database.

[0300] Step 2:

[0301] The server analyzes communication information stored in the database using information analysis tools. This analysis uses AI algorithms and machine learning techniques to identify trends in the input data and detect anomalies. For example, it includes processes to identify areas where signal strength is low.

[0302] Step 3:

[0303] The server uses a prioritization mechanism based on the analysis results to determine the priority of communication devices within the factory. User density and trouble history are input, and based on this, critical devices are identified. Prioritizing from high importance to low importance enables efficient response.

[0304] Step 4:

[0305] The server automatically generates specific response procedures using the generative AI model according to the determined priorities. In this process, using the prompt text, an optimal response method for the input trouble information and priorities is constructed, and the procedure is output.

[0306] Step 5:

[0307] The generated procedure is transmitted to the terminal of the responsible technician by the procedure output means. The terminal receives and displays the output of this procedure. The procedure is presented clearly so that the technician can quickly start problem-solving.

[0308] Step 6:

[0309] The user can inquire about the information system using the terminal. The inquiry response means receives the inquiry content from the user as input, generates an optimal answer using the generative AI model, and presents it to the user as the output. As a specific example, there is the prompt text "Please generate the troubleshooting procedures when communication between robots fails in a specific area."

[0310] Step 7:

[0311] The feedback provided by the user is collected by the server and used as learning data for the generative AI model. The input data of the feedback is utilized in the calculations to improve the accuracy of the model, contributing to the improvement of the quality of future troubleshooting.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0313] The system of the present invention incorporates an emotion engine for recognizing the user's emotion in addition to the conventional data collection, analysis, priority judgment, response procedure generation, and output in order to improve the operation of the wireless communication network and the user experience.

[0314] The server stores traffic data acquired from the wireless communication network in a database and analyzes this data using machine learning models to determine which base stations should be prioritized and which require troubleshooting. Subsequently, it uses a generative AI model to generate response procedures based on priorities and provides them to the user through a procedure output device.

[0315] Furthermore, when receiving inquiries from users, the device uses an emotion engine to analyze the user's emotions in real time. This allows it to assess the user's stress level and satisfaction level, and provide a more personalized service tailored to their emotions. Specifically, the emotion engine recognizes the user's emotions from voice and text data, and adjusts the advice and response procedures provided by the AI ​​model based on the results. This approach reduces user frustration and irritation, enabling a better user experience.

[0316] For example, if a user is dissatisfied with a base station problem, the server will use feedback from the emotion engine in addition to analysis results to offer a more considerate solution. Furthermore, if the server detects that the user is feeling anxious, it can respond flexibly by adding information to provide reassurance.

[0317] Furthermore, emotional data is incorporated into the learning process of the generative AI model, allowing the server to continuously optimize the model. As a result, the accuracy of responses and the quality of emotion-based personalized responses are improved, and the system is designed to make the management of wireless communication networks more efficient and effective.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] The server acquires traffic data from the wireless communication network and stores it in a database. This data includes signal quality for each base station, the number of users, connection errors, and past trouble history.

[0321] Step 2:

[0322] The server analyzes the accumulated data using a machine learning model. This analysis identifies potential problem areas and points requiring improvement within the network. The analysis results are used to prioritize specific base stations.

[0323] Step 3:

[0324] The server determines the priority of base stations based on the analysis results. This process evaluates the user utilization rate, past trouble frequency, and current load status of each base station and lists the order in which they should be addressed.

[0325] Step 4:

[0326] The server uses an AI model to automatically generate appropriate response procedures based on priority. These procedures include specific troubleshooting steps, necessary tools, and estimated response times. The procedures are generated in a format that is easy for the user to understand.

[0327] Step 5:

[0328] When a user makes an inquiry about a problem via their device, the device acquires the voice and text data and performs sentiment analysis using an emotion engine. The emotion engine evaluates dissatisfaction, stress, satisfaction, etc., and sends the results to the server.

[0329] Step 6:

[0330] The server receives information from the sentiment engine and adjusts the generated response procedures and answers. If the user expresses strong dissatisfaction, procedures including faster, more detailed explanations and a more benevolent response are provided.

[0331] Step 7:

[0332] The server collects user-provided feedback and sentiment data, which it then uses as training data for its AI model. The server continuously improves the model's accuracy based on this new data, resulting in better future responses.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] In recent years, information and communication networks have become increasingly complex and the users more diverse, making it difficult to troubleshoot and support users quickly and effectively using only traditional data analysis. Furthermore, users are increasingly seeking not only technical solutions but also emotional considerations to alleviate stress and anxiety. To address these challenges, there is a growing need for systems that offer the precision and flexibility that conventional technologies could not provide.

[0336] 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.

[0337] In this invention, the server includes data collection means for acquiring traffic data from a communication network, data analysis means for analyzing the acquired traffic data using a machine learning model, and an emotion analysis engine for analyzing user emotions. This enables rapid and highly accurate troubleshooting and allows for flexible responses that take user emotions into consideration.

[0338] A "data collection means for acquiring traffic data from a communication network" is a component that has the function of collecting various types of data from a wireless communication network, and is a system that plays the role of acquiring packet information and connection status in real time.

[0339] "Data analysis methods using machine learning models" refer to components that apply machine learning techniques to analyze collected data, with the aim of detecting network performance and anomalies.

[0340] "Priority determination means for determining the priority of communication management devices" refers to a component that executes methods or algorithms for determining which communication management device should be prioritized for processing based on the analyzed data.

[0341] A "procedure generation method that automatically generates using a generative AI model" is a system that utilizes generative AI technology to automatically generate response procedures and design proposals according to specific situations and priorities.

[0342] A "procedure output means" is a component that has an interface or mechanism for providing the user with the generated corresponding procedures or design proposals.

[0343] A "sentiment analysis engine that analyzes user emotions" is a technology that analyzes voice or text input from users and evaluates the user's emotional state in real time based on the content.

[0344] A "user inquiry response method that performs real-time sentiment analysis of user inquiries" refers to a set of components that include a process for quickly analyzing the sentiment of a user inquiry and deriving the optimal response.

[0345] A "feedback learning method" is a function that provides a way to collect user feedback and emotional data and use it as data to improve and optimize the generated AI model.

[0346] This invention is a system for achieving efficient and advanced communication network management. Servers, terminals, and users function as follows, providing a consistent service as a whole.

[0347] Server role:

[0348] The server collects traffic data from the communication network in real time. This data collection function is designed to monitor and analyze network performance and connection stability, and to store detailed data from each base station in a database. The server analyzes this data using machine learning models to detect anomalies and analyze network load. Based on the analysis results, it identifies communication management devices that require priority attention and passes that information to the next process.

[0349] Terminal role:

[0350] The device activates its sentiment analysis engine when it receives an inquiry from a user. This sentiment analysis evaluates the user's stress and satisfaction levels through voice or text data, and grasps their emotional state in real time. Based on this information, the device generates optimal response procedures and advice tailored to the user's state through a generative AI model.

[0351] System operation:

[0352] The system can automatically generate troubleshooting steps using a generative AI model based on data analyzed on the server. This enables rapid and highly accurate troubleshooting. For example, if a user complains of slow internet speed, the system uses historical data and real-time sentiment assessment to suggest appropriate solutions. In this process, the generative AI model receives prompts such as, "Analyze the emotions expressed by the user and generate the optimal troubleshooting steps corresponding to those emotions. For example, suggest solutions for a user who is dissatisfied with slow internet speed."

[0353] This system design simultaneously achieves flexible responses that take user emotions into consideration and streamlines network management.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The server continuously collects traffic data from the wireless communication network. Specifically, data such as packet information, connection status, and communication speed transmitted from each base station are stored in a database in real time. This process generates raw data for understanding the overall performance of the network using data collection methods.

[0357] Step 2:

[0358] The server analyzes the collected traffic data using a machine learning model. Calculations are performed to identify abnormal communication patterns and network load trends. It receives raw network data as input and outputs anomaly detection results and a list of degraded base stations. This output helps determine which items require priority attention.

[0359] Step 3:

[0360] When the device receives an inquiry from a user, it uses an emotion engine to analyze the user's emotions. Voice and text data are input, and based on this, data evaluating stress levels and satisfaction levels is output. This allows for support that takes into account the emotional factors behind what the user is dissatisfied with.

[0361] Step 4:

[0362] The server uses a generative AI model to generate specific troubleshooting steps based on the analyzed network data and feedback from the emotion engine. This process takes pre-configured prompts as input and outputs the most suitable troubleshooting steps for the user. For example, a prompt such as "Analyze the emotions the user is expressing and generate the most appropriate troubleshooting steps based on those emotions" might be used.

[0363] Step 5:

[0364] The server provides the generated response procedures to the user through a procedure output mechanism. For example, if the user is dissatisfied with slow communication speeds, the procedures will include suggestions for improvement and additional information that takes their feelings into consideration. Feedback from the user is also returned to the system and used to optimize the model in the next learning cycle. By forming this feedback loop, the overall accuracy and effectiveness of the system are improved.

[0365] (Application Example 2)

[0366] 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."

[0367] In the operation of communication networks, stress and dissatisfaction caused by responses that disregard user emotions are a major challenge. Traditional systems have a problem where improving the user experience is difficult because they do not understand user emotions and troubleshoot problems uniformly. Furthermore, in security services, it is necessary to provide appropriate reassuring information to users who are feeling anxious.

[0368] 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.

[0369] In this invention, the server includes data collection means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of wireless communication devices based on the analyzed information, procedure generation means for automatically generating response procedures based on the determined priority, emotion recognition means for recognizing the user's emotions from voice or text, and emotion response adjustment means for adjusting the response based on the recognized emotions. This enables detailed responses that respond to the user's emotions, thereby improving the efficiency of network operations and enhancing the user experience.

[0370] "Past communication information" refers to data and records that were previously sent and received via the network.

[0371] "Data collection means" refers to a device or system that has the function of acquiring and storing communication information.

[0372] "Information analysis means" refers to methods and tools used to recognize patterns, identify problems, or discover areas for improvement based on collected communication information.

[0373] A "prioritization decision mechanism" refers to a system that uses analyzed information to evaluate the importance and urgency of communication devices and processes, and to determine countermeasures.

[0374] A "procedure generation means" refers to a system that has the function of automatically constructing necessary processing procedures and countermeasures based on the results of priority determination.

[0375] "Emotion recognition means" refers to technology or devices that analyze audio data or text data to identify the emotional state of a user.

[0376] An "emotional response adjustment mechanism" refers to a system that has the function of adjusting the responses and notifications it provides in accordance with the recognized emotions.

[0377] The system of this invention aims to improve the effective management of communication networks and the user experience. The server acquires past communication information using data collection means and analyzes it using information analysis means. The importance of the analysis results is evaluated by priority determination means, and procedure generation means automatically creates corresponding procedures.

[0378] Furthermore, the device is equipped with emotion recognition means to identify emotions expressed by the user through voice or text. Emotion response adjustment means adjusts the response to the user accordingly, providing a flexible response that reduces stress.

[0379] This system utilizes hardware such as smartphones and computers, and software such as Google Cloud's TensorFlow and natural language processing toolkits. Generative AI models are used to learn user sentiment data and generate more accurate responses.

[0380] As a concrete example, if a user accesses the security system via their smartphone and asks about the status of their home, and the emotion recognition system detects anxiety, the server can immediately analyze past monitoring data and provide information indicating safety. An example of a prompt message for the generating AI model would be, "Please provide the most appropriate reassurance information to alleviate the user's anxiety."

[0381] This system will enable more human-centered and efficient monitoring and troubleshooting of communication networks.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server retrieves past communication information from a network database using data collection methods. This input data includes communication volume, connection time, and error rate. The collected data forms the basis for subsequent information analysis.

[0385] Step 2:

[0386] The server analyzes the collected communication information using information analysis tools. Specifically, it identifies traffic patterns using machine learning algorithms and identifies potential problems. The output of this step provides information to determine what needs to be improved.

[0387] Step 3:

[0388] The server uses a prioritization mechanism to evaluate the importance of wireless communication devices based on the analyzed information. This step prioritizes devices and issues that are likely to affect users. The output is a list of priorities.

[0389] Step 4:

[0390] The server uses a procedure generation mechanism to automatically generate response procedures based on priority. Prompt messages are input into a generation AI model to create appropriate responses and procedures. The output of this step is specific countermeasures or improvement suggestions.

[0391] Step 5:

[0392] The device uses emotion recognition to analyze the emotions in the user's voice and text. A dedicated emotion analysis engine is used to determine the user's emotional state. The input is the user's voice or text, and the output is the result of the emotion evaluation.

[0393] Step 6:

[0394] The device uses emotion response adjustment mechanisms to adjust its response based on recognized emotions. For example, if anxiety is detected, it adds reassuring information. The output is a customized notification or explanation for the user.

[0395] Step 7:

[0396] The user receives responses and notifications from the device and checks for specific instructions and situation-appropriate information. The input here is the tailored notification, and the output is the information provided to the user.

[0397] This series of processes makes communication network management more efficient and based on emotions, improving the user experience.

[0398] 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.

[0399] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] The system of the present invention is designed to streamline the operation of wireless communication networks and enable operation that does not rely on specialized knowledge. In this system, a server takes the lead in collecting historical communication data, and each component works in cooperation to perform data analysis, priority determination, procedure generation, and output.

[0415] The server stores traffic data acquired from the wireless network in a database. This data includes the number of users, signal strength, connection history, and troubleshooting status. The server periodically analyzes this data and uses machine learning models to detect potential trouble trends and performance changes within the network.

[0416] Based on the analyzed data, the server determines the priority of each base station. This process evaluates parameters such as user density, trouble history, and current network load, and automatically identifies base stations that require attention.

[0417] Next, the server utilizes a generation AI model to generate specific troubleshooting steps. These steps clearly outline the necessary items and procedures for troubleshooting and are provided in a format that even a novice engineer can understand.

[0418] Users can use their devices to submit inquiries about specific problems or operational issues. These inquiries are then processed on the server, where a generating AI model selects the most appropriate answer and provides it to the user.

[0419] As a concrete example, if a communication failure occurs at a base station, the server analyzes the collected data to identify the cause and prioritize it appropriately. Next, the generated response procedure is sent to the terminal, supporting the assigned engineer in responding quickly on-site.

[0420] Furthermore, user feedback is collected by the server and used as training data to improve the accuracy of the generated AI model. This iterative process is designed to continuously improve the overall operational efficiency of the system.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The server retrieves historical traffic data from the communication network. This includes daily log data from each base station, signal strength, connection count, and troubleshooting information. The server periodically stores this data in the relevant database.

[0424] Step 2:

[0425] The server analyzes the accumulated data. Using machine learning models, it analyzes communication patterns and signs of anomalies to identify the location and time of potential problems.

[0426] Step 3:

[0427] The server determines the priority of communication devices within the network based on the results of data analysis. This process considers factors such as the trouble history of a particular base station, current user density, and signal quality, and lists the locations that require priority attention.

[0428] Step 4:

[0429] The server uses an AI model to generate specific action plans based on the determined priorities. These plans include detailed descriptions of necessary equipment, troubleshooting steps, and points to note.

[0430] Step 5:

[0431] Users make inquiries about problems or operational issues through their devices. The device sends the inquiry to the server, which uses a generative AI model to generate optimal solutions and advice, which are then provided to the user.

[0432] Step 6:

[0433] The server collects user feedback and uses it as training data for the generating AI model. The server analyzes the content of the feedback to improve the accuracy of future analyses and the quality of response procedures.

[0434] (Example 1)

[0435] 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."

[0436] In the operation of wireless communication networks, there is a need for systems that can detect potential communication failures and performance degradation early and enable efficient and appropriate responses even without specialized knowledge. Furthermore, a key challenge is to effectively utilize inquiries and feedback from users and engineers to continuously improve the overall operational efficiency and response accuracy of the system.

[0437] 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.

[0438] In this invention, the server includes information gathering means for acquiring communication information from a wireless communication network in real time, data analysis means for performing data preprocessing and data analysis using a machine learning model based on the collected communication information, priority determination means for evaluating the priority of devices used based on the analysis results and identifying devices of high importance, automatic procedure creation means for automatically creating corresponding procedures using a generated AI model based on the identified priorities, and procedure transmission means for transmitting the created procedures to a display device. This enables efficient and expertise-independent operation of the wireless communication network.

[0439] "Information gathering means" refers to a device or method that has the function of acquiring communication information in real time from a wireless communication network.

[0440] "Data analysis means" refers to a device or method that performs data preprocessing based on collected communication information and performs data analysis using a machine learning model.

[0441] A "priority determination means" is a device or method that utilizes analysis results to evaluate the priority of the devices used and identify the most important devices.

[0442] "Automatic procedure generation means" refers to a device or method that automatically generates corresponding procedures using a generation AI model based on identified priorities.

[0443] "Procedure transmission means" refers to a device or method for transmitting a created procedure to a display device.

[0444] A "generative AI model" is an artificial intelligence model that learns from past examples and data to generate appropriate responses and procedures for new data inputs.

[0445] "User" refers to someone who operates the system or provides inquiries or feedback.

[0446] This invention provides a system that streamlines the operation of wireless communication networks and enables rapid troubleshooting without requiring specialized knowledge. The central component of this system is a server, which collects and analyzes communication data from the wireless communication network in real time.

[0447] The server utilizes sensors and network monitoring tools during the data collection phase. The collected data is stored in databases such as MySQL and PostgreSQL. Data preprocessing is performed using Python and its libraries, Pandas and NumPy, for data analysis. Machine learning models are also executed using TensorFlow and PyTorch to analyze communication patterns and predict problems.

[0448] Based on the analysis results, the server executes an algorithm to determine the priority of the devices to be used. This is done by evaluating parameters such as the number of users, signal strength, connection history, and trouble occurrence status obtained from communication data. For devices of high importance, a specific response procedure is automatically created using a generation AI model (for example, OpenAI's GPT series). The generated response procedure is sent to the terminal of the person in charge.

[0449] Users can contact the server via their device to inquire about specific problems or operational issues. The server receives the inquiry, uses a generative AI model to generate the most appropriate response, and provides it to the user.

[0450] For example, if a communication failure occurs at one of the base stations, the server immediately accesses the base station's historical data to identify the cause of the problem. Then, a generative AI model is used to create troubleshooting steps, which are sent to the engineer's terminal, enabling a rapid response on-site.

[0451] As an example of a prompt, by inputting an instruction such as "Please suggest a rapid response procedure for a communication failure at base station 123" into the generating AI model, an appropriate procedure will be suggested.

[0452] This system collects user feedback and uses it as training data for the generated AI model, thereby improving the model's accuracy. This design not only streamlines operations but also accelerates the system's own evolution.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The server collects communication data from the wireless communication network in real time. Inputs include the number of users, signal strength, connection history, and troubleshooting status. This data is collected using a stream processing module and stored in a MySQL database. Specifically, the server periodically polls the network for data and writes it to the database in real time.

[0456] Step 2:

[0457] The server periodically analyzes the accumulated communication data. This analysis involves preprocessing using Pandas to clean and format the data. The input is the raw data collected in step 1, from which features are extracted, and machine learning models are run using TensorFlow to analyze traffic trends and detect anomalies. The output consists of anomaly detection results and a traffic analysis report. Specifically, the server executes scheduled jobs and logs the analysis results.

[0458] Step 3:

[0459] The server evaluates the priority of each device based on the analysis results. The input is the output of step 2, which analyzes user density, trouble frequency, and current network load according to a specific algorithm. The output is a list of high-priority devices. Specifically, the server executes a priority determination algorithm internally and temporarily stores the results in memory.

[0460] Step 4:

[0461] The server automatically generates response procedures using a generative AI model based on the priority determination results. The input is the priority list from step 3, and the output is the specific response procedure. The generative AI model references past successes and best practices to create new procedures. In practice, the server invokes the AI ​​and saves the generated procedure in text format.

[0462] Step 5:

[0463] The server sends the generated procedure to the engineer's terminal. The input is the corresponding procedure created in step 4, and the output is the procedure notification to the terminal. Specifically, the server pushes the procedure to the engineer's terminal via the notification system and displays it.

[0464] Step 6:

[0465] The user uses an engineer's terminal to send a query to the server, seeking advice on a specific problem. The input is the query created by the user, and the server uses a generative AI model to find the best solution. The output is the response to the user. Specifically, the terminal sends the query to the server and displays the received response to the user.

[0466] Step 7:

[0467] The server collects feedback from users and engineers and uses it as training data for generative AI models. The input is feedback data, and the output is an improvement in the performance of the generative AI model. Specifically, the server stores the feedback in a database and incorporates it into training new models.

[0468] (Application Example 1)

[0469] 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."

[0470] The present invention aims to prevent potential communication failures and enable real-time anomaly detection and rapid problem resolution by achieving efficient operation of communication networks in factory environments. This will improve the operational efficiency of machinery within factories and solve the problem of enabling even novice engineers to easily manage the network.

[0471] 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.

[0472] In this invention, the server includes information gathering means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of communication devices based on the analyzed information, procedure generation means for automatically generating response procedures based on the determined priority, procedure output means for outputting the generated procedures and design proposals, and means for analyzing traffic information of a wireless communication network to improve the operation of machinery in a factory environment and detecting potential communication failures in real time. This enables the advance detection of communication failures within the factory and efficient operation through rapid response.

[0473] An "information gathering means" is an element that has the function of acquiring past communication information and storing or recording that information.

[0474] An "information analysis tool" is an element that can analyze and evaluate data trends and anomalies based on collected communication information.

[0475] A "priority determination method" is a component used to rank the importance of communication devices and problems using analyzed information and to determine the appropriate processing order.

[0476] A "procedure generation means" is an element that has the function of automatically generating necessary response procedures and solutions based on the priority ranking determined by the priority determination means.

[0477] A "procedure output means" is an element that has the function of presenting the generated procedures or design proposals to the user or operator.

[0478] "Communication failure" refers to a phenomenon in wireless communication networks where normal data transmission and reception become impossible or difficult.

[0479] "Factory environment" refers to the work area where machinery and equipment are operated within facilities where industrial production takes place or in their surrounding areas.

[0480] "Real-time problem solving" refers to responding quickly and without delay to problems that arise and implementing solutions immediately.

[0481] To implement this invention, the server first acquires historical and real-time communication information from a wireless communication network in a factory environment. The acquired communication information includes the number of users, signal strength, connection history, and trouble occurrence status, and is stored in a database.

[0482] Next, the server analyzes the collected information using data analysis tools to identify potential communication failures and changes in communication equipment performance. Here, AI algorithms and machine learning techniques are used to explore data trends and detect anomalies. Specifically, data analysis software such as Python and R may be used.

[0483] Subsequently, the server uses a priority determination mechanism to determine the priority of communication devices within the factory. This ensures that more critical communication devices are addressed first, enabling efficient responses when problems occur.

[0484] The generated data and response procedures are converted into specific steps by an automated generation system and sent to the assigned technician's terminal by a procedure output system. This process utilizes a generation AI model designed to provide the optimal steps for problem solving based on the original data.

[0485] Furthermore, users can submit inquiries to the system through their terminals. The inquiry response method, which uses a generative AI model, selects the most appropriate answer and presents it to the user. An example of a prompt used in this process would be, "Please generate a procedure for dealing with the situation when communication between robots is interrupted in a specific area."

[0486] This system allows for the proactive detection of communication network failures within the factory, enabling rapid resolution. Furthermore, by collecting user feedback and using it as training data for the generated AI model, continuous accuracy improvements can be achieved.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] The server acquires historical and real-time communication information from the factory's wireless communication network. This input data includes the number of users, signal strength, connection history, and trouble occurrences. Information is collected by storing this information in a database.

[0490] Step 2:

[0491] The server analyzes communication information stored in the database using information analysis tools. This analysis uses AI algorithms and machine learning techniques to identify trends in the input data and detect anomalies. For example, it includes processes to identify areas where signal strength is low.

[0492] Step 3:

[0493] The server uses a prioritization mechanism based on the analysis results to determine the priority of communication devices within the factory. User density and trouble history are input, and based on this, critical devices are identified. Prioritizing from high importance to low importance enables efficient response.

[0494] Step 4:

[0495] The server automatically generates specific response procedures using a generative AI model, according to the determined priorities. In this process, prompts are used to construct the optimal response method for the input trouble information and priorities, and the procedure is output.

[0496] Step 5:

[0497] The generated procedure is sent to the technician's terminal via the procedure output device. The terminal receives and displays the output of this procedure. The procedure is clearly presented so that the technician can quickly begin resolving the problem.

[0498] Step 6:

[0499] Users can use a terminal to make inquiries to the information system. The inquiry response method receives the user's inquiry as input, generates the optimal answer using a generation AI model, and presents it to the user as output. A specific example is the prompt message, "Generate the procedure for dealing with the situation when communication between robots is interrupted in a specific area."

[0500] Step 7:

[0501] User feedback is collected on the server and used as training data for the generated AI model. The input data from the feedback is used in calculations to improve the model's accuracy, contributing to improved troubleshooting quality in the future.

[0502] 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.

[0503] The system of the present invention incorporates an emotion engine that recognizes user emotions, in addition to conventional data collection, analysis, prioritization, response procedure generation, and output, in order to improve the operation of wireless communication networks and the user experience.

[0504] The server stores traffic data acquired from the wireless communication network in a database and analyzes this data using machine learning models to determine which base stations should be prioritized and which require troubleshooting. Subsequently, it uses a generative AI model to generate response procedures based on priorities and provides them to the user through a procedure output device.

[0505] Furthermore, when receiving inquiries from users, the device uses an emotion engine to analyze the user's emotions in real time. This allows it to assess the user's stress level and satisfaction level, and provide a more personalized service tailored to their emotions. Specifically, the emotion engine recognizes the user's emotions from voice and text data, and adjusts the advice and response procedures provided by the AI ​​model based on the results. This approach reduces user frustration and irritation, enabling a better user experience.

[0506] For example, if a user is dissatisfied with a base station problem, the server will use feedback from the emotion engine in addition to analysis results to offer a more considerate solution. Furthermore, if the server detects that the user is feeling anxious, it can respond flexibly by adding information to provide reassurance.

[0507] Furthermore, emotional data is incorporated into the learning process of the generative AI model, allowing the server to continuously optimize the model. As a result, the accuracy of responses and the quality of emotion-based personalized responses are improved, and the system is designed to make the management of wireless communication networks more efficient and effective.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The server acquires traffic data from the wireless communication network and stores it in a database. This data includes signal quality for each base station, the number of users, connection errors, and past trouble history.

[0511] Step 2:

[0512] The server analyzes the accumulated data using a machine learning model. This analysis identifies potential problem areas and points requiring improvement within the network. The analysis results are used to prioritize specific base stations.

[0513] Step 3:

[0514] The server determines the priority of base stations based on the analysis results. This process evaluates the user utilization rate, past trouble frequency, and current load status of each base station and lists the order in which they should be addressed.

[0515] Step 4:

[0516] The server uses an AI model to automatically generate appropriate response procedures based on priority. These procedures include specific troubleshooting steps, necessary tools, and estimated response times. The procedures are generated in a format that is easy for the user to understand.

[0517] Step 5:

[0518] When a user makes an inquiry about a problem via their device, the device acquires the voice and text data and performs sentiment analysis using an emotion engine. The emotion engine evaluates dissatisfaction, stress, satisfaction, etc., and sends the results to the server.

[0519] Step 6:

[0520] The server receives information from the sentiment engine and adjusts the generated response procedures and answers. If the user expresses strong dissatisfaction, procedures including faster, more detailed explanations and a more benevolent response are provided.

[0521] Step 7:

[0522] The server collects user-provided feedback and sentiment data, which it then uses as training data for its AI model. The server continuously improves the model's accuracy based on this new data, resulting in better future responses.

[0523] (Example 2)

[0524] 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."

[0525] In recent years, information and communication networks have become increasingly complex and the users more diverse, making it difficult to troubleshoot and support users quickly and effectively using only traditional data analysis. Furthermore, users are increasingly seeking not only technical solutions but also emotional considerations to alleviate stress and anxiety. To address these challenges, there is a growing need for systems that offer the precision and flexibility that conventional technologies could not provide.

[0526] 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.

[0527] In this invention, the server includes data collection means for acquiring traffic data from a communication network, data analysis means for analyzing the acquired traffic data using a machine learning model, and an emotion analysis engine for analyzing user emotions. This enables rapid and highly accurate troubleshooting and allows for flexible responses that take user emotions into consideration.

[0528] A "data collection means for acquiring traffic data from a communication network" is a component that has the function of collecting various types of data from a wireless communication network, and is a system that plays the role of acquiring packet information and connection status in real time.

[0529] "Data analysis methods using machine learning models" refer to components that apply machine learning techniques to analyze collected data, with the aim of detecting network performance and anomalies.

[0530] "Priority determination means for determining the priority of communication management devices" refers to a component that executes methods or algorithms for determining which communication management device should be prioritized for processing based on the analyzed data.

[0531] A "procedure generation method that automatically generates using a generative AI model" is a system that utilizes generative AI technology to automatically generate response procedures and design proposals according to specific situations and priorities.

[0532] A "procedure output means" is a component that has an interface or mechanism for providing the user with the generated corresponding procedures or design proposals.

[0533] A "sentiment analysis engine that analyzes user emotions" is a technology that analyzes voice or text input from users and evaluates the user's emotional state in real time based on the content.

[0534] A "user inquiry response method that performs real-time sentiment analysis of user inquiries" refers to a set of components that include a process for quickly analyzing the sentiment of a user inquiry and deriving the optimal response.

[0535] A "feedback learning method" is a function that provides a way to collect user feedback and emotional data and use it as data to improve and optimize the generated AI model.

[0536] This invention is a system for achieving efficient and advanced communication network management. Servers, terminals, and users function as follows, providing a consistent service as a whole.

[0537] Server role:

[0538] The server collects traffic data from the communication network in real time. This data collection function is designed to monitor and analyze network performance and connection stability, and to store detailed data from each base station in a database. The server analyzes this data using machine learning models to detect anomalies and analyze network load. Based on the analysis results, it identifies communication management devices that require priority attention and passes that information to the next process.

[0539] Terminal role:

[0540] The device activates its sentiment analysis engine when it receives an inquiry from a user. This sentiment analysis evaluates the user's stress and satisfaction levels through voice or text data, and grasps their emotional state in real time. Based on this information, the device generates optimal response procedures and advice tailored to the user's state through a generative AI model.

[0541] System operation:

[0542] The system can automatically generate troubleshooting steps using a generative AI model based on data analyzed on the server. This enables rapid and highly accurate troubleshooting. For example, if a user complains of slow internet speed, the system uses historical data and real-time sentiment assessment to suggest appropriate solutions. In this process, the generative AI model receives prompts such as, "Analyze the emotions expressed by the user and generate the optimal troubleshooting steps corresponding to those emotions. For example, suggest solutions for a user who is dissatisfied with slow internet speed."

[0543] This system design simultaneously achieves flexible responses that take user emotions into consideration and streamlines network management.

[0544] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0545] Step 1:

[0546] The server continuously collects traffic data from the wireless communication network. Specifically, data such as packet information, connection status, and communication speed transmitted from each base station are stored in a database in real time. This process generates raw data for understanding the overall performance of the network using data collection methods.

[0547] Step 2:

[0548] The server analyzes the collected traffic data using a machine learning model. Calculations are performed to identify abnormal communication patterns and network load trends. It receives raw network data as input and outputs anomaly detection results and a list of degraded base stations. This output helps determine which items require priority attention.

[0549] Step 3:

[0550] When the device receives an inquiry from a user, it uses an emotion engine to analyze the user's emotions. Voice and text data are input, and based on this, data evaluating stress levels and satisfaction levels is output. This allows for support that takes into account the emotional factors behind what the user is dissatisfied with.

[0551] Step 4:

[0552] The server uses a generative AI model to generate specific troubleshooting steps based on the analyzed network data and feedback from the emotion engine. This process takes pre-configured prompts as input and outputs the most suitable troubleshooting steps for the user. For example, a prompt such as "Analyze the emotions the user is expressing and generate the most appropriate troubleshooting steps based on those emotions" might be used.

[0553] Step 5:

[0554] The server provides the generated response procedures to the user through a procedure output mechanism. For example, if the user is dissatisfied with slow communication speeds, the procedures will include suggestions for improvement and additional information that takes their feelings into consideration. Feedback from the user is also returned to the system and used to optimize the model in the next learning cycle. By forming this feedback loop, the overall accuracy and effectiveness of the system are improved.

[0555] (Application Example 2)

[0556] 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."

[0557] In the operation of communication networks, stress and dissatisfaction caused by responses that disregard user emotions are a major challenge. Traditional systems have a problem where improving the user experience is difficult because they do not understand user emotions and troubleshoot problems uniformly. Furthermore, in security services, it is necessary to provide appropriate reassuring information to users who are feeling anxious.

[0558] 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.

[0559] In this invention, the server includes data collection means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of wireless communication devices based on the analyzed information, procedure generation means for automatically generating response procedures based on the determined priority, emotion recognition means for recognizing the user's emotions from voice or text, and emotion response adjustment means for adjusting the response based on the recognized emotions. This enables detailed responses that respond to the user's emotions, thereby improving the efficiency of network operations and enhancing the user experience.

[0560] "Past communication information" refers to data and records that were previously sent and received via the network.

[0561] "Data collection means" refers to a device or system that has the function of acquiring and storing communication information.

[0562] "Information analysis means" refers to methods and tools used to recognize patterns, identify problems, or discover areas for improvement based on collected communication information.

[0563] A "prioritization decision mechanism" refers to a system that uses analyzed information to evaluate the importance and urgency of communication devices and processes, and to determine countermeasures.

[0564] A "procedure generation means" refers to a system that has the function of automatically constructing necessary processing procedures and countermeasures based on the results of priority determination.

[0565] "Emotion recognition means" refers to technology or devices that analyze audio data or text data to identify the emotional state of a user.

[0566] An "emotional response adjustment mechanism" refers to a system that has the function of adjusting the responses and notifications it provides in accordance with the recognized emotions.

[0567] The system of this invention aims to improve the effective management of communication networks and the user experience. The server acquires past communication information using data collection means and analyzes it using information analysis means. The importance of the analysis results is evaluated by priority determination means, and procedure generation means automatically creates corresponding procedures.

[0568] Furthermore, the device is equipped with emotion recognition means to identify emotions expressed by the user through voice or text. Emotion response adjustment means adjusts the response to the user accordingly, providing a flexible response that reduces stress.

[0569] This system utilizes hardware such as smartphones and computers, and software such as Google Cloud's TensorFlow and natural language processing toolkits. Generative AI models are used to learn user sentiment data and generate more accurate responses.

[0570] As a concrete example, if a user accesses the security system via their smartphone and asks about the status of their home, and the emotion recognition system detects anxiety, the server can immediately analyze past monitoring data and provide information indicating safety. An example of a prompt message for the generating AI model would be, "Please provide the most appropriate reassurance information to alleviate the user's anxiety."

[0571] This system will enable more human-centered and efficient monitoring and troubleshooting of communication networks.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server retrieves past communication information from a network database using data collection methods. This input data includes communication volume, connection time, and error rate. The collected data forms the basis for subsequent information analysis.

[0575] Step 2:

[0576] The server analyzes the collected communication information using information analysis tools. Specifically, it identifies traffic patterns using machine learning algorithms and identifies potential problems. The output of this step provides information to determine what needs to be improved.

[0577] Step 3:

[0578] The server uses a prioritization mechanism to evaluate the importance of wireless communication devices based on the analyzed information. This step prioritizes devices and issues that are likely to affect users. The output is a list of priorities.

[0579] Step 4:

[0580] The server uses a procedure generation mechanism to automatically generate response procedures based on priority. Prompt messages are input into a generation AI model to create appropriate responses and procedures. The output of this step is specific countermeasures or improvement suggestions.

[0581] Step 5:

[0582] The device uses emotion recognition to analyze the emotions in the user's voice and text. A dedicated emotion analysis engine is used to determine the user's emotional state. The input is the user's voice or text, and the output is the result of the emotion evaluation.

[0583] Step 6:

[0584] The device uses emotion response adjustment mechanisms to adjust its response based on recognized emotions. For example, if anxiety is detected, it adds reassuring information. The output is a customized notification or explanation for the user.

[0585] Step 7:

[0586] The user receives responses and notifications from the device and checks for specific instructions and situation-appropriate information. The input here is the tailored notification, and the output is the information provided to the user.

[0587] This series of processes makes communication network management more efficient and based on emotions, improving the user experience.

[0588] 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.

[0589] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] The system of the present invention is designed to streamline the operation of wireless communication networks and enable operation that does not rely on specialized knowledge. In this system, a server takes the lead in collecting historical communication data, and each component works in cooperation to perform data analysis, priority determination, procedure generation, and output.

[0606] The server stores traffic data acquired from the wireless network in a database. This data includes the number of users, signal strength, connection history, and troubleshooting status. The server periodically analyzes this data and uses machine learning models to detect potential trouble trends and performance changes within the network.

[0607] Based on the analyzed data, the server determines the priority of each base station. This process evaluates parameters such as user density, trouble history, and current network load, and automatically identifies base stations that require attention.

[0608] Next, the server utilizes a generation AI model to generate specific troubleshooting steps. These steps clearly outline the necessary items and procedures for troubleshooting and are provided in a format that even a novice engineer can understand.

[0609] Users can use their devices to submit inquiries about specific problems or operational issues. These inquiries are then processed on the server, where a generating AI model selects the most appropriate answer and provides it to the user.

[0610] As a concrete example, if a communication failure occurs at a base station, the server analyzes the collected data to identify the cause and prioritize it appropriately. Next, the generated response procedure is sent to the terminal, supporting the assigned engineer in responding quickly on-site.

[0611] Furthermore, user feedback is collected by the server and used as training data to improve the accuracy of the generated AI model. This iterative process is designed to continuously improve the overall operational efficiency of the system.

[0612] The following describes the processing flow.

[0613] Step 1:

[0614] The server retrieves historical traffic data from the communication network. This includes daily log data from each base station, signal strength, connection count, and troubleshooting information. The server periodically stores this data in the relevant database.

[0615] Step 2:

[0616] The server analyzes the accumulated data. Using machine learning models, it analyzes communication patterns and signs of anomalies to identify the location and time of potential problems.

[0617] Step 3:

[0618] The server determines the priority of communication devices within the network based on the results of data analysis. This process considers factors such as the trouble history of a particular base station, current user density, and signal quality, and lists the locations that require priority attention.

[0619] Step 4:

[0620] The server uses an AI model to generate specific action plans based on the determined priorities. These plans include detailed descriptions of necessary equipment, troubleshooting steps, and points to note.

[0621] Step 5:

[0622] Users make inquiries about problems or operational issues through their devices. The device sends the inquiry to the server, which uses a generative AI model to generate optimal solutions and advice, which are then provided to the user.

[0623] Step 6:

[0624] The server collects user feedback and uses it as training data for the generating AI model. The server analyzes the content of the feedback to improve the accuracy of future analyses and the quality of response procedures.

[0625] (Example 1)

[0626] 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".

[0627] In the operation of wireless communication networks, there is a need for systems that can detect potential communication failures and performance degradation early and enable efficient and appropriate responses even without specialized knowledge. Furthermore, a key challenge is to effectively utilize inquiries and feedback from users and engineers to continuously improve the overall operational efficiency and response accuracy of the system.

[0628] 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.

[0629] In this invention, the server includes information gathering means for acquiring communication information from a wireless communication network in real time, data analysis means for performing data preprocessing and data analysis using a machine learning model based on the collected communication information, priority determination means for evaluating the priority of devices used based on the analysis results and identifying devices of high importance, automatic procedure creation means for automatically creating corresponding procedures using a generated AI model based on the identified priorities, and procedure transmission means for transmitting the created procedures to a display device. This enables efficient and expertise-independent operation of the wireless communication network.

[0630] "Information gathering means" refers to a device or method that has the function of acquiring communication information in real time from a wireless communication network.

[0631] "Data analysis means" refers to a device or method that performs data preprocessing based on collected communication information and performs data analysis using a machine learning model.

[0632] A "priority determination means" is a device or method that utilizes analysis results to evaluate the priority of the devices used and identify the most important devices.

[0633] "Automatic procedure generation means" refers to a device or method that automatically generates corresponding procedures using a generation AI model based on identified priorities.

[0634] "Procedure transmission means" refers to a device or method for transmitting a created procedure to a display device.

[0635] A "generative AI model" is an artificial intelligence model that learns from past examples and data to generate appropriate responses and procedures for new data inputs.

[0636] "User" refers to someone who operates the system or provides inquiries or feedback.

[0637] This invention provides a system that streamlines the operation of wireless communication networks and enables rapid troubleshooting without requiring specialized knowledge. The central component of this system is a server, which collects and analyzes communication data from the wireless communication network in real time.

[0638] The server utilizes sensors and network monitoring tools during the data collection phase. The collected data is stored in databases such as MySQL and PostgreSQL. Data preprocessing is performed using Python and its libraries, Pandas and NumPy, for data analysis. Machine learning models are also executed using TensorFlow and PyTorch to analyze communication patterns and predict problems.

[0639] Based on the analysis results, the server executes an algorithm to determine the priority of the devices to be used. This is done by evaluating parameters such as the number of users, signal strength, connection history, and trouble occurrence status obtained from communication data. For devices of high importance, a specific response procedure is automatically created using a generation AI model (for example, OpenAI's GPT series). The generated response procedure is sent to the terminal of the person in charge.

[0640] Users can contact the server via their device to inquire about specific problems or operational issues. The server receives the inquiry, uses a generative AI model to generate the most appropriate response, and provides it to the user.

[0641] For example, if a communication failure occurs at one of the base stations, the server immediately accesses the base station's historical data to identify the cause of the problem. Then, a generative AI model is used to create troubleshooting steps, which are sent to the engineer's terminal, enabling a rapid response on-site.

[0642] As an example of a prompt, by inputting an instruction such as "Please suggest a rapid response procedure for a communication failure at base station 123" into the generating AI model, an appropriate procedure will be suggested.

[0643] This system collects user feedback and uses it as training data for the generated AI model, thereby improving the model's accuracy. This design not only streamlines operations but also accelerates the system's own evolution.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The server collects communication data from the wireless communication network in real time. Inputs include the number of users, signal strength, connection history, and troubleshooting status. This data is collected using a stream processing module and stored in a MySQL database. Specifically, the server periodically polls the network for data and writes it to the database in real time.

[0647] Step 2:

[0648] The server periodically analyzes the accumulated communication data. This analysis involves preprocessing using Pandas to clean and format the data. The input is the raw data collected in step 1, from which features are extracted, and machine learning models are run using TensorFlow to analyze traffic trends and detect anomalies. The output consists of anomaly detection results and a traffic analysis report. Specifically, the server executes scheduled jobs and logs the analysis results.

[0649] Step 3:

[0650] The server evaluates the priority of each device based on the analysis results. The input is the output of step 2, which analyzes user density, trouble frequency, and current network load according to a specific algorithm. The output is a list of high-priority devices. Specifically, the server executes a priority determination algorithm internally and temporarily stores the results in memory.

[0651] Step 4:

[0652] The server automatically generates response procedures using a generative AI model based on the priority determination results. The input is the priority list from step 3, and the output is the specific response procedure. The generative AI model references past successes and best practices to create new procedures. In practice, the server invokes the AI ​​and saves the generated procedure in text format.

[0653] Step 5:

[0654] The server sends the generated procedure to the engineer's terminal. The input is the corresponding procedure created in step 4, and the output is the procedure notification to the terminal. Specifically, the server pushes the procedure to the engineer's terminal via the notification system and displays it.

[0655] Step 6:

[0656] The user uses an engineer's terminal to send a query to the server, seeking advice on a specific problem. The input is the query created by the user, and the server uses a generative AI model to find the best solution. The output is the response to the user. Specifically, the terminal sends the query to the server and displays the received response to the user.

[0657] Step 7:

[0658] The server collects feedback from users and engineers and uses it as training data for generative AI models. The input is feedback data, and the output is an improvement in the performance of the generative AI model. Specifically, the server stores the feedback in a database and incorporates it into training new models.

[0659] (Application Example 1)

[0660] 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".

[0661] The present invention aims to prevent potential communication failures and enable real-time anomaly detection and rapid problem resolution by achieving efficient operation of communication networks in factory environments. This will improve the operational efficiency of machinery within factories and solve the problem of enabling even novice engineers to easily manage the network.

[0662] 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.

[0663] In this invention, the server includes information gathering means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of communication devices based on the analyzed information, procedure generation means for automatically generating response procedures based on the determined priority, procedure output means for outputting the generated procedures and design proposals, and means for analyzing traffic information of a wireless communication network to improve the operation of machinery in a factory environment and detecting potential communication failures in real time. This enables the advance detection of communication failures within the factory and efficient operation through rapid response.

[0664] An "information gathering means" is an element that has the function of acquiring past communication information and storing or recording that information.

[0665] An "information analysis tool" is an element that can analyze and evaluate data trends and anomalies based on collected communication information.

[0666] A "priority determination method" is a component used to rank the importance of communication devices and problems using analyzed information and to determine the appropriate processing order.

[0667] A "procedure generation means" is an element that has the function of automatically generating necessary response procedures and solutions based on the priority ranking determined by the priority determination means.

[0668] A "procedure output means" is an element that has the function of presenting the generated procedures or design proposals to the user or operator.

[0669] "Communication failure" refers to a phenomenon in wireless communication networks where normal data transmission and reception become impossible or difficult.

[0670] "Factory environment" refers to the work area where machinery and equipment are operated within facilities where industrial production takes place or in their surrounding areas.

[0671] "Real-time problem solving" refers to responding quickly and without delay to problems that arise and implementing solutions immediately.

[0672] To implement this invention, the server first acquires historical and real-time communication information from a wireless communication network in a factory environment. The acquired communication information includes the number of users, signal strength, connection history, and trouble occurrence status, and is stored in a database.

[0673] Next, the server analyzes the collected information using data analysis tools to identify potential communication failures and changes in communication equipment performance. Here, AI algorithms and machine learning techniques are used to explore data trends and detect anomalies. Specifically, data analysis software such as Python and R may be used.

[0674] Subsequently, the server uses a priority determination mechanism to determine the priority of communication devices within the factory. This ensures that more critical communication devices are addressed first, enabling efficient responses when problems occur.

[0675] The generated data and response procedures are converted into specific steps by an automated generation system and sent to the assigned technician's terminal by a procedure output system. This process utilizes a generation AI model designed to provide the optimal steps for problem solving based on the original data.

[0676] Furthermore, users can submit inquiries to the system through their terminals. The inquiry response method, which uses a generative AI model, selects the most appropriate answer and presents it to the user. An example of a prompt used in this process would be, "Please generate a procedure for dealing with the situation when communication between robots is interrupted in a specific area."

[0677] This system allows for the proactive detection of communication network failures within the factory, enabling rapid resolution. Furthermore, by collecting user feedback and using it as training data for the generated AI model, continuous accuracy improvements can be achieved.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The server acquires historical and real-time communication information from the factory's wireless communication network. This input data includes the number of users, signal strength, connection history, and trouble occurrences. Information is collected by storing this information in a database.

[0681] Step 2:

[0682] The server analyzes communication information stored in the database using information analysis tools. This analysis uses AI algorithms and machine learning techniques to identify trends in the input data and detect anomalies. For example, it includes processes to identify areas where signal strength is low.

[0683] Step 3:

[0684] The server uses a prioritization mechanism based on the analysis results to determine the priority of communication devices within the factory. User density and trouble history are input, and based on this, critical devices are identified. Prioritizing from high importance to low importance enables efficient response.

[0685] Step 4:

[0686] The server automatically generates specific response procedures using a generative AI model, according to the determined priorities. In this process, prompts are used to construct the optimal response method for the input trouble information and priorities, and the procedure is output.

[0687] Step 5:

[0688] The generated procedure is sent to the technician's terminal via the procedure output device. The terminal receives and displays the output of this procedure. The procedure is clearly presented so that the technician can quickly begin resolving the problem.

[0689] Step 6:

[0690] Users can use a terminal to make inquiries to the information system. The inquiry response method receives the user's inquiry as input, generates the optimal answer using a generation AI model, and presents it to the user as output. A specific example is the prompt message, "Generate the procedure for dealing with the situation when communication between robots is interrupted in a specific area."

[0691] Step 7:

[0692] User feedback is collected on the server and used as training data for the generated AI model. The input data from the feedback is used in calculations to improve the model's accuracy, contributing to improved troubleshooting quality in the future.

[0693] 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.

[0694] The system of the present invention incorporates an emotion engine that recognizes user emotions, in addition to conventional data collection, analysis, prioritization, response procedure generation, and output, in order to improve the operation of wireless communication networks and the user experience.

[0695] The server stores traffic data acquired from the wireless communication network in a database and analyzes this data using machine learning models to determine which base stations should be prioritized and which require troubleshooting. Subsequently, it uses a generative AI model to generate response procedures based on priorities and provides them to the user through a procedure output device.

[0696] Furthermore, when receiving inquiries from users, the device uses an emotion engine to analyze the user's emotions in real time. This allows it to assess the user's stress level and satisfaction level, and provide a more personalized service tailored to their emotions. Specifically, the emotion engine recognizes the user's emotions from voice and text data, and adjusts the advice and response procedures provided by the AI ​​model based on the results. This approach reduces user frustration and irritation, enabling a better user experience.

[0697] For example, if a user is dissatisfied with a base station problem, the server will use feedback from the emotion engine in addition to analysis results to offer a more considerate solution. Furthermore, if the server detects that the user is feeling anxious, it can respond flexibly by adding information to provide reassurance.

[0698] Furthermore, emotional data is incorporated into the learning process of the generative AI model, allowing the server to continuously optimize the model. As a result, the accuracy of responses and the quality of emotion-based personalized responses are improved, and the system is designed to make the management of wireless communication networks more efficient and effective.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server acquires traffic data from the wireless communication network and stores it in a database. This data includes signal quality for each base station, the number of users, connection errors, and past trouble history.

[0702] Step 2:

[0703] The server analyzes the accumulated data using a machine learning model. This analysis identifies potential problem areas and points requiring improvement within the network. The analysis results are used to prioritize specific base stations.

[0704] Step 3:

[0705] The server determines the priority of base stations based on the analysis results. This process evaluates the user utilization rate, past trouble frequency, and current load status of each base station and lists the order in which they should be addressed.

[0706] Step 4:

[0707] The server uses an AI model to automatically generate appropriate response procedures based on priority. These procedures include specific troubleshooting steps, necessary tools, and estimated response times. The procedures are generated in a format that is easy for the user to understand.

[0708] Step 5:

[0709] When a user makes an inquiry about a problem via their device, the device acquires the voice and text data and performs sentiment analysis using an emotion engine. The emotion engine evaluates dissatisfaction, stress, satisfaction, etc., and sends the results to the server.

[0710] Step 6:

[0711] The server receives information from the sentiment engine and adjusts the generated response procedures and answers. If the user expresses strong dissatisfaction, procedures including faster, more detailed explanations and a more benevolent response are provided.

[0712] Step 7:

[0713] The server collects user-provided feedback and sentiment data, which it then uses as training data for its AI model. The server continuously improves the model's accuracy based on this new data, resulting in better future responses.

[0714] (Example 2)

[0715] 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".

[0716] In recent years, information and communication networks have become increasingly complex and the users more diverse, making it difficult to troubleshoot and support users quickly and effectively using only traditional data analysis. Furthermore, users are increasingly seeking not only technical solutions but also emotional considerations to alleviate stress and anxiety. To address these challenges, there is a growing need for systems that offer the precision and flexibility that conventional technologies could not provide.

[0717] 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.

[0718] In this invention, the server includes data collection means for acquiring traffic data from a communication network, data analysis means for analyzing the acquired traffic data using a machine learning model, and an emotion analysis engine for analyzing user emotions. This enables rapid and highly accurate troubleshooting and allows for flexible responses that take user emotions into consideration.

[0719] A "data collection means for acquiring traffic data from a communication network" is a component that has the function of collecting various types of data from a wireless communication network, and is a system that plays the role of acquiring packet information and connection status in real time.

[0720] "Data analysis methods using machine learning models" refer to components that apply machine learning techniques to analyze collected data, with the aim of detecting network performance and anomalies.

[0721] "Priority determination means for determining the priority of communication management devices" refers to a component that executes methods or algorithms for determining which communication management device should be prioritized for processing based on the analyzed data.

[0722] A "procedure generation method that automatically generates using a generative AI model" is a system that utilizes generative AI technology to automatically generate response procedures and design proposals according to specific situations and priorities.

[0723] A "procedure output means" is a component that has an interface or mechanism for providing the user with the generated corresponding procedures or design proposals.

[0724] A "sentiment analysis engine that analyzes user emotions" is a technology that analyzes voice or text input from users and evaluates the user's emotional state in real time based on the content.

[0725] A "user inquiry response method that performs real-time sentiment analysis of user inquiries" refers to a set of components that include a process for quickly analyzing the sentiment of a user inquiry and deriving the optimal response.

[0726] A "feedback learning method" is a function that provides a way to collect user feedback and emotional data and use it as data to improve and optimize the generated AI model.

[0727] This invention is a system for achieving efficient and advanced communication network management. Servers, terminals, and users function as follows, providing a consistent service as a whole.

[0728] Server role:

[0729] The server collects traffic data from the communication network in real time. This data collection function is designed to monitor and analyze network performance and connection stability, and to store detailed data from each base station in a database. The server analyzes this data using machine learning models to detect anomalies and analyze network load. Based on the analysis results, it identifies communication management devices that require priority attention and passes that information to the next process.

[0730] Terminal role:

[0731] The device activates its sentiment analysis engine when it receives an inquiry from a user. This sentiment analysis evaluates the user's stress and satisfaction levels through voice or text data, and grasps their emotional state in real time. Based on this information, the device generates optimal response procedures and advice tailored to the user's state through a generative AI model.

[0732] System operation:

[0733] The system can automatically generate troubleshooting steps using a generative AI model based on data analyzed on the server. This enables rapid and highly accurate troubleshooting. For example, if a user complains of slow internet speed, the system uses historical data and real-time sentiment assessment to suggest appropriate solutions. In this process, the generative AI model receives prompts such as, "Analyze the emotions expressed by the user and generate the optimal troubleshooting steps corresponding to those emotions. For example, suggest solutions for a user who is dissatisfied with slow internet speed."

[0734] This system design simultaneously achieves flexible responses that take user emotions into consideration and streamlines network management.

[0735] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0736] Step 1:

[0737] The server continuously collects traffic data from the wireless communication network. Specifically, data such as packet information, connection status, and communication speed transmitted from each base station are stored in a database in real time. This process generates raw data for understanding the overall performance of the network using data collection methods.

[0738] Step 2:

[0739] The server analyzes the collected traffic data using a machine learning model. Calculations are performed to identify abnormal communication patterns and network load trends. It receives raw network data as input and outputs anomaly detection results and a list of degraded base stations. This output helps determine which items require priority attention.

[0740] Step 3:

[0741] When the device receives an inquiry from a user, it uses an emotion engine to analyze the user's emotions. Voice and text data are input, and based on this, data evaluating stress levels and satisfaction levels is output. This allows for support that takes into account the emotional factors behind what the user is dissatisfied with.

[0742] Step 4:

[0743] The server uses a generative AI model to generate specific troubleshooting steps based on the analyzed network data and feedback from the emotion engine. This process takes pre-configured prompts as input and outputs the most suitable troubleshooting steps for the user. For example, a prompt such as "Analyze the emotions the user is expressing and generate the most appropriate troubleshooting steps based on those emotions" might be used.

[0744] Step 5:

[0745] The server provides the generated response procedures to the user through a procedure output mechanism. For example, if the user is dissatisfied with slow communication speeds, the procedures will include suggestions for improvement and additional information that takes their feelings into consideration. Feedback from the user is also returned to the system and used to optimize the model in the next learning cycle. By forming this feedback loop, the overall accuracy and effectiveness of the system are improved.

[0746] (Application Example 2)

[0747] 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".

[0748] In the operation of communication networks, stress and dissatisfaction caused by responses that disregard user emotions are a major challenge. Traditional systems have a problem where improving the user experience is difficult because they do not understand user emotions and troubleshoot problems uniformly. Furthermore, in security services, it is necessary to provide appropriate reassuring information to users who are feeling anxious.

[0749] 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.

[0750] In this invention, the server includes data collection means for acquiring past communication information, information analysis means for performing information analysis based on the acquired past communication information, priority determination means for determining the priority of wireless communication devices based on the analyzed information, procedure generation means for automatically generating response procedures based on the determined priority, emotion recognition means for recognizing the user's emotions from voice or text, and emotion response adjustment means for adjusting the response based on the recognized emotions. This enables detailed responses that respond to the user's emotions, thereby improving the efficiency of network operations and enhancing the user experience.

[0751] "Past communication information" refers to data and records that were previously sent and received via the network.

[0752] "Data collection means" refers to a device or system that has the function of acquiring and storing communication information.

[0753] "Information analysis means" refers to methods and tools used to recognize patterns, identify problems, or discover areas for improvement based on collected communication information.

[0754] A "prioritization decision mechanism" refers to a system that uses analyzed information to evaluate the importance and urgency of communication devices and processes, and to determine countermeasures.

[0755] A "procedure generation means" refers to a system that has the function of automatically constructing necessary processing procedures and countermeasures based on the results of priority determination.

[0756] "Emotion recognition means" refers to technology or devices that analyze audio data or text data to identify the emotional state of a user.

[0757] An "emotional response adjustment mechanism" refers to a system that has the function of adjusting the responses and notifications it provides in accordance with the recognized emotions.

[0758] The system of this invention aims to improve the effective management of communication networks and the user experience. The server acquires past communication information using data collection means and analyzes it using information analysis means. The importance of the analysis results is evaluated by priority determination means, and procedure generation means automatically creates corresponding procedures.

[0759] Furthermore, the device is equipped with emotion recognition means to identify emotions expressed by the user through voice or text. Emotion response adjustment means adjusts the response to the user accordingly, providing a flexible response that reduces stress.

[0760] This system utilizes hardware such as smartphones and computers, and software such as Google Cloud's TensorFlow and natural language processing toolkits. Generative AI models are used to learn user sentiment data and generate more accurate responses.

[0761] As a concrete example, if a user accesses the security system via their smartphone and asks about the status of their home, and the emotion recognition system detects anxiety, the server can immediately analyze past monitoring data and provide information indicating safety. An example of a prompt message for the generating AI model would be, "Please provide the most appropriate reassurance information to alleviate the user's anxiety."

[0762] This system will enable more human-centered and efficient monitoring and troubleshooting of communication networks.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The server retrieves past communication information from a network database using data collection methods. This input data includes communication volume, connection time, and error rate. The collected data forms the basis for subsequent information analysis.

[0766] Step 2:

[0767] The server analyzes the collected communication information using information analysis tools. Specifically, it identifies traffic patterns using machine learning algorithms and identifies potential problems. The output of this step provides information to determine what needs to be improved.

[0768] Step 3:

[0769] The server uses a prioritization mechanism to evaluate the importance of wireless communication devices based on the analyzed information. This step prioritizes devices and issues that are likely to affect users. The output is a list of priorities.

[0770] Step 4:

[0771] The server uses a procedure generation mechanism to automatically generate response procedures based on priority. Prompt messages are input into a generation AI model to create appropriate responses and procedures. The output of this step is specific countermeasures or improvement suggestions.

[0772] Step 5:

[0773] The device uses emotion recognition to analyze the emotions in the user's voice and text. A dedicated emotion analysis engine is used to determine the user's emotional state. The input is the user's voice or text, and the output is the result of the emotion evaluation.

[0774] Step 6:

[0775] The device uses emotion response adjustment mechanisms to adjust its response based on recognized emotions. For example, if anxiety is detected, it adds reassuring information. The output is a customized notification or explanation for the user.

[0776] Step 7:

[0777] The user receives responses and notifications from the device and checks for specific instructions and situation-appropriate information. The input here is the tailored notification, and the output is the information provided to the user.

[0778] This series of processes makes communication network management more efficient and based on emotions, improving the user experience.

[0779] 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.

[0780] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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."

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] A data collection method for acquiring past communication data,

[0803] A data analysis means that performs data analysis based on acquired past communication data,

[0804] A priority determination means for determining the priority of communication devices based on analyzed data,

[0805] A procedure generation means that automatically generates response procedures based on the determined priority,

[0806] A system that includes a procedure output means for outputting generated procedures and design proposals.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising a means for receiving inquiries from users, analyzing the content of the inquiries, and generating and providing the optimal answer using the generation AI model.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising a feedback learning means for collecting user-provided feedback and using it as training data for the generated AI model.

[0811] "Example 1"

[0812] (Claim 1)

[0813] An information gathering method that acquires communication information in real time from a wireless communication network,

[0814] A data analysis means that performs data preprocessing and data analysis using a machine learning model based on collected communication information,

[0815] A priority determination means that evaluates the priority of the devices to be used based on the analysis results and identifies the devices that are of high importance,

[0816] A procedure creation means that automatically creates response procedures using a generated AI model based on identified priorities,

[0817] A system including a procedure transmission means for transmitting the created procedure to a display device.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising an inquiry response means for receiving inquiries from users, creating an optimal response to the content of the inquiries using a generation AI model, and responding to the user device.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising a feedback utilization means for collecting user feedback and using it as training data for an AI model.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] Information gathering means for acquiring past communication information,

[0825] Information analysis means that performs information analysis based on acquired past communication information,

[0826] Priority determination means for determining the priority of communication devices based on analyzed information,

[0827] A procedure generation means that automatically generates response procedures based on the determined priority,

[0828] A procedure output means that outputs the generated procedures and design proposals,

[0829] A system that includes means for analyzing wireless communication network traffic information and detecting potential communication failures in real time in order to improve the operation of machinery in a factory environment.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising: an inquiry handling means for receiving inquiries from users, analyzing the content of the inquiries, and generating and providing the optimal answer using the generation AI model; and a means for presenting a resolution procedure based on anomaly detection within the factory.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising a feedback learning means for collecting feedback provided by users and using it as learning information for the generated AI model, and a means for utilizing data to continuously improve operational efficiency in a factory.

[0834] "Example 2 of combining an emotion engine"

[0835] (Claim 1)

[0836] A data collection method for acquiring traffic data from a communication network,

[0837] A data analysis method that analyzes acquired traffic data using a machine learning model,

[0838] A priority determination means for determining the priority of communication management devices based on analyzed data,

[0839] A procedure generation means that automatically generates response procedures based on priority using an AI model,

[0840] A procedure output means that outputs the generated procedures and design proposals,

[0841] A system that includes an emotion analysis engine to analyze user emotions.

[0842] (Claim 2)

[0843] The system according to claim 1, further comprising a means for handling inquiries, which analyzes the sentiment of user inquiries in real time and generates and provides an optimal and emotionally sensitive response using an AI model based on the content of the inquiry.

[0844] (Claim 3)

[0845] The system according to claim 1, further comprising a feedback learning means for collecting user-provided feedback and sentiment data, using it as training data for a generative AI model, and optimizing the model.

[0846] "Application example 2 when combining with an emotional engine"

[0847] (Claim 1)

[0848] A data collection method for acquiring past communication information,

[0849] Information analysis means that performs information analysis based on acquired past communication information,

[0850] Priority determination means for determining the priority of wireless communication devices based on analyzed information,

[0851] A procedure generation means that automatically generates response procedures based on the determined priority,

[0852] A procedure output means that outputs the generated procedures and design proposals,

[0853] An emotion recognition method that recognizes the user's emotions from voice or text,

[0854] A system including emotion response adjustment means that adjusts the response based on recognized emotions.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising an inquiry handling means for receiving inquiries from users, analyzing the content of the inquiries and the emotions associated with them, and generating and providing optimal answers and additional information based on those emotions using the generation AI model.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising an emotional feedback learning means for collecting emotional feedback provided by a user and using it as training data for the generating AI model. [Explanation of Symbols]

[0859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data collection method for acquiring past communication data, A data analysis means that performs data analysis based on acquired past communication data, A priority determination means for determining the priority of communication devices based on analyzed data, A procedure generation means that automatically generates response procedures based on the determined priority, A system that includes a procedure output means for outputting generated procedures and design proposals.

2. The system according to claim 1, further comprising a means for receiving inquiries from users, analyzing the content of the inquiries, and generating and providing the optimal answer using the generation AI model.

3. The system according to claim 1, further comprising a feedback learning means for collecting user-provided feedback and using it as training data for the generated AI model.

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