System

Abstract

To provide a communication network facility layout that minimizes costs while providing high quality communication services.SOLUTION: A system includes means for inputting a specific prompt sentence into a generative AI model to deploy a network including base station equipment on the basis of data including geographical information, demographic statistics, urban planning, and demand forecasts from around the world, anomaly detection means for detecting abnormalities in the base station equipment by periodically executing a self-diagnosis program for the base station equipment, means for activating an automatic repair function that automatically repairs the base station equipment in which the abnormality has been detected, and means for providing information necessary for understanding the current situation to maintenance staff who maintain and manage the base station equipment when the base station equipment cannot be repaired even by the automatic repair function.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] The layout of communication network facilities must take into account a variety of factors, including geographic information, demographics, urban planning, and demand forecasts, and it is difficult to combine these factors appropriately to achieve optimal layout. In addition, interruptions to communication services due to equipment failure or aging will reduce the quality of the service and impair the user experience. Another important issue is to minimize costs while providing high-quality communication services. [Means for solving the problem]

[0005] The system of the present invention includes: means for configuring a network including base station equipment based on data including geographical information, demographic statistics, urban planning, and demand forecasts from around the world by inputting a specific prompt sentence into a generative AI model; anomaly detection means for detecting anomalies in the base station equipment by periodically executing a self-diagnosis program for the base station equipment; means for activating an automatic repair function for automatically repairing the base station equipment in which the abnormality is detected; means for providing information necessary to understand the current situation to maintenance staff who maintain and manage the base station equipment when the base station equipment cannot be repaired even by the automatic repair function; an emotion engine for recognizing the emotions of a user who uses a terminal connected to the base station equipment; and adjustment means for adjusting the operation of the self-diagnosis of the base station equipment by the self-diagnosis program and the operation of the automatic repair function based on the emotion. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 20] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the embodiment 3 of the form example 3 in the case where an emotion engine is combined. [Figure 22] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)). In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored, and is used as a working memory by the processor.

[0010] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0011] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0012] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0013] [First embodiment]

[0014] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0015] 1, a 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.

[0016] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0017] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0018] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0019] 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 of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0020] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0021] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0022] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0023] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0024] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0025] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0026] "Example 1"

[0027] In one embodiment of the present invention, the AI ​​performs optimal network equipment placement based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts. Specifically, the AI ​​analyzes these data and places network equipment preferentially in areas and time periods with high communication demand, areas with high population density, etc. In addition, the AI ​​uses urban planning data to plan the placement of network equipment based on predictions of future population movement and urban development. This improves the quality of communication services and makes it possible to make capital investments more efficient.

[0028] "Example 2"

[0029] Furthermore, in one embodiment of the present invention, a self-diagnosis and automatic repair function is introduced from the base station equipment information. Specifically, the base station equipment periodically performs self-diagnosis to detect signs of failure or aging. If detected, the automatic repair function corrects the problem or notifies maintenance staff as necessary. This makes it possible to minimize interruptions to communication services and improve stability.

[0030] "Example 3"

[0031] In addition, one embodiment of the present invention provides an ultra-high-speed, low-latency communication experience while minimizing costs. Specifically, by introducing optimal network equipment layout using AI and self-diagnosis and automatic repair functions, high-quality communication services are provided while minimizing capital investment and maintenance costs. For example, it becomes possible to provide services that require high-speed transmission and reception of large amounts of data, such as 4K and 8K video streaming and real-time online games, with low latency.

[0032] The process flow of each embodiment will be described below.

[0033] "Example 1"

[0034] Step 1: AI collects big data such as geographic information, demographics, urban planning, and demand forecasts.

[0035] Step 2: Analyze the collected data and identify areas, time periods with high demand for communications, areas with high population density, etc.

[0036] Step 3: Based on the identified information, prioritize the placement of network facilities.

[0037] Step 4: Using urban planning data, plan the placement of network facilities based on predictions of future population movement and urban development.

[0038] "Example 2"

[0039] Step 1: The base station equipment periodically performs self-diagnosis.

[0040] Step 2: Detect signs of malfunction or deterioration through self-diagnosis.

[0041] Step 3: If detected, auto-remediation will fix the issue or notify maintenance staff as appropriate.

[0042] "Example 3"

[0043] Step 1: Minimize capital investment and maintenance costs by using AI to optimize network equipment placement and introduce self-diagnosis and automatic repair functions.

[0044] Step 2: Provide services that require high-speed transmission and reception of large amounts of data with low latency, such as 4K and 8K video streaming and real-time online gaming.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0047] Modern communications infrastructure is required to respond quickly to sudden fluctuations in communications demand and changes in population density. However, with conventional methods, it is difficult to optimally deploy network equipment to deal with these fluctuations, which can lead to a decline in the quality of communications services. In addition, the efficiency of capital investment is also an issue. Furthermore, it is important to detect early signs of failure or aging of base station equipment and minimize interruptions to communications services.

[0048] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for collecting big data such as geographic information, demographic statistics, urban planning, and demand forecasts, a means for preprocessing the collected data, a means for analyzing the preprocessed data and identifying areas, time periods, and areas with high communication demand and high population density, a means for generating an optimal network facility layout plan based on the analysis results, a means for outputting the generated layout plan in a report format, and a means for providing high-quality communication services. This makes it possible to quickly respond to fluctuations in communication demand and changes in population density and to optimally layout network facilities. In addition, it is possible to improve the efficiency of capital investment and the quality of communication services.

[0049] "Geographic information" is data relating to geographic location, topography, land use, etc.

[0050] "Demographics" refers to statistical data such as the distribution, age structure, gender, and occupation of the population in a particular area.

[0051] "Urban planning" refers to planning data regarding urban land use, transportation, infrastructure development, etc.

[0052] "Demand forecast" is data for predicting future fluctuations in demand.

[0053] "Big data" refers to extremely large and complex data sets that cannot be handled using traditional data processing methods.

[0054] "Network equipment" is a general term for the hardware and software required to provide communication services.

[0055] "Preprocessing" refers to cleaning, normalizing, integrating, and other processes on data before performing data analysis.

[0056] "Analysis" refers to the process of using collected data to discover specific patterns or trends.

[0057] "Layout planning" refers to planning where and how network equipment will be laid out.

[0058] The "report format" is a format in which analysis results and deployment plans are compiled as a document.

[0059] "High-quality communication services" refers to services that provide stable, high-speed, and low-latency communications.

[0060] "Self-diagnosis" refers to the system's ability to monitor its own status and detect abnormalities.

[0061] "Automatic repair" refers to the system's ability to automatically correct any abnormalities it detects.

[0062] "Signs of failure or deterioration" refer to symptoms that appear before equipment breaks down or deteriorates.

[0063] "Minimizing interruptions to communication services" refers to minimizing the duration and area of ​​interruptions to communication services.

[0064] "Improving stability" refers to maintaining a state in which communication services are provided continuously and without interruption.

[0065] "Lowest cost" refers to keeping costs to a minimum.

[0066] "Ultra-fast, low-latency communication experience" refers to the provision of extremely fast data transfer speeds and extremely short communication latency.

[0067] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0068] The present invention relates to a system for analyzing big data such as geographic information, demographic statistics, urban planning, demand forecasts, etc., and for optimally arranging network facilities. A specific embodiment of this system is described below.

[0069] 1. Program Generation

[0070] The server generates programs to analyze big data such as geographic information, demographic statistics, urban planning, and demand forecasts. The programs use generative AI models to optimally allocate network equipment.

[0071] 2. Program Processing Explanation

[0072] The server analyzes the data using the following procedure to plan the optimal placement of network equipment.

[0073] 1. Data collection:

[0074] The server collects geographic information using Geographic Information System (GIS) software (e.g., ArcGIS), demographic data is obtained from government statistical databases (e.g., census data), urban planning data is collected from public databases of local governments, and demand forecasting data is derived from the carrier's historical traffic data.

[0075] 2. Data preprocessing:

[0076] The server converts the collected data into a format that is easier to analyze: it cleans, normalizes, and consolidates the data.

[0077] 3. Data Analysis:

[0078] The server analyzes the preprocessed data using AI analysis tools (e.g., TENSORFLOW (registered trademark), PyTorch). Specifically, it trains a machine learning model to identify areas and times of day with high communication demand and areas with high population density.

[0079] 4. Generate optimal layout plan:

[0080] The server then uses the analysis results to plan the optimal placement of network equipment. For example, it will add base stations to areas with high demand for communications and set up Wi-Fi hotspots in areas with high population density. It also uses urban planning data to plan the placement of network equipment based on predictions of future population movements and urban development.

[0081] 5. Result output:

[0082] The server outputs the optimal layout plan in the form of a report and provides it to the user. This report includes specific layout locations, the types of equipment required, installation times, etc.

[0083] 3. Examples and prompts

[0084] As a concrete example, consider the following scenario.

[0085] Examples:

[0086] A user uses the system to optimize the communication infrastructure of city A. City A has a rapidly growing population, and communication traffic is increasing rapidly in certain areas. The user inputs geographic information, demographic statistics, urban planning, and past communication traffic data of city A to the system. In other words, the server may be provided with a means for placing a network including base station equipment based on data including geographic information, demographic statistics, urban planning, and demand forecasts from around the world by inputting a specific prompt sentence into the generative AI model.

[0087] Example prompt:

[0088] "Plan the optimal placement of network facilities based on the geographic information, demographics, urban planning, and past communication traffic data of City A. In particular, prioritize placement of facilities in areas and time periods with high communication demand and in areas with high population density, while also taking into consideration predictions of future population movement and urban development."

[0089] By inputting this prompt sentence into the generative AI model, the server generates an optimal network equipment layout plan and provides it to the user.

[0090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0091] Step 1: Data collection

[0092] The server collects geographic information using Geographic Information System (GIS) software. Specifically, it uses tools such as ArcGIS to obtain geographic information for City A. It also downloads demographic data for City A from a government statistical database. It also collects urban planning data for City A from the public database of the local government and obtains historical traffic data from telecommunications carriers. These data are inputs, and the collected data set is output.

[0093] Step 2: Data Preprocessing

[0094] The server converts the collected data into a format that is easy to analyze. Specifically, it cleans the data and removes missing values ​​and outliers. Next, it normalizes the data to unify the scale. Finally, it integrates data from different data sources and combines them into a single dataset. The input is the collected dataset, and the output is the preprocessed dataset.

[0095] Step 3: Data analysis

[0096] The server uses the preprocessed data to perform analysis. Specifically, it uses AI analysis tools (e.g., TensorFlow, PyTorch) to train machine learning models to identify areas and times of high demand for communications, as well as areas with high population density. The trained models are then used to predict future demand for communications and population movements. The input is the preprocessed dataset, and the analysis results are output.

[0097] Step 4: Generate optimal layout plans

[0098] The server generates an optimal network equipment layout plan based on the analysis results. Specifically, it adds base stations in areas with high demand for communications and installs Wi-Fi hotspots in areas with high population density. It also creates a layout plan that takes into account future population movements and urban development based on urban planning data. The input is the analysis results, and the generated layout plan is output.

[0099] Step 5: Output the results

[0100] The server outputs the optimal deployment plan in report format and provides it to the user. Specifically, it generates a report that includes details of the deployment location, the type of equipment required, the installation time, predicted fluctuations in communication demand, etc. The input is the generated deployment plan, and the output is the deployment plan in report format.

[0101] (Application example 1)

[0102] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0103] In modern society, optimizing communication infrastructure and providing high-quality communication services are important issues. However, conventional methods have limitations when it comes to responding to fluctuations in communication demand, changes in population density, and progress in urban planning. In addition, the operation management of autonomous vehicles requires providing optimal routes and stopping points in real time. In order to solve these issues, advanced analysis technology that utilizes big data is required.

[0104] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0105] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, and a means for analyzing big data such as geographic information, demographic statistics, urban planning, and demand forecasts to provide optimal routes and stopping points in real time in order to optimize operation management of autonomous vehicles. This makes it possible to optimize communication infrastructure and provide high-quality communication services, as well as realize efficient operation management of autonomous vehicles.

[0106] "AI" is an abbreviation for artificial intelligence, a technology that enables computer systems to mimic human intelligence to learn, reason, and self-correct.

[0107] "Geographic information" is data about specific locations on Earth, including maps, location information, and topographical data.

[0108] "Demographics" refers to data concerning the distribution, composition, and changes of the population in a particular area.

[0109] "Urban planning" refers to plans and policies concerning urban land use, development, and infrastructure development.

[0110] "Demand forecasting" refers to data analysis and models used to predict future fluctuations in demand.

[0111] "NW equipment" is an abbreviation for network equipment, and refers to the hardware and software that make up the communications infrastructure.

[0112] "High-quality communication services" refers to services that provide stable, high-speed, and low-latency communications.

[0113] An "autonomous vehicle" refers to a vehicle that drives autonomously using artificial intelligence and sensor technology.

[0114] "Traffic management" refers to the management work of planning, monitoring and controlling vehicle operations.

[0115] An "optimal route" refers to the most efficient route selected taking into account traffic conditions, distance to the destination, time, etc.

[0116] A "stopping point" refers to a specific location for a vehicle to stop.

[0117] "Real-time" refers to data and information being processed immediately and provided without delay.

[0118] To implement this invention, the following system configuration is required. The server collects big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and implements an AI model to analyze this data. Specifically, software libraries such as Python, Pandas, NumPy, Scikit-learn, and GeoPandas are used.

[0119] The server first loads data such as geographic information, demographic statistics, urban planning, and demand forecasts, and then integrates this data. Next, it classifies regions using KMeans clustering and builds a model to forecast future demand using RandomForestRegressor. This allows it to calculate the optimal placement of network facilities and saves the results.

[0120] As a concrete example, consider the case of optimizing the operation management of autonomous vehicles in Tokyo. The server collects road network data in Tokyo, population density data for each ward, construction plan data for new roads and buildings, and demand forecast data based on past traffic data, and analyzes this data. Based on the analysis results, it provides optimal routes and stopping points in real time.

[0121] Users can receive real-time information on optimal routes and stopping points via a smartphone application. Based on data provided by the server, the application provides information to avoid traffic congestion and ensure efficient operation.

[0122] An example of a prompt sentence might be:

[0123] "In order to optimize the operation management of autonomous vehicles in Tokyo, please develop an application that analyzes big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and provides optimal routes and stopping points in real time."

[0124] By feeding this prompt into a generative AI model, it is possible to obtain detailed advice on the design and implementation of a specific application.

[0125] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0126] Step 1:

[0127] The server collects data such as geographic information, demographics, urban planning, and demand forecasts. Specifically, it retrieves data from various databases and APIs and integrates them. The inputs are geographic information data, demographic data, urban planning data, and demand forecast data, and the output is an integrated data set.

[0128] Step 2:

[0129] The server preprocesses the merged dataset, i.e. imputing missing values, normalizing the data, removing outliers, etc. As input, we have the merged dataset, and as output, we have the preprocessed dataset.

[0130] Step 3:

[0131] The server uses the preprocessed dataset to perform KMeans clustering, classifying regions based on population density and demand forecasts. The input is the preprocessed dataset, and the output is the cluster information for each region.

[0132] Step 4:

[0133] The server uses the cluster information to build a demand forecasting model using RandomForestRegressor. Specifically, it trains a model to forecast future demand using population density, urban development index, and current demand as inputs. The inputs are the cluster information and a preprocessed dataset, and the output is a demand forecasting model.

[0134] Step 5:

[0135] The server uses the demand forecasting model to calculate the optimal network equipment placement. Specifically, it predicts future demand in each region and optimizes the placement of network equipment based on that. The inputs are the demand forecasting model and a pre-processed dataset, and the output is the optimal network equipment placement information.

[0136] Step 6:

[0137] The server stores the optimal network equipment layout information and updates it as necessary. Specifically, the calculation results are stored in a database and periodically recalculated. The input is the optimal network equipment layout information, and the output is the stored layout information.

[0138] Step 7:

[0139] Users receive information on optimal routes and stopping points in real time through a smartphone application. Specifically, information is provided to avoid traffic congestion and achieve efficient operation based on data provided by the server. The input is real-time data from the server, and the output is information on optimal routes and stopping points provided to users.

[0140] Example 2

[0141] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0142] Conventional communication systems have problems with frequent interruptions to communication services due to failures and aging of base station equipment, resulting in reduced stability. In addition, optimal network equipment placement and efficient maintenance have not been implemented sufficiently, resulting in increased costs and reduced communication quality. Furthermore, delayed response after anomaly detection can prolong the interruption of communication services.

[0143] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0144] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, a means for collecting status data of base station equipment, a means for performing self-diagnosis based on the collected data, a means for performing automatic repair when an abnormality is detected, a means for notifying maintenance staff when the automatic repair fails, and a means for storing the results of the self-diagnosis and automatic repair in a database and analyzing the long-term status of the equipment. This makes it possible to minimize interruptions to communication services and improve stability.

[0145] "AI" is an abbreviation for artificial intelligence, a technology that mimics human intelligence through machine learning and data analysis.

[0146] "Geographic information" refers to data such as location, topography, and climate for specific locations on Earth.

[0147] "Demographics" refers to statistical data such as the distribution of the population in a particular area, age structure, gender, birth rate, and death rate.

[0148] "Urban planning" refers to the designs and policies for the planned arrangement and management of a city's land use, transportation, infrastructure, etc.

[0149] "Demand forecasting" is a method that uses data analysis and models to predict future demand.

[0150] "Network equipment" is a general term for the hardware and software required to provide communication services.

[0151] The term "base station equipment" refers to fixed communication equipment for communicating with mobile communication terminals in a wireless communication network.

[0152] "Self-diagnosis" is a function that enables a system or device to check its own status and detect signs of abnormalities or failures.

[0153] "Automatic repair" is a function that automatically corrects detected abnormalities or failures.

[0154] "Maintenance staff" refers to professional personnel who maintain and manage systems and equipment.

[0155] A "database" is a system for efficiently storing, searching, and managing data.

[0156] "Analyzing the long-term condition of equipment" means evaluating the condition of equipment over a long period of time based on collected data and predicting future breakdowns and the need for maintenance.

[0157] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0158] The present invention relates to a communication system having a self-diagnosis and automatic repair function for base station equipment. A specific embodiment of this system will be described below.

[0159] 1. Program Generation

[0160] The server generates a program with self-diagnosis and automatic repair functions based on the base station equipment information. This program is developed using programming languages ​​such as Python and Java (registered trademark).

[0161] 2. Program Processing Explanation

[0162] The server uses the generated program to perform the following processes.

[0163] Self-diagnosis function:

[0164] The server periodically checks the status of the base station equipment using sensors and monitoring software (e.g., Nagios, Zabbix) installed in the equipment.

[0165] It collects data on equipment temperature, voltage, communication status, etc., and runs algorithms to detect abnormal values ​​and signs of aging.

[0166] Automatic repair function:

[0167] If the server detects a problem during self-diagnosis, it will execute an automatic repair script, such as restarting the software or resetting settings.

[0168] If the problem cannot be automatically fixed, the server will notify maintenance staff via email, SMS, or a dedicated maintenance app (e.g. PagerDuty).

[0169] Data storage and analysis:

[0170] The server stores the results of self-diagnosis and automatic repair in a database (e.g., MySQL (registered trademark), PostgreSQL).

[0171] Based on the stored data, the long-term condition of the equipment is analyzed, future failures are predicted, and maintenance plans are optimized.

[0172] 3. Examples of concrete examples and prompts

[0173] Examples:

[0174] When a user wants to check the status of base station equipment, the following steps are taken.

[0175] 1. The user sends a request to check the status of the base station equipment from a dedicated management terminal.

[0176] 2. The server displays the equipment status based on the latest self-diagnosis results.

[0177] 3. If an abnormality is detected, the server will display the automatic repair history and current response status.

[0178] Example prompt:

[0179] By inputting the following prompt sentence into the generative AI model, a report on the status of base station equipment can be generated.

[0180] "Based on the latest self-diagnosis results of the base station equipment, please generate a report of the current status and the anomaly detection history for the past week."

[0181] In this way, the user can grasp the status of the base station equipment in real time and take the necessary measures quickly.

[0182] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0183] Step 1:

[0184] The server periodically collects equipment status data using sensors and monitoring software (e.g., Nagios, Zabbix) installed in the base station equipment.

[0185] Input: Sensor data such as temperature, voltage, and communication status from base station equipment.

[0186] Data processing: The server collects the sensor data and formats it for storage in a database.

[0187] Output: Formatted asset condition data.

[0188] Specific operation: The server runs a script to obtain data from the sensor at midnight every day and stores it in a database.

[0189] Step 2:

[0190] The server runs a self-diagnosis algorithm based on the collected data.

[0191] Input: Formatted equipment condition data.

[0192] Data computation: The server analyzes the data using Python's Pandas library to detect outliers and signs of aging.

[0193] Output: Anomaly detection results.

[0194] What happens: The server compares the collected data with past data to see if there are any outliers.

[0195] Step 3:

[0196] If the self-diagnosis detects an abnormality, the server executes an automatic repair script.

[0197] Input: Anomaly detection results.

[0198] Data processing: The server selects an appropriate repair script depending on the type of anomaly.

[0199] Output: The repair result.

[0200] Specific action: If an abnormality is detected, the server executes a Bash script and restarts the relevant software.

[0201] Step 4:

[0202] The server sends notifications to maintenance staff if automatic repairs fail or if a critical anomaly is detected.

[0203] Input: The repair result.

[0204] Data processing: The server generates notification content and sends it via email, SMS, or a dedicated maintenance app.

[0205] Output: Informational message.

[0206] Specific action: The server sends an email using the SMTP protocol to notify the maintenance staff of the details of the anomaly and the need for action.

[0207] Step 5:

[0208] The server stores the results of self-diagnosis and automatic repair in a database and analyzes the long-term condition of the equipment.

[0209] Input: Self-diagnosis and repair results.

[0210] Data processing: The server inserts the data into the database and periodically generates analysis reports.

[0211] Output: Analysis report.

[0212] What it does: The server executes SQL queries to store the data in a database and uses Python data analysis libraries to analyze long-term equipment health.

[0213] (Application example 2)

[0214] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] In modern factories, the downtime of production lines due to machine breakdowns or aging is a major problem. This reduces manufacturing efficiency and increases costs. Similarly, interruptions to communication services are a major problem, and there is a demand to provide a stable communication environment. To solve these problems, it is necessary to constantly monitor the condition of machines and equipment, detect early signs of breakdowns or aging, and respond quickly.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0217] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for machines in a factory to periodically perform self-diagnosis and detect signs of failure or aging, a means for automatically correcting the detected problems, a means for notifying maintenance staff as necessary, a means for minimizing interruptions to communication services and improving stability, and a means for minimizing costs while providing an ultra-high-speed, low-latency communication experience. This makes it possible to detect failures and aging of machines and communication equipment in a factory early and respond quickly.

[0218] "AI" stands for artificial intelligence, a technology that mimics human intelligence through machine learning and data analysis.

[0219] "Geographic information" is information relating to specific locations on the Earth, including map data and location information.

[0220] "Demographics" refers to statistical data concerning the composition and changes of the population in a particular area or group.

[0221] "Urban planning" refers to the designs and policies for planned development and development of cities.

[0222] "Demand forecasting" refers to the analysis and calculations used to predict future demand.

[0223] "Network equipment" is a general term for the hardware and software used to build a communications network.

[0224] "Communication services" refers to services for sending and receiving data and voice.

[0225] "Factory machinery" refers to production equipment and devices used in a factory.

[0226] "Self-diagnosis" is a function that allows a machine or system to check its own condition and detect abnormalities.

[0227] "Signs of failure or deterioration" refer to symptoms or signs that appear before a machine or equipment breaks down or deteriorates.

[0228] An "automatic remediation mechanism" is a feature or method for automatically repairing a detected problem.

[0229] "Maintenance staff" are specialized technicians who maintain and repair machinery and equipment.

[0230] "Interruption of communications services" means a temporary cessation of communications.

[0231] "Stability" refers to the ability of a system or service to continue to operate stably.

[0232] "Cost minimization" refers to methods or means for minimizing costs.

[0233] "Ultra-high speed and low latency" refers to the sending and receiving of data at extremely high speeds with almost no latency.

[0234] "Communication experience" refers to the experience and sensations a user has when using a communication service.

[0235] The system for carrying out this invention is configured as follows: The server has a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, and also has a means for providing high-quality communication services.

[0236] In addition, the machines in the factory will have the means to periodically perform self-diagnosis to detect signs of breakdowns or aging. They will also have the means to automatically correct any problems detected, and will also notify maintenance staff as necessary. This will minimize interruptions to communication services and improve stability.

[0237] Specifically, the server runs a self-diagnosis and auto-repair program written in Python that allows the factory's machines to periodically self-diagnose and detect abnormalities in motors or sensor failures. If detected, the auto-repair function corrects the problem and notifies maintenance staff if necessary.

[0238] The hardware used is the factory robot itself and its sensors. The software is a self-diagnosis and self-repair program written in Python. This makes it possible to detect failures and deterioration of machinery and communication equipment in the factory at an early stage and respond quickly.

[0239] A concrete example is a system in which a robot in a factory periodically performs self-diagnosis to detect motor anomalies or sensor failures. If a problem is detected, the system automatically corrects the problem and notifies maintenance staff if necessary. This system minimizes downtime of the factory's production line.

[0240] Example of a prompt to be input to a generative AI model:

[0241] Design a system where factory robots periodically self-diagnose to detect signs of failure or aging. If a problem is detected, create an application that can automatically fix the problem or notify maintenance staff if necessary.

[0242] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0243] Step 1:

[0244] The server collects sensor information from machines in the factory.

[0245] Input: Sensor information from machines in the factory

[0246] Output: Collected sensor information

[0247] Specific operation: The server periodically collects data such as temperature, vibration, and current from sensors installed on each machine.

[0248] Step 2:

[0249] The server analyzes the collected sensor information and detects abnormal values.

[0250] Input: Collected sensor information

[0251] Output: Anomaly detection results

[0252] Specific operation: The server uses statistical methods and machine learning algorithms based on the collected data to detect values ​​that are outside the normal range.

[0253] Step 3:

[0254] The server performs a self-diagnosis if an abnormal value is detected.

[0255] Input: Anomaly detection results

[0256] Output: Self-diagnosis result

[0257] Specific operation: The server performs a detailed diagnosis of each part of the machine depending on the type and severity of the abnormality, and identifies signs of malfunction or deterioration.

[0258] Step 4:

[0259] The server will attempt to automatically repair itself based on the results of the self-diagnosis.

[0260] Input: Self-diagnosis result

[0261] Output: Result of automatic repair

[0262] Specific actions: Based on the diagnostic results, the server will automatically perform tasks such as restarting the software, resetting settings, and making simple hardware adjustments.

[0263] Step 5:

[0264] The server notifies maintenance staff if the automatic repair is unsuccessful.

[0265] Input: Automatic repair result

[0266] Output: Maintenance notification

[0267] Specific behavior: If the server determines that repair is impossible or insufficient, it will send an email or alert to maintenance staff, reporting detailed diagnostic results and repair attempts.

[0268] Step 6:

[0269] The server logs all processing results for future analysis.

[0270] Input: The results of each processing step

[0271] Output: Recorded log data

[0272] Specific operation: The server records detailed information about each step, such as input data, processing results, and execution time, and stores the information in a database.

[0273] Example 3

[0274] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0275] Modern communication services require optimal deployment of network equipment, early detection of failures, and rapid repair. However, meeting these requirements requires a great deal of cost and effort, and in particular, to provide high-quality communication services, real-time monitoring and rapid response are essential. With conventional systems, it was difficult to efficiently solve these problems.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0277] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, a means for monitoring the network status in real time, a means for detecting anomalies and identifying their causes, a means for performing automatic repair processing, and a means for notifying users of the results. This makes it possible to provide an ultra-high-speed, low-latency communication experience at the lowest cost, minimize interruptions to communication services, and improve stability.

[0278] "AI" is an abbreviation for artificial intelligence, a technology that uses techniques such as machine learning and deep learning to analyze data and derive optimal solutions.

[0279] "Network equipment layout" refers to the physical layout and layout plan of various devices and infrastructure in a communications network, and is an important element for achieving efficient data communications.

[0280] A "high-quality communication service" is a service that provides low-latency, high-speed, large-capacity data communication, and offers a stable communication environment that meets user demands.

[0281] "Real-time monitoring" is the process of constantly monitoring the network status and instantly detecting abnormalities, and is important for maintaining the health of the network.

[0282] "Anomaly detection" refers to identifying conditions that deviate from the network's normal operation, enabling early detection and countermeasures for problems.

[0283] "Cause identification" is the process of clarifying the source or cause of a detected anomaly and is a prerequisite for taking appropriate corrective action.

[0284] "Automatic recovery processing" is a process in which recovery work is automatically carried out in response to a detected abnormality, and is a function for quickly restoring normal operation of the network.

[0285] "Notifying the user of the results" refers to the process of reporting the network status and the results of the repair work to the user, and providing the user with the information necessary to understand the current situation. If the base station equipment cannot be repaired even by the automatic repair function, the server may be equipped with a means for providing the maintenance staff who maintain and manage the base station equipment with the information necessary to understand the current situation.

[0286] "Lowest cost" refers to providing the required functions and services at the lowest possible cost, and is the pursuit of economic efficiency.

[0287] "Ultra-high speed and low latency" refers to extremely fast data communication speeds with extremely little communication latency, and is an important element in providing a high-quality communication experience.

[0288] This invention is a system that uses AI to optimize the placement of network equipment and provide high-quality communication services. A specific embodiment of this system is described below.

[0289] 1. Program Generation

[0290] The user inputs specific requirements to the system as prompts, for example, "Please generate a system with optimal network equipment layout and self-diagnosis and self-repair functions to provide 4K video streaming services."

[0291] 2. Program Processing

[0292] The server executes the generated program and performs the following processes.

[0293] Optimizing network equipment layout:

[0294] The server uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal placement of network equipment. This calculation includes geographical data, user traffic patterns, and information on existing network infrastructure. Specifically, the server inputs these data into the AI ​​model and obtains the optimal placement as the output.

[0295] Self-diagnosis function:

[0296] The server monitors the network status in real time and detects anomalies. If an anomaly is detected, the server identifies the cause and proposes appropriate measures. This function uses log data analysis and anomaly detection algorithms (e.g., machine learning models for anomaly detection).

[0297] Automatic repair function:

[0298] The server automatically takes corrective action in response to the detected anomaly, for example by reconfiguring the network or reallocating resources, using an orchestration tool (e.g., Kubernetes) and / or a scripting language (e.g., Python).

[0299] Notification of results:

[0300] After the repair process is completed, the server notifies the user of the result by email, updating the dashboard, etc. Specifically, the server reports the details of the repair process and the current network status to the user.

[0301] 3. Examples of concrete examples and prompts

[0302] Examples:

[0303] Consider a case where a user uses this system to provide a 4K video streaming service. The user inputs the following prompt to the system:

[0304] Example prompt:

[0305] "Create a system with optimal network equipment layout and self-diagnosis and self-repair functions to provide 4K video streaming services."

[0306] In this way, a system can be realized that provides the user with an ultra-high speed, low latency communication experience at the lowest cost. The flow of the specific process in the third embodiment will be described with reference to FIG.

[0307] Step 1:

[0308] The user enters a prompt sentence.

[0309] The user inputs specific requirements to the system as a prompt, for example, "Please generate a system with optimal network equipment layout and self-diagnosis and self-repair functions to provide 4K video streaming services." This prompt becomes the input to the system.

[0310] Step 2:

[0311] The server uses an AI model to calculate the optimal placement of network equipment.

[0312] The server receives the user's prompt and uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal placement of network equipment. This calculation includes geographical data, user traffic patterns, and information on existing network infrastructure. Specifically, the server inputs these data into the AI ​​model and obtains the optimal placement as the output.

[0313] Step 3:

[0314] The server monitors the network status in real time.

[0315] The server uses a network monitoring tool (such as Nagios or Zabbix) to monitor the network status in real time. The server periodically checks the status and traffic volume of each network device to see if there are any abnormalities. This monitoring data is the input, and the presence or absence of abnormalities is the output.

[0316] Step 4:

[0317] The server detects the abnormality and identifies the cause.

[0318] The server analyzes data from the network monitoring tool and detects anomalies. If an anomaly is detected, the server uses log data analysis and anomaly detection algorithms (for example, machine learning models for anomaly detection) to identify the cause. Specifically, the server identifies the location and time of the anomaly, the extent of its impact, etc. The results of this analysis are the output.

[0319] Step 5:

[0320] The server performs an automatic repair process.

[0321] The server automatically performs repair processing for the detected anomaly. For example, this can include reconfiguring the network and reallocating resources. This processing is performed using an orchestration tool (e.g., Kubernetes) or a scripting language (e.g., Python). Specifically, the server reconfigures the part where the anomaly occurred and returns it to a normal state. The result of this repair processing is the output.

[0322] Step 6:

[0323] The server notifies the user of the result.

[0324] After the repair process is completed, the server notifies the user of the result. The notification can be done by email or by updating the dashboard. Specifically, the server reports the details of the repair process and the current network status to the user. This notification is the output.

[0325] (Application example 3)

[0326] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0327] Modern communication services require the transmission and reception of large volumes of data at high speeds and with low latency, for example in 4K or 8K high-resolution video streaming or real-time online gaming. However, conventional communication systems lack optimal layout of network equipment and sufficient self-diagnosis and automatic repair functions, which often results in degradation of communication quality and interruptions to services. Furthermore, the capital investment and maintenance costs required to solve these problems are high, so a cost-effective solution is required.

[0328] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means. In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for streaming 4K or 8K high-resolution video at ultra-high speed and low latency through an application installed on a smartphone, and a network optimization means with self-diagnosis and automatic repair functions. This makes it possible to provide a cost-effective, high-quality communication service while improving communication quality and minimizing service interruptions.

[0329] "AI" is an abbreviation for artificial intelligence, a technology that enables computer systems to mimic human intelligence to learn, reason, and self-correct.

[0330] "Geographic information" is information relating to a specific place on Earth, and includes map data and location information.

[0331] "Demographics" refers to statistical data concerning the composition and trends of the population in a particular area or group.

[0332] "Urban planning" is the activity of formulating plans for urban land use, transportation, infrastructure, etc., and managing urban development.

[0333] "Demand forecasting" is an analytical method for predicting future demand, and is based on past data and trends.

[0334] "Network equipment placement" refers to placing network equipment in the most optimal location, with the aim of improving communications quality and optimizing cost efficiency.

[0335] A "high-quality communication service" is a communication service that enables high-speed and stable data transmission and reception with few delays or interruptions.

[0336] A "smartphone" is a type of mobile phone and a multi-function device that can connect to the Internet and use applications.

[0337] An "Application" is a software program designed to provide a particular function or service.

[0338] "4K and 8K high-resolution video" refers to video with extremely high resolution, with 4K referring to a resolution of 3840 x 2160 pixels and 8K referring to a resolution of 7680 x 4320 pixels.

[0339] "Ultra-high speed and low latency" refers to a state in which data can be sent and received extremely quickly with almost no latency.

[0340] "Streaming viewing" refers to the playback of video and audio in real time over the Internet.

[0341] "Self-diagnosis" is the function of the system to check its own status and detect problems.

[0342] "Automatic repair function" is a function that automatically corrects problems that the system detects.

[0343] "Network optimization means" refers to techniques and technologies for optimizing network performance.

[0344] The following system configuration will be described as an embodiment of the present invention.

[0345] System Configuration

[0346] The server will include: a means for using AI to optimally place network equipment based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world; a means for providing high-quality communication services; a means for enabling ultra-fast, low-latency streaming of 4K or 8K high-resolution video via an application installed on a smartphone; and a network optimization means with self-diagnosis and automatic repair functions.

[0347] Program Processing

[0348] The server first uses an AI model to analyze big data such as geographic information, demographics, urban planning, and demand forecasts to determine the optimal placement of network equipment. The AI ​​model is built using machine learning frameworks such as TensorFlow.

[0349] The server then performs network self-diagnosis and auto-repair functions to provide high-quality communication services, detecting signs of failure or aging in base station equipment and automatically carrying out necessary repairs. This process uses network optimization algorithms.

[0350] Furthermore, the server enables a user to stream 4K or 8K high-resolution video at ultra-high speed and low latency through an application installed on the smartphone. This application is designed to enable a user to watch high-quality video in real time, and automatically adjusts optimal streaming settings according to the network status. The server may include an adjustment means for adjusting the operation of the self-diagnosis of the base station equipment by the self-diagnosis program and the operation of the automatic repair function based on an emotion recognized by an emotion engine that recognizes the emotion of a user using a terminal connected to the base station equipment. The adjustment means may prioritize the repair of the base station equipment when the user is angry, and suppress the operation of the self-diagnosis and the automatic repair function when the user is happy.

[0351] Hardware and Software Used

[0352] Hardware: Servers, smartphones, base station equipment

[0353] Software: TensorFlow (AI model building), network optimization algorithms, streaming applications

[0354] Examples

[0355] For example, consider a scenario where a user wants to watch 4K video on a smartphone. In this case, the user launches an application and selects the 4K video they want to watch. The server self-diagnoses the network status, automatically repairs it if necessary, and then applies the optimal streaming settings to deliver the video. This allows the user to watch high-quality video without interruption.

[0356] Examples of prompt statements

[0357] "Please develop an application that allows users to watch 4K video on smartphones at ultra-high speeds with low latency. The application will use AI to optimize network equipment and have self-diagnosis and self-repair functions."

[0358] In this way, the present invention provides high quality communication services and realizes an environment in which users can comfortably view high resolution video.

[0359] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0360] Step 1:

[0361] The server uses the AI ​​model to analyze big data such as geographic information, demographics, urban planning, and demand forecasts to determine the optimal placement of network equipment. It receives geographic information, demographics, urban planning, and demand forecast data as input, and the AI ​​model calculates the optimal placement of network equipment based on this data. It generates optimal network equipment placement information as output.

[0362] Step 2:

[0363] The server performs self-diagnosis of the network. It receives base station equipment information as input and diagnoses the network status. Specifically, it analyzes the equipment's operating status and performance data to detect signs of failure or aging. It generates the diagnosis results as output.

[0364] Step 3:

[0365] The server performs automatic repair based on the results of self-diagnosis. It receives the diagnosis results as input and automatically executes the necessary repair work, such as restarting the failed equipment or adjusting the settings. It generates the post-repair network state as output.

[0366] Step 4:

[0367] A user launches an application installed on a smartphone and selects the 4K or 8K high-resolution video they want to watch. As input, the application receives the user's selection information and sends it to the server. As output, the application generates a request for the selected video.

[0368] Step 5:

[0369] The server receives video requests from users and distributes the video by applying optimal streaming settings. As input, it receives user request information and network status information, and adjusts streaming settings. Specifically, it optimizes the video bitrate and buffer size. As output, it generates optimized video data and sends it to the user's smartphone.

[0370] Step 6:

[0371] The user plays the received video data on the smartphone and watches high-quality video. As input, the video data sent from the server is received and played through the application. As output, the high-quality video is displayed.

[0372] In this way, through the specific operations and data flows performed at each step, users can enjoy high-quality communication services and comfortably watch high-definition video.

[0373] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0374] "Example 1"

[0375] The emotion engine recognizes emotions from the user's tone of voice, facial expressions, behavioral patterns, etc. For example, when a user is angry, their voice tone gets higher, and when they are happy, they smile more. AI analyzes this information and recognizes the user's emotions. The emotion engine may recognize the emotions of users who use terminals connected to base station equipment.

[0376] "Example 2"

[0377] The method of providing communication services is optimized based on the user's emotions recognized by the emotion engine. For example, when a user is angry, the communication speed is increased to reduce stress. When a user is happy, content that allows them to share their joy is recommended.

[0378] "Example 3"

[0379] The operation of the self-diagnosis and auto-repair functions is adjusted based on the user's emotions recognized by the emotion engine. For example, when the user is angry, the system is more sensitive to detecting signs of failure and repairing them earlier. On the other hand, when the user is happy, the system minimizes the operation of the self-diagnosis and auto-repair functions to avoid interrupting communication services.

[0380] The process flow of each embodiment will be described below.

[0381] "Example 1"

[0382] Step 1: The emotion engine collects the user's tone of voice, facial expressions, behavioral patterns, etc.

[0383] Step 2: AI analyzes the collected data.

[0384] Step 3: The AI ​​recognizes the user's emotions from the analysis results and feeds that information back to the system.

[0385] "Example 2"

[0386] Step 1: The system receives feedback from the emotion engine.

[0387] Step 2: The system optimizes how the communication service is provided based on the received emotion information.

[0388] "Example 3"

[0389] Step 1: The system receives feedback from the emotion engine.

[0390] Step 2: The system adjusts the behavior of its self-diagnosis and auto-repair functions based on the received emotional information.

[0391] Example 1

[0392] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0393] In modern society, the demand for communication services is rapidly increasing, and there is a demand for optimal placement of communication infrastructure, especially in urban areas. However, conventional methods have been unable to effectively utilize big data such as geographic information, demographic statistics, urban planning, and demand forecasts, making it difficult to place appropriate network facilities in areas and times of high demand for communication, or in densely populated areas. It has also been difficult to recognize user emotions in real time and provide appropriate feedback. As a result, improvements in the quality of communication services and the efficiency of capital investment have not been fully achieved.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0395] In this invention, the server includes a means for collecting big data such as geographic information, demographic statistics, urban planning, and demand forecasts, a means for analyzing the collected data and identifying areas and time periods with high communication demand and areas with high population density, a means for generating an optimal network facility layout plan based on the analysis results, a means for collecting data such as the user's tone of voice, facial expression, and behavioral patterns, a means for analyzing the collected emotion data and recognizing the user's emotion, and a means for providing feedback to the user based on the analysis results. This makes it possible to allocate optimal network facilities in areas with high communication demand, improve the quality of communication services, and recognize the user's emotion in real time and provide appropriate feedback.

[0396] "Geographic information" is data relating to geographical locations, topography, land use, transportation networks, etc.

[0397] "Demographics" refers to data concerning the distribution of the population in a particular area, its age structure, gender, number of households, etc.

[0398] "Urban planning" refers to planning data regarding urban land use, infrastructure development, transportation planning, housing development, etc.

[0399] "Demand forecast" refers to data for predicting future fluctuations in demand, and in particular to predictive data regarding demand for communication services.

[0400] "Big data" refers to a collection of data that is so large and diverse that it cannot be handled by conventional data processing technologies.

[0401] "Network equipment" is a general term for the hardware and software required to provide telecommunications services, and includes base stations, routers, switches, etc.

[0402] "Analysis" is the process of analyzing collected data using statistical methods and machine learning algorithms to extract useful information.

[0403] "Communications demand" refers to the amount of usage and necessity of communication services in a specific area or time period.

[0404] "Vocal tone" refers to characteristics such as pitch, strength, and rhythm of the voice, and is an element related to the expression of emotions.

[0405] "Facial expression" refers to emotions and intentions expressed through the movement of facial muscles.

[0406] "Behavioral patterns" refer to the user's behavioral tendencies and habits, and include characteristics of reactions and actions in specific situations.

[0407] "Emotion data" is data that indicates the user's emotional state, and includes tone of voice, facial expressions, behavior patterns, and the like.

[0408] "Feedback" refers to information or advice provided to the user based on the analysis results.

[0409] This invention is a system that optimally allocates network facilities by collecting and analyzing big data such as geographic information, demographic statistics, urban planning, and demand forecasts. It also includes a function to collect data such as the user's tone of voice, facial expressions, and behavioral patterns, recognize emotions, and provide feedback.

[0410] Hardware and Software Used

[0411] server

[0412] The server collects big data such as geographic information, demographics, urban planning, and demand forecasts. This includes public government data, commercial databases, and sensor data. APIs and database connections are used to collect data. The collected data is analyzed using big data processing frameworks such as Hadoop and Spark. Specific analysis methods include clustering and regression analysis.

[0413] Terminal

[0414] The device collects data such as the user's tone of voice, facial expressions, and behavioral patterns. This is done using hardware such as a microphone and a camera. The collected data is temporarily stored in local storage within the device and later sent to a server. The device provides data to recognize the user's emotions in real time.

[0415] User

[0416] Users provide data to the device through their daily actions, such as the tone of voice when the user speaks, changes in facial expressions, and behavioral patterns. This provides data for the emotion engine to recognize the user's emotions.

[0417] Examples

[0418] For example, suppose a new residential area is planned to be developed in a city. The server analyzes urban planning data and predicts that the population in that area will increase in the future. Based on this, the server makes a plan to prioritize the placement of network equipment in that area. Also, when a user is speaking to the terminal, the terminal collects the tone of the user's voice with a microphone and sends it to the server. The server uses a voice analysis algorithm to detect that the tone of the user's voice is getting higher and recognizes that the user is angry. Based on this, the server displays advice such as "Take a deep breath to relax" through the terminal.

[0419] Examples of prompt statements

[0420] "Develop a plan for optimal network deployment in an area where new housing developments are planned. Also, explain how to analyze the tone of a user's voice to recognize emotions when they are angry."

[0421] In this way, by clarifying the roles of servers, terminals, and users, and naming specific hardware and software, the processing of the system's programs can be explained in natural language.

[0422] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0423] Step 1:

[0424] The server collects big data such as geographic information, demographics, urban planning, and demand forecasts. As input, it uses public government data, commercial databases, and sensor data. These data are obtained through APIs and database connections. As output, the collected data is stored in a database. In concrete terms, the server periodically calls the API to obtain new data and add it to the database.

[0425] Step 2:

[0426] The server analyzes the collected data using big data processing frameworks such as Hadoop or Spark. The data collected in step 1 is used as input. Methods such as clustering and regression analysis are used for data analysis. As output, areas and time periods with high communication demand, as well as areas with high population density, are identified. In concrete terms, the server runs Spark jobs, analyzes the data, and obtains the results.

[0427] Step 3:

[0428] The server generates an optimal network equipment placement plan based on the analysis results. The analysis results obtained in step 2 are used as input. The network equipment placement plan is generated as output. In concrete terms, the server executes an algorithm based on the analysis results to create an optimal placement plan.

[0429] Step 4:

[0430] The device collects data such as the user's tone of voice, facial expressions, and behavioral patterns. As input, the device uses the user's audio and video data. This is achieved using hardware such as a microphone and a camera. As output, the collected data is temporarily stored in local storage. In concrete terms, the device collects audio with a microphone and captures facial expressions with a camera.

[0431] Step 5:

[0432] The server analyzes the emotion data sent by the device. As input, it uses the data collected in step 4. Voice analysis and image analysis techniques are used for the data analysis. As output, the user's emotion is recognized. Specifically, the server runs a voice analysis algorithm to analyze the tone of the user's voice to determine the emotion.

[0433] Step 6:

[0434] The server provides feedback to the user based on the analysis results. As input, it uses the emotion analysis results obtained in step 5. As output, feedback for the user is generated. In concrete terms, the server generates appropriate advice or information based on the analysis results and displays it to the user via the terminal.

[0435] (Application example 1)

[0436] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0437] Conventional network equipment placement systems can optimize placement by utilizing big data such as geographic information and demographic statistics, but there are limitations to improving the quality of communication services and the efficiency of capital investment. In addition, autonomous vehicles cannot recognize passenger emotions and respond appropriately, making it difficult to ensure passenger comfort.

[0438] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for optimally arranging NW facilities based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for analyzing passengers' tone of voice, facial expressions, and behavioral patterns to recognize their emotions, a means for responding according to the emotions, and a means for selecting an optimal route in real time. This not only makes it possible to improve the quality of communication services and streamline capital investment, but also makes it possible to ensure passenger comfort by recognizing passenger emotions in an autonomous vehicle and responding appropriately.

[0439] "AI" is an abbreviation for artificial intelligence, a technology that allows computers to mimic human intelligence to learn, reason, and self-correct.

[0440] "Geographic information" is data relating to geographic location, topography, land use, etc., and includes maps and GPS data.

[0441] "Demographic statistics" are data concerning the distribution, composition, and dynamics of the population in a particular area.

[0442] "Urban planning" refers to planning for the development and management of cities, including land use, infrastructure development, and transportation planning.

[0443] "Demand forecasting" is a data analysis method for predicting future fluctuations in demand, and is based on economic activity, consumer behavior, etc.

[0444] "NW equipment" is an abbreviation for network equipment, and refers to the hardware and software used to build and operate a communications network.

[0445] "Communication services" refers to various services provided via a network, such as data communications and voice communications.

[0446] "Tone of voice" refers to elements that indicate characteristics of the voice, such as pitch, strength, and intonation.

[0447] "Facial expressions" are those that indicate emotions or intentions expressed through the movement of facial muscles.

[0448] A "behavioral pattern" refers to a tendency or habit of human behavior in a particular situation.

[0449] "Emotion recognition" is a technology that analyzes and identifies human emotions from voice, facial expressions, behavioral patterns, etc.

[0450] An "optimal route" refers to the most efficient path to reach a particular destination.

[0451] "Real-time" refers to data processing and information being provided immediately.

[0452] "Self-diagnosis" is a function that allows a system to monitor its own status and detect abnormalities or failures.

[0453] "Automatic repair" is a function that automatically corrects abnormalities or failures that the system detects.

[0454] "Ultra-high speed and low latency" refers to data communication that is carried out at extremely high speeds with extremely little communication latency.

[0455] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[0456] System Configuration

[0457] The system includes the following main components:

[0458] 1. Server: Analyzes big data such as geographic information, demographic statistics, urban planning, and demand forecasts to optimize network (NW) equipment layout.

[0459] 2. Self-driving cars: They will be equipped with cameras and microphones to analyse passenger tone of voice, facial expressions and behavioural patterns to recognise emotions.

[0460] 3. Emotion recognition engine: Software that analyzes passenger emotions and responds appropriately.

[0461] 4. Navigation system: Software for selecting the optimal route in real time.

[0462] Hardware and Software Used

[0463] Camera: Used to capture passengers' facial expressions.

[0464] Microphone: Used to capture the passenger's tone of voice.

[0465] Self-driving car computers: Used for data analysis and emotion recognition.

[0466] Python: Used to implement the program.

[0467] Pandas: Used to load and preprocess data.

[0468] KMeans (scikit-learn): A clustering algorithm.

[0469] Keras: An implementation of an emotion recognition model.

[0470] OpenCV: Face detection and video processing.

[0471] Data processing and calculation

[0472] The server integrates big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and removes missing values. It then uses a clustering algorithm to determine the optimal network equipment layout. The autonomous vehicle's computer uses data acquired from the camera and microphone to analyze passenger emotions with an emotion recognition engine. Based on the analysis results, it takes appropriate action. The navigation system also selects the optimal route in real time.

[0473] Examples

[0474] For example, if a passenger is upset, the system will automatically play relaxing music, and if the passenger is happy, no action is required.

[0475] Examples of prompt statements

[0476] "Generate a program that recognizes passengers' emotions and plays relaxing music if they are angry."

[0477] The above is an embodiment of the present invention.

[0478] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0479] Step 1:

[0480] The server collects big data such as geographic information, demographics, urban planning, and demand forecasts. These data are obtained from various databases and APIs. The inputs include geographic information data, demographics data, urban planning data, and demand forecast data. The server integrates these data and performs preprocessing to remove missing values. The output is the preprocessed integrated data.

[0481] Step 2:

[0482] The server runs a clustering algorithm (KMeans) using the preprocessed integrated data. The input includes the preprocessed integrated data. The server performs clustering and determines the optimal network (NW) equipment layout. The output is location information for the optimal NW equipment layout.

[0483] Step 3:

[0484] The autonomous vehicle's terminal uses a camera and microphone to capture passengers' facial expressions and tone of voice. The inputs include real-time video and audio data. The terminal sends these data to an emotion recognition engine. The output is the captured video and audio data.

[0485] Step 4:

[0486] The autonomous vehicle terminal uses an emotion recognition engine to analyze the passenger's emotions. Inputs include captured video and audio data. The terminal uses an emotion recognition model (Keras) to predict the passenger's emotions. The output is the passenger's emotional state (e.g., anger, joy).

[0487] Step 5:

[0488] The autonomous vehicle's terminal responds according to the passenger's emotional state. The input includes the passenger's emotional state. For example, if the passenger is angry, the terminal plays relaxing music. The output is the execution of a response according to the passenger's emotion.

[0489] Step 6:

[0490] The autonomous vehicle's terminal uses a navigation system to select the optimal route in real time. The inputs include geographical and traffic information data. The terminal analyzes this data and calculates the optimal route. The output is optimal route information updated in real time.

[0491] The above are the specific process steps for carrying out the present invention.

[0492] Example 2

[0493] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0494] Conventional communication systems had problems with frequent interruptions to communication services due to failures and aging of base station equipment, reducing stability. In addition, there was a lack of technology to optimize communication services based on user emotions, making improving the user experience a challenge. Furthermore, it was difficult to optimally allocate network equipment and provide high-quality communication services, and there was a demand to simultaneously achieve the lowest costs and an ultra-high-speed, low-latency communication experience.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0496] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for periodically executing a self-diagnosis program for base station equipment, a means for analyzing the diagnosis results and detecting abnormalities, a means for starting an automatic repair function when an abnormality is detected, a means for notifying a maintenance staff when automatic repair is impossible, a means for starting an emotion engine for recognizing the user's emotions, a means for analyzing the user's voice and facial expressions and identifying the emotion, a means for optimizing the method of providing communication services based on the recognized emotion, and a means for recommending content for sharing the user's joy when the user is happy. This makes it possible to improve the stability of communication services, optimize the user experience, and simultaneously realize the lowest cost and ultra-high-speed, low-latency communication experience.

[0497] "AI" stands for artificial intelligence, a technology that enables computer systems to mimic human intelligence to learn, reason, and self-correct.

[0498] "Geographic information" is data about specific places on Earth, including maps and location information.

[0499] "Demographics" refers to statistical data concerning the composition and changes of the population in a particular area or group.

[0500] "Urban planning" refers to the designs and policies for the planned development of a city's land use, transportation, infrastructure, etc.

[0501] "Demand forecasting" is an analytical method for predicting future demand, and is based on past data and trends.

[0502] "Network equipment" is a general term for the hardware and software used to configure a communications network.

[0503] A "base station facility" is a facility for communicating with mobile communication terminals in a wireless communication network.

[0504] A "self-diagnostic program" is software that enables a system or equipment to check its own status and detect abnormalities.

[0505] An "automatic repair function" is a function that enables the system to automatically correct abnormalities.

[0506] "Maintenance staff" refers to engineers in charge of maintaining and repairing systems and equipment.

[0507] An "emotion engine" is software that recognizes the user's emotions and has the ability to analyze voice and facial expressions.

[0508] A "communication service" is a service for sending and receiving information such as voice, data, and video.

[0509] "Content" refers collectively to information and entertainment provided to users.

[0510] "Big data" refers to data sets that are so large and complex that they cannot be handled by traditional data processing methods.

[0511] A "high-quality communication service" is a communication service that has characteristics such as stable connection, low latency, and high-speed data transfer.

[0512] "Ultra-high speed and low latency" refers to extremely high data transfer speeds and extremely short communication latency.

[0513] The present invention provides a technique for improving the stability and user experience of a communication system. A specific embodiment of this system will be described below.

[0514] Self-diagnosis and automatic repair function for base station equipment

[0515] 1. The server periodically executes a self-diagnosis program for the base station equipment. This program is used to check the status of the hardware and software of the base station equipment and detect signs of failure or deterioration. The hardware used includes general base station equipment, specifically, various sensors and monitoring devices. The software used includes a self-diagnosis program. In other words, the server may be equipped with an anomaly detection means that periodically executes the self-diagnosis program for the base station equipment to detect anomalies in the base station equipment. The anomaly detection means may detect signs that appear before the base station equipment fails or deteriorates by checking the status of the hardware and software of the base station equipment using the self-diagnosis program.

[0516] Example: A server schedules a self-diagnosis program to run every day at 2 a.m. The program collects data on CPU usage, memory usage, and temperature sensors to detect abnormal values.

[0517] 2. The server analyzes the diagnostic results and detects any abnormalities. If an abnormality is detected, it launches an automatic repair function that restarts the software, resets settings, and so on to fix the problem.

[0518] Example: If the server detects a memory leak, it restarts the relevant process.

[0519] 3. If automatic repair is not possible, the server notifies the maintenance staff. This notification is sent via email and / or SMS.

[0520] Example: The server detects a hardware failure and notifies the maintenance staff that "there is an abnormality in the power supply unit of base station 123."

[0521] Optimizing communication services with emotion engine

[0522] 1. The device is equipped with an emotion engine for recognizing the user's emotions. This engine is used to analyze the user's voice and facial expressions to identify emotions. The hardware used includes devices such as smartphones and tablets. The software used includes the emotion recognition engine.

[0523] Example: When a user launches an app, the device launches an emotion engine, which uses the camera and microphone to collect the user's facial expressions and voice.

[0524] 2. The device analyzes the user's voice and facial expressions to identify emotions. It optimizes the way it provides communication services based on the recognized emotions. For example, if the user is angry, it will increase the communication speed.

[0525] Example: The device determines that the user is angry and increases the communication speed to 1Gbps.

[0526] 3. If the user is happy, the device will recommend content to help them share their joy.

[0527] Example: The device determines that the user is happy and displays a pop-up to encourage sharing on social media, for example displaying a message saying, "Would you like to share this moment with your friends?"

[0528] Examples of prompt statements

[0529] "Please explain the specific processing steps and operations of the self-diagnosis and automatic repair functions of base station equipment."

[0530] Please explain the specific process steps and operations of how to use an emotion engine to optimize communication services based on user emotions.

[0531] In this way, it is possible to improve the stability of the communication system and optimize the user experience.

[0532] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0533] Self-diagnosis and automatic repair function for base station equipment

[0534] Step 1:

[0535] The server periodically runs a self-diagnostic program.

[0536] Input: Base station equipment hardware and software status data (e.g. CPU usage, memory usage, temperature sensor data)

[0537] Data processing: The server collects these data and records them in log files.

[0538] Output: Log file of diagnostic results

[0539] Specific Actions: The server will schedule a self-diagnosis program to run every day at 2:00 AM.

[0540] Step 2:

[0541] The server analyzes the diagnostic results and detects any abnormalities.

[0542] Input: Diagnostics log file

[0543] Data calculation: The server compares the collected data with the set threshold.

[0544] Output: Anomaly detection flag (e.g., if CPU usage exceeds 90%, it is determined to be an anomaly)

[0545] Specific operation: The server analyzes the data in the log file and detects outliers.

[0546] Step 3:

[0547] If an abnormality is detected, the server will initiate an automatic repair function.

[0548] Input: Anomaly detection flag

[0549] Data manipulation: The server takes action to correct the anomaly (e.g. restarting the software, resetting settings).

[0550] Output: Log file of repair results

[0551] Specific behavior: If the server detects a memory leak, it will restart the relevant process.

[0552] Step 4:

[0553] The server will notify maintenance staff if automatic repair is not possible.

[0554] Input: Log file of repair results

[0555] Data processing: The server generates notification messages and sends them to the maintenance staff.

[0556] Output: Notification message (e.g. "There is an abnormality in the power supply unit of base station 123")

[0557] Specific action: The server detects a hardware failure and sends an email to the maintenance staff.

[0558] Optimizing communication services with emotion engine

[0559] Step 1:

[0560] The terminal activates an emotion engine for recognizing the user's emotions.

[0561] Input: User's voice and facial expression data

[0562] Data processing: The device uses a camera and microphone to collect the user's facial expressions and voice.

[0563] Output: A dataset for emotion recognition.

[0564] Specific operation: The device launches the emotion engine when the user launches the app.

[0565] Step 2:

[0566] The device analyzes the user's voice and facial expressions to identify emotions.

[0567] Input: Dataset for emotion recognition

[0568] Data calculation: The device sends the collected data to an emotion recognition engine to identify the user's emotions.

[0569] Output: Emotion recognition results (e.g. anger, joy, sadness, etc.)

[0570] Specific behavior: The device detects anger from changes in voice tone and facial expressions.

[0571] Step 3:

[0572] The terminal optimizes the way in which it provides communication services based on the recognized emotions.

[0573] Input: Emotion recognition results

[0574] Data processing: The device changes communication service settings based on the recognized emotions (e.g. throttling communication speeds).

[0575] Output: Optimized communication service settings

[0576] Specific behavior: The device determines that the user is angry and increases the communication speed to 1Gbps.

[0577] Step 4:

[0578] If the user is happy, the terminal recommends content to share that happiness.

[0579] Input: Emotion recognition results

[0580] Data processing: The device selects appropriate content based on the user's emotions.

[0581] Output: Content recommendation message

[0582] Specific behavior: The device determines that the user is happy and displays a pop-up encouraging them to share on social media.

[0583] (Application example 2)

[0584] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0585] In modern communication systems, maintaining the stability and quality of communication services is an important issue. In particular, in autonomous vehicles, communication interruptions and delays directly affect safety, so self-diagnosis and self-repair functions are required for the communication system. In addition, providing services according to passenger emotions also contributes to improving the user experience. However, there is still no system that realizes these functions in an integrated manner.

[0586] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0587] In this invention, the server includes: means for optimally arranging network equipment using AI based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world; means for providing high-quality communication services; means for introducing self-diagnosis and automatic repair functions from base station equipment information and detecting signs of failure or aging; means for having self-diagnosis and automatic repair functions for the communication system and recognizing passenger emotions to optimize in-car entertainment and communication services; means for minimizing interruptions to communication services and improving stability; and means for minimizing costs while providing an ultra-high-speed, low-latency communication experience. This makes it possible to provide optimal services according to passenger emotions while maintaining the stability and quality of the communication system.

[0588] "AI" is an abbreviation for artificial intelligence, a technology that allows computers to imitate human intelligence to learn and make inferences.

[0589] "Geographic information" is information relating to a specific location on the Earth, and includes map data and location information.

[0590] "Demographics" refers to statistical data concerning the composition and trends of the population in a particular area or group.

[0591] "Urban planning" refers to the activities and plans for planning and managing urban land use and infrastructure development.

[0592] "Demand forecasting" refers to the analysis and calculations used to predict future demand, and plays an important role in business and economic activities.

[0593] "Network equipment" is a general term for the hardware and software used to configure a communications network.

[0594] The term "base station equipment" refers to fixed communication equipment for communicating with mobile communication terminals in a wireless communication network.

[0595] "Self-diagnosis" is a function that enables a system or device to check its own status and detect abnormalities or failures.

[0596] "Automatic repair" is a function that automatically corrects abnormalities or failures detected by a system or device.

[0597] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions, voice, etc.

[0598] "In-car entertainment" is a general term for entertainment content and services provided within a vehicle.

[0599] "Communication services" refers to services for sending and receiving data, and includes Internet connections and voice calls.

[0600] "Ultra-high speed and low latency" means extremely high data communication speeds and extremely low communication latency.

[0601] The system for implementing this invention has the following configuration. The server has a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world. This enables efficient arrangement of communication networks.

[0602] The server also has the means to provide high-quality communication services and provide users with a stable communication environment. Furthermore, it has the means to introduce self-diagnosis and automatic repair functions from base station equipment information and to detect signs of failure or aging. This improves the stability of the communication system and minimizes interruptions to communication services.

[0603] In addition, the server has the ability to self-diagnose and self-repair the communication system, and has the means to recognize passenger emotions and optimize in-car entertainment and communication services. This allows the server to provide optimal services according to the passenger's emotions. For example, if a passenger is angry, the server can increase communication speed to reduce stress, and if a passenger is happy, the server can recommend content that will help the passenger share their joy.

[0604] The following hardware and software are used to realize this system. For hardware, a smartphone and a head-mounted display are used. For software, Python and emotion recognition libraries (e.g., OpenCV and TensorFlow) are used.

[0605] Specifically, the server periodically performs self-diagnosis of the communication system and attempts to automatically repair it if an abnormality is detected. It also uses an emotion recognition library to analyze the user's facial expressions and voice to recognize their emotions. Based on this, it provides optimal communication services and entertainment content.

[0606] Concrete examples of this could include the following scenarios: If a passenger is angry, increase the speed of communication to reduce stress If a passenger is happy, recommend content that helps them share their joy.

[0607] Examples of prompts to be input to a generative AI model include the following:

[0608] "If users are angry, tell them how to make their connection faster."

[0609] "If a user is happy, how can we recommend content that helps them share that happiness?"

[0610] Thus, specific embodiments for carrying out the invention are provided.

[0611] The flow of the identification process in the application example 2 will be described with reference to FIG.

[0612] Step 1:

[0613] The server uses AI to collect big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world. Inputs include information from various databases and public data sources. These data are analyzed to calculate the optimal network equipment layout. The output is an optimal network equipment layout plan. Specific operations include data collection, data analysis, and application of optimization algorithms.

[0614] Step 2:

[0615] The server constructs a communication infrastructure based on the network equipment deployment plan to provide high-quality communication services. The input includes the deployment plan obtained in step 1. The output is the completion of the construction of the actual communication infrastructure. Specific operations include the installation, configuration, and testing of the network equipment.

[0616] Step 3:

[0617] The server periodically collects base station equipment information and performs self-diagnosis. Input includes status information from the base stations. Output provides a health status report for the base stations. Specific operations include data collection, application of anomaly detection algorithms, and report generation.

[0618] Step 4:

[0619] The server attempts to automatically heal itself based on the results of the self-diagnosis. Inputs include the health report from step 3. Outputs include a normal state if the repair was successful, or a maintenance notification if repair is required. Specific actions include applying a repair algorithm, performing a repair operation, and sending a notification.

[0620] Step 5:

[0621] The device uses an emotion recognition library to analyze the user's facial expressions and voice to recognize the user's emotions. Inputs include the user's facial expression data and voice data. Outputs include the user's emotional state. Specific operations include data collection, application of emotion recognition algorithms, and determination of the emotional state.

[0622] Step 6:

[0623] The server provides optimal communication services and entertainment contents based on the user's emotional state. The input includes the emotional state obtained in step 5. The output includes optimized communication services and recommended contents. Specific operations include applying a service optimization algorithm, running a content recommendation engine, and providing the service.

[0624] Step 7:

[0625] The user uses the provided communication services and entertainment content. The inputs include the services and content provided in step 6. The outputs include the user's satisfaction and feedback. Specific actions include using the service, watching the content, and providing feedback.

[0626] Example 3

[0627] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0628] In conventional communication systems, the quality and stability of communication services can decline due to insufficient optimal placement of network equipment and detection of signs of failure. In addition, because dynamic system adjustments based on user emotions are not performed, there is an issue that the user experience is uniform and cannot respond to individual needs. Furthermore, it is difficult to provide a high-speed, low-latency communication experience while keeping communication costs down.

[0629] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0630] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for recognizing user emotions and adjusting the operation of self-diagnosis and automatic repair functions based on the emotions, and a means for providing a high-speed, low-latency communication experience. This enables optimal arrangement of network equipment and early detection of signs of failure, improving the quality and stability of communication services. In addition, dynamic system adjustment based on user emotions makes it possible to provide a high-quality communication experience that meets individual needs. Furthermore, it becomes possible to realize a high-speed, low-latency communication experience while suppressing communication costs.

[0631] "AI" stands for artificial intelligence, a technology that automatically makes judgments and predictions through machine learning and data analysis.

[0632] "Network equipment" is a general term for hardware and software used to configure a communications network, and includes routers, switches, base stations, etc.

[0633] "Optimal placement" is a placement method that maximizes communication performance and minimizes costs by efficiently placing network facilities.

[0634] "Big data" refers to extremely large and complex data sets, amounts of data that cannot be handled by traditional data processing techniques.

[0635] A "high-quality communication service" is a communication service that excels in such areas as stability, speed, and low latency, and provides users with a comfortable communication experience.

[0636] "User emotion" refers to the psychological state that the user is feeling, such as joy, anger, sadness, etc., and is information that is used to adjust the system's operation by recognizing this state.

[0637] "Self-diagnosis" is a function that enables the system to check its own status and detect signs of abnormalities or failures.

[0638] The "automatic restoration function" is a function that automatically restores abnormalities or failures detected by the system, in order to minimize interruptions to communication services. The server may be provided with a means for starting the automatic restoration function that automatically restores base station equipment in which an abnormality has been detected.

[0639] A "high-speed, low-latency communication experience" is one in which data is sent and received quickly and with extremely little communication latency.

[0640] "Geographic information" refers to information about geographic locations and topography, and is used to optimize the placement of network facilities.

[0641] "Demographics" refers to data about the distribution and composition of the population in a particular area, and is used for demand forecasting and the placement of network facilities.

[0642] "Urban planning" refers to plans for the development and development of cities, and influences the long-term placement strategy of network facilities.

[0643] "Demand forecasting" refers to predicting future demand for communications, and is useful for optimal placement of network equipment and efficient use of resources.

[0644] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0645] This invention is a system that provides high-quality communication services by utilizing AI technology to optimize the placement of network equipment and adjusts self-diagnosis and automatic repair functions based on user emotions. A specific embodiment of this system will be described below.

[0646] 1. Generating the system program

[0647] The server uses AI algorithms to calculate the optimal placement of network equipment. The AI ​​algorithms are implemented using machine learning frameworks such as TensorFlow and PyTorch. The server collects network traffic data and equipment operation status data, and determines the optimal placement based on this data.

[0648] The device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine is implemented using emotion recognition software such as OpenCV and Emotion API. The device transmits the user's emotion data to the server.

[0649] 2. Explanation of program processing

[0650] The server adjusts the operation of the self-diagnosis and auto-repair functions based on the received emotion data. For example, when the user is angry, the server is more sensitive to detect signs of failure and performs early repairs. On the other hand, when the user is happy, the server minimizes the operation of the self-diagnosis and auto-repair functions to avoid interrupting communication services.

[0651] The servers provide high-speed, low-latency communication services based on optimal network equipment layout and coordinated self-diagnosis and automatic repair functions, allowing users to send and receive large amounts of data with low latency, such as 4K and 8K video streaming and real-time online games.

[0652] 3. Examples of concrete examples and prompts

[0653] As a concrete example, consider services that require high-speed transmission and reception of large amounts of data, such as 4K or 8K video streaming and real-time online games. By using this system, users can enjoy these services with low latency.

[0654] Example of a prompt to be input to a generative AI model:

[0655] "Please explain how to detect early signs of network equipment failure and quickly repair it when users are angry."

[0656] Using this prompt sentence, the generative AI model can provide detailed instructions on how to manage network equipment based on the user's emotions.

[0657] As described above, the present invention combines AI technology and emotion recognition technology to realize optimal placement of network facilities and dynamic system adjustment, thereby providing high-quality communication services. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0658] Step 1: Recognizing user emotions

[0659] The device captures the user's face with a camera and records the user's voice with a microphone. As input, it obtains the user's facial expression data and voice data. The device uses OpenCV to analyze facial expressions and Emotion API to recognize emotions from voice. This outputs the user's emotional state, such as whether they are angry, happy, or sad.

[0660] Step 2: Calculate the optimal layout of network equipment

[0661] The server collects network traffic data and equipment operation status data. It receives these data as input. The server uses machine learning frameworks such as TensorFlow and PyTorch to calculate the optimal network equipment placement. As a result of the calculation, it outputs a diagram of the optimal network equipment placement.

[0662] Step 3: Adjust the self-diagnosis and auto-repair functions

[0663] The server receives the user's emotional data. As input, the server receives the user's emotional state. If the user is angry, the server increases the sensitivity of the self-diagnosis function to detect the signs of failure more sensitively. If the user is happy, the server minimizes the operation of the self-diagnosis and auto-repair functions to avoid interruption of the communication service. This results in outputting the adjusted self-diagnosis and auto-repair function settings.

[0664] Step 4: Providing high-speed, low-latency communications

[0665] The server provides high-speed, low-latency communication services based on optimal network equipment layout and adjusted self-diagnosis and auto-repair functions. As input, it receives an optimal network equipment layout diagram and settings for adjusted self-diagnosis and auto-repair functions. This enables users to send and receive large amounts of data with low latency, such as 4K or 8K video streaming and real-time online games. As output, it provides high-speed, low-latency communication services.

[0666] (Application example 3)

[0667] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0668] Conventional communication systems require huge costs for the deployment and maintenance of network equipment, and often suffer from poor communication quality and delays. In addition, they are unable to flexibly respond to user emotions, leading to problems with interruptions and instability in communication services. This has led to issues with the degradation of user experience, particularly in services that require the high-speed transmission and reception of large amounts of data, such as high-quality video streaming and real-time online games.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0670] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for recognizing a user's emotions in real time and automatically adjusting network settings, a means for adjusting the operation of self-diagnosis and automatic repair functions based on the emotions, and a means including an application installed on a smartphone, smart glasses, a head-mounted display, or a robot. This makes it possible to provide an ultra-high-speed, low-latency communication experience at the lowest cost and to respond flexibly to the user's emotions.

[0671] "AI" stands for artificial intelligence, a technology that allows computers to imitate human intelligence and learn and make inferences.

[0672] "Geographic information" is information relating to a specific place on the earth, and includes map data and location information.

[0673] "Demographics" refers to statistical data concerning the composition and changes of the population in a particular area or group.

[0674] "Urban planning" refers to the activities and plans for planning and managing urban land use and infrastructure development.

[0675] "Demand forecasting" is the analysis and calculation used to predict future demand, and plays an important role in business and economic activities.

[0676] "Network equipment placement" refers to placing network equipment in optimal locations, and is a means of improving communications quality and cost efficiency.

[0677] A "high-quality communication service" is a service that provides low-latency, high-speed, large-capacity data communication.

[0678] "User emotion" refers to an emotional state such as happiness, anger, sadness, etc., felt by a user.

[0679] "Real-time" refers to instantaneous processing or reaction without delay.

[0680] "Network settings" refer to various settings and parameters for controlling the operation of a communications network.

[0681] "Self-diagnosis" is a function that enables the system to check its own status and detect signs of abnormalities or failures.

[0682] An "automatic repair function" is a function that enables a system to automatically repair failures or abnormalities.

[0683] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and use applications.

[0684] "Smart glasses" are a wearable device in the form of glasses that has the functions of displaying information and communicating with others.

[0685] A "head-mounted display" is a display device worn on the head and is a device for providing visual information.

[0686] A "robot" is a mechanical device that operates autonomously and performs specific tasks.

[0687] An "application" is a software program designed to provide a specific function or service.

[0688] The following system configuration will be described as an embodiment of the present invention.

[0689] The servers use AI to optimize network equipment layout based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, improving communication quality while minimizing capital investment and maintenance costs.

[0690] The device (smartphone, smart glasses, head-mounted display, or robot) recognizes the user's emotions in real time and automatically adjusts network settings. Emotion recognition is performed using software called EmotionRecognizer, which analyzes the user's facial expressions through a camera. Network settings are adjusted using software called NetworkOptimizer.

[0691] Specifically, when a user is emotionally excited while watching a movie, the network's self-diagnosis and auto-repair functions will be more responsive to prevent communication interruptions, allowing users to enjoy high-quality video streaming with low latency.

[0692] The server also adjusts the operation of the self-diagnosis and auto-repair functions based on the user's emotions. For example, when the user is angry, the server is more sensitive to detecting signs of failure and repairing them earlier. On the other hand, when the user is happy, the server minimizes the operation of the self-diagnosis and auto-repair functions to avoid interrupting the communication service.

[0693] Here are some example prompts using a generative AI model to provide optimal network settings based on the user’s emotions:

[0694] If a user is watching a movie and is emotional, how can the network's self-diagnosis and auto-repair capabilities be more responsive and prevent communication interruptions?

[0695] In this way, it is possible to provide an ultra-fast, low-latency communication experience at the lowest cost, and to respond flexibly to user emotions.

[0696] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0697] Step 1:

[0698] The server uses AI to collect big data such as geographic information, demographics, urban planning, and demand forecasts from around the world, and calculates the optimal network equipment layout. It receives geographic information, demographics, urban planning, and demand forecast data as input, and generates an optimal network equipment layout plan as output. Specifically, it analyzes this data and determines the layout that maximizes communication quality while minimizing costs.

[0699] Step 2:

[0700] The device uses a camera to capture the user's facial expressions and uses EmotionRecognizer software to recognize emotions in real time. It receives camera footage as input and generates the user's emotional state (happiness, anger, sadness, etc.) as output. Specifically, it analyzes the video data, extracts facial features, and classifies emotions.

[0701] Step 3:

[0702] The device automatically adjusts network settings based on the user's recognized emotions using the NetworkOptimizer software. It takes the user's emotional state as input and generates optimal network settings as output. Specifically, it adjusts communication priority and bandwidth according to the user's emotional state to optimize communication quality.

[0703] Step 4:

[0704] The server performs self-diagnosis and automatic repair functions based on network equipment information to detect signs of failure or aging. It receives network equipment information as input and generates detection results of signs of failure as output. Specifically, it analyzes equipment operation data and detects abnormal patterns.

[0705] Step 5:

[0706] The server adjusts the operation of the self-diagnosis and auto-repair functions based on the user's emotions. It receives the user's emotional state and the results of the detection of signs of failure as input, and generates an adjusted operation plan for self-diagnosis and auto-repair as output. Specifically, if the user is angry, the server will be more sensitive to detect signs of failure and perform repairs early, while if the user is happy, the server will minimize the operation to avoid interrupting the communication service.

[0707] Step 6:

[0708] The terminal provides users with high-quality communication services based on the adjusted network settings and self-diagnosis and automatic repair functions. It receives the adjusted network settings and operation plan as input and provides a low-latency, high-quality communication experience as output. Specifically, it applies the network settings and performs self-diagnosis and automatic repair when necessary to maintain the stability and quality of communication.

[0709] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0710] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0711] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0712] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0713] [Second embodiment]

[0714] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0715] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0716] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0717] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0718] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0719] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0720] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0721] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0722] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0723] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0724] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0725] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0726] "Example 1"

[0727] In one embodiment of the present invention, the AI ​​performs optimal network equipment placement based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts. Specifically, the AI ​​analyzes these data and places network equipment preferentially in areas and time periods with high communication demand, areas with high population density, etc. In addition, the AI ​​uses urban planning data to plan the placement of network equipment based on predictions of future population movement and urban development. This improves the quality of communication services and makes it possible to make capital investments more efficient.

[0728] "Example 2"

[0729] Furthermore, in one embodiment of the present invention, a self-diagnosis and automatic repair function is introduced from the base station equipment information. Specifically, the base station equipment periodically performs self-diagnosis to detect signs of failure or aging. If detected, the automatic repair function corrects the problem or notifies maintenance staff as necessary. This makes it possible to minimize interruptions to communication services and improve stability.

[0730] "Example 3"

[0731] In addition, one embodiment of the present invention provides an ultra-high-speed, low-latency communication experience while minimizing costs. Specifically, by introducing optimal network equipment layout using AI and self-diagnosis and automatic repair functions, high-quality communication services are provided while minimizing capital investment and maintenance costs. For example, it becomes possible to provide services that require high-speed transmission and reception of large amounts of data, such as 4K and 8K video streaming and real-time online games, with low latency.

[0732] The process flow of each embodiment will be described below.

[0733] "Example 1"

[0734] Step 1: AI collects big data such as geographic information, demographics, urban planning, and demand forecasts.

[0735] Step 2: Analyze the collected data and identify areas, time periods with high demand for communications, areas with high population density, etc.

[0736] Step 3: Based on the identified information, prioritize the placement of network facilities.

[0737] Step 4: Using urban planning data, plan the placement of network facilities based on predictions of future population movement and urban development.

[0738] "Example 2"

[0739] Step 1: The base station equipment periodically performs self-diagnosis.

[0740] Step 2: Detect signs of malfunction or deterioration through self-diagnosis.

[0741] Step 3: If detected, auto-remediation will fix the issue or notify maintenance staff as appropriate.

[0742] "Example 3"

[0743] Step 1: Minimize capital investment and maintenance costs by using AI to optimize network equipment placement and introduce self-diagnosis and automatic repair functions.

[0744] Step 2: Provide services that require high-speed transmission and reception of large amounts of data with low latency, such as 4K and 8K video streaming and real-time online gaming.

[0745] Example 1

[0746] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0747] Modern communications infrastructure is required to respond quickly to sudden fluctuations in communications demand and changes in population density. However, with conventional methods, it is difficult to optimally deploy network equipment to deal with these fluctuations, which can lead to a decline in the quality of communications services. In addition, the efficiency of capital investment is also an issue. Furthermore, it is important to detect early signs of failure or aging of base station equipment and minimize interruptions to communications services.

[0748] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for collecting big data such as geographic information, demographic statistics, urban planning, and demand forecasts, a means for preprocessing the collected data, a means for analyzing the preprocessed data and identifying areas, time periods, and areas with high communication demand and high population density, a means for generating an optimal network facility layout plan based on the analysis results, a means for outputting the generated layout plan in a report format, and a means for providing high-quality communication services. This makes it possible to quickly respond to fluctuations in communication demand and changes in population density and to optimally layout network facilities. In addition, it is possible to improve the efficiency of capital investment and the quality of communication services.

[0749] "Geographic information" is data relating to geographic location, topography, land use, etc.

[0750] "Demographics" refers to statistical data such as the distribution, age structure, gender, and occupation of the population in a particular area.

[0751] "Urban planning" refers to planning data regarding urban land use, transportation, infrastructure development, etc.

[0752] "Demand forecast" is data for predicting future fluctuations in demand.

[0753] "Big data" refers to extremely large and complex data sets that cannot be handled using traditional data processing methods.

[0754] "Network equipment" is a general term for the hardware and software required to provide communication services.

[0755] "Preprocessing" refers to cleaning, normalizing, integrating, and other processes on data before performing data analysis.

[0756] "Analysis" refers to the process of using collected data to discover specific patterns or trends.

[0757] "Layout planning" refers to planning where and how network equipment will be laid out.

[0758] The "report format" is a format in which analysis results and deployment plans are compiled as a document.

[0759] "High-quality communication services" refers to services that provide stable, high-speed, and low-latency communications.

[0760] "Self-diagnosis" refers to the system's ability to monitor its own status and detect abnormalities.

[0761] "Automatic repair" refers to the system's ability to automatically correct any abnormalities it detects.

[0762] "Signs of failure or deterioration" refer to symptoms that appear before equipment breaks down or deteriorates.

[0763] "Minimizing interruptions to communication services" refers to minimizing the duration and area of ​​interruptions to communication services.

[0764] "Improving stability" refers to maintaining a state in which communication services are provided continuously and without interruption.

[0765] "Lowest cost" refers to keeping costs to a minimum.

[0766] "Ultra-fast, low-latency communication experience" refers to the provision of extremely fast data transfer speeds and extremely short communication latency.

[0767] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0768] The present invention relates to a system for analyzing big data such as geographic information, demographic statistics, urban planning, demand forecasts, etc., and for optimally arranging network facilities. A specific embodiment of this system is described below.

[0769] 1. Program Generation

[0770] The server generates programs to analyze big data such as geographic information, demographic statistics, urban planning, and demand forecasts. The programs use generative AI models to optimally allocate network equipment.

[0771] 2. Program Processing Explanation

[0772] The server analyzes the data using the following procedure to plan the optimal placement of network equipment.

[0773] 1. Data collection:

[0774] The server collects geographic information using Geographic Information System (GIS) software (e.g., ArcGIS), demographic data is obtained from government statistical databases (e.g., census data), urban planning data is collected from public databases of local governments, and demand forecasting data is derived from the carrier's historical traffic data.

[0775] 2. Data preprocessing:

[0776] The server converts the collected data into a format that is easier to analyze: it cleans, normalizes, and consolidates the data.

[0777] 3. Data Analysis:

[0778] The server then analyzes the preprocessed data using AI analysis tools (e.g., TensorFlow, PyTorch) to train machine learning models to identify areas and times of day with high demand for communications, as well as areas with high population density.

[0779] 4. Generate optimal layout plan:

[0780] The server then uses the analysis results to plan the optimal placement of network equipment. For example, it will add base stations to areas with high demand for communications and set up Wi-Fi hotspots in areas with high population density. It also uses urban planning data to plan the placement of network equipment based on predictions of future population movements and urban development.

[0781] 5. Result output:

[0782] The server outputs the optimal layout plan in the form of a report and provides it to the user. This report includes specific layout locations, the types of equipment required, installation times, etc.

[0783] 3. Examples and prompts

[0784] As a concrete example, consider the following scenario.

[0785] Examples:

[0786] A user uses this system to optimize the communication infrastructure of city A. City A is experiencing rapid population growth, and communication traffic is increasing rapidly in certain areas. The user inputs the geographic information, demographic statistics, urban planning, and past communication traffic data of city A into the system.

[0787] Example prompt:

[0788] "Plan the optimal placement of network facilities based on the geographic information, demographics, urban planning, and past communication traffic data of City A. In particular, prioritize placement of facilities in areas and time periods with high communication demand and in areas with high population density, while also taking into consideration predictions of future population movement and urban development."

[0789] By inputting this prompt sentence into the generative AI model, the server generates an optimal network equipment layout plan and provides it to the user.

[0790] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0791] Step 1: Data collection

[0792] The server collects geographic information using Geographic Information System (GIS) software. Specifically, it uses tools such as ArcGIS to obtain geographic information for City A. It also downloads demographic data for City A from a government statistical database. It also collects urban planning data for City A from the public database of the local government and obtains historical traffic data from telecommunications carriers. These data are inputs, and the collected data set is output.

[0793] Step 2: Data Preprocessing

[0794] The server converts the collected data into a format that is easy to analyze. Specifically, it cleans the data and removes missing values ​​and outliers. Next, it normalizes the data to unify the scale. Finally, it integrates data from different data sources and combines them into a single dataset. The input is the collected dataset, and the output is the preprocessed dataset.

[0795] Step 3: Data analysis

[0796] The server uses the preprocessed data to perform analysis. Specifically, it uses AI analysis tools (e.g., TensorFlow, PyTorch) to train machine learning models to identify areas and times of high demand for communications, as well as areas with high population density. The trained models are then used to predict future demand for communications and population movements. The input is the preprocessed dataset, and the analysis results are output.

[0797] Step 4: Generate optimal layout plans

[0798] The server generates an optimal network equipment layout plan based on the analysis results. Specifically, it adds base stations in areas with high demand for communications and installs Wi-Fi hotspots in areas with high population density. It also creates a layout plan that takes into account future population movements and urban development based on urban planning data. The input is the analysis results, and the generated layout plan is output.

[0799] Step 5: Output the results

[0800] The server outputs the optimal deployment plan in report format and provides it to the user. Specifically, it generates a report that includes details of the deployment location, the type of equipment required, the installation time, predicted fluctuations in communication demand, etc. The input is the generated deployment plan, and the output is the deployment plan in report format.

[0801] (Application example 1)

[0802] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0803] In modern society, optimizing communication infrastructure and providing high-quality communication services are important issues. However, conventional methods have limitations when it comes to responding to fluctuations in communication demand, changes in population density, and progress in urban planning. In addition, the operation management of autonomous vehicles requires providing optimal routes and stopping points in real time. In order to solve these issues, advanced analysis technology that utilizes big data is required.

[0804] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0805] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, and a means for analyzing big data such as geographic information, demographic statistics, urban planning, and demand forecasts to provide optimal routes and stopping points in real time in order to optimize operation management of autonomous vehicles. This makes it possible to optimize communication infrastructure and provide high-quality communication services, as well as realize efficient operation management of autonomous vehicles.

[0806] "AI" is an abbreviation for artificial intelligence, a technology that enables computer systems to mimic human intelligence to learn, reason, and self-correct.

[0807] "Geographic information" is data about specific locations on Earth, including maps, location information, and topographical data.

[0808] "Demographics" refers to data concerning the distribution, composition, and changes of the population in a particular area.

[0809] "Urban planning" refers to plans and policies concerning urban land use, development, and infrastructure development.

[0810] "Demand forecasting" refers to data analysis and models used to predict future fluctuations in demand.

[0811] "NW equipment" is an abbreviation for network equipment, and refers to the hardware and software that make up the communications infrastructure.

[0812] "High-quality communication services" refers to services that provide stable, high-speed, and low-latency communications.

[0813] An "autonomous vehicle" refers to a vehicle that drives autonomously using artificial intelligence and sensor technology.

[0814] "Traffic management" refers to the management work of planning, monitoring and controlling vehicle operations.

[0815] An "optimal route" refers to the most efficient route selected taking into account traffic conditions, distance to the destination, time, etc.

[0816] A "stopping point" refers to a specific location for a vehicle to stop.

[0817] "Real-time" refers to data and information being processed immediately and provided without delay.

[0818] To implement this invention, the following system configuration is required. The server collects big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and implements an AI model to analyze this data. Specifically, software libraries such as Python, Pandas, NumPy, Scikit-learn, and GeoPandas are used.

[0819] The server first loads data such as geographic information, demographic statistics, urban planning, and demand forecasts, and then integrates this data. Next, it classifies regions using KMeans clustering and builds a model to forecast future demand using RandomForestRegressor. This allows it to calculate the optimal placement of network facilities and saves the results.

[0820] As a concrete example, consider the case of optimizing the operation management of autonomous vehicles in Tokyo. The server collects road network data in Tokyo, population density data for each ward, construction plan data for new roads and buildings, and demand forecast data based on past traffic data, and analyzes this data. Based on the analysis results, it provides optimal routes and stopping points in real time.

[0821] Users can receive real-time information on optimal routes and stopping points via a smartphone application. Based on data provided by the server, the application provides information to avoid traffic congestion and ensure efficient operation.

[0822] An example of a prompt sentence might be:

[0823] "In order to optimize the operation management of autonomous vehicles in Tokyo, please develop an application that analyzes big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and provides optimal routes and stopping points in real time."

[0824] By feeding this prompt into a generative AI model, it is possible to obtain detailed advice on the design and implementation of a specific application.

[0825] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0826] Step 1:

[0827] The server collects data such as geographic information, demographics, urban planning, and demand forecasts. Specifically, it retrieves data from various databases and APIs and integrates them. The inputs are geographic information data, demographic data, urban planning data, and demand forecast data, and the output is an integrated data set.

[0828] Step 2:

[0829] The server preprocesses the merged dataset, i.e. imputing missing values, normalizing the data, removing outliers, etc. As input, we have the merged dataset, and as output, we have the preprocessed dataset.

[0830] Step 3:

[0831] The server uses the preprocessed dataset to perform KMeans clustering, classifying regions based on population density and demand forecasts. The input is the preprocessed dataset, and the output is the cluster information for each region.

[0832] Step 4:

[0833] The server uses the cluster information to build a demand forecasting model using RandomForestRegressor. Specifically, it trains a model to forecast future demand using population density, urban development index, and current demand as inputs. The inputs are the cluster information and a preprocessed dataset, and the output is a demand forecasting model.

[0834] Step 5:

[0835] The server uses the demand forecasting model to calculate the optimal network equipment placement. Specifically, it predicts future demand in each region and optimizes the placement of network equipment based on that. The inputs are the demand forecasting model and a pre-processed dataset, and the output is the optimal network equipment placement information.

[0836] Step 6:

[0837] The server stores the optimal network equipment layout information and updates it as necessary. Specifically, the calculation results are stored in a database and periodically recalculated. The input is the optimal network equipment layout information, and the output is the stored layout information.

[0838] Step 7:

[0839] Users receive information on optimal routes and stopping points in real time through a smartphone application. Specifically, information is provided to avoid traffic congestion and achieve efficient operation based on data provided by the server. The input is real-time data from the server, and the output is information on optimal routes and stopping points provided to users.

[0840] Example 2

[0841] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0842] Conventional communication systems have problems with frequent interruptions to communication services due to failures and aging of base station equipment, resulting in reduced stability. In addition, optimal network equipment placement and efficient maintenance have not been implemented sufficiently, resulting in increased costs and reduced communication quality. Furthermore, delayed response after anomaly detection can prolong the interruption of communication services.

[0843] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0844] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, a means for collecting status data of base station equipment, a means for performing self-diagnosis based on the collected data, a means for performing automatic repair when an abnormality is detected, a means for notifying maintenance staff when the automatic repair fails, and a means for storing the results of the self-diagnosis and automatic repair in a database and analyzing the long-term status of the equipment. This makes it possible to minimize interruptions to communication services and improve stability.

[0845] "AI" is an abbreviation for artificial intelligence, a technology that mimics human intelligence through machine learning and data analysis.

[0846] "Geographic information" refers to data such as location, topography, and climate for specific locations on Earth.

[0847] "Demographics" refers to statistical data such as the distribution of the population in a particular area, age structure, gender, birth rate, and death rate.

[0848] "Urban planning" refers to the designs and policies for the planned arrangement and management of a city's land use, transportation, infrastructure, etc.

[0849] "Demand forecasting" is a method that uses data analysis and models to predict future demand.

[0850] "Network equipment" is a general term for the hardware and software required to provide communication services.

[0851] The term "base station equipment" refers to fixed communication equipment for communicating with mobile communication terminals in a wireless communication network.

[0852] "Self-diagnosis" is a function that enables a system or device to check its own status and detect signs of abnormalities or failures.

[0853] "Automatic repair" is a function that automatically corrects detected abnormalities or failures.

[0854] "Maintenance staff" refers to professional personnel who maintain and manage systems and equipment.

[0855] A "database" is a system for efficiently storing, searching, and managing data.

[0856] "Analyzing the long-term condition of equipment" means evaluating the condition of equipment over a long period of time based on collected data and predicting future breakdowns and the need for maintenance.

[0857] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0858] The present invention relates to a communication system having a self-diagnosis and automatic repair function for base station equipment. A specific embodiment of this system will be described below.

[0859] 1. Program Generation

[0860] The server generates a program with self-diagnosis and automatic repair functions based on the base station equipment information. This program is developed using programming languages ​​such as Python and Java.

[0861] 2. Program Processing Explanation

[0862] The server uses the generated program to perform the following processes.

[0863] Self-diagnosis function:

[0864] The server periodically checks the status of the base station equipment using sensors and monitoring software (e.g., Nagios, Zabbix) installed in the equipment.

[0865] It collects data on equipment temperature, voltage, communication status, etc., and runs algorithms to detect abnormal values ​​and signs of aging.

[0866] Automatic repair function:

[0867] If the server detects a problem during self-diagnosis, it will execute an automatic repair script, such as restarting the software or resetting settings.

[0868] If the problem cannot be automatically fixed, the server will notify maintenance staff via email, SMS, or a dedicated maintenance app (e.g. PagerDuty).

[0869] Data storage and analysis:

[0870] The server stores the results of self-diagnosis and automatic repair in a database (e.g., MySQL, PostgreSQL).

[0871] Based on the stored data, the long-term condition of the equipment is analyzed, future failures are predicted, and maintenance plans are optimized.

[0872] 3. Examples of concrete examples and prompts

[0873] Examples:

[0874] When a user wants to check the status of base station equipment, the following steps are taken.

[0875] 1. The user sends a request to check the status of the base station equipment from a dedicated management terminal.

[0876] 2. The server displays the equipment status based on the latest self-diagnosis results.

[0877] 3. If an abnormality is detected, the server will display the automatic repair history and current response status.

[0878] Example prompt:

[0879] By inputting the following prompt sentence into the generative AI model, a report on the status of base station equipment can be generated.

[0880] "Based on the latest self-diagnosis results of the base station equipment, please generate a report of the current status and the anomaly detection history for the past week."

[0881] In this way, the user can grasp the status of the base station equipment in real time and take the necessary measures quickly.

[0882] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0883] Step 1:

[0884] The server periodically collects equipment status data using sensors and monitoring software (e.g., Nagios, Zabbix) installed in the base station equipment.

[0885] Input: Sensor data such as temperature, voltage, and communication status from base station equipment.

[0886] Data processing: The server collects the sensor data and formats it for storage in a database.

[0887] Output: Formatted asset condition data.

[0888] Specific operation: The server runs a script to obtain data from the sensor at midnight every day and stores it in a database.

[0889] Step 2:

[0890] The server runs a self-diagnosis algorithm based on the collected data.

[0891] Input: Formatted equipment condition data.

[0892] Data computation: The server analyzes the data using Python's Pandas library to detect outliers and signs of aging.

[0893] Output: Anomaly detection results.

[0894] What happens: The server compares the collected data with past data to see if there are any outliers.

[0895] Step 3:

[0896] If the self-diagnosis detects an abnormality, the server executes an automatic repair script.

[0897] Input: Anomaly detection results.

[0898] Data processing: The server selects an appropriate repair script depending on the type of anomaly.

[0899] Output: The repair result.

[0900] Specific action: If an abnormality is detected, the server executes a Bash script and restarts the relevant software.

[0901] Step 4:

[0902] The server sends notifications to maintenance staff if automatic repairs fail or if a critical anomaly is detected.

[0903] Input: The repair result.

[0904] Data processing: The server generates notification content and sends it via email, SMS, or a dedicated maintenance app.

[0905] Output: Informational message.

[0906] Specific action: The server sends an email using the SMTP protocol to notify the maintenance staff of the details of the anomaly and the need for action.

[0907] Step 5:

[0908] The server stores the results of self-diagnosis and automatic repair in a database and analyzes the long-term condition of the equipment.

[0909] Input: Self-diagnosis and repair results.

[0910] Data processing: The server inserts the data into the database and periodically generates analysis reports.

[0911] Output: Analysis report.

[0912] What it does: The server executes SQL queries to store the data in a database and uses Python data analysis libraries to analyze long-term equipment health.

[0913] (Application example 2)

[0914] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0915] In modern factories, the downtime of production lines due to machine breakdowns or aging is a major problem. This reduces manufacturing efficiency and increases costs. Similarly, interruptions to communication services are a major problem, and there is a demand to provide a stable communication environment. To solve these problems, it is necessary to constantly monitor the condition of machines and equipment, detect early signs of breakdowns or aging, and respond quickly.

[0916] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0917] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for machines in a factory to periodically perform self-diagnosis and detect signs of failure or aging, a means for automatically correcting the detected problems, a means for notifying maintenance staff as necessary, a means for minimizing interruptions to communication services and improving stability, and a means for minimizing costs while providing an ultra-high-speed, low-latency communication experience. This makes it possible to detect failures and aging of machines and communication equipment in a factory early and respond quickly.

[0918] "AI" stands for artificial intelligence, a technology that mimics human intelligence through machine learning and data analysis.

[0919] "Geographic information" is information relating to specific locations on the Earth, including map data and location information.

[0920] "Demographics" refers to statistical data concerning the composition and changes of the population in a particular area or group.

[0921] "Urban planning" refers to the designs and policies for planned development and development of cities.

[0922] "Demand forecasting" refers to the analysis and calculations used to predict future demand.

[0923] "Network equipment" is a general term for the hardware and software used to build a communications network.

[0924] "Communication services" refers to services for sending and receiving data and voice.

[0925] "Factory machinery" refers to production equipment and devices used in a factory.

[0926] "Self-diagnosis" is a function that allows a machine or system to check its own condition and detect abnormalities.

[0927] "Signs of failure or deterioration" refer to symptoms or signs that appear before a machine or equipment breaks down or deteriorates.

[0928] An "automatic remediation mechanism" is a feature or method for automatically repairing a detected problem.

[0929] "Maintenance staff" are specialized technicians who maintain and repair machinery and equipment.

[0930] "Interruption of communications services" means a temporary cessation of communications.

[0931] "Stability" refers to the ability of a system or service to continue to operate stably.

[0932] "Cost minimization" refers to methods or means for minimizing costs.

[0933] "Ultra-high speed and low latency" refers to the sending and receiving of data at extremely high speeds with almost no latency.

[0934] "Communication experience" refers to the experience and sensations a user has when using a communication service.

[0935] The system for carrying out this invention is configured as follows: The server has a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, and also has a means for providing high-quality communication services.

[0936] In addition, the machines in the factory will have the means to periodically perform self-diagnosis to detect signs of breakdowns or aging. They will also have the means to automatically correct any problems detected, and will also notify maintenance staff as necessary. This will minimize interruptions to communication services and improve stability.

[0937] Specifically, the server runs a self-diagnosis and auto-repair program written in Python that allows the factory's machines to periodically self-diagnose and detect abnormalities in motors or sensor failures. If detected, the auto-repair function corrects the problem and notifies maintenance staff if necessary.

[0938] The hardware used is the factory robot itself and its sensors. The software is a self-diagnosis and self-repair program written in Python. This makes it possible to detect failures and deterioration of machinery and communication equipment in the factory at an early stage and respond quickly.

[0939] A concrete example is a system in which a robot in a factory periodically performs self-diagnosis to detect motor anomalies or sensor failures. If a problem is detected, the system automatically corrects the problem and notifies maintenance staff if necessary. This system minimizes downtime of the factory's production line.

[0940] Example of a prompt to be input to a generative AI model:

[0941] Design a system where factory robots periodically self-diagnose to detect signs of failure or aging. If a problem is detected, create an application that can automatically fix the problem or notify maintenance staff if necessary.

[0942] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0943] Step 1:

[0944] The server collects sensor information from machines in the factory.

[0945] Input: Sensor information from machines in the factory

[0946] Output: Collected sensor information

[0947] Specific operation: The server periodically collects data such as temperature, vibration, and current from sensors installed on each machine.

[0948] Step 2:

[0949] The server analyzes the collected sensor information and detects abnormal values.

[0950] Input: Collected sensor information

[0951] Output: Anomaly detection results

[0952] Specific operation: The server uses statistical methods and machine learning algorithms based on the collected data to detect values ​​that are outside the normal range.

[0953] Step 3:

[0954] The server performs a self-diagnosis if an abnormal value is detected.

[0955] Input: Anomaly detection results

[0956] Output: Self-diagnosis result

[0957] Specific operation: The server performs a detailed diagnosis of each part of the machine depending on the type and severity of the abnormality, and identifies signs of malfunction or deterioration.

[0958] Step 4:

[0959] The server will attempt to automatically repair itself based on the results of the self-diagnosis.

[0960] Input: Self-diagnosis result

[0961] Output: Result of automatic repair

[0962] Specific actions: Based on the diagnostic results, the server will automatically perform tasks such as restarting the software, resetting settings, and making simple hardware adjustments.

[0963] Step 5:

[0964] The server notifies maintenance staff if the automatic repair is unsuccessful.

[0965] Input: Automatic repair result

[0966] Output: Maintenance notification

[0967] Specific behavior: If the server determines that repair is impossible or insufficient, it will send an email or alert to maintenance staff, reporting detailed diagnostic results and repair attempts.

[0968] Step 6:

[0969] The server logs all processing results for future analysis.

[0970] Input: The results of each processing step

[0971] Output: Recorded log data

[0972] Specific operation: The server records detailed information about each step, such as input data, processing results, and execution time, and stores the information in a database.

[0973] Example 3

[0974] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0975] Modern communication services require optimal deployment of network equipment, early detection of failures, and rapid repair. However, meeting these requirements requires a great deal of cost and effort, and in particular, to provide high-quality communication services, real-time monitoring and rapid response are essential. With conventional systems, it was difficult to efficiently solve these problems.

[0976] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0977] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, a means for monitoring the network status in real time, a means for detecting anomalies and identifying their causes, a means for performing automatic repair processing, and a means for notifying users of the results. This makes it possible to provide an ultra-high-speed, low-latency communication experience at the lowest cost, minimize interruptions to communication services, and improve stability.

[0978] "AI" is an abbreviation for artificial intelligence, a technology that uses techniques such as machine learning and deep learning to analyze data and derive optimal solutions.

[0979] "Network equipment layout" refers to the physical layout and layout plan of various devices and infrastructure in a communications network, and is an important element for achieving efficient data communications.

[0980] A "high-quality communication service" is a service that provides low-latency, high-speed, large-capacity data communication, and offers a stable communication environment that meets user demands.

[0981] "Real-time monitoring" is the process of constantly monitoring the network status and instantly detecting abnormalities, and is important for maintaining the health of the network.

[0982] "Anomaly detection" refers to identifying conditions that deviate from the network's normal operation, enabling early detection and countermeasures for problems.

[0983] "Cause identification" is the process of clarifying the source or cause of a detected anomaly and is a prerequisite for taking appropriate corrective action.

[0984] "Automatic recovery processing" is a process in which recovery work is automatically carried out in response to a detected abnormality, and is a function for quickly restoring normal operation of the network.

[0985] "Notifying the user of the results" refers to the process of reporting the network status and the results of the repair work to the user, and providing the information necessary for the user to understand the current situation.

[0986] "Lowest cost" refers to providing the required functions and services at the lowest possible cost, and is the pursuit of economic efficiency.

[0987] "Ultra-high speed and low latency" refers to extremely fast data communication speeds with extremely little communication latency, and is an important element in providing a high-quality communication experience.

[0988] This invention is a system that uses AI to optimize the placement of network equipment and provide high-quality communication services. A specific embodiment of this system is described below.

[0989] 1. Program Generation

[0990] The user inputs specific requirements to the system as prompts, for example, "Please generate a system with optimal network equipment layout and self-diagnosis and self-repair functions to provide 4K video streaming service."

[0991] 2. Program Processing

[0992] The server executes the generated program and performs the following processes.

[0993] Optimizing network equipment layout:

[0994] The server uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal placement of network equipment. This calculation includes geographical data, user traffic patterns, and information on existing network infrastructure. Specifically, the server inputs these data into the AI ​​model and obtains the optimal placement as the output.

[0995] Self-diagnosis function:

[0996] The server monitors the network status in real time and detects anomalies. If an anomaly is detected, the server identifies the cause and proposes appropriate measures. This function uses log data analysis and anomaly detection algorithms (e.g., machine learning models for anomaly detection).

[0997] Automatic repair function:

[0998] The server automatically takes corrective action in response to the detected anomaly, for example by reconfiguring the network or reallocating resources, using an orchestration tool (e.g., Kubernetes) and / or a scripting language (e.g., Python).

[0999] Notification of results:

[1000] After the repair process is completed, the server notifies the user of the result by email, updating the dashboard, etc. Specifically, the server reports the details of the repair process and the current network status to the user.

[1001] 3. Examples of concrete examples and prompts

[1002] Examples:

[1003] Consider a case where a user uses this system to provide a 4K video streaming service. The user inputs the following prompt to the system:

[1004] Example prompt:

[1005] "Create a system with optimal network equipment layout and self-diagnosis and self-repair functions to provide 4K video streaming services."

[1006] In this way, a system can be realized that provides the user with an ultra-high speed, low latency communication experience at the lowest cost. The flow of the specific process in the third embodiment will be described with reference to FIG.

[1007] Step 1:

[1008] The user enters a prompt sentence.

[1009] The user inputs specific requirements to the system as a prompt, for example, "Please generate a system with optimal network equipment layout and self-diagnosis and self-repair functions to provide 4K video streaming services." This prompt becomes the input to the system.

[1010] Step 2:

[1011] The server uses an AI model to calculate the optimal placement of network equipment.

[1012] The server receives the user's prompt and uses an AI model (e.g., TensorFlow or PyTorch) to calculate the optimal placement of network equipment. This calculation includes geographical data, user traffic patterns, and information on existing network infrastructure. Specifically, the server inputs these data into the AI ​​model and obtains the optimal placement as the output.

[1013] Step 3:

[1014] The server monitors the network status in real time.

[1015] The server uses a network monitoring tool (such as Nagios or Zabbix) to monitor the network status in real time. The server periodically checks the status and traffic volume of each network device to see if there are any abnormalities. This monitoring data is the input, and the presence or absence of abnormalities is the output.

[1016] Step 4:

[1017] The server detects the abnormality and identifies the cause.

[1018] The server analyzes data from the network monitoring tool and detects anomalies. If an anomaly is detected, the server uses log data analysis and anomaly detection algorithms (for example, machine learning models for anomaly detection) to identify the cause. Specifically, the server identifies the location and time of the anomaly, the extent of its impact, etc. The results of this analysis are the output.

[1019] Step 5:

[1020] The server performs an automatic repair process.

[1021] The server automatically performs repair processing for the detected anomaly. For example, this can include reconfiguring the network and reallocating resources. This processing is performed using an orchestration tool (e.g., Kubernetes) or a scripting language (e.g., Python). Specifically, the server reconfigures the part where the anomaly occurred and returns it to a normal state. The result of this repair processing is the output.

[1022] Step 6:

[1023] The server notifies the user of the result.

[1024] After the repair process is completed, the server notifies the user of the result. The notification can be done by email or by updating the dashboard. Specifically, the server reports the details of the repair process and the current network status to the user. This notification is the output.

[1025] (Application example 3)

[1026] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1027] Modern communication services require the transmission and reception of large volumes of data at high speeds and with low latency, for example in 4K or 8K high-resolution video streaming or real-time online gaming. However, conventional communication systems lack optimal layout of network equipment and sufficient self-diagnosis and automatic repair functions, which often results in degradation of communication quality and interruptions to services. Furthermore, the capital investment and maintenance costs required to solve these problems are high, so a cost-effective solution is required.

[1028] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means. In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for streaming 4K or 8K high-resolution video at ultra-high speed and low latency through an application installed on a smartphone, and a network optimization means with self-diagnosis and automatic repair functions. This makes it possible to provide a cost-effective, high-quality communication service while improving communication quality and minimizing service interruptions.

[1029] "AI" is an abbreviation for artificial intelligence, a technology that enables computer systems to mimic human intelligence to learn, reason, and self-correct.

[1030] "Geographic information" is information relating to a specific place on Earth, and includes map data and location information.

[1031] "Demographics" refers to statistical data concerning the composition and trends of the population in a particular area or group.

[1032] "Urban planning" is the activity of formulating plans for urban land use, transportation, infrastructure, etc., and managing urban development.

[1033] "Demand forecasting" is an analytical method for predicting future demand, and is based on past data and trends.

[1034] "Network equipment placement" refers to placing network equipment in the most optimal location, with the aim of improving communications quality and optimizing cost efficiency.

[1035] A "high-quality communication service" is a communication service that enables high-speed and stable data transmission and reception with few delays or interruptions.

[1036] A "smartphone" is a type of mobile phone and a multi-function device that can connect to the Internet and use applications.

[1037] An "Application" is a software program designed to provide a particular function or service.

[1038] "4K and 8K high-resolution video" refers to video with extremely high resolution, with 4K referring to a resolution of 3840 x 2160 pixels and 8K referring to a resolution of 7680 x 4320 pixels.

[1039] "Ultra-high speed and low latency" refers to a state in which data can be sent and received extremely quickly with almost no latency.

[1040] "Streaming viewing" refers to the playback of video and audio in real time over the Internet.

[1041] "Self-diagnosis" is the function of the system to check its own status and detect problems.

[1042] "Automatic repair function" is a function that automatically corrects problems that the system detects.

[1043] "Network optimization means" refers to techniques and technologies for optimizing network performance.

[1044] The following system configuration will be described as an embodiment of the present invention.

[1045] System Configuration

[1046] The server will include: a means for using AI to optimally place network equipment based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world; a means for providing high-quality communication services; a means for enabling ultra-fast, low-latency streaming of 4K or 8K high-resolution video via an application installed on a smartphone; and a network optimization means with self-diagnosis and automatic repair functions.

[1047] Program Processing

[1048] The server first uses an AI model to analyze big data such as geographic information, demographics, urban planning, and demand forecasts to determine the optimal placement of network equipment. The AI ​​model is built using machine learning frameworks such as TensorFlow.

[1049] The server then performs network self-diagnosis and auto-repair functions to provide high-quality communication services, detecting signs of failure or aging in base station equipment and automatically carrying out necessary repairs. This process uses network optimization algorithms.

[1050] In addition, the server will enable ultra-fast, low-latency streaming of 4K and 8K high-definition video through an application installed on smartphones. The application is designed to enable users to watch high-quality video in real time, automatically adjusting the optimal streaming settings according to network conditions.

[1051] Hardware and Software Used

[1052] Hardware: Servers, smartphones, base station equipment

[1053] Software: TensorFlow (AI model building), network optimization algorithms, streaming applications

[1054] Examples

[1055] For example, consider a scenario where a user wants to watch 4K video on a smartphone. In this case, the user launches an application and selects the 4K video they want to watch. The server self-diagnoses the network status, automatically repairs it if necessary, and then applies the optimal streaming settings to deliver the video. This allows the user to watch high-quality video without interruption.

[1056] Examples of prompt statements

[1057] "Please develop an application that allows users to watch 4K video on smartphones at ultra-high speeds with low latency. The application will use AI to optimize network equipment and have self-diagnosis and self-repair functions."

[1058] In this way, the present invention provides high quality communication services and realizes an environment in which users can comfortably view high resolution video.

[1059] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1060] Step 1:

[1061] The server uses the AI ​​model to analyze big data such as geographic information, demographics, urban planning, and demand forecasts to determine the optimal placement of network equipment. It receives geographic information, demographics, urban planning, and demand forecast data as input, and the AI ​​model calculates the optimal placement of network equipment based on this data. It generates optimal network equipment placement information as output.

[1062] Step 2:

[1063] The server performs self-diagnosis of the network. It receives base station equipment information as input and diagnoses the network status. Specifically, it analyzes the equipment's operating status and performance data to detect signs of failure or aging. It generates the diagnosis results as output.

[1064] Step 3:

[1065] The server performs automatic repair based on the results of self-diagnosis. It receives the diagnosis results as input and automatically executes the necessary repair work, such as restarting the failed equipment or adjusting the settings. It generates the post-repair network state as output.

[1066] Step 4:

[1067] A user launches an application installed on a smartphone and selects the 4K or 8K high-resolution video they want to watch. As input, the application receives the user's selection information and sends it to the server. As output, the application generates a request for the selected video.

[1068] Step 5:

[1069] The server receives video requests from users and distributes the video by applying optimal streaming settings. As input, it receives user request information and network status information, and adjusts streaming settings. Specifically, it optimizes the video bitrate and buffer size. As output, it generates optimized video data and sends it to the user's smartphone.

[1070] Step 6:

[1071] The user plays the received video data on the smartphone and watches high-quality video. As input, the video data sent from the server is received and played through the application. As output, the high-quality video is displayed.

[1072] In this way, through the specific operations and data flows performed at each step, users can enjoy high-quality communication services and comfortably watch high-definition video.

[1073] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1074] "Example 1"

[1075] The emotion engine recognizes emotions from the user's tone of voice, facial expressions, behavioral patterns, etc. For example, when a user is angry, their voice tone gets higher, and when they are happy, their smile increases. AI analyzes this information and recognizes the user's emotions.

[1076] "Example 2"

[1077] The method of providing communication services is optimized based on the user's emotions recognized by the emotion engine. For example, when a user is angry, the communication speed is increased to reduce stress. When a user is happy, content that allows them to share their joy is recommended.

[1078] "Example 3"

[1079] The operation of the self-diagnosis and auto-repair functions is adjusted based on the user's emotions recognized by the emotion engine. For example, when the user is angry, the system is more sensitive to detecting signs of failure and repairing them earlier. On the other hand, when the user is happy, the system minimizes the operation of the self-diagnosis and auto-repair functions to avoid interrupting communication services.

[1080] The process flow of each embodiment will be described below.

[1081] "Example 1"

[1082] Step 1: The emotion engine collects the user's tone of voice, facial expressions, behavioral patterns, etc.

[1083] Step 2: AI analyzes the collected data.

[1084] Step 3: The AI ​​recognizes the user's emotions from the analysis results and feeds that information back to the system.

[1085] "Example 2"

[1086] Step 1: The system receives feedback from the emotion engine.

[1087] Step 2: The system optimizes how the communication service is provided based on the received emotion information.

[1088] "Example 3"

[1089] Step 1: The system receives feedback from the emotion engine.

[1090] Step 2: The system adjusts the behavior of its self-diagnosis and auto-repair functions based on the received emotional information.

[1091] Example 1

[1092] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1093] In modern society, the demand for communication services is rapidly increasing, and there is a demand for optimal placement of communication infrastructure, especially in urban areas. However, conventional methods have been unable to effectively utilize big data such as geographic information, demographic statistics, urban planning, and demand forecasts, making it difficult to place appropriate network facilities in areas and times of high demand for communication, or in densely populated areas. It has also been difficult to recognize user emotions in real time and provide appropriate feedback. As a result, improvements in the quality of communication services and the efficiency of capital investment have not been fully achieved.

[1094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1095] In this invention, the server includes a means for collecting big data such as geographic information, demographic statistics, urban planning, and demand forecasts, a means for analyzing the collected data and identifying areas and time periods with high communication demand and areas with high population density, a means for generating an optimal network facility layout plan based on the analysis results, a means for collecting data such as the user's tone of voice, facial expression, and behavioral patterns, a means for analyzing the collected emotion data and recognizing the user's emotion, and a means for providing feedback to the user based on the analysis results. This makes it possible to allocate optimal network facilities in areas with high communication demand, improve the quality of communication services, and recognize the user's emotion in real time and provide appropriate feedback.

[1096] "Geographic information" is data relating to geographical locations, topography, land use, transportation networks, etc.

[1097] "Demographics" refers to data concerning the distribution of the population in a particular area, its age structure, gender, number of households, etc.

[1098] "Urban planning" refers to planning data regarding urban land use, infrastructure development, transportation planning, housing development, etc.

[1099] "Demand forecast" refers to data for predicting future fluctuations in demand, and in particular to predictive data regarding demand for communication services.

[1100] "Big data" refers to a collection of data that is so large and diverse that it cannot be handled by conventional data processing technologies.

[1101] "Network equipment" is a general term for the hardware and software required to provide telecommunications services, and includes base stations, routers, switches, etc.

[1102] "Analysis" is the process of analyzing collected data using statistical methods and machine learning algorithms to extract useful information.

[1103] "Communications demand" refers to the amount of usage and necessity of communication services in a specific area or time period.

[1104] "Vocal tone" refers to characteristics such as pitch, strength, and rhythm of the voice, and is an element related to the expression of emotions.

[1105] "Facial expression" refers to emotions and intentions expressed through the movement of facial muscles.

[1106] "Behavioral patterns" refer to the user's behavioral tendencies and habits, and include characteristics of reactions and actions in specific situations.

[1107] "Emotion data" is data that indicates the user's emotional state, and includes tone of voice, facial expressions, behavior patterns, and the like.

[1108] "Feedback" refers to information or advice provided to the user based on the analysis results.

[1109] This invention is a system that optimally allocates network facilities by collecting and analyzing big data such as geographic information, demographic statistics, urban planning, and demand forecasts. It also includes a function to collect data such as the user's tone of voice, facial expressions, and behavioral patterns, recognize emotions, and provide feedback.

[1110] Hardware and Software Used

[1111] server

[1112] The server collects big data such as geographic information, demographics, urban planning, and demand forecasts. This includes public government data, commercial databases, and sensor data. APIs and database connections are used to collect data. The collected data is analyzed using big data processing frameworks such as Hadoop and Spark. Specific analysis methods include clustering and regression analysis.

[1113] Terminal

[1114] The device collects data such as the user's tone of voice, facial expressions, and behavioral patterns. This is done using hardware such as a microphone and a camera. The collected data is temporarily stored in local storage within the device and later sent to a server. The device provides data to recognize the user's emotions in real time.

[1115] User

[1116] Users provide data to the device through their daily actions, such as the tone of voice when the user speaks, changes in facial expressions, and behavioral patterns. This provides data for the emotion engine to recognize the user's emotions.

[1117] Examples

[1118] For example, suppose a new residential area is planned to be developed in a city. The server analyzes urban planning data and predicts that the population in that area will increase in the future. Based on this, the server makes a plan to prioritize the placement of network equipment in that area. Also, when a user is speaking to the terminal, the terminal collects the tone of the user's voice with a microphone and sends it to the server. The server uses a voice analysis algorithm to detect that the tone of the user's voice is getting higher and recognizes that the user is angry. Based on this, the server displays advice such as "Take a deep breath to relax" through the terminal.

[1119] Examples of prompt statements

[1120] "Develop a plan for optimal network deployment in an area where new housing developments are planned. Also, explain how to analyze the tone of a user's voice to recognize emotions when they are angry."

[1121] In this way, by clarifying the roles of servers, terminals, and users, and naming specific hardware and software, the processing of the system's programs can be explained in natural language.

[1122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1123] Step 1:

[1124] The server collects big data such as geographic information, demographics, urban planning, and demand forecasts. As input, it uses public government data, commercial databases, and sensor data. These data are obtained through APIs and database connections. As output, the collected data is stored in a database. In concrete terms, the server periodically calls the API to obtain new data and add it to the database.

[1125] Step 2:

[1126] The server analyzes the collected data using big data processing frameworks such as Hadoop or Spark. The data collected in step 1 is used as input. Methods such as clustering and regression analysis are used for data analysis. As output, areas and time periods with high communication demand, as well as areas with high population density, are identified. In concrete terms, the server runs Spark jobs, analyzes the data, and obtains the results.

[1127] Step 3:

[1128] The server generates an optimal network equipment placement plan based on the analysis results. The analysis results obtained in step 2 are used as input. The network equipment placement plan is generated as output. In concrete terms, the server executes an algorithm based on the analysis results to create an optimal placement plan.

[1129] Step 4:

[1130] The device collects data such as the user's tone of voice, facial expressions, and behavioral patterns. As input, the device uses the user's audio and video data. This is achieved using hardware such as a microphone and a camera. As output, the collected data is temporarily stored in local storage. In concrete terms, the device collects audio with a microphone and captures facial expressions with a camera.

[1131] Step 5:

[1132] The server analyzes the emotion data sent by the device. As input, it uses the data collected in step 4. Voice analysis and image analysis techniques are used for the data analysis. As output, the user's emotion is recognized. Specifically, the server runs a voice analysis algorithm to analyze the tone of the user's voice to determine the emotion.

[1133] Step 6:

[1134] The server provides feedback to the user based on the analysis results. As input, it uses the emotion analysis results obtained in step 5. As output, feedback for the user is generated. In concrete terms, the server generates appropriate advice or information based on the analysis results and displays it to the user via the terminal.

[1135] (Application example 1)

[1136] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1137] Conventional network equipment placement systems can optimize placement by utilizing big data such as geographic information and demographic statistics, but there are limitations to improving the quality of communication services and the efficiency of capital investment. In addition, autonomous vehicles cannot recognize passenger emotions and respond appropriately, making it difficult to ensure passenger comfort.

[1138] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for optimally arranging NW facilities based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for analyzing passengers' tone of voice, facial expressions, and behavioral patterns to recognize their emotions, a means for responding according to the emotions, and a means for selecting an optimal route in real time. This not only makes it possible to improve the quality of communication services and streamline capital investment, but also makes it possible to ensure passenger comfort by recognizing passenger emotions in an autonomous vehicle and responding appropriately.

[1139] "AI" is an abbreviation for artificial intelligence, a technology that allows computers to mimic human intelligence to learn, reason, and self-correct.

[1140] "Geographic information" is data relating to geographic location, topography, land use, etc., and includes maps and GPS data.

[1141] "Demographic statistics" are data concerning the distribution, composition, and dynamics of the population in a particular area.

[1142] "Urban planning" refers to planning for the development and management of cities, including land use, infrastructure development, and transportation planning.

[1143] "Demand forecasting" is a data analysis method for predicting future fluctuations in demand, and is based on economic activity, consumer behavior, etc.

[1144] "NW equipment" is an abbreviation for network equipment, and refers to the hardware and software used to build and operate a communications network.

[1145] "Communication services" refers to various services provided via a network, such as data communications and voice communications.

[1146] "Tone of voice" refers to elements that indicate characteristics of the voice, such as pitch, strength, and intonation.

[1147] "Facial expressions" are those that indicate emotions or intentions expressed through the movement of facial muscles.

[1148] A "behavioral pattern" refers to a tendency or habit of human behavior in a particular situation.

[1149] "Emotion recognition" is a technology that analyzes and identifies human emotions from voice, facial expressions, behavioral patterns, etc.

[1150] An "optimal route" refers to the most efficient path to reach a particular destination.

[1151] "Real-time" refers to data processing and information being provided immediately.

[1152] "Self-diagnosis" is a function that allows a system to monitor its own status and detect abnormalities or failures.

[1153] "Automatic repair" is a function that automatically corrects abnormalities or failures that the system detects.

[1154] "Ultra-high speed and low latency" refers to data communication that is carried out at extremely high speeds with extremely little communication latency.

[1155] The following system configuration and processing procedure will be described as an embodiment of the present invention.

[1156] System Configuration

[1157] The system includes the following main components:

[1158] 1. Server: Analyzes big data such as geographic information, demographic statistics, urban planning, and demand forecasts to optimize network (NW) equipment layout.

[1159] 2. Self-driving cars: They will be equipped with cameras and microphones to analyse passenger tone of voice, facial expressions and behavioural patterns to recognise emotions.

[1160] 3. Emotion recognition engine: Software that analyzes passenger emotions and responds appropriately.

[1161] 4. Navigation system: Software for selecting the optimal route in real time.

[1162] Hardware and Software Used

[1163] Camera: Used to capture passengers' facial expressions.

[1164] Microphone: Used to capture the passenger's tone of voice.

[1165] Self-driving car computers: Used for data analysis and emotion recognition.

[1166] Python: Used to implement the program.

[1167] Pandas: Used to load and preprocess data.

[1168] KMeans (scikit-learn): A clustering algorithm.

[1169] Keras: An implementation of an emotion recognition model.

[1170] OpenCV: Face detection and video processing.

[1171] Data processing and calculation

[1172] The server integrates big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and removes missing values. It then uses a clustering algorithm to determine the optimal network equipment layout. The autonomous vehicle's computer uses data acquired from the camera and microphone to analyze passenger emotions with an emotion recognition engine. Based on the analysis results, it takes appropriate action. The navigation system also selects the optimal route in real time.

[1173] Examples

[1174] For example, if a passenger is upset, the system will automatically play relaxing music, and if the passenger is happy, no action is required.

[1175] Examples of prompt statements

[1176] "Generate a program that recognizes passengers' emotions and plays relaxing music if they are angry."

[1177] The above is an embodiment of the present invention.

[1178] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] The server collects big data such as geographic information, demographics, urban planning, and demand forecasts. These data are obtained from various databases and APIs. The inputs include geographic information data, demographics data, urban planning data, and demand forecast data. The server integrates these data and performs preprocessing to remove missing values. The output is the preprocessed integrated data.

[1181] Step 2:

[1182] The server runs a clustering algorithm (KMeans) using the preprocessed integrated data. The input includes the preprocessed integrated data. The server performs clustering and determines the optimal network (NW) equipment layout. The output is location information for the optimal NW equipment layout.

[1183] Step 3:

[1184] The autonomous vehicle's terminal uses a camera and microphone to capture passengers' facial expressions and tone of voice. The inputs include real-time video and audio data. The terminal sends these data to an emotion recognition engine. The output is the captured video and audio data.

[1185] Step 4:

[1186] The autonomous vehicle terminal uses an emotion recognition engine to analyze the passenger's emotions. Inputs include captured video and audio data. The terminal uses an emotion recognition model (Keras) to predict the passenger's emotions. The output is the passenger's emotional state (e.g., anger, joy).

[1187] Step 5:

[1188] The autonomous vehicle's terminal responds according to the passenger's emotional state. The input includes the passenger's emotional state. For example, if the passenger is angry, the terminal plays relaxing music. The output is the execution of a response according to the passenger's emotion.

[1189] Step 6:

[1190] The autonomous vehicle's terminal uses a navigation system to select the optimal route in real time. The inputs include geographical and traffic information data. The terminal analyzes this data and calculates the optimal route. The output is optimal route information updated in real time.

[1191] The above are the specific process steps for carrying out the present invention.

[1192] Example 2

[1193] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1194] Conventional communication systems had problems with frequent interruptions to communication services due to failures and aging of base station equipment, reducing stability. In addition, there was a lack of technology to optimize communication services based on user emotions, making improving the user experience a challenge. Furthermore, it was difficult to optimally allocate network equipment and provide high-quality communication services, and there was a demand to simultaneously achieve the lowest costs and an ultra-high-speed, low-latency communication experience.

[1195] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1196] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for periodically executing a self-diagnosis program for base station equipment, a means for analyzing the diagnosis results and detecting abnormalities, a means for starting an automatic repair function when an abnormality is detected, a means for notifying a maintenance staff when automatic repair is impossible, a means for starting an emotion engine for recognizing the user's emotions, a means for analyzing the user's voice and facial expressions and identifying the emotion, a means for optimizing the method of providing communication services based on the recognized emotion, and a means for recommending content for sharing the user's joy when the user is happy. This makes it possible to improve the stability of communication services, optimize the user experience, and simultaneously realize the lowest cost and ultra-high-speed, low-latency communication experience.

[1197] "AI" stands for artificial intelligence, a technology that enables computer systems to mimic human intelligence to learn, reason, and self-correct.

[1198] "Geographic information" is data about specific places on Earth, including maps and location information.

[1199] "Demographics" refers to statistical data concerning the composition and changes of the population in a particular area or group.

[1200] "Urban planning" refers to the designs and policies for the planned development of a city's land use, transportation, infrastructure, etc.

[1201] "Demand forecasting" is an analytical method for predicting future demand, and is based on past data and trends.

[1202] "Network equipment" is a general term for the hardware and software used to configure a communications network.

[1203] A "base station facility" is a facility for communicating with mobile communication terminals in a wireless communication network.

[1204] A "self-diagnostic program" is software that enables a system or equipment to check its own status and detect abnormalities.

[1205] An "automatic repair function" is a function that enables the system to automatically correct abnormalities.

[1206] "Maintenance staff" refers to engineers in charge of maintaining and repairing systems and equipment.

[1207] An "emotion engine" is software that recognizes the user's emotions and has the ability to analyze voice and facial expressions.

[1208] A "communication service" is a service for sending and receiving information such as voice, data, and video.

[1209] "Content" refers collectively to information and entertainment provided to users.

[1210] "Big data" refers to data sets that are so large and complex that they cannot be handled by traditional data processing methods.

[1211] A "high-quality communication service" is a communication service that has characteristics such as stable connection, low latency, and high-speed data transfer.

[1212] "Ultra-high speed and low latency" refers to extremely high data transfer speeds and extremely short communication latency.

[1213] The present invention provides a technique for improving the stability and user experience of a communication system. A specific embodiment of this system will be described below.

[1214] Self-diagnosis and automatic repair function for base station equipment

[1215] 1. The server periodically executes a self-diagnosis program for the base station equipment. This program is used to check the status of the hardware and software of the base station equipment and detect signs of failure or aging. The hardware used includes general base station equipment, specifically various sensors and monitoring devices. The software used includes the self-diagnosis program.

[1216] Example: A server schedules a self-diagnosis program to run every day at 2 a.m. The program collects data on CPU usage, memory usage, and temperature sensors to detect abnormal values.

[1217] 2. The server analyzes the diagnostic results and detects any abnormalities. If an abnormality is detected, it launches an automatic repair function that restarts the software, resets settings, and so on to fix the problem.

[1218] Example: If the server detects a memory leak, it restarts the relevant process.

[1219] 3. If automatic repair is not possible, the server notifies the maintenance staff. This notification is sent via email and / or SMS.

[1220] Example: The server detects a hardware failure and notifies the maintenance staff that "there is an abnormality in the power supply unit of base station 123."

[1221] Optimizing communication services with emotion engine

[1222] 1. The device is equipped with an emotion engine for recognizing the user's emotions. This engine is used to analyze the user's voice and facial expressions to identify emotions. The hardware used includes devices such as smartphones and tablets. The software used includes the emotion recognition engine.

[1223] Example: When a user launches an app, the device launches an emotion engine, which uses the camera and microphone to collect the user's facial expressions and voice.

[1224] 2. The device analyzes the user's voice and facial expressions to identify emotions. It optimizes the way it provides communication services based on the recognized emotions. For example, if the user is angry, it will increase the communication speed.

[1225] Example: The device determines that the user is angry and increases the communication speed to 1Gbps.

[1226] 3. If the user is happy, the device will recommend content to help them share their joy.

[1227] Example: The device determines that the user is happy and displays a pop-up to encourage sharing on social media, for example displaying a message saying, "Would you like to share this moment with your friends?"

[1228] Examples of prompt statements

[1229] "Please explain the specific processing steps and operations of the self-diagnosis and automatic repair functions of base station equipment."

[1230] Please explain the specific process steps and operations of how to use an emotion engine to optimize communication services based on user emotions.

[1231] In this way, it is possible to improve the stability of the communication system and optimize the user experience.

[1232] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1233] Self-diagnosis and automatic repair function for base station equipment

[1234] Step 1:

[1235] The server periodically runs a self-diagnostic program.

[1236] Input: Base station equipment hardware and software status data (e.g. CPU usage, memory usage, temperature sensor data)

[1237] Data processing: The server collects these data and records them in log files.

[1238] Output: Log file of diagnostic results

[1239] Specific Actions: The server will schedule a self-diagnosis program to run every day at 2:00 AM.

[1240] Step 2:

[1241] The server analyzes the diagnostic results and detects any abnormalities.

[1242] Input: Diagnostics log file

[1243] Data calculation: The server compares the collected data with the set threshold.

[1244] Output: Anomaly detection flag (e.g., if CPU usage exceeds 90%, it is determined to be an anomaly)

[1245] Specific operation: The server analyzes the data in the log file and detects outliers.

[1246] Step 3:

[1247] If an abnormality is detected, the server will initiate an automatic repair function.

[1248] Input: Anomaly detection flag

[1249] Data manipulation: The server takes action to correct the anomaly (e.g. restarting the software, resetting settings).

[1250] Output: Log file of repair results

[1251] Specific behavior: If the server detects a memory leak, it will restart the relevant process.

[1252] Step 4:

[1253] The server will notify maintenance staff if automatic repair is not possible.

[1254] Input: Log file of repair results

[1255] Data processing: The server generates notification messages and sends them to the maintenance staff.

[1256] Output: Notification message (e.g. "There is an abnormality in the power supply unit of base station 123")

[1257] Specific action: The server detects a hardware failure and sends an email to the maintenance staff.

[1258] Optimizing communication services with emotion engine

[1259] Step 1:

[1260] The terminal activates an emotion engine for recognizing the user's emotions.

[1261] Input: User's voice and facial expression data

[1262] Data processing: The device uses a camera and microphone to collect the user's facial expressions and voice.

[1263] Output: A dataset for emotion recognition.

[1264] Specific operation: The device launches the emotion engine when the user launches the app.

[1265] Step 2:

[1266] The device analyzes the user's voice and facial expressions to identify emotions.

[1267] Input: Dataset for emotion recognition

[1268] Data calculation: The device sends the collected data to an emotion recognition engine to identify the user's emotions.

[1269] Output: Emotion recognition results (e.g. anger, joy, sadness, etc.)

[1270] Specific behavior: The device detects anger from changes in voice tone and facial expressions.

[1271] Step 3:

[1272] The terminal optimizes the way in which it provides communication services based on the recognized emotions.

[1273] Input: Emotion recognition results

[1274] Data processing: The device changes communication service settings based on the recognized emotions (e.g. throttling communication speeds).

[1275] Output: Optimized communication service settings

[1276] Specific behavior: The device determines that the user is angry and increases the communication speed to 1Gbps.

[1277] Step 4:

[1278] If the user is happy, the terminal recommends content to share that happiness.

[1279] Input: Emotion recognition results

[1280] Data processing: The device selects appropriate content based on the user's emotions.

[1281] Output: Content recommendation message

[1282] Specific behavior: The device determines that the user is happy and displays a pop-up encouraging them to share on social media.

[1283] (Application example 2)

[1284] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1285] In modern communication systems, maintaining the stability and quality of communication services is an important issue. In particular, in autonomous vehicles, communication interruptions and delays directly affect safety, so self-diagnosis and self-repair functions are required for the communication system. In addition, providing services according to passenger emotions also contributes to improving the user experience. However, there is still no system that realizes these functions in an integrated manner.

[1286] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1287] In this invention, the server includes: means for optimally arranging network equipment using AI based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world; means for providing high-quality communication services; means for introducing self-diagnosis and automatic repair functions from base station equipment information and detecting signs of failure or aging; means for having self-diagnosis and automatic repair functions for the communication system and recognizing passenger emotions to optimize in-car entertainment and communication services; means for minimizing interruptions to communication services and improving stability; and means for minimizing costs while providing an ultra-high-speed, low-latency communication experience. This makes it possible to provide optimal services according to passenger emotions while maintaining the stability and quality of the communication system.

[1288] "AI" is an abbreviation for artificial intelligence, a technology that allows computers to imitate human intelligence to learn and make inferences.

[1289] "Geographic information" is information relating to a specific location on the Earth, and includes map data and location information.

[1290] "Demographics" refers to statistical data concerning the composition and trends of the population in a particular area or group.

[1291] "Urban planning" refers to the activities and plans for planning and managing urban land use and infrastructure development.

[1292] "Demand forecasting" refers to the analysis and calculations used to predict future demand, and plays an important role in business and economic activities.

[1293] "Network equipment" is a general term for the hardware and software used to configure a communications network.

[1294] The term "base station equipment" refers to fixed communication equipment for communicating with mobile communication terminals in a wireless communication network.

[1295] "Self-diagnosis" is a function that enables a system or device to check its own status and detect abnormalities or failures.

[1296] "Automatic repair" is a function that automatically corrects abnormalities or failures detected by a system or device.

[1297] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions, voice, etc.

[1298] "In-car entertainment" is a general term for entertainment content and services provided within a vehicle.

[1299] "Communication services" refers to services for sending and receiving data, and includes Internet connections and voice calls.

[1300] "Ultra-high speed and low latency" means extremely high data communication speeds and extremely low communication latency.

[1301] The system for implementing this invention has the following configuration. The server has a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world. This enables efficient arrangement of communication networks.

[1302] The server also has the means to provide high-quality communication services and provide users with a stable communication environment. Furthermore, it has the means to introduce self-diagnosis and automatic repair functions from base station equipment information and to detect signs of failure or aging. This improves the stability of the communication system and minimizes interruptions to communication services.

[1303] In addition, the server has the ability to self-diagnose and self-repair the communication system, and has the means to recognize passenger emotions and optimize in-car entertainment and communication services. This allows the server to provide optimal services according to the passenger's emotions. For example, if a passenger is angry, the server can increase communication speed to reduce stress, and if a passenger is happy, the server can recommend content that will help the passenger share their joy.

[1304] The following hardware and software are used to realize this system. For hardware, a smartphone and a head-mounted display are used. For software, Python and emotion recognition libraries (e.g., OpenCV and TensorFlow) are used.

[1305] Specifically, the server periodically performs self-diagnosis of the communication system and attempts to automatically repair it if an abnormality is detected. It also uses an emotion recognition library to analyze the user's facial expressions and voice to recognize their emotions. Based on this, it provides optimal communication services and entertainment content.

[1306] Concrete examples of this could include the following scenarios: If a passenger is angry, increase the speed of communication to reduce stress If a passenger is happy, recommend content that helps them share their joy.

[1307] Examples of prompts to be input to a generative AI model include the following:

[1308] "If users are angry, tell them how to make their connection faster."

[1309] "If a user is happy, how can we recommend content that helps them share that happiness?"

[1310] Thus, specific embodiments for carrying out the invention are provided.

[1311] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1312] Step 1:

[1313] The server uses AI to collect big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world. Inputs include information from various databases and public data sources. These data are analyzed to calculate the optimal network equipment layout. The output is an optimal network equipment layout plan. Specific operations include data collection, data analysis, and application of optimization algorithms.

[1314] Step 2:

[1315] The server constructs a communication infrastructure based on the network equipment deployment plan to provide high-quality communication services. The input includes the deployment plan obtained in step 1. The output is the completion of the construction of the actual communication infrastructure. Specific operations include the installation, configuration, and testing of the network equipment.

[1316] Step 3:

[1317] The server periodically collects base station equipment information and performs self-diagnosis. Input includes status information from the base stations. Output provides a health status report for the base stations. Specific operations include data collection, application of anomaly detection algorithms, and report generation.

[1318] Step 4:

[1319] The server attempts to automatically heal itself based on the results of the self-diagnosis. Inputs include the health report from step 3. Outputs include a normal state if the repair was successful, or a maintenance notification if repair is required. Specific actions include applying a repair algorithm, performing a repair operation, and sending a notification.

[1320] Step 5:

[1321] The device uses an emotion recognition library to analyze the user's facial expressions and voice to recognize the user's emotions. Inputs include the user's facial expression data and voice data. Outputs include the user's emotional state. Specific operations include data collection, application of emotion recognition algorithms, and determination of the emotional state.

[1322] Step 6:

[1323] The server provides optimal communication services and entertainment contents based on the user's emotional state. The input includes the emotional state obtained in step 5. The output includes optimized communication services and recommended contents. Specific operations include applying a service optimization algorithm, running a content recommendation engine, and providing the service.

[1324] Step 7:

[1325] The user uses the provided communication services and entertainment content. The inputs include the services and content provided in step 6. The outputs include the user's satisfaction and feedback. Specific actions include using the service, watching the content, and providing feedback.

[1326] Example 3

[1327] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[1328] In conventional communication systems, the quality and stability of communication services can decline due to insufficient optimal placement of network equipment and detection of signs of failure. In addition, because dynamic system adjustments based on user emotions are not performed, there is an issue that the user experience is uniform and cannot respond to individual needs. Furthermore, it is difficult to provide a high-speed, low-latency communication experience while keeping communication costs down.

[1329] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1330] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for recognizing user emotions and adjusting the operation of self-diagnosis and automatic repair functions based on the emotions, and a means for providing a high-speed, low-latency communication experience. This enables optimal arrangement of network equipment and early detection of signs of failure, improving the quality and stability of communication services. In addition, dynamic system adjustment based on user emotions makes it possible to provide a high-quality communication experience that meets individual needs. Furthermore, it becomes possible to realize a high-speed, low-latency communication experience while suppressing communication costs.

[1331] "AI" stands for artificial intelligence, a technology that automatically makes judgments and predictions through machine learning and data analysis.

[1332] "Network equipment" is a general term for hardware and software used to configure a communications network, and includes routers, switches, base stations, etc.

[1333] "Optimal placement" is a placement method that maximizes communication performance and minimizes costs by efficiently placing network facilities.

[1334] "Big data" refers to extremely large and complex data sets, amounts of data that cannot be handled by traditional data processing techniques.

[1335] A "high-quality communication service" is a communication service that excels in such areas as stability, speed, and low latency, and provides users with a comfortable communication experience.

[1336] "User emotion" refers to the psychological state that the user is feeling, such as joy, anger, sadness, etc., and is information that is used to adjust the system's operation by recognizing this state.

[1337] "Self-diagnosis" is a function that enables the system to check its own status and detect signs of abnormalities or failures.

[1338] The "automatic repair function" is a function that automatically repairs any abnormalities or failures detected by the system, in order to minimize interruptions to communication services.

[1339] A "high-speed, low-latency communication experience" is one in which data is sent and received quickly and with extremely little communication latency.

[1340] "Geographic information" refers to information about geographic locations and topography, and is used to optimize the placement of network facilities.

[1341] "Demographics" refers to data about the distribution and composition of the population in a particular area, and is used for demand forecasting and the placement of network facilities.

[1342] "Urban planning" refers to plans for the development and development of cities, and influences the long-term placement strategy of network facilities.

[1343] "Demand forecasting" refers to predicting future demand for communications, and is useful for optimal placement of network equipment and efficient use of resources.

[1344] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1345] This invention is a system that provides high-quality communication services by utilizing AI technology to optimize the placement of network equipment and adjusts self-diagnosis and automatic repair functions based on user emotions. A specific embodiment of this system will be described below.

[1346] 1. Generating the system program

[1347] The server uses AI algorithms to calculate the optimal placement of network equipment. The AI ​​algorithms are implemented using machine learning frameworks such as TensorFlow and PyTorch. The server collects network traffic data and equipment operation status data, and determines the optimal placement based on this data.

[1348] The device is equipped with an emotion engine that recognizes the user's emotions in real time. The emotion engine is implemented using emotion recognition software such as OpenCV and Emotion API. The device transmits the user's emotion data to the server.

[1349] 2. Explanation of program processing

[1350] The server adjusts the operation of the self-diagnosis and auto-repair functions based on the received emotion data. For example, when the user is angry, the server is more sensitive to detect signs of failure and performs early repairs. On the other hand, when the user is happy, the server minimizes the operation of the self-diagnosis and auto-repair functions to avoid interrupting communication services.

[1351] The servers provide high-speed, low-latency communication services based on optimal network equipment layout and coordinated self-diagnosis and automatic repair functions, allowing users to send and receive large amounts of data with low latency, such as 4K and 8K video streaming and real-time online games.

[1352] 3. Examples of concrete examples and prompts

[1353] As a concrete example, consider services that require high-speed transmission and reception of large amounts of data, such as 4K or 8K video streaming and real-time online games. By using this system, users can enjoy these services with low latency.

[1354] Example of a prompt to be input to a generative AI model:

[1355] "Please explain how to detect early signs of network equipment failure and quickly repair it when users are angry."

[1356] Using this prompt sentence, the generative AI model can provide detailed instructions on how to manage network equipment based on the user's emotions.

[1357] As described above, the present invention combines AI technology and emotion recognition technology to realize optimal placement of network facilities and dynamic system adjustment, thereby providing high-quality communication services. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1358] Step 1: Recognizing user emotions

[1359] The device captures the user's face with a camera and records the user's voice with a microphone. As input, it obtains the user's facial expression data and voice data. The device uses OpenCV to analyze facial expressions and Emotion API to recognize emotions from voice. This outputs the user's emotional state, such as whether they are angry, happy, or sad.

[1360] Step 2: Calculate the optimal layout of network equipment

[1361] The server collects network traffic data and equipment operation status data. It receives these data as input. The server uses machine learning frameworks such as TensorFlow and PyTorch to calculate the optimal network equipment placement. As a result of the calculation, it outputs a diagram of the optimal network equipment placement.

[1362] Step 3: Adjust the self-diagnosis and auto-repair functions

[1363] The server receives the user's emotional data. As input, the server receives the user's emotional state. If the user is angry, the server increases the sensitivity of the self-diagnosis function to detect the signs of failure more sensitively. If the user is happy, the server minimizes the operation of the self-diagnosis and auto-repair functions to avoid interruption of the communication service. This results in outputting the adjusted self-diagnosis and auto-repair function settings.

[1364] Step 4: Providing high-speed, low-latency communications

[1365] The server provides high-speed, low-latency communication services based on optimal network equipment layout and adjusted self-diagnosis and auto-repair functions. As input, it receives an optimal network equipment layout diagram and settings for adjusted self-diagnosis and auto-repair functions. This enables users to send and receive large amounts of data with low latency, such as 4K or 8K video streaming and real-time online games. As output, it provides high-speed, low-latency communication services.

[1366] (Application example 3)

[1367] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1368] Conventional communication systems require huge costs for the deployment and maintenance of network equipment, and often suffer from poor communication quality and delays. In addition, they are unable to flexibly respond to user emotions, leading to problems with interruptions and instability in communication services. This has led to issues with the degradation of user experience, particularly in services that require the high-speed transmission and reception of large amounts of data, such as high-quality video streaming and real-time online games.

[1369] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1370] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for recognizing a user's emotions in real time and automatically adjusting network settings, a means for adjusting the operation of self-diagnosis and automatic repair functions based on the emotions, and a means including an application installed on a smartphone, smart glasses, a head-mounted display, or a robot. This makes it possible to provide an ultra-high-speed, low-latency communication experience at the lowest cost and to respond flexibly to the user's emotions.

[1371] "AI" stands for artificial intelligence, a technology that allows computers to imitate human intelligence and learn and make inferences.

[1372] "Geographic information" is information relating to a specific place on the earth, and includes map data and location information.

[1373] "Demographics" refers to statistical data concerning the composition and changes of the population in a particular area or group.

[1374] "Urban planning" refers to the activities and plans for planning and managing urban land use and infrastructure development.

[1375] "Demand forecasting" is the analysis and calculation used to predict future demand, and plays an important role in business and economic activities.

[1376] "Network equipment placement" refers to placing network equipment in optimal locations, and is a means of improving communications quality and cost efficiency.

[1377] A "high-quality communication service" is a service that provides low-latency, high-speed, large-capacity data communication.

[1378] "User emotion" refers to an emotional state such as happiness, anger, sadness, etc., felt by a user.

[1379] "Real-time" refers to instantaneous processing or reaction without delay.

[1380] "Network settings" refer to various settings and parameters for controlling the operation of a communications network.

[1381] "Self-diagnosis" is a function that enables the system to check its own status and detect signs of abnormalities or failures.

[1382] An "automatic repair function" is a function that enables a system to automatically repair failures or abnormalities.

[1383] A "smartphone" is a type of mobile phone, a multi-function device that can connect to the Internet and use applications.

[1384] "Smart glasses" are a wearable device in the form of glasses that has the functions of displaying information and communicating with others.

[1385] A "head-mounted display" is a display device worn on the head and is a device for providing visual information.

[1386] A "robot" is a mechanical device that operates autonomously and performs specific tasks.

[1387] An "application" is a software program designed to provide a specific function or service.

[1388] The following system configuration will be described as an embodiment of the present invention.

[1389] The servers use AI to optimize network equipment layout based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, improving communication quality while minimizing capital investment and maintenance costs.

[1390] The device (smartphone, smart glasses, head-mounted display, or robot) recognizes the user's emotions in real time and automatically adjusts network settings. Emotion recognition is performed using software called EmotionRecognizer, which analyzes the user's facial expressions through a camera. Network settings are adjusted using software called NetworkOptimizer.

[1391] Specifically, when a user is emotionally excited while watching a movie, the network's self-diagnosis and auto-repair functions will be more responsive to prevent communication interruptions, allowing users to enjoy high-quality video streaming with low latency.

[1392] The server also adjusts the operation of the self-diagnosis and auto-repair functions based on the user's emotions. For example, when the user is angry, the server is more sensitive to detecting signs of failure and repairing them earlier. On the other hand, when the user is happy, the server minimizes the operation of the self-diagnosis and auto-repair functions to avoid interrupting the communication service.

[1393] Here are some example prompts using a generative AI model to provide optimal network settings based on the user’s emotions:

[1394] If a user is watching a movie and is emotional, how can the network's self-diagnosis and auto-repair capabilities be more responsive and prevent communication interruptions?

[1395] In this way, it is possible to provide an ultra-fast, low-latency communication experience at the lowest cost, and to respond flexibly to user emotions.

[1396] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1397] Step 1:

[1398] The server uses AI to collect big data such as geographic information, demographics, urban planning, and demand forecasts from around the world, and calculates the optimal network equipment layout. It receives geographic information, demographics, urban planning, and demand forecast data as input, and generates an optimal network equipment layout plan as output. Specifically, it analyzes this data and determines the layout that maximizes communication quality while minimizing costs.

[1399] Step 2:

[1400] The device uses a camera to capture the user's facial expressions and uses EmotionRecognizer software to recognize emotions in real time. It receives camera footage as input and generates the user's emotional state (happiness, anger, sadness, etc.) as output. Specifically, it analyzes the video data, extracts facial features, and classifies emotions.

[1401] Step 3:

[1402] The device automatically adjusts network settings based on the user's recognized emotions using the NetworkOptimizer software. It takes the user's emotional state as input and generates optimal network settings as output. Specifically, it adjusts communication priority and bandwidth according to the user's emotional state to optimize communication quality.

[1403] Step 4:

[1404] The server performs self-diagnosis and automatic repair functions based on network equipment information to detect signs of failure or aging. It receives network equipment information as input and generates detection results of signs of failure as output. Specifically, it analyzes equipment operation data and detects abnormal patterns.

[1405] Step 5:

[1406] The server adjusts the operation of the self-diagnosis and auto-repair functions based on the user's emotions. It receives the user's emotional state and the results of the detection of signs of failure as input, and generates an adjusted operation plan for self-diagnosis and auto-repair as output. Specifically, if the user is angry, the server will be more sensitive to detect signs of failure and perform repairs early, while if the user is happy, the server will minimize the operation to avoid interrupting the communication service.

[1407] Step 6:

[1408] The terminal provides users with high-quality communication services based on the adjusted network settings and self-diagnosis and automatic repair functions. It receives the adjusted network settings and operation plan as input and provides a low-latency, high-quality communication experience as output. Specifically, it applies the network settings and performs self-diagnosis and automatic repair when necessary to maintain the stability and quality of communication.

[1409] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1410] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1411] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1412] 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 the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1413] [Third embodiment]

[1414] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1415] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1416] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1417] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1418] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1419] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1420] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1421] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1422] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1423] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1424] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1425] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1426] "Example 1"

[1427] In one embodiment of the present invention, the AI ​​performs optimal network equipment placement based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts. Specifically, the AI ​​analyzes these data and places network equipment preferentially in areas and time periods with high communication demand, areas with high population density, etc. In addition, the AI ​​uses urban planning data to plan the placement of network equipment based on predictions of future population movement and urban development. This improves the quality of communication services and makes it possible to make capital investments more efficient.

[1428] "Example 2"

[1429] Furthermore, in one embodiment of the present invention, a self-diagnosis and automatic repair function is introduced from the base station equipment information. Specifically, the base station equipment periodically performs self-diagnosis to detect signs of failure or aging. If detected, the automatic repair function corrects the problem or notifies maintenance staff as necessary. This makes it possible to minimize interruptions to communication services and improve stability.

[1430] "Example 3"

[1431] In addition, one embodiment of the present invention provides an ultra-high-speed, low-latency communication experience while minimizing costs. Specifically, by introducing optimal network equipment layout using AI and self-diagnosis and automatic repair functions, high-quality communication services are provided while minimizing capital investment and maintenance costs. For example, it becomes possible to provide services that require high-speed transmission and reception of large amounts of data, such as 4K and 8K video streaming and real-time online games, with low latency.

[1432] The process flow of each embodiment will be described below.

[1433] "Example 1"

[1434] Step 1: AI collects big data such as geographic information, demographics, urban planning, and demand forecasts.

[1435] Step 2: Analyze the collected data and identify areas, time periods with high demand for communications, areas with high population density, etc.

[1436] Step 3: Based on the identified information, prioritize the placement of network facilities.

[1437] Step 4: Using urban planning data, predict future population movements and urban development

[1438] Plan the layout of network equipment.

[1439] "Example 2"

[1440] Step 1: The base station equipment periodically performs self-diagnosis.

[1441] Step 2: Detect signs of malfunction or deterioration through self-diagnosis.

[1442] Step 3: If detected, auto-remediation will fix the issue or notify maintenance staff as appropriate.

[1443] "Example 3"

[1444] Step 1: Minimize capital investment and maintenance costs by using AI to optimize network equipment placement and introduce self-diagnosis and automatic repair functions.

[1445] Step 2: Provide services that require high-speed transmission and reception of large amounts of data with low latency, such as 4K and 8K video streaming and real-time online gaming.

[1446] Example 1

[1447] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1448] Modern communications infrastructure is required to respond quickly to sudden fluctuations in communications demand and changes in population density. However, with conventional methods, it is difficult to optimally deploy network equipment to deal with these fluctuations, which can lead to a decline in the quality of communications services. In addition, the efficiency of capital investment is also an issue. Furthermore, it is important to detect early signs of failure or aging of base station equipment and minimize interruptions to communications services.

[1449] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for collecting big data such as geographic information, demographic statistics, urban planning, and demand forecasts, a means for preprocessing the collected data, a means for analyzing the preprocessed data and identifying areas, time periods, and areas with high communication demand and high population density, a means for generating an optimal network facility layout plan based on the analysis results, a means for outputting the generated layout plan in a report format, and a means for providing high-quality communication services. This makes it possible to quickly respond to fluctuations in communication demand and changes in population density and to optimally layout network facilities. In addition, it is possible to improve the efficiency of capital investment and the quality of communication services.

[1450] "Geographic information" is data relating to geographic location, topography, land use, etc.

[1451] "Demographics" refers to statistical data such as the distribution, age structure, gender, and occupation of the population in a particular area.

[1452] "Urban planning" refers to planning data regarding urban land use, transportation, infrastructure development, etc.

[1453] "Demand forecast" is data for predicting future fluctuations in demand.

[1454] "Big data" refers to extremely large and complex data sets that cannot be handled using traditional data processing methods.

[1455] "Network equipment" is a general term for the hardware and software required to provide communication services.

[1456] "Preprocessing" refers to cleaning, normalizing, integrating, and other processes on data before performing data analysis.

[1457] "Analysis" refers to the process of using collected data to discover specific patterns or trends.

[1458] "Layout planning" refers to planning where and how network equipment will be laid out.

[1459] The "report format" is a format in which analysis results and deployment plans are compiled as a document.

[1460] "High-quality communication services" refers to services that provide stable, high-speed, and low-latency communications.

[1461] "Self-diagnosis" refers to the system's ability to monitor its own status and detect abnormalities.

[1462] "Automatic repair" refers to the system's ability to automatically correct any abnormalities it detects.

[1463] "Signs of failure or deterioration" refer to symptoms that appear before equipment breaks down or deteriorates.

[1464] "Minimizing interruptions to communication services" refers to minimizing the duration and area of ​​interruptions to communication services.

[1465] "Improving stability" refers to maintaining a state in which communication services are provided continuously and without interruption.

[1466] "Lowest cost" refers to keeping costs to a minimum.

[1467] "Ultra-fast, low-latency communication experience" refers to the provision of extremely fast data transfer speeds and extremely short communication latency.

[1468] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1469] The present invention relates to a system for analyzing big data such as geographic information, demographic statistics, urban planning, demand forecasts, etc., and for optimally arranging network facilities. A specific embodiment of this system is described below.

[1470] 1. Program Generation

[1471] The server generates programs to analyze big data such as geographic information, demographic statistics, urban planning, and demand forecasts. The programs use generative AI models to optimally allocate network equipment.

[1472] 2. Program Processing Explanation

[1473] The server analyzes the data using the following procedure to plan the optimal placement of network equipment.

[1474] 1. Data collection:

[1475] The server collects geographic information using Geographic Information System (GIS) software (e.g., ArcGIS), demographic data is obtained from government statistical databases (e.g., census data), urban planning data is collected from public databases of local governments, and demand forecasting data is derived from the carrier's historical traffic data.

[1476] 2. Data preprocessing:

[1477] The server converts the collected data into a format that is easier to analyze: it cleans, normalizes, and consolidates the data.

[1478] 3. Data Analysis:

[1479] The server then analyzes the preprocessed data using AI analysis tools (e.g., TensorFlow, PyTorch) to train machine learning models to identify areas and times of day with high demand for communications, as well as areas with high population density.

[1480] 4. Generate optimal layout plan:

[1481] The server then uses the analysis results to plan the optimal placement of network equipment. For example, it will add base stations to areas with high demand for communications and set up Wi-Fi hotspots in areas with high population density. It also uses urban planning data to plan the placement of network equipment based on predictions of future population movements and urban development.

[1482] 5. Result output:

[1483] The server outputs the optimal layout plan in the form of a report and provides it to the user. This report includes specific layout locations, the types of equipment required, installation times, etc.

[1484] 3. Examples and prompts

[1485] As a concrete example, consider the following scenario.

[1486] Examples:

[1487] A user uses this system to optimize the communication infrastructure of city A. City A is experiencing rapid population growth, and communication traffic is increasing rapidly in certain areas. The user inputs the geographic information, demographic statistics, urban planning, and past communication traffic data of city A into the system.

[1488] Example prompt:

[1489] "Plan the optimal placement of network facilities based on the geographic information, demographics, urban planning, and past communication traffic data of City A. In particular, prioritize placement of facilities in areas and time periods with high communication demand and in areas with high population density, while also taking into consideration predictions of future population movement and urban development."

[1490] By inputting this prompt sentence into the generative AI model, the server generates an optimal network equipment layout plan and provides it to the user.

[1491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1492] Step 1: Data collection

[1493] The server collects geographic information using Geographic Information System (GIS) software. Specifically, it uses tools such as ArcGIS to obtain geographic information for City A. It also downloads demographic data for City A from a government statistical database. It also collects urban planning data for City A from the public database of the local government and obtains historical traffic data from telecommunications carriers. These data are inputs, and the collected data set is output.

[1494] Step 2: Data Preprocessing

[1495] The server converts the collected data into a format that is easy to analyze. Specifically, it cleans the data and removes missing values ​​and outliers. Next, it normalizes the data to unify the scale. Finally, it integrates data from different data sources and combines them into a single dataset. The input is the collected dataset, and the output is the preprocessed dataset.

[1496] Step 3: Data analysis

[1497] The server uses the preprocessed data to perform analysis. Specifically, it uses AI analysis tools (e.g., TensorFlow, PyTorch) to train machine learning models to identify areas and times of high demand for communications, as well as areas with high population density. The trained models are then used to predict future demand for communications and population movements. The input is the preprocessed dataset, and the analysis results are output.

[1498] Step 4: Generate optimal layout plans

[1499] The server generates an optimal network equipment layout plan based on the analysis results. Specifically, it adds base stations in areas with high demand for communications and installs Wi-Fi hotspots in areas with high population density. It also creates a layout plan that takes into account future population movements and urban development based on urban planning data. The input is the analysis results, and the generated layout plan is output.

[1500] Step 5: Output the results

[1501] The server outputs the optimal deployment plan in report format and provides it to the user. Specifically, it generates a report that includes details of the deployment location, the type of equipment required, the installation time, predicted fluctuations in communication demand, etc. The input is the generated deployment plan, and the output is the deployment plan in report format.

[1502] (Application example 1)

[1503] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1504] In modern society, optimizing communication infrastructure and providing high-quality communication services are important issues. However, conventional methods have limitations when it comes to responding to fluctuations in communication demand, changes in population density, and progress in urban planning. In addition, the operation management of autonomous vehicles requires providing optimal routes and stopping points in real time. In order to solve these issues, advanced analysis technology that utilizes big data is required.

[1505] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1506] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, and a means for analyzing big data such as geographic information, demographic statistics, urban planning, and demand forecasts to provide optimal routes and stopping points in real time in order to optimize operation management of autonomous vehicles. This makes it possible to optimize communication infrastructure and provide high-quality communication services, as well as realize efficient operation management of autonomous vehicles.

[1507] "AI" is an abbreviation for artificial intelligence, a technology that enables computer systems to mimic human intelligence to learn, reason, and self-correct.

[1508] "Geographic information" is data about specific locations on Earth, including maps, location information, and topographical data.

[1509] "Demographics" refers to data concerning the distribution, composition, and changes of the population in a particular area.

[1510] "Urban planning" refers to plans and policies concerning urban land use, development, and infrastructure development.

[1511] "Demand forecasting" refers to data analysis and models used to predict future fluctuations in demand.

[1512] "NW equipment" is an abbreviation for network equipment, and refers to the hardware and software that make up the communications infrastructure.

[1513] "High-quality communication services" refers to services that provide stable, high-speed, and low-latency communications.

[1514] An "autonomous vehicle" refers to a vehicle that drives autonomously using artificial intelligence and sensor technology.

[1515] "Traffic management" refers to the management work of planning, monitoring and controlling vehicle operations.

[1516] An "optimal route" refers to the most efficient route selected taking into account traffic conditions, distance to the destination, time, etc.

[1517] A "stopping point" refers to a specific location for a vehicle to stop.

[1518] "Real-time" refers to data and information being processed immediately and provided without delay.

[1519] To implement this invention, the following system configuration is required. The server collects big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and implements an AI model to analyze this data. Specifically, software libraries such as Python, Pandas, NumPy, Scikit-learn, and GeoPandas are used.

[1520] The server first loads data such as geographic information, demographic statistics, urban planning, and demand forecasts, and then integrates this data. Next, it classifies regions using KMeans clustering and builds a model to forecast future demand using RandomForestRegressor. This allows it to calculate the optimal placement of network facilities and saves the results.

[1521] As a concrete example, consider the case of optimizing the operation management of autonomous vehicles in Tokyo. The server collects road network data in Tokyo, population density data for each ward, construction plan data for new roads and buildings, and demand forecast data based on past traffic data, and analyzes this data. Based on the analysis results, it provides optimal routes and stopping points in real time.

[1522] Users can receive real-time information on optimal routes and stopping points via a smartphone application. Based on data provided by the server, the application provides information to avoid traffic congestion and ensure efficient operation.

[1523] An example of a prompt sentence might be:

[1524] "In order to optimize the operation management of autonomous vehicles in Tokyo, please develop an application that analyzes big data such as geographic information, demographic statistics, urban planning, and demand forecasts, and provides optimal routes and stopping points in real time."

[1525] By feeding this prompt into a generative AI model, it is possible to obtain detailed advice on the design and implementation of a specific application.

[1526] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1527] Step 1:

[1528] The server collects data such as geographic information, demographics, urban planning, and demand forecasts. Specifically, it retrieves data from various databases and APIs and integrates them. The inputs are geographic information data, demographic data, urban planning data, and demand forecast data, and the output is an integrated data set.

[1529] Step 2:

[1530] The server preprocesses the merged dataset, i.e. imputing missing values, normalizing the data, removing outliers, etc. As input, we have the merged dataset, and as output, we have the preprocessed dataset.

[1531] Step 3:

[1532] The server uses the preprocessed dataset to perform KMeans clustering, classifying regions based on population density and demand forecasts. The input is the preprocessed dataset, and the output is the cluster information for each region.

[1533] Step 4:

[1534] The server uses the cluster information to build a demand forecasting model using RandomForestRegressor. Specifically, it trains a model to forecast future demand using population density, urban development index, and current demand as inputs. The inputs are the cluster information and a preprocessed dataset, and the output is a demand forecasting model.

[1535] Step 5:

[1536] The server uses the demand forecasting model to calculate the optimal network equipment placement. Specifically, it predicts future demand in each region and optimizes the placement of network equipment based on that. The inputs are the demand forecasting model and a pre-processed dataset, and the output is the optimal network equipment placement information.

[1537] Step 6:

[1538] The server stores the optimal network equipment layout information and updates it as necessary. Specifically, the calculation results are stored in a database and periodically recalculated. The input is the optimal network equipment layout information, and the output is the stored layout information.

[1539] Step 7:

[1540] Users receive information on optimal routes and stopping points in real time through a smartphone application. Specifically, information is provided to avoid traffic congestion and achieve efficient operation based on data provided by the server. The input is real-time data from the server, and the output is information on optimal routes and stopping points provided to users.

[1541] Example 2

[1542] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1543] Conventional communication systems have problems with frequent interruptions to communication services due to failures and aging of base station equipment, resulting in reduced stability. In addition, optimal network equipment placement and efficient maintenance have not been implemented sufficiently, resulting in increased costs and reduced communication quality. Furthermore, delayed response after anomaly detection can prolong the interruption of communication services.

[1544] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1545] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, a means for collecting status data of base station equipment, a means for performing self-diagnosis based on the collected data, a means for performing automatic repair when an abnormality is detected, a means for notifying maintenance staff when the automatic repair fails, and a means for storing the results of the self-diagnosis and automatic repair in a database and analyzing the long-term status of the equipment. This makes it possible to minimize interruptions to communication services and improve stability.

[1546] "AI" is an abbreviation for artificial intelligence, a technology that mimics human intelligence through machine learning and data analysis.

[1547] "Geographic information" refers to data such as location, topography, and climate for specific locations on Earth.

[1548] "Demographics" refers to statistical data such as the distribution of the population in a particular area, age structure, gender, birth rate, and death rate.

[1549] "Urban planning" refers to the designs and policies for the planned arrangement and management of a city's land use, transportation, infrastructure, etc.

[1550] "Demand forecasting" is a method that uses data analysis and models to predict future demand.

[1551] "Network equipment" is a general term for the hardware and software required to provide communication services.

[1552] The term "base station equipment" refers to fixed communication equipment for communicating with mobile communication terminals in a wireless communication network.

[1553] "Self-diagnosis" is a function that enables a system or device to check its own status and detect signs of abnormalities or failures.

[1554] "Automatic repair" is a function that automatically corrects detected abnormalities or failures.

[1555] "Maintenance staff" refers to professional personnel who maintain and manage systems and equipment.

[1556] A "database" is a system for efficiently storing, searching, and managing data.

[1557] "Analyzing the long-term condition of equipment" means evaluating the condition of equipment over a long period of time based on collected data and predicting future breakdowns and the need for maintenance.

[1558] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1559] The present invention relates to a communication system having a self-diagnosis and automatic repair function for base station equipment. A specific embodiment of this system will be described below.

[1560] 1. Program Generation

[1561] The server generates a program with self-diagnosis and automatic repair functions based on the base station equipment information. This program is developed using programming languages ​​such as Python and Java.

[1562] 2. Program Processing Explanation

[1563] The server uses the generated program to perform the following processes.

[1564] Self-diagnosis function:

[1565] The server periodically checks the status of the base station equipment using sensors and monitoring software (e.g., Nagios, Zabbix) installed in the equipment.

[1566] It collects data on equipment temperature, voltage, communication status, etc., and runs algorithms to detect abnormal values ​​and signs of aging.

[1567] Automatic repair function:

[1568] If the server detects a problem during self-diagnosis, it will execute an automatic repair script, such as restarting the software or resetting settings.

[1569] If the problem cannot be automatically fixed, the server will notify maintenance staff via email, SMS, or a dedicated maintenance app (e.g. PagerDuty).

[1570] Data storage and analysis:

[1571] The server stores the results of self-diagnosis and automatic repair in a database (e.g., MySQL, PostgreSQL).

[1572] Based on the stored data, the long-term condition of the equipment is analyzed, future failures are predicted, and maintenance plans are optimized.

[1573] 3. Examples of concrete examples and prompts

[1574] Examples:

[1575] When a user wants to check the status of base station equipment, the following steps are taken.

[1576] 1. The user sends a request to check the status of the base station equipment from a dedicated management terminal.

[1577] 2. The server displays the equipment status based on the latest self-diagnosis results.

[1578] 3. If an abnormality is detected, the server will display the automatic repair history and current response status.

[1579] Example prompt:

[1580] By inputting the following prompt sentence into the generative AI model, a report on the status of base station equipment can be generated.

[1581] "Based on the latest self-diagnosis results of the base station equipment, please generate a report of the current status and the anomaly detection history for the past week."

[1582] In this way, the user can grasp the status of the base station equipment in real time and take the necessary measures quickly.

[1583] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1584] Step 1:

[1585] The server periodically collects equipment status data using sensors and monitoring software (e.g., Nagios, Zabbix) installed in the base station equipment.

[1586] Input: Sensor data such as temperature, voltage, and communication status from base station equipment.

[1587] Data processing: The server collects the sensor data and formats it for storage in a database.

[1588] Output: Formatted asset condition data.

[1589] Specific operation: The server runs a script to obtain data from the sensor at midnight every day and stores it in a database.

[1590] Step 2:

[1591] The server runs a self-diagnosis algorithm based on the collected data.

[1592] Input: Formatted equipment condition data.

[1593] Data computation: The server analyzes the data using Python's Pandas library to detect outliers and signs of aging.

[1594] Output: Anomaly detection results.

[1595] What happens: The server compares the collected data with past data to see if there are any outliers.

[1596] Step 3:

[1597] If the self-diagnosis detects an abnormality, the server executes an automatic repair script.

[1598] Input: Anomaly detection results.

[1599] Data processing: The server selects an appropriate repair script depending on the type of anomaly.

[1600] Output: The repair result.

[1601] Specific action: If an abnormality is detected, the server executes a Bash script and restarts the relevant software.

[1602] Step 4:

[1603] The server sends notifications to maintenance staff if automatic repairs fail or if a critical anomaly is detected.

[1604] Input: The repair result.

[1605] Data processing: The server generates notification content and sends it via email, SMS, or a dedicated maintenance app.

[1606] Output: Informational message.

[1607] Specific action: The server sends an email using the SMTP protocol to notify the maintenance staff of the details of the anomaly and the need for action.

[1608] Step 5:

[1609] The server stores the results of self-diagnosis and automatic repair in a database and analyzes the long-term condition of the equipment.

[1610] Input: Self-diagnosis and repair results.

[1611] Data processing: The server inserts the data into the database and periodically generates analysis reports.

[1612] Output: Analysis report.

[1613] What it does: The server executes SQL queries to store the data in a database and uses Python data analysis libraries to analyze long-term equipment health.

[1614] (Application example 2)

[1615] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1616] In modern factories, the downtime of production lines due to machine breakdowns or aging is a major problem. This reduces manufacturing efficiency and increases costs. Similarly, interruptions to communication services are a major problem, and there is a demand to provide a stable communication environment. To solve these problems, it is necessary to constantly monitor the condition of machines and equipment, detect early signs of breakdowns or aging, and respond quickly.

[1617] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1618] In this invention, the server includes a means for optimally arranging network equipment based on big data such as geographic information, demographic statistics, urban planning, and demand forecasts from around the world using AI, a means for providing high-quality communication services, a means for machines in a factory to periodically perform self-diagnosis and detect signs of failure or aging, a means for automatically correcting the detected problems, a means for notifying maintenance staff as necessary, a means for minimizing interruptions to communication services and improving stability, and a means for minimizing costs while providing an ultra-high-speed, low-latency communication experience. This makes it possible to detect failures and aging of machines and communication equipment in a factory early and respond quickly.

[1619] "AI" stands for artificial intelligence, a technology that mimics human intelligence through machine learning and data analysis.

[1620] "Geographic information" is information relating to specific locations on the Earth, including map data and location information.

[1621] "Demographics" refers to statistical data concerning the composition and changes of the population in a particular area or group.

[1622] "Urban planning" refers to the designs and policies for planned development and development of cities.

[1623] "Demand forecasting" refers to the analysis and calculations used to predict future demand.

[1624] "Network equipment" is a general term for the hardware and software used to build a communications network.

[1625] "Communication services" refers to services for sending and receiving data and voice.

[1626] "Factory machinery" refers to production equipment and devices used in a factory.

[1627] "Self-diagnosis" is a function that allows a machine or system to check its own condition and detect abnormalities.

[1628] "Signs of failure or deterioration" refer to symptoms or signs that appear before a machine or equipment breaks down or deteriorates.

[1629] An "automatic remediation mechanism" is a feature or method for automatically repairing a detected problem.

[1630] "Maintenance staff" are specialized technicians who maintain and repair machinery and equipment.

[1631] "Interruption of communications services" means a temporary cessation of communications.

[1632] "Stability" refers to the ability of a system or service to continue to operate stably.

[1633] "Cost minimization" refers to methods or means for minimizing costs.

[1634] "Ultra-high speed and low latency" refers to the sending and receiving of data at extremely high speeds with almost no latency.

[1635] "Communication experience" refers to the experience and sensations a user has when using a communication service.

[1636] The system for carrying out this invention is configured as follows: The server has a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, and also has a means for providing high-quality communication services.

[1637] In addition, the machines in the factory will have the means to periodically perform self-diagnosis to detect signs of breakdowns or aging. They will also have the means to automatically correct any problems detected, and will also notify maintenance staff as necessary. This will minimize interruptions to communication services and improve stability.

[1638] Specifically, the server runs a self-diagnosis and auto-repair program written in Python that allows the factory's machines to periodically self-diagnose and detect abnormalities in motors or sensor failures. If detected, the auto-repair function corrects the problem and notifies maintenance staff if necessary.

[1639] The hardware used is the factory robot itself and its sensors. The software is a self-diagnosis and self-repair program written in Python. This makes it possible to detect failures and deterioration of machinery and communication equipment in the factory at an early stage and respond quickly.

[1640] A concrete example is a system in which a robot in a factory periodically performs self-diagnosis to detect motor anomalies or sensor failures. If a problem is detected, the system automatically corrects the problem and notifies maintenance staff if necessary. This system minimizes downtime of the factory's production line.

[1641] Example of a prompt to be input to a generative AI model:

[1642] Design a system where factory robots periodically self-diagnose to detect signs of failure or aging. If a problem is detected, create an application that can automatically fix the problem or notify maintenance staff if necessary.

[1643] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1644] Step 1:

[1645] The server collects sensor information from machines in the factory.

[1646] Input: Sensor information from machines in the factory

[1647] Output: Collected sensor information

[1648] Specific operation: The server periodically collects data such as temperature, vibration, and current from sensors installed on each machine.

[1649] Step 2:

[1650] The server analyzes the collected sensor information and detects abnormal values.

[1651] Input: Collected sensor information

[1652] Output: Anomaly detection results

[1653] Specific operation: The server uses statistical methods and machine learning algorithms based on the collected data to detect values ​​that are outside the normal range.

[1654] Step 3:

[1655] The server performs a self-diagnosis if an abnormal value is detected.

[1656] Input: Anomaly detection results

[1657] Output: Self-diagnosis result

[1658] Specific operation: The server performs a detailed diagnosis of each part of the machine depending on the type and severity of the abnormality, and identifies signs of malfunction or deterioration.

[1659] Step 4:

[1660] The server will attempt to automatically repair itself based on the results of the self-diagnosis.

[1661] Input: Self-diagnosis result

[1662] Output: Result of automatic repair

[1663] Specific actions: Based on the diagnostic results, the server will automatically perform tasks such as restarting the software, resetting settings, and making simple hardware adjustments.

[1664] Step 5:

[1665] The server notifies maintenance staff if the automatic repair is unsuccessful.

[1666] Input: Automatic repair result

[1667] Output: Maintenance notification

[1668] Specific behavior: If the server determines that repair is impossible or insufficient, it will send an email or alert to maintenance staff, reporting detailed diagnostic results and repair attempts.

[1669] Step 6:

[1670] The server logs all processing results for future analysis.

[1671] Input: The results of each processing step

[1672] Output: Recorded log data

[1673] Specific operation: The server records detailed information about each step, such as input data, processing results, and execution time, and stores the information in a database.

[1674] Example 3

[1675] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1676] Modern communication services require optimal deployment of network equipment, early detection of failures, and rapid repair. However, meeting these requirements requires a great deal of cost and effort, and in particular, to provide high-quality communication services, real-time monitoring and rapid response are essential. With conventional systems, it was difficult to efficiently solve these problems.

[1677] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1678] In this invention, the server includes a means for optimally arranging network equipment using AI based on big data such as geographical information, demographic statistics, urban planning, and demand forecasts from around the world, a means for providing high-quality communication services, a means for monitoring the network status in real time, a means for detecting anomalies and identifying their causes, a means for performing automatic repair processing, and a means for notifying users of the results. This makes it possible to provide an ultra-high-speed, low-latency communication experience at the lowest cost, minimize interruptions to communication services, and improve stability.

[1679] "AI" is an abbreviation for artificial intelligence, a technology that uses techniques such as machine learning and deep learning to analyze data and derive optimal solutions.

[1680] "Network equipment layout" refers to the physical layout and layout plan of various devices and infrastructure in a communications network, and is an important element for achieving efficient data communications.

[1681] A "high-quality communication service" is a servi...

Claims

1. A means for inputting a specific prompt sentence into a generative AI model to place a network including base station equipment based on data including geographic information, demographic statistics, urban planning, and demand forecasts from around the world; an anomaly detection means for detecting an anomaly in the base station equipment by periodically executing a self-diagnosis program of the base station equipment; means for starting an automatic restoration function for automatically restoring the base station equipment in which the abnormality is detected; means for providing information necessary for a maintenance staff who maintains and manages the base station equipment to grasp a current state of the base station equipment when the base station equipment cannot be restored even by the automatic restoration function; An emotion engine that recognizes emotions of a user who uses a terminal connected to the base station equipment; an adjustment means for adjusting the operation of the self-diagnosis of the base station equipment by the self-diagnosis program and the operation of the automatic recovery function based on the emotion; A system including:

2. 2. The system according to claim 1, wherein said adjustment means prioritizes repair of said base station equipment when said user is angry, and inhibits operation of said self-diagnosis and said automatic repair function when said user is happy.

3. 2. The system according to claim 1, wherein the abnormality detection means detects symptoms that appear before the base station equipment breaks down or deteriorates by checking the state of the hardware and software of the base station equipment using the self-diagnosis program.

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