system

Abstract

We provide the system. [Solution] Means of obtaining event information, A means for predicting communication demand based on the aforementioned event information, A means for determining the need to arrange mobile communication equipment or adjust existing communication equipment based on the predicted communication demand, A means for generating an implementation plan based on the aforementioned judgment and notifying users of congestion information on the communication infrastructure, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the increase in events, the demand for communication infrastructure has increased rapidly, and there is a problem that the labor and time required to take prompt and efficient countermeasures have increased. In the conventional method, since it depends on manual information collection and countermeasure judgment, the load on human resources is large, and there is a possibility of a delay in response. For this reason, a method that can appropriately and quickly respond to events by a more automated process is required.

Means for Solving the Problems

[0005] This invention provides a system that automatically acquires event information, predicts communication demand, and determines whether it is necessary to deploy mobile communication equipment or adjust existing communication equipment based on the prediction. This system collects event information from a digital platform on the internet and stores it in a database. It then constructs a prediction model using past event data and base station communication logs, and simulates communication demand by inputting event information into the model. This enables the automatic generation of implementation plans and rapid response.

[0006] "Event information" refers to data about gatherings or events held at a specific date and time, including information such as the event name, date, location, and expected number of participants.

[0007] "Communication demand" refers to the expected degree or amount of use of communication networks at a specific location and time, and is subject to change depending on the scale and nature of the event.

[0008] "Mobile communication equipment" refers to mobile network devices that are temporarily installed in specific locations and are used to supplement communication capabilities that are insufficient with existing communication equipment alone.

[0009] "Existing communication equipment" refers to permanent communication network infrastructure already installed in a specific area, and is essential equipment for providing normal communication services.

[0010] An "implementation plan" refers to a specific action plan necessary to meet communication demand, including the placement of mobile communication equipment and the adjustment of existing communication equipment.

[0011] A "digital platform" is a foundation for providing various services and information over the internet, and is an online system that serves as a primary source for collecting event information.

[0012] A "predictive model" is a mathematical and statistical framework built to predict future events based on past data.

[0013] "Simulation" is an experimental method for testing scenarios in a virtual environment by imitating real-world physical states and behaviors. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system for efficiently managing the communication demands of an event, with a server primarily handling the processing of various types of information. Specific embodiments of this system are described in detail below.

[0036] This system first includes a function that allows the server to automatically retrieve event information from various digital platforms on the internet. The server accesses data sources such as event booking sites, social media, and local news sites, and collects relevant event information to store in a database.

[0037] Next, the server analyzes the stored event information and extracts attributes such as the expected number of participants, the venue, and the scale of the event. Based on this analysis, the server simulates communication demand through a predictive model that utilizes past event data and base station communication logs. This allows the server to identify the expected communication demand on the day of the event.

[0038] Subsequently, the server determines, based on the simulation results, whether the existing communication equipment can provide sufficient communication services. If necessary, and if the deployment of mobile communication equipment is required, the server automatically generates an implementation plan and reallocates the necessary resources according to its guidelines. The server outputs the generated implementation plan as a report and notifies the responsible person, helping to enable swift action.

[0039] As a concrete example, consider a scenario where a server detects a large-scale concert event that a user plans to attend. The server analyzes the scale and location of this event and determines that it cannot be handled by the normal communication infrastructure. Based on this, the server decides on the deployment of mobile communication equipment and creates a plan. Through this process, an automated process enables rapid and efficient communication response.

[0040] In this way, by predicting the demand for communication infrastructure accompanying the increase in events and taking appropriate measures, companies can improve the quality of their communication services.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server accesses a pre-registered list of digital platforms. It uses scraping techniques and APIs to collect information from each platform, such as event name, date, location, and estimated number of attendees. The collected event information is automatically stored in a database by the server.

[0044] Step 2:

[0045] The server analyzes event information stored in the database. The purpose of this analysis is to extract key elements such as the scale, type, and location of the event. This analysis allows for advance knowledge of how events will impact communication demand.

[0046] Step 3:

[0047] The server runs a model to predict communication demand using historical event data and current event information. This predictive model is built using machine learning algorithms and statistical methods to calculate the expected data traffic on the day of the event.

[0048] Step 4:

[0049] The server compares the predicted communication demand with the capacity of existing communication equipment. If it determines that the demand exceeds capacity, it determines the need to deploy mobile communication equipment or adjust existing equipment. This allows for planning to prevent communication service shortages.

[0050] Step 5:

[0051] The server will create a detailed implementation plan as needed. This plan will include the location and quantity of mobile communication equipment, as well as proposed changes to the configuration of existing communication equipment. The server will output this plan as a report and notify the communication equipment team.

[0052] Step 6:

[0053] After the event ends, the server monitors the actual data traffic and analyzes the difference between the prediction and the actual data. Based on this, a feedback loop is formed to improve the accuracy of the prediction model and improve future prediction accuracy.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In recent years, the demand for communication infrastructure has surged due to the diversification of events and the increase in the number of participants. However, existing communication equipment is sometimes unable to cope with this demand, resulting in problems such as reduced communication speeds and connection failures on the day of the event. Such situations not only diminish participant satisfaction but also pose a major challenge for communication carriers.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes a processing unit for acquiring event information, a processing unit for analyzing the event information and extracting features, and a device equipped with an algorithm for predicting communication demand based on the extracted features. This makes it possible to accurately predict communication demand in an event in advance and to arrange appropriate communication equipment or adjust existing facilities.

[0059] "Event information" refers to detailed data about an event, including attribute information such as the specific date and time, location, scale, and number of participants.

[0060] A "processing device" is a computer or a device with the functionality to automatically analyze and manipulate digital information.

[0061] "Storage medium" refers to a device or part of a device capable of storing and retrieving digital data, such as a database or cloud storage.

[0062] "Communication demand" refers to the total volume and frequency of communication expected to occur in connection with an event.

[0063] An "algorithm" is a series of computational procedures for solving a specific problem, and in particular refers to mathematical models and formulas used for forecasting communication demand.

[0064] A "control device" is a device used to manage and regulate the operation of other devices or systems.

[0065] The following describes the specific configuration and operation of the system as an embodiment for carrying out the present invention.

[0066] This invention is a system aimed at predicting communication demand and optimizing the placement of communication equipment. The central server functions as follows:

[0067] The server acquires data from various digital information sources to efficiently collect event information. Here, the server uses APIs and web scraping techniques. The acquired data is stored on the server's storage media, such as a database. This creates a system that continuously updates the latest event information from event booking sites, social media, local news sites, and other sources.

[0068] Next, the server analyzes the event information stored in the database. Using natural language processing (NLP) techniques, it extracts features from information such as event name, date, location, and expected number of participants. This analysis is essential for predicting communication demand.

[0069] Based on the extracted features, the server runs an algorithm to predict communication demand. The server is equipped with a prediction model that utilizes AI. This model is built on data from similar past events and communication logs, and provides accurate simulation results when event information is input.

[0070] As a concrete example, when a large-scale music festival is held, the server quickly collects and analyzes event information. Using predictive models, it identifies peak times and areas for communication demand and automatically generates a plan proposing the optimal placement of mobile communication equipment. This entire process enables efficient and rapid communication response.

[0071] An example of a prompt message is provided: "Predict communication demand based on the number of participants and location of the next event, and propose necessary countermeasures." This allows for the proposal of specific countermeasures using a synthetic AI model.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server accesses digital information sources on the internet to retrieve event information. Input requires API and web scraping settings or URLs, and output is the raw data of the retrieved events. The server maintains information consistency by updating this information in real time and storing it in a storage medium.

[0075] Step 2:

[0076] The server analyzes event information stored on a storage medium. Its inputs are raw data and natural language processing (NLP) algorithms for analysis, while its output consists of extracted event features (e.g., event name, date, location, expected number of attendees). The server extracts each item in text format, efficiently organizes and stores them in a database.

[0077] Step 3:

[0078] The server predicts communication demand based on the characteristics of extracted events. Inputs include event characteristics, data from similar past events, and communication logs. A generative AI model is used as the prediction algorithm, and simulations are performed. The output is a detailed report of the expected communication demand. The server analyzes historical data and displays future demand numerically and graphically.

[0079] Step 4:

[0080] The server generates a plan for appropriate communication equipment placement or adjustment of existing facilities based on predicted communication demand. The input is a communication demand report, and the output is a detailed implementation plan. The server uses an algorithm for optimal placement to calculate and report on efficient equipment placement.

[0081] Step 5:

[0082] The server notifies the responsible parties of the generated plan. The input is the implementation plan document, and the output is a notification via email or dashboard. The server has the capability to automatically format the plan and quickly distribute it to the relevant parties.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] In recent years, the load on communication infrastructure has increased dramatically as a variety of events are held. In particular, large-scale events experience communication congestion, leading to a poor communication experience for participants. Therefore, there is a need for a system that can efficiently and quickly predict communication demand, and then appropriately allocate equipment and provide advance notification to participants.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for generating an implementation plan and notifying users of communication infrastructure congestion information. This makes it possible to provide event participants with appropriate ways to use the communication infrastructure and ensure smooth communication.

[0088] "Event information" refers to detailed information about an event held at a specific date, time, and location, and includes data such as the number of participants, location, and scale.

[0089] "Communication demand" is an indicator that shows the predicted amount of usage and load on communication networks at a specific time and place.

[0090] "Mobile communication equipment" refers to mobile communication devices and infrastructure used to temporarily supplement the load on a communication network.

[0091] "Existing communication equipment" refers to currently installed, fixed communication-related infrastructure and devices.

[0092] An "implementation plan" is a document that specifically instructs the optimal placement and adjustment of necessary communication equipment based on predicted communication demand.

[0093] "Notification" refers to a means of transmitting specific information to specific recipients, and in this context, it refers to providing event participants with information regarding the use of communication infrastructure.

[0094] This invention efficiently manages event communication demands through a system in which a server plays a central role. The server first automatically retrieves event information from multiple digital platforms on the internet. In doing so, it connects with event reservation sites and social media via APIs and stores the latest event information in real time in a database. The server then analyzes the stored data to determine the expected number of participants and the venue for each event.

[0095] Next, the server uses machine learning algorithms to predict communication demand based on past event data and communication equipment logs. The scikit-learn library in Python is used for the prediction, and a highly accurate prediction model is built.

[0096] Subsequently, based on the prediction results, the server generates a specific implementation plan for deploying necessary mobile communication equipment and adjusting existing communication equipment. Once the plan is generated, the server notifies users of congestion information via a smartphone application. The notification uses an application created with a smartphone app development framework. Through the app, users can learn about the status of the communication infrastructure in advance before participating in an event.

[0097] As a concrete example, consider the case of a large-scale music festival. When the server retrieves event information and anticipates high communication demand, a notification message such as "Normal network traffic may be congested during the music festival, so we recommend using Wi-Fi" is sent to participants' smartphones. This allows participants to understand the communication situation in advance and take appropriate measures smoothly.

[0098] By using a generative AI model, an example prompt message could be, "Please tell me the expected state of the communication infrastructure for the event I plan to attend this weekend." This would allow event participants to use communication services with peace of mind.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server automatically collects event information from multiple digital platforms on the internet. Specifically, it uses APIs to retrieve the latest event information from event booking sites and social media, and stores it in a database as structured data. The input is event data obtained from the APIs, and the output is event information stored in the database maintained by the server.

[0102] Step 2:

[0103] The server analyzes event information in the database and extracts attribute data such as the expected number of participants and the event location. The analysis results are compiled into a dataset necessary for predicting communication demand for each event. The input is event information stored in the database, and the output is analysis data including event attributes.

[0104] Step 3:

[0105] The server uses historical event data and base station communication logs to build a generative AI model and predict communication demand. The processing uses the Python scikit-learn library, and the prediction simulation is performed by inputting the analyzed data into the prediction model. The input is the analyzed event attribute data and communication logs, and the output is the result of the predicted communication demand.

[0106] Step 4:

[0107] The server determines the optimal placement of mobile communication equipment based on the prediction results and generates a specific implementation plan. The plan is compiled as specific placement instructions to mitigate the predicted communication load. The input is the predicted communication demand, and the output is the implementation plan.

[0108] Step 5:

[0109] The server notifies the user of the generated implementation plan via a smartphone application. The device receives real-time information on areas where congestion is expected, allowing the user to gain prior knowledge about the communication environment. The input is the implementation plan, and the output is a notification message sent to the user's device.

[0110] Step 6:

[0111] Based on notifications from the server, users take appropriate communication measures before participating in an event. For example, they might refer to prompts included in the notification, such as "Please tell me the expected communication infrastructure conditions for the event you plan to attend this weekend," and take measures such as using Wi-Fi in congested areas. The input is the notification message from the server, and the output is the user's countermeasures.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention aims to efficiently predict and manage communication demand related to events, while also optimizing the entire system by considering the emotional state of users. This system primarily operates around a server, collecting and analyzing various types of information and formulating implementation plans.

[0114] First, the server automatically collects event information from multiple digital platforms. This includes detailed information such as the event name, date, location, and expected number of attendees, which is then stored in a database.

[0115] Next, the server analyzes the event information and calculates communication demand using a predictive model. This predictive model is built on data from past events and base station communication logs, and has the ability to specifically simulate the data traffic on the day of the event.

[0116] Furthermore, this system incorporates an emotion engine to recognize user emotions. Based on user emotion data acquired from terminals and various devices, the server understands the emotional tendencies of event participants. This emotion data is not only used to improve the accuracy of communication demand predictions, but is also utilized as an important factor in determining whether or not to hold an event and adjusting its scale.

[0117] As a concrete example, consider an implementation at a music festival. In this festival, a server receives participant emotional data in real time and, based on this data, predicts an increase in communication demand when participants are highly excited. As a result, the server quickly decides to deploy additional mobile communication equipment, helping to ensure the smooth operation of the festival.

[0118] This series of processes provides a sense of security to both event organizers and participants, and improves the overall efficiency of the system. This form of invention visualizes and utilizes the invisible element of emotion, and is highly effective in optimizing communication infrastructure.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The server connects to multiple pre-registered digital platforms to collect event information. Using scraping and APIs, it retrieves information such as event name, date, location, and estimated number of attendees, and stores it in a database.

[0122] Step 2:

[0123] The server analyzes the stored event information. It compares it with data from similar past events to understand the characteristics and scale of the event, and prepares to set up the variables necessary for the communication demand forecasting model.

[0124] Step 3:

[0125] The server inputs the analyzed event information into a predictive model to forecast communication demand. This model is built on past event data and communication logs, and uses machine learning algorithms to simulate communication demand on the day of the event.

[0126] Step 4:

[0127] The device acquires emotional data from event participants in real time. This utilizes the participants' smartphone sensors and wearable devices. The device then transmits this data to an emotion engine.

[0128] Step 5:

[0129] The server analyzes the emotional data received from the emotion engine. It evaluates the user's level of excitement and stress, and integrates this data into a predictive model to forecast the impact on communication demand during an event.

[0130] Step 6:

[0131] Based on all analysis results, the server develops a plan for the placement of mobile communication equipment and the adjustment of existing communication equipment. If necessary, it instructs the responsible team to implement the new placement plan and outputs the plan as a report.

[0132] Step 7:

[0133] Users enjoy guaranteed communication quality while participating in the event. After the event, the server analyzes the difference between actual data traffic and predictions and uses this as feedback to improve the accuracy of the learning model.

[0134] (Example 2)

[0135] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0136] The challenge lies in preventing a decline in communication services due to the sudden increase in communication demand during events, and in providing a comfortable communication environment. Furthermore, it is necessary to adjust the communication infrastructure more appropriately by taking user emotions into consideration.

[0137] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0138] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for analyzing user sentiment data and understanding sentiment trends. This improves the accuracy of communication demand prediction and enables the rapid formulation of an optimal communication equipment deployment plan.

[0139] "Event information" refers to specific details about an event, such as the event name, date, location, and expected number of participants.

[0140] "Communication demand" refers to the predicted amount of data traffic at a specific time and place, and is a factor that directly affects the load on communication infrastructure.

[0141] "Mobile communication equipment" refers to additional communication devices and equipment installed to meet the increased communication needs during events.

[0142] "Emotional data" refers to information that indicates a user's emotional state, and is collected from data such as voice tone, heart rate, and social media content.

[0143] "Emotional tendencies" refer to the overall emotional trends and patterns obtained by analyzing users' emotional data.

[0144] An "implementation plan" refers to the specific procedures and plans for planning the placement and adjustment of communication equipment based on predicted communication demand and sentiment trends.

[0145] This invention provides a system that efficiently predicts communication demand related to events and optimizes the communication infrastructure while considering the emotional state of users. The system primarily operates around a server, which collects and analyzes necessary data and formulates implementation plans.

[0146] The server first automatically collects event information from multiple sources on the internet using APIs. This information includes the event name, date, location, and expected number of attendees, and is stored in a database. Social networking platforms and event announcement services are used as information sources.

[0147] Next, the server applies a predictive model built using libraries such as Python's scikit-learn to forecast communication demand based on collected event information. This model is based on historical event data and communication logs, and simulations can identify times and locations where communication is concentrated.

[0148] The server further acquires user sentiment data, such as voice tone, heart rate, and social media posts from terminals and devices. This data is analyzed, for example, through a sentiment analysis API using Microsoft® Azure® Cognitive Services. Based on the analysis results, the server understands the emotional tendencies of participants and improves the accuracy of communication demand predictions.

[0149] As a concrete example, consider its use in a music festival. The server receives participant emotional data in real time, allowing it to predict increased communication demand based on high levels of excitement. Based on these results, the server can quickly decide on the deployment of additional mobile communication equipment to support smooth event management.

[0150] An example of a prompt would be, "Predict communication demand at the next music festival and plan the optimal placement of mobile communication equipment based on participant sentiment data." This prompt allows the system to generate a flexible plan that responds to real-time conditions.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The server automatically collects event information from multiple sources on the internet via APIs. This input includes specific keywords and topics. Specifically, the server retrieves information such as event name, date, location, and expected number of attendees from social networking platforms and event announcement services, and stores it in a database. The output is a dataset containing the retrieved event information.

[0154] Step 2:

[0155] The server retrieves event information stored in a database and applies a predictive model using the Python scikit-learn library to forecast communication demand. The inputs are an event information dataset, historical event data, and communication logs. Specifically, the server trains the model based on historical data and simulates communication traffic on the day of the event. The output is an indicator of the predicted communication demand.

[0156] Step 3:

[0157] The terminal collects user emotion data in real time from smartphones and wearable devices. This data includes voice tone, heart rate, and social media posts. The server analyzes this data using emotion analysis APIs such as Microsoft Azure Cognitive Services. The input is the emotion data collected in real time. Specifically, it estimates emotion tendencies via the emotion analysis API. The output is a result showing the participant's emotion tendencies.

[0158] Step 4:

[0159] The server plans the optimal placement of mobile communication equipment based on predicted communication demand and analyzed sentiment trends. Inputs are communication demand indicators and sentiment trend results. Specifically, the server evaluates the communication load in each area and determines the necessary mobile communication equipment. Output is an implementation plan for the placement and adjustment of communication equipment.

[0160] Step 5:

[0161] The user manages the actual event based on the proposed communication equipment layout plan. The server monitors this process and makes real-time adjustments as needed. The input is the implementation plan provided by the server. Specifically, the user appropriately allocates communication resources and supports the smooth running of the event. The output is the event with a comfortable communication environment ensured.

[0162] (Application Example 2)

[0163] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0164] In modern cities, the surge in communication demand and the resulting congestion during events pose a significant challenge to urban management. In particular, there is a need to optimize communication infrastructure efficiently, taking into account the impact of participants' emotional states on communication demand. However, conventional systems have struggled to utilize emotional data for demand forecasting and to provide participants with appropriate guidance to avoid congestion.

[0165] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0166] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for collecting and analyzing emotional information from user devices. This enables highly accurate prediction of communication demand that takes into account the emotional tendencies of event participants. It also makes it possible to predict congestion in urban environments and provide participants with guidance on the optimal travel route.

[0167] "Means for acquiring event information" refers to a system that automatically collects detailed information about an event from multiple digital platforms on the internet and stores that information in a database.

[0168] "Methods for predicting communication demand" refer to technologies that utilize predictive models built on past event data and communication logs, and input acquired event information to simulate communication traffic on a given day.

[0169] "Means for determining the need to deploy mobile communication equipment or adjust existing communication equipment" refers to an evaluation process for determining necessary adjustments or additions to communication infrastructure based on predicted communication demand.

[0170] "Means for generating implementation plans" refers to the function of formulating a plan for the specific placement and adjustment of communication infrastructure based on predictions and judgment results.

[0171] "Means for collecting and analyzing emotional information from user devices" refers to technologies that collect data on a user's emotions from smartphones and wearable devices, and then analyze that data to understand the user's emotional state.

[0172] "Methods for evaluating participants' emotional tendencies" refer to methods for analyzing and evaluating the overall emotional trends and tendencies of event participants based on collected emotional data.

[0173] "A guidance system that predicts congestion in urban environments and supports the optimization of travel routes" refers to a mechanism that predicts the flow of people within a city in real time and provides participants with optimized travel guidance.

[0174] The system for realizing this invention is centered around a server. This server collects event information from various digital platforms on the internet. Specifically, it automatically retrieves information such as the event name, date and time, location, and expected number of participants, and stores it in a database. The hardware used for this is typically server equipment provided by a cloud service provider.

[0175] Next, the server uses the collected event information to predict communication demand. A prediction model that utilizes past event data and base station communication logs is incorporated and used for data analysis. Machine learning libraries such as Scikit-learn are sometimes used. Furthermore, sentiment information is collected from the user's terminal and analyzed. For analysis, the natural language processing library NLTK is used to estimate the user's emotional state.

[0176] Users can learn about congestion levels in urban environments via their smartphones or smart glasses and receive guidance on optimal travel routes. Real-time guidance allows users to plan their travel to avoid congested areas, thus supporting a less stressful urban life.

[0177] As a concrete example, when a music festival is held, the server analyzes participants' emotional data in real time and predicts communication demand based on the results. Based on these results, appropriate communication equipment is deployed and adjusted, ensuring the smooth operation of the event. An example of a prompt message would be, "Please tell me the route to avoid congestion during the music festival." In this way, the invention brings peace of mind and efficiency to both event participants and organizers.

[0178] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0179] Step 1:

[0180] The server collects event information from multiple digital platforms. Specifically, it uses APIs to retrieve information such as the event name, date and time, location, and estimated number of attendees from the internet, and stores this information in a database. During this process, the server executes data collection scripts and saves the collected data as structured data.

[0181] Step 2:

[0182] The server predicts communication demand based on event information stored in the database. By inputting event information into a prediction model built using past event data and base station communication logs, it simulates communication traffic on the day of the event. This simulation is performed using machine learning libraries such as Scikit-learn.

[0183] Step 3:

[0184] The server collects emotional data from the user's terminal and analyzes it. It collects facial expression data and sensor data via the user's smart devices (smartphones and smart glasses). Based on this data, it performs natural language processing using the NLTK library to determine the emotional state.

[0185] Step 4:

[0186] Based on the analyzed emotional information, the server evaluates the emotional tendencies of participants and improves the accuracy of communication demand forecasting. Specifically, it predicts that communication demand will increase when users are highly agitated, and then incorporates this into the prediction model to run the simulation again.

[0187] Step 5:

[0188] Based on the prediction results, the server determines whether mobile communication equipment needs to be deployed or existing communication equipment needs to be adjusted. This allows it to formulate an implementation plan to adequately meet communication demand. After formulating the plan, it determines the necessary deployment adjustments and outputs a specific plan.

[0189] Step 6:

[0190] Users receive information on congestion levels within urban environments and guidance on optimal routes from a server via their smart devices. The server feeds data back to the user's device in real time, helping users avoid congestion. This includes UI displays and voice guidance in the device application.

[0191] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0192] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0193] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0194] [Second Embodiment]

[0195] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0196] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0197] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0198] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0199] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0200] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0201] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0202] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0203] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0204] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0205] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0206] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0207] This invention is a system for efficiently managing the communication demands of an event, with a server primarily handling the processing of various types of information. Specific embodiments of this system are described in detail below.

[0208] This system first includes a function that allows the server to automatically retrieve event information from various digital platforms on the internet. The server accesses data sources such as event booking sites, social media, and local news sites, and collects relevant event information to store in a database.

[0209] Next, the server analyzes the stored event information and extracts attributes such as the expected number of participants, the venue, and the scale of the event. Based on this analysis, the server simulates communication demand through a predictive model that utilizes past event data and base station communication logs. This allows the server to identify the expected communication demand on the day of the event.

[0210] Subsequently, the server determines, based on the simulation results, whether the existing communication equipment can provide sufficient communication services. If necessary, and if the deployment of mobile communication equipment is required, the server automatically generates an implementation plan and reallocates the necessary resources according to its guidelines. The server outputs the generated implementation plan as a report and notifies the responsible person, helping to enable swift action.

[0211] As a concrete example, consider a scenario where a server detects a large-scale concert event that a user plans to attend. The server analyzes the scale and location of this event and determines that it cannot be handled by the normal communication infrastructure. Based on this, the server decides on the deployment of mobile communication equipment and creates a plan. Through this process, an automated process enables rapid and efficient communication response.

[0212] In this way, by predicting the demand for communication infrastructure accompanying the increase in events and taking appropriate measures, companies can improve the quality of their communication services.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The server accesses a pre-registered list of digital platforms. It uses scraping techniques and APIs to collect information from each platform, such as event name, date, location, and estimated number of attendees. The collected event information is automatically stored in a database by the server.

[0216] Step 2:

[0217] The server analyzes event information stored in the database. The purpose of this analysis is to extract key elements such as the scale, type, and location of the event. This analysis allows for advance knowledge of how events will impact communication demand.

[0218] Step 3:

[0219] The server runs a model to predict communication demand using historical event data and current event information. This predictive model is built using machine learning algorithms and statistical methods to calculate the expected data traffic on the day of the event.

[0220] Step 4:

[0221] The server compares the predicted communication demand with the capacity of existing communication equipment. If it determines that the demand exceeds capacity, it determines the need to deploy mobile communication equipment or adjust existing equipment. This allows for planning to prevent communication service shortages.

[0222] Step 5:

[0223] The server will create a detailed implementation plan as needed. This plan will include the location and quantity of mobile communication equipment, as well as proposed changes to the configuration of existing communication equipment. The server will output this plan as a report and notify the communication equipment team.

[0224] Step 6:

[0225] After the event ends, the server monitors the actual data traffic and analyzes the difference between the prediction and the actual data. Based on this, a feedback loop is formed to improve the accuracy of the prediction model and improve future prediction accuracy.

[0226] (Example 1)

[0227] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0228] In recent years, the demand for communication infrastructure has surged due to the diversification of events and the increase in the number of participants. However, existing communication equipment is sometimes unable to cope with this demand, resulting in problems such as reduced communication speeds and connection failures on the day of the event. Such situations not only diminish participant satisfaction but also pose a major challenge for communication carriers.

[0229] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0230] In this invention, the server includes a processing unit for acquiring event information, a processing unit for analyzing the event information and extracting features, and a device equipped with an algorithm for predicting communication demand based on the extracted features. This makes it possible to accurately predict communication demand in an event in advance and to arrange appropriate communication equipment or adjust existing facilities.

[0231] "Event information" refers to detailed data about an event, including attribute information such as the specific date and time, location, scale, and number of participants.

[0232] A "processing device" is a computer or a device with the functionality to automatically analyze and manipulate digital information.

[0233] "Storage medium" refers to a device or part of a device capable of storing and retrieving digital data, such as a database or cloud storage.

[0234] "Communication demand" refers to the total volume and frequency of communication expected to occur in connection with an event.

[0235] An "algorithm" is a series of computational procedures for solving a specific problem, and in particular refers to mathematical models and formulas used for forecasting communication demand.

[0236] A "control device" is a device used to manage and regulate the operation of other devices or systems.

[0237] The following describes the specific configuration and operation of the system as an embodiment for carrying out the present invention.

[0238] This invention is a system aimed at predicting communication demand and optimizing the placement of communication equipment. The central server functions as follows:

[0239] The server acquires data from various digital information sources to efficiently collect event information. Here, the server uses APIs and web scraping techniques. The acquired data is stored on the server's storage media, such as a database. This creates a system that continuously updates the latest event information from event booking sites, social media, local news sites, and other sources.

[0240] Next, the server analyzes the event information stored in the database. Using natural language processing (NLP) techniques, it extracts features from information such as event name, date, location, and expected number of participants. This analysis is essential for predicting communication demand.

[0241] Based on the extracted features, the server runs an algorithm to predict communication demand. The server is equipped with a prediction model that utilizes AI. This model is built on data from similar past events and communication logs, and provides accurate simulation results when event information is input.

[0242] As a concrete example, when a large-scale music festival is held, the server quickly collects and analyzes event information. Using predictive models, it identifies peak times and areas for communication demand and automatically generates a plan proposing the optimal placement of mobile communication equipment. This entire process enables efficient and rapid communication response.

[0243] An example of a prompt message is provided: "Predict communication demand based on the number of participants and location of the next event, and propose necessary countermeasures." This allows for the proposal of specific countermeasures using a synthetic AI model.

[0244] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0245] Step 1:

[0246] The server accesses digital information sources on the internet to retrieve event information. Input requires API and web scraping settings or URLs, and output is the raw data of the retrieved events. The server maintains information consistency by updating this information in real time and storing it in a storage medium.

[0247] Step 2:

[0248] The server analyzes event information stored on a storage medium. Its inputs are raw data and natural language processing (NLP) algorithms for analysis, while its output consists of extracted event features (e.g., event name, date, location, expected number of attendees). The server extracts each item in text format, efficiently organizes and stores them in a database.

[0249] Step 3:

[0250] The server predicts communication demand based on the characteristics of extracted events. Inputs include event characteristics, data from similar past events, and communication logs. A generative AI model is used as the prediction algorithm, and simulations are performed. The output is a detailed report of the expected communication demand. The server analyzes historical data and displays future demand numerically and graphically.

[0251] Step 4:

[0252] The server generates a plan for appropriate communication equipment placement or adjustment of existing facilities based on predicted communication demand. The input is a communication demand report, and the output is a detailed implementation plan. The server uses an algorithm for optimal placement to calculate and report on efficient equipment placement.

[0253] Step 5:

[0254] The server notifies the responsible parties of the generated plan. The input is the implementation plan document, and the output is a notification via email or dashboard. The server has the capability to automatically format the plan and quickly distribute it to the relevant parties.

[0255] (Application Example 1)

[0256] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] In recent years, the load on communication infrastructure has increased dramatically as a variety of events are held. In particular, large-scale events experience communication congestion, leading to a poor communication experience for participants. Therefore, there is a need for a system that can efficiently and quickly predict communication demand, and then appropriately allocate equipment and provide advance notification to participants.

[0258] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0259] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for generating an implementation plan and notifying users of communication infrastructure congestion information. This makes it possible to provide event participants with appropriate ways to use the communication infrastructure and ensure smooth communication.

[0260] "Event information" refers to detailed information about an event held at a specific date, time, and location, and includes data such as the number of participants, location, and scale.

[0261] "Communication demand" is an indicator that shows the predicted amount of usage and load on communication networks at a specific time and place.

[0262] "Mobile communication equipment" refers to mobile communication devices and infrastructure used to temporarily supplement the load on a communication network.

[0263] "Existing communication equipment" refers to currently installed, fixed communication-related infrastructure and devices.

[0264] An "implementation plan" is a document that specifically instructs the optimal placement and adjustment of necessary communication equipment based on predicted communication demand.

[0265] "Notification" refers to a means of transmitting specific information to specific recipients, and in this context, it refers to providing event participants with information regarding the use of communication infrastructure.

[0266] This invention efficiently manages event communication demands through a system in which a server plays a central role. The server first automatically retrieves event information from multiple digital platforms on the internet. In doing so, it connects with event reservation sites and social media via APIs and stores the latest event information in real time in a database. The server then analyzes the stored data to determine the expected number of participants and the venue for each event.

[0267] Next, the server uses machine learning algorithms to predict communication demand based on past event data and communication equipment logs. The scikit-learn library in Python is used for the prediction, and a highly accurate prediction model is built.

[0268] Subsequently, based on the prediction results, the server generates a specific implementation plan for deploying necessary mobile communication equipment and adjusting existing communication equipment. Once the plan is generated, the server notifies users of congestion information via a smartphone application. The notification uses an application created with a smartphone app development framework. Through the app, users can learn about the status of the communication infrastructure in advance before participating in an event.

[0269] As a concrete example, consider the case of a large-scale music festival. When the server retrieves event information and anticipates high communication demand, a notification message such as "Normal network traffic may be congested during the music festival, so we recommend using Wi-Fi" is sent to participants' smartphones. This allows participants to understand the communication situation in advance and take appropriate measures smoothly.

[0270] By using a generative AI model, an example prompt message could be, "Please tell me the expected state of the communication infrastructure for the event I plan to attend this weekend." This would allow event participants to use communication services with peace of mind.

[0271] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0272] Step 1:

[0273] The server automatically collects event information from multiple digital platforms on the internet. Specifically, it uses APIs to retrieve the latest event information from event booking sites and social media, and stores it in a database as structured data. The input is event data obtained from the APIs, and the output is event information stored in the database maintained by the server.

[0274] Step 2:

[0275] The server analyzes event information in the database and extracts attribute data such as the expected number of participants and the event location. The analysis results are compiled into a dataset necessary for predicting communication demand for each event. The input is event information stored in the database, and the output is analysis data including event attributes.

[0276] Step 3:

[0277] The server uses historical event data and base station communication logs to build a generative AI model and predict communication demand. The processing uses the Python scikit-learn library, and the prediction simulation is performed by inputting the analyzed data into the prediction model. The input is the analyzed event attribute data and communication logs, and the output is the result of the predicted communication demand.

[0278] Step 4:

[0279] The server determines the optimal placement of mobile communication equipment based on the prediction results and generates a specific implementation plan. The plan is compiled as specific placement instructions to mitigate the predicted communication load. The input is the predicted communication demand, and the output is the implementation plan.

[0280] Step 5:

[0281] The server notifies the user of the generated implementation plan through an application for smartphones. The terminal receives information on areas where congestion is expected in real time, and the user can obtain prior knowledge about the communication environment. The input is the implementation plan, and the output is a notification message to the user terminal.

[0282] Step 6:

[0283] Based on the notification from the server, the user takes appropriate communication countermeasures before participating in the event. For example, referring to a prompt sentence such as "Please tell me the predicted communication infrastructure situation for the event you plan to participate in this weekend" included in the notification, the user takes measures such as using Wi-Fi in congested communication areas. The input is the notification message from the server, and the output is the user's countermeasure action.

[0284] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0285] The present invention efficiently predicts and manages the communication demand related to an event and optimizes the entire system in consideration of the user's emotional state. This system mainly operates centering around a server, collects various information, analyzes it, and formulates an implementation plan.

[0286] First, the server automatically collects event information from multiple digital platforms. This includes detailed information such as the event name, date of holding, location, and expected number of participants, and these are stored in a database.

[0287] Next, the server analyzes the event information and calculates the communication demand using a prediction model. This prediction model is constructed based on data related to past events and communication logs of base stations, and has the ability to specifically simulate the data traffic on the event day.

[0288] Furthermore, this system incorporates an emotion engine to recognize user emotions. Based on user emotion data acquired from terminals and various devices, the server understands the emotional tendencies of event participants. This emotion data is not only used to improve the accuracy of communication demand predictions, but is also utilized as an important factor in determining whether or not to hold an event and adjusting its scale.

[0289] As a concrete example, consider an implementation at a music festival. In this festival, a server receives participant emotional data in real time and, based on this data, predicts an increase in communication demand when participants are highly excited. As a result, the server quickly decides to deploy additional mobile communication equipment, helping to ensure the smooth operation of the festival.

[0290] This series of processes provides a sense of security to both event organizers and participants, and improves the overall efficiency of the system. This form of invention visualizes and utilizes the invisible element of emotion, and is highly effective in optimizing communication infrastructure.

[0291] The following describes the processing flow.

[0292] Step 1:

[0293] The server connects to multiple pre-registered digital platforms to collect event information. Using scraping and APIs, it retrieves information such as event name, date, location, and estimated number of attendees, and stores it in a database.

[0294] Step 2:

[0295] The server analyzes the stored event information. It compares it with data from similar past events to understand the characteristics and scale of the event, and prepares to set up the variables necessary for the communication demand forecasting model.

[0296] Step 3:

[0297] The server inputs the analyzed event information into a prediction model to predict communication demand. This model is constructed based on data of past events and communication logs, and uses machine learning algorithms to simulate the communication demand on the event day.

[0298] Step 4:

[0299] The terminal acquires the emotional data of event participants in real time. This utilizes the participants' smartphone sensors and wearable devices. The terminal transmits this data to the emotion engine.

[0300] Step 5:

[0301] The server analyzes the emotional data received from the emotion engine. It evaluates the excitement level and stress level of the user, and integrates this data into the prediction model to predict the impact on communication demand during the event.

[0302] Step 6:

[0303] Based on all the analysis results, the server formulates plans for the deployment of mobile communication facilities and the adjustment of existing communication facilities. If necessary, it issues instructions to the responsible team to execute a new deployment plan and outputs the plan as a report.

[0304] Step 7:

[0305] The user enjoys the guaranteed communication quality during the event. The server analyzes the difference between the actual data traffic and the prediction after the event, and utilizes it as feedback to improve the accuracy of the learning model.

[0306] (Example 2)

[0307] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] The challenge lies in preventing a decline in communication services due to the sudden increase in communication demand during events, and in providing a comfortable communication environment. Furthermore, it is necessary to adjust the communication infrastructure more appropriately by taking user emotions into consideration.

[0309] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0310] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for analyzing user sentiment data and understanding sentiment trends. This improves the accuracy of communication demand prediction and enables the rapid formulation of an optimal communication equipment deployment plan.

[0311] "Event information" refers to specific details about an event, such as the event name, date, location, and expected number of participants.

[0312] "Communication demand" refers to the predicted amount of data traffic at a specific time and place, and is a factor that directly affects the load on communication infrastructure.

[0313] "Mobile communication equipment" refers to additional communication devices and equipment installed to meet the increased communication needs during events.

[0314] "Emotional data" refers to information that indicates a user's emotional state, and is collected from data such as voice tone, heart rate, and social media content.

[0315] "Emotional tendencies" refer to the overall emotional trends and patterns obtained by analyzing users' emotional data.

[0316] An "implementation plan" refers to the specific procedures and plans for planning the placement and adjustment of communication equipment based on predicted communication demand and sentiment trends.

[0317] This invention provides a system that efficiently predicts communication demand related to events and optimizes the communication infrastructure while considering the emotional state of users. The system primarily operates around a server, which collects and analyzes necessary data and formulates implementation plans.

[0318] The server first automatically collects event information from multiple sources on the internet using APIs. This information includes the event name, date, location, and expected number of attendees, and is stored in a database. Social networking platforms and event announcement services are used as information sources.

[0319] Next, the server applies a predictive model built using libraries such as Python's scikit-learn to forecast communication demand based on collected event information. This model is based on historical event data and communication logs, and simulations can identify times and locations where communication is concentrated.

[0320] The server also acquires user sentiment data, such as voice tone, heart rate, and social media posts from terminals and devices. This data is analyzed, for example, through a sentiment analysis API using Microsoft Azure Cognitive Services. Based on the analysis results, the server understands the emotional tendencies of participants and improves the accuracy of communication demand predictions.

[0321] As a concrete example, consider its use in a music festival. The server receives participant emotional data in real time, allowing it to predict increased communication demand based on high levels of excitement. Based on these results, the server can quickly decide on the deployment of additional mobile communication equipment to support smooth event management.

[0322] An example of a prompt would be, "Predict communication demand at the next music festival and plan the optimal placement of mobile communication equipment based on participant sentiment data." This prompt allows the system to generate a flexible plan that responds to real-time conditions.

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] Step 1:

[0325] The server automatically collects event information from multiple sources on the internet via APIs. This input includes specific keywords and topics. Specifically, the server retrieves information such as event name, date, location, and expected number of attendees from social networking platforms and event announcement services, and stores it in a database. The output is a dataset containing the retrieved event information.

[0326] Step 2:

[0327] The server retrieves event information stored in a database and applies a predictive model using the Python scikit-learn library to forecast communication demand. The inputs are an event information dataset, historical event data, and communication logs. Specifically, the server trains the model based on historical data and simulates communication traffic on the day of the event. The output is an indicator of the predicted communication demand.

[0328] Step 3:

[0329] The terminal collects user emotion data in real time from smartphones and wearable devices. This data includes voice tone, heart rate, and social media posts. The server analyzes this data using emotion analysis APIs such as Microsoft Azure Cognitive Services. The input is the emotion data collected in real time. Specifically, it estimates emotion tendencies via the emotion analysis API. The output is a result showing the participant's emotion tendencies.

[0330] Step 4:

[0331] The server plans the optimal placement of mobile communication equipment based on predicted communication demand and analyzed sentiment trends. Inputs are communication demand indicators and sentiment trend results. Specifically, the server evaluates the communication load in each area and determines the necessary mobile communication equipment. Output is an implementation plan for the placement and adjustment of communication equipment.

[0332] Step 5:

[0333] The user manages the actual event based on the proposed communication equipment layout plan. The server monitors this process and makes real-time adjustments as needed. The input is the implementation plan provided by the server. Specifically, the user appropriately allocates communication resources and supports the smooth running of the event. The output is the event with a comfortable communication environment ensured.

[0334] (Application Example 2)

[0335] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0336] In modern cities, the surge in communication demand and the resulting congestion during events pose a significant challenge to urban management. In particular, there is a need to optimize communication infrastructure efficiently, taking into account the impact of participants' emotional states on communication demand. However, conventional systems have struggled to utilize emotional data for demand forecasting and to provide participants with appropriate guidance to avoid congestion.

[0337] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0338] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for collecting and analyzing emotional information from user devices. This enables highly accurate prediction of communication demand that takes into account the emotional tendencies of event participants. It also makes it possible to predict congestion in urban environments and provide participants with guidance on the optimal travel route.

[0339] "Means for acquiring event information" refers to a system that automatically collects detailed information about an event from multiple digital platforms on the internet and stores that information in a database.

[0340] "Methods for predicting communication demand" refer to technologies that utilize predictive models built on past event data and communication logs, and input acquired event information to simulate communication traffic on a given day.

[0341] "Means for determining the need to deploy mobile communication equipment or adjust existing communication equipment" refers to an evaluation process for determining necessary adjustments or additions to communication infrastructure based on predicted communication demand.

[0342] "Means for generating implementation plans" refers to the function of formulating a plan for the specific placement and adjustment of communication infrastructure based on predictions and judgment results.

[0343] "Means for collecting and analyzing emotional information from user devices" refers to technologies that collect data on a user's emotions from smartphones and wearable devices, and then analyze that data to understand the user's emotional state.

[0344] "Methods for evaluating participants' emotional tendencies" refer to methods for analyzing and evaluating the overall emotional trends and tendencies of event participants based on collected emotional data.

[0345] "A guidance system that predicts congestion in urban environments and supports the optimization of travel routes" refers to a mechanism that predicts the flow of people within a city in real time and provides participants with optimized travel guidance.

[0346] The system for realizing this invention is centered around a server. This server collects event information from various digital platforms on the internet. Specifically, it automatically retrieves information such as the event name, date and time, location, and expected number of participants, and stores it in a database. The hardware used for this is typically server equipment provided by a cloud service provider.

[0347] Next, the server uses the collected event information to predict communication demand. A prediction model that utilizes past event data and base station communication logs is incorporated and used for data analysis. Machine learning libraries such as Scikit-learn are sometimes used. Furthermore, sentiment information is collected from the user's terminal and analyzed. For analysis, the natural language processing library NLTK is used to estimate the user's emotional state.

[0348] Users can learn about congestion levels in urban environments via their smartphones or smart glasses and receive guidance on optimal travel routes. Real-time guidance allows users to plan their travel to avoid congested areas, thus supporting a less stressful urban life.

[0349] As a concrete example, when a music festival is held, the server analyzes participants' emotional data in real time and predicts communication demand based on the results. Based on these results, appropriate communication equipment is deployed and adjusted, ensuring the smooth operation of the event. An example of a prompt message would be, "Please tell me the route to avoid congestion during the music festival." In this way, the invention brings peace of mind and efficiency to both event participants and organizers.

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Step 1:

[0352] The server collects event information from multiple digital platforms. Specifically, it uses APIs to retrieve information such as the event name, date and time, location, and estimated number of attendees from the internet, and stores this information in a database. During this process, the server executes data collection scripts and saves the collected data as structured data.

[0353] Step 2:

[0354] The server predicts communication demand based on event information stored in the database. By inputting event information into a prediction model built using past event data and base station communication logs, it simulates communication traffic on the day of the event. This simulation is performed using machine learning libraries such as Scikit-learn.

[0355] Step 3:

[0356] The server collects emotional data from the user's terminal and analyzes it. It collects facial expression data and sensor data via the user's smart devices (smartphones and smart glasses). Based on this data, it performs natural language processing using the NLTK library to determine the emotional state.

[0357] Step 4:

[0358] Based on the analyzed emotional information, the server evaluates the emotional tendencies of participants and improves the accuracy of communication demand forecasting. Specifically, it predicts that communication demand will increase when users are highly agitated, and then incorporates this into the prediction model to run the simulation again.

[0359] Step 5:

[0360] Based on the prediction results, the server determines whether mobile communication equipment needs to be deployed or existing communication equipment needs to be adjusted. This allows it to formulate an implementation plan to adequately meet communication demand. After formulating the plan, it determines the necessary deployment adjustments and outputs a specific plan.

[0361] Step 6:

[0362] Users receive information on congestion levels within urban environments and guidance on optimal routes from a server via their smart devices. The server feeds data back to the user's device in real time, helping users avoid congestion. This includes UI displays and voice guidance in the device application.

[0363] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0364] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0365] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0366] [Third Embodiment]

[0367] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0368] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0369] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0370] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0371] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0373] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0374] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0375] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0376] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0377] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0378] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0379] This invention is a system for efficiently managing the communication demands of an event, with a server primarily handling the processing of various types of information. Specific embodiments of this system are described in detail below.

[0380] This system first includes a function that allows the server to automatically retrieve event information from various digital platforms on the internet. The server accesses data sources such as event booking sites, social media, and local news sites, and collects relevant event information to store in a database.

[0381] Next, the server analyzes the stored event information and extracts attributes such as the expected number of participants, the venue, and the scale of the event. Based on this analysis, the server simulates communication demand through a predictive model that utilizes past event data and base station communication logs. This allows the server to identify the expected communication demand on the day of the event.

[0382] Subsequently, the server determines, based on the simulation results, whether the existing communication equipment can provide sufficient communication services. If necessary, and if the deployment of mobile communication equipment is required, the server automatically generates an implementation plan and reallocates the necessary resources according to its guidelines. The server outputs the generated implementation plan as a report and notifies the responsible person, helping to enable swift action.

[0383] As a concrete example, consider a scenario where a server detects a large-scale concert event that a user plans to attend. The server analyzes the scale and location of this event and determines that it cannot be handled by the normal communication infrastructure. Based on this, the server decides on the deployment of mobile communication equipment and creates a plan. Through this process, an automated process enables rapid and efficient communication response.

[0384] In this way, by predicting the demand for communication infrastructure accompanying the increase in events and taking appropriate measures, companies can improve the quality of their communication services.

[0385] The following describes the processing flow.

[0386] Step 1:

[0387] The server accesses a pre-registered list of digital platforms. It uses scraping techniques and APIs to collect information from each platform, such as event name, date, location, and estimated number of attendees. The collected event information is automatically stored in a database by the server.

[0388] Step 2:

[0389] The server analyzes event information stored in the database. The purpose of this analysis is to extract key elements such as the scale, type, and location of the event. This analysis allows for advance knowledge of how events will impact communication demand.

[0390] Step 3:

[0391] The server runs a model to predict communication demand using historical event data and current event information. This predictive model is built using machine learning algorithms and statistical methods to calculate the expected data traffic on the day of the event.

[0392] Step 4:

[0393] The server compares the predicted communication demand with the capacity of existing communication equipment. If it determines that the demand exceeds capacity, it determines the need to deploy mobile communication equipment or adjust existing equipment. This allows for planning to prevent communication service shortages.

[0394] Step 5:

[0395] The server will create a detailed implementation plan as needed. This plan will include the location and quantity of mobile communication equipment, as well as proposed changes to the configuration of existing communication equipment. The server will output this plan as a report and notify the communication equipment team.

[0396] Step 6:

[0397] After the event ends, the server monitors the actual data traffic and analyzes the difference between the prediction and the actual data. Based on this, a feedback loop is formed to improve the accuracy of the prediction model and improve future prediction accuracy.

[0398] (Example 1)

[0399] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0400] In recent years, the demand for communication infrastructure has surged due to the diversification of events and the increase in the number of participants. However, existing communication equipment is sometimes unable to cope with this demand, resulting in problems such as reduced communication speeds and connection failures on the day of the event. Such situations not only diminish participant satisfaction but also pose a major challenge for communication carriers.

[0401] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0402] In this invention, the server includes a processing unit for acquiring event information, a processing unit for analyzing the event information and extracting features, and a device equipped with an algorithm for predicting communication demand based on the extracted features. This makes it possible to accurately predict communication demand in an event in advance and to arrange appropriate communication equipment or adjust existing facilities.

[0403] "Event information" refers to detailed data about an event, including attribute information such as the specific date and time, location, scale, and number of participants.

[0404] A "processing device" is a computer or a device with the functionality to automatically analyze and manipulate digital information.

[0405] "Storage medium" refers to a device or part of a device capable of storing and retrieving digital data, such as a database or cloud storage.

[0406] "Communication demand" refers to the total volume and frequency of communication expected to occur in connection with an event.

[0407] An "algorithm" is a series of computational procedures for solving a specific problem, and in particular refers to mathematical models and formulas used for forecasting communication demand.

[0408] A "control device" is a device used to manage and regulate the operation of other devices or systems.

[0409] The following describes the specific configuration and operation of the system as an embodiment for carrying out the present invention.

[0410] This invention is a system aimed at predicting communication demand and optimizing the placement of communication equipment. The central server functions as follows:

[0411] The server acquires data from various digital information sources to efficiently collect event information. Here, the server uses APIs and web scraping techniques. The acquired data is stored on the server's storage media, such as a database. This creates a system that continuously updates the latest event information from event booking sites, social media, local news sites, and other sources.

[0412] Next, the server analyzes the event information stored in the database. Using natural language processing (NLP) techniques, it extracts features from information such as event name, date, location, and expected number of participants. This analysis is essential for predicting communication demand.

[0413] Based on the extracted features, the server runs an algorithm to predict communication demand. The server is equipped with a prediction model that utilizes AI. This model is built on data from similar past events and communication logs, and provides accurate simulation results when event information is input.

[0414] As a concrete example, when a large-scale music festival is held, the server quickly collects and analyzes event information. Using predictive models, it identifies peak times and areas for communication demand and automatically generates a plan proposing the optimal placement of mobile communication equipment. This entire process enables efficient and rapid communication response.

[0415] An example of a prompt message is provided: "Predict communication demand based on the number of participants and location of the next event, and propose necessary countermeasures." This allows for the proposal of specific countermeasures using a synthetic AI model.

[0416] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0417] Step 1:

[0418] The server accesses digital information sources on the internet to retrieve event information. Input requires API and web scraping settings or URLs, and output is the raw data of the retrieved events. The server maintains information consistency by updating this information in real time and storing it in a storage medium.

[0419] Step 2:

[0420] The server analyzes event information stored on a storage medium. Its inputs are raw data and natural language processing (NLP) algorithms for analysis, while its output consists of extracted event features (e.g., event name, date, location, expected number of attendees). The server extracts each item in text format, efficiently organizes and stores them in a database.

[0421] Step 3:

[0422] The server predicts communication demand based on the characteristics of extracted events. Inputs include event characteristics, data from similar past events, and communication logs. A generative AI model is used as the prediction algorithm, and simulations are performed. The output is a detailed report of the expected communication demand. The server analyzes historical data and displays future demand numerically and graphically.

[0423] Step 4:

[0424] The server generates a plan for appropriate communication equipment placement or adjustment of existing facilities based on predicted communication demand. The input is a communication demand report, and the output is a detailed implementation plan. The server uses an algorithm for optimal placement to calculate and report on efficient equipment placement.

[0425] Step 5:

[0426] The server notifies the responsible parties of the generated plan. The input is the implementation plan document, and the output is a notification via email or dashboard. The server has the capability to automatically format the plan and quickly distribute it to the relevant parties.

[0427] (Application Example 1)

[0428] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0429] In recent years, the load on communication infrastructure has increased dramatically as a variety of events are held. In particular, large-scale events experience communication congestion, leading to a poor communication experience for participants. Therefore, there is a need for a system that can efficiently and quickly predict communication demand, and then appropriately allocate equipment and provide advance notification to participants.

[0430] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0431] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for generating an implementation plan and notifying users of communication infrastructure congestion information. This makes it possible to provide event participants with appropriate ways to use the communication infrastructure and ensure smooth communication.

[0432] "Event information" refers to detailed information about an event held at a specific date, time, and location, and includes data such as the number of participants, location, and scale.

[0433] "Communication demand" is an indicator that shows the predicted amount of usage and load on communication networks at a specific time and place.

[0434] "Mobile communication equipment" refers to mobile communication devices and infrastructure used to temporarily supplement the load on a communication network.

[0435] "Existing communication equipment" refers to currently installed, fixed communication-related infrastructure and devices.

[0436] An "implementation plan" is a document that specifically instructs the optimal placement and adjustment of necessary communication equipment based on predicted communication demand.

[0437] "Notification" refers to a means of transmitting specific information to specific recipients, and in this context, it refers to providing event participants with information regarding the use of communication infrastructure.

[0438] This invention efficiently manages event communication demands through a system in which a server plays a central role. The server first automatically retrieves event information from multiple digital platforms on the internet. In doing so, it connects with event reservation sites and social media via APIs and stores the latest event information in real time in a database. The server then analyzes the stored data to determine the expected number of participants and the venue for each event.

[0439] Next, the server uses machine learning algorithms to predict communication demand based on past event data and communication equipment logs. The scikit-learn library in Python is used for the prediction, and a highly accurate prediction model is built.

[0440] Subsequently, based on the prediction results, the server generates a specific implementation plan for deploying necessary mobile communication equipment and adjusting existing communication equipment. Once the plan is generated, the server notifies users of congestion information via a smartphone application. The notification uses an application created with a smartphone app development framework. Through the app, users can learn about the status of the communication infrastructure in advance before participating in an event.

[0441] As a concrete example, consider the case of a large-scale music festival. When the server retrieves event information and anticipates high communication demand, a notification message such as "Normal network traffic may be congested during the music festival, so we recommend using Wi-Fi" is sent to participants' smartphones. This allows participants to understand the communication situation in advance and take appropriate measures smoothly.

[0442] By using a generative AI model, an example prompt message could be, "Please tell me the expected state of the communication infrastructure for the event I plan to attend this weekend." This would allow event participants to use communication services with peace of mind.

[0443] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0444] Step 1:

[0445] The server automatically collects event information from multiple digital platforms on the internet. Specifically, it uses APIs to retrieve the latest event information from event booking sites and social media, and stores it in a database as structured data. The input is event data obtained from the APIs, and the output is event information stored in the database maintained by the server.

[0446] Step 2:

[0447] The server analyzes event information in the database and extracts attribute data such as the expected number of participants and the event location. The analysis results are compiled into a dataset necessary for predicting communication demand for each event. The input is event information stored in the database, and the output is analysis data including event attributes.

[0448] Step 3:

[0449] The server uses historical event data and base station communication logs to build a generative AI model and predict communication demand. The processing uses the Python scikit-learn library, and the prediction simulation is performed by inputting the analyzed data into the prediction model. The input is the analyzed event attribute data and communication logs, and the output is the result of the predicted communication demand.

[0450] Step 4:

[0451] The server determines the optimal placement of mobile communication equipment based on the prediction results and generates a specific implementation plan. The plan is compiled as specific placement instructions to mitigate the predicted communication load. The input is the predicted communication demand, and the output is the implementation plan.

[0452] Step 5:

[0453] The server notifies the user of the generated implementation plan via a smartphone application. The device receives real-time information on areas where congestion is expected, allowing the user to gain prior knowledge about the communication environment. The input is the implementation plan, and the output is a notification message sent to the user's device.

[0454] Step 6:

[0455] Based on notifications from the server, users take appropriate communication measures before participating in an event. For example, they might refer to prompts included in the notification, such as "Please tell me the expected communication infrastructure conditions for the event you plan to attend this weekend," and take measures such as using Wi-Fi in congested areas. The input is the notification message from the server, and the output is the user's countermeasures.

[0456] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0457] This invention aims to efficiently predict and manage communication demand related to events, while also optimizing the entire system by considering the emotional state of users. This system primarily operates around a server, collecting and analyzing various types of information and formulating implementation plans.

[0458] First, the server automatically collects event information from multiple digital platforms. This includes detailed information such as the event name, date, location, and expected number of attendees, which is then stored in a database.

[0459] Next, the server analyzes the event information and calculates communication demand using a predictive model. This predictive model is built on data from past events and base station communication logs, and has the ability to specifically simulate the data traffic on the day of the event.

[0460] Furthermore, this system incorporates an emotion engine to recognize user emotions. Based on user emotion data acquired from terminals and various devices, the server understands the emotional tendencies of event participants. This emotion data is not only used to improve the accuracy of communication demand predictions, but is also utilized as an important factor in determining whether or not to hold an event and adjusting its scale.

[0461] As a concrete example, consider an implementation at a music festival. In this festival, a server receives participant emotional data in real time and, based on this data, predicts an increase in communication demand when participants are highly excited. As a result, the server quickly decides to deploy additional mobile communication equipment, helping to ensure the smooth operation of the festival.

[0462] This series of processes provides a sense of security to both event organizers and participants, and improves the overall efficiency of the system. This form of invention visualizes and utilizes the invisible element of emotion, and is highly effective in optimizing communication infrastructure.

[0463] The following describes the processing flow.

[0464] Step 1:

[0465] The server connects to multiple pre-registered digital platforms to collect event information. Using scraping and APIs, it retrieves information such as event name, date, location, and estimated number of attendees, and stores it in a database.

[0466] Step 2:

[0467] The server analyzes the stored event information. It compares it with data from similar past events to understand the characteristics and scale of the event, and prepares to set up the variables necessary for the communication demand forecasting model.

[0468] Step 3:

[0469] The server inputs the analyzed event information into a predictive model to forecast communication demand. This model is built on past event data and communication logs, and uses machine learning algorithms to simulate communication demand on the day of the event.

[0470] Step 4:

[0471] The device acquires emotional data from event participants in real time. This utilizes the participants' smartphone sensors and wearable devices. The device then transmits this data to an emotion engine.

[0472] Step 5:

[0473] The server analyzes the emotional data received from the emotion engine. It evaluates the user's level of excitement and stress, and integrates this data into a predictive model to forecast the impact on communication demand during an event.

[0474] Step 6:

[0475] Based on all analysis results, the server develops a plan for the placement of mobile communication equipment and the adjustment of existing communication equipment. If necessary, it instructs the responsible team to implement the new placement plan and outputs the plan as a report.

[0476] Step 7:

[0477] Users enjoy guaranteed communication quality while participating in the event. After the event, the server analyzes the difference between actual data traffic and predictions and uses this as feedback to improve the accuracy of the learning model.

[0478] (Example 2)

[0479] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] The challenge lies in preventing a decline in communication services due to the sudden increase in communication demand during events, and in providing a comfortable communication environment. Furthermore, it is necessary to adjust the communication infrastructure more appropriately by taking user emotions into consideration.

[0481] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0482] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for analyzing user sentiment data and understanding sentiment trends. This improves the accuracy of communication demand prediction and enables the rapid formulation of an optimal communication equipment deployment plan.

[0483] "Event information" refers to specific details about an event, such as the event name, date, location, and expected number of participants.

[0484] "Communication demand" refers to the predicted amount of data traffic at a specific time and place, and is a factor that directly affects the load on communication infrastructure.

[0485] "Mobile communication equipment" refers to additional communication devices and equipment installed to meet the increased communication needs during events.

[0486] "Emotional data" refers to information that indicates a user's emotional state, and is collected from data such as voice tone, heart rate, and social media content.

[0487] "Emotional tendencies" refer to the overall emotional trends and patterns obtained by analyzing users' emotional data.

[0488] An "implementation plan" refers to the specific procedures and plans for planning the placement and adjustment of communication equipment based on predicted communication demand and sentiment trends.

[0489] This invention provides a system that efficiently predicts communication demand related to events and optimizes the communication infrastructure while considering the emotional state of users. The system primarily operates around a server, which collects and analyzes necessary data and formulates implementation plans.

[0490] The server first automatically collects event information from multiple sources on the internet using APIs. This information includes the event name, date, location, and expected number of attendees, and is stored in a database. Social networking platforms and event announcement services are used as information sources.

[0491] Next, the server applies a predictive model built using libraries such as Python's scikit-learn to forecast communication demand based on collected event information. This model is based on historical event data and communication logs, and simulations can identify times and locations where communication is concentrated.

[0492] The server also acquires user sentiment data, such as voice tone, heart rate, and social media posts from terminals and devices. This data is analyzed, for example, through a sentiment analysis API using Microsoft Azure Cognitive Services. Based on the analysis results, the server understands the emotional tendencies of participants and improves the accuracy of communication demand predictions.

[0493] As a concrete example, consider its use in a music festival. The server receives participant emotional data in real time, allowing it to predict increased communication demand based on high levels of excitement. Based on these results, the server can quickly decide on the deployment of additional mobile communication equipment to support smooth event management.

[0494] An example of a prompt would be, "Predict communication demand at the next music festival and plan the optimal placement of mobile communication equipment based on participant sentiment data." This prompt allows the system to generate a flexible plan that responds to real-time conditions.

[0495] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0496] Step 1:

[0497] The server automatically collects event information from multiple sources on the internet via APIs. This input includes specific keywords and topics. Specifically, the server retrieves information such as event name, date, location, and expected number of attendees from social networking platforms and event announcement services, and stores it in a database. The output is a dataset containing the retrieved event information.

[0498] Step 2:

[0499] The server retrieves event information stored in a database and applies a predictive model using the Python scikit-learn library to forecast communication demand. The inputs are an event information dataset, historical event data, and communication logs. Specifically, the server trains the model based on historical data and simulates communication traffic on the day of the event. The output is an indicator of the predicted communication demand.

[0500] Step 3:

[0501] The terminal collects user emotion data in real time from smartphones and wearable devices. This data includes voice tone, heart rate, and social media posts. The server analyzes this data using emotion analysis APIs such as Microsoft Azure Cognitive Services. The input is the emotion data collected in real time. Specifically, it estimates emotion tendencies via the emotion analysis API. The output is a result showing the participant's emotion tendencies.

[0502] Step 4:

[0503] The server plans the optimal placement of mobile communication equipment based on predicted communication demand and analyzed sentiment trends. Inputs are communication demand indicators and sentiment trend results. Specifically, the server evaluates the communication load in each area and determines the necessary mobile communication equipment. Output is an implementation plan for the placement and adjustment of communication equipment.

[0504] Step 5:

[0505] The user manages the actual event based on the proposed communication equipment layout plan. The server monitors this process and makes real-time adjustments as needed. The input is the implementation plan provided by the server. Specifically, the user appropriately allocates communication resources and supports the smooth running of the event. The output is the event with a comfortable communication environment ensured.

[0506] (Application Example 2)

[0507] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0508] In modern cities, the surge in communication demand and the resulting congestion during events pose a significant challenge to urban management. In particular, there is a need to optimize communication infrastructure efficiently, taking into account the impact of participants' emotional states on communication demand. However, conventional systems have struggled to utilize emotional data for demand forecasting and to provide participants with appropriate guidance to avoid congestion.

[0509] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0510] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for collecting and analyzing emotional information from user devices. This enables highly accurate prediction of communication demand that takes into account the emotional tendencies of event participants. It also makes it possible to predict congestion in urban environments and provide participants with guidance on the optimal travel route.

[0511] "Means for acquiring event information" refers to a system that automatically collects detailed information about an event from multiple digital platforms on the internet and stores that information in a database.

[0512] "Methods for predicting communication demand" refer to technologies that utilize predictive models built on past event data and communication logs, and input acquired event information to simulate communication traffic on a given day.

[0513] "Means for determining the need to deploy mobile communication equipment or adjust existing communication equipment" refers to an evaluation process for determining necessary adjustments or additions to communication infrastructure based on predicted communication demand.

[0514] "Means for generating implementation plans" refers to the function of formulating a plan for the specific placement and adjustment of communication infrastructure based on predictions and judgment results.

[0515] "Means for collecting and analyzing emotional information from user devices" refers to technologies that collect data on a user's emotions from smartphones and wearable devices, and then analyze that data to understand the user's emotional state.

[0516] "Methods for evaluating participants' emotional tendencies" refer to methods for analyzing and evaluating the overall emotional trends and tendencies of event participants based on collected emotional data.

[0517] "A guidance system that predicts congestion in urban environments and supports the optimization of travel routes" refers to a mechanism that predicts the flow of people within a city in real time and provides participants with optimized travel guidance.

[0518] The system for realizing this invention is centered around a server. This server collects event information from various digital platforms on the internet. Specifically, it automatically retrieves information such as the event name, date and time, location, and expected number of participants, and stores it in a database. The hardware used for this is typically server equipment provided by a cloud service provider.

[0519] Next, the server uses the collected event information to predict communication demand. A prediction model that utilizes past event data and base station communication logs is incorporated and used for data analysis. Machine learning libraries such as Scikit-learn are sometimes used. Furthermore, sentiment information is collected from the user's terminal and analyzed. For analysis, the natural language processing library NLTK is used to estimate the user's emotional state.

[0520] Users can learn about congestion levels in urban environments via their smartphones or smart glasses and receive guidance on optimal travel routes. Real-time guidance allows users to plan their travel to avoid congested areas, thus supporting a less stressful urban life.

[0521] As a concrete example, when a music festival is held, the server analyzes participants' emotional data in real time and predicts communication demand based on the results. Based on these results, appropriate communication equipment is deployed and adjusted, ensuring the smooth operation of the event. An example of a prompt message would be, "Please tell me the route to avoid congestion during the music festival." In this way, the invention brings peace of mind and efficiency to both event participants and organizers.

[0522] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0523] Step 1:

[0524] The server collects event information from multiple digital platforms. Specifically, it uses APIs to retrieve information such as the event name, date and time, location, and estimated number of attendees from the internet, and stores this information in a database. During this process, the server executes data collection scripts and saves the collected data as structured data.

[0525] Step 2:

[0526] The server predicts communication demand based on event information stored in the database. By inputting event information into a prediction model built using past event data and base station communication logs, it simulates communication traffic on the day of the event. This simulation is performed using machine learning libraries such as Scikit-learn.

[0527] Step 3:

[0528] The server collects emotional data from the user's terminal and analyzes it. It collects facial expression data and sensor data via the user's smart devices (smartphones and smart glasses). Based on this data, it performs natural language processing using the NLTK library to determine the emotional state.

[0529] Step 4:

[0530] Based on the analyzed emotional information, the server evaluates the emotional tendencies of participants and improves the accuracy of communication demand forecasting. Specifically, it predicts that communication demand will increase when users are highly agitated, and then incorporates this into the prediction model to run the simulation again.

[0531] Step 5:

[0532] Based on the prediction results, the server determines whether mobile communication equipment needs to be deployed or existing communication equipment needs to be adjusted. This allows it to formulate an implementation plan to adequately meet communication demand. After formulating the plan, it determines the necessary deployment adjustments and outputs a specific plan.

[0533] Step 6:

[0534] Users receive information on congestion levels within urban environments and guidance on optimal routes from a server via their smart devices. The server feeds data back to the user's device in real time, helping users avoid congestion. This includes UI displays and voice guidance in the device application.

[0535] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0536] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0537] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0538] [Fourth Embodiment]

[0539] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0540] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0541] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0542] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0543] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0544] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0545] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0546] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0547] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0548] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0549] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0550] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0551] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0552] This invention is a system for efficiently managing the communication demands of an event, with a server primarily handling the processing of various types of information. Specific embodiments of this system are described in detail below.

[0553] This system first includes a function that allows the server to automatically retrieve event information from various digital platforms on the internet. The server accesses data sources such as event booking sites, social media, and local news sites, and collects relevant event information to store in a database.

[0554] Next, the server analyzes the stored event information and extracts attributes such as the expected number of participants, the venue, and the scale of the event. Based on this analysis, the server simulates communication demand through a predictive model that utilizes past event data and base station communication logs. This allows the server to identify the expected communication demand on the day of the event.

[0555] Subsequently, the server determines, based on the simulation results, whether the existing communication equipment can provide sufficient communication services. If necessary, and if the deployment of mobile communication equipment is required, the server automatically generates an implementation plan and reallocates the necessary resources according to its guidelines. The server outputs the generated implementation plan as a report and notifies the responsible person, helping to enable swift action.

[0556] As a concrete example, consider a scenario where a server detects a large-scale concert event that a user plans to attend. The server analyzes the scale and location of this event and determines that it cannot be handled by the normal communication infrastructure. Based on this, the server decides on the deployment of mobile communication equipment and creates a plan. Through this process, an automated process enables rapid and efficient communication response.

[0557] In this way, by predicting the demand for communication infrastructure accompanying the increase in events and taking appropriate measures, companies can improve the quality of their communication services.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] The server accesses a pre-registered list of digital platforms. It uses scraping techniques and APIs to collect information from each platform, such as event name, date, location, and estimated number of attendees. The collected event information is automatically stored in a database by the server.

[0561] Step 2:

[0562] The server analyzes event information stored in the database. The purpose of this analysis is to extract key elements such as the scale, type, and location of the event. This analysis allows for advance knowledge of how events will impact communication demand.

[0563] Step 3:

[0564] The server runs a model to predict communication demand using historical event data and current event information. This predictive model is built using machine learning algorithms and statistical methods to calculate the expected data traffic on the day of the event.

[0565] Step 4:

[0566] The server compares the predicted communication demand with the capacity of existing communication equipment. If it determines that the demand exceeds capacity, it determines the need to deploy mobile communication equipment or adjust existing equipment. This allows for planning to prevent communication service shortages.

[0567] Step 5:

[0568] The server will create a detailed implementation plan as needed. This plan will include the location and quantity of mobile communication equipment, as well as proposed changes to the configuration of existing communication equipment. The server will output this plan as a report and notify the communication equipment team.

[0569] Step 6:

[0570] After the event ends, the server monitors the actual data traffic and analyzes the difference between the prediction and the actual data. Based on this, a feedback loop is formed to improve the accuracy of the prediction model and improve future prediction accuracy.

[0571] (Example 1)

[0572] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0573] In recent years, the demand for communication infrastructure has surged due to the diversification of events and the increase in the number of participants. However, existing communication equipment is sometimes unable to cope with this demand, resulting in problems such as reduced communication speeds and connection failures on the day of the event. Such situations not only diminish participant satisfaction but also pose a major challenge for communication carriers.

[0574] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0575] In this invention, the server includes a processing unit for acquiring event information, a processing unit for analyzing the event information and extracting features, and a device equipped with an algorithm for predicting communication demand based on the extracted features. This makes it possible to accurately predict communication demand in an event in advance and to arrange appropriate communication equipment or adjust existing facilities.

[0576] "Event information" refers to detailed data about an event, including attribute information such as the specific date and time, location, scale, and number of participants.

[0577] A "processing device" is a computer or a device with the functionality to automatically analyze and manipulate digital information.

[0578] "Storage medium" refers to a device or part of a device capable of storing and retrieving digital data, such as a database or cloud storage.

[0579] "Communication demand" refers to the total volume and frequency of communication expected to occur in connection with an event.

[0580] An "algorithm" is a series of computational procedures for solving a specific problem, and in particular refers to mathematical models and formulas used for forecasting communication demand.

[0581] A "control device" is a device used to manage and regulate the operation of other devices or systems.

[0582] The following describes the specific configuration and operation of the system as an embodiment for carrying out the present invention.

[0583] This invention is a system aimed at predicting communication demand and optimizing the placement of communication equipment. The central server functions as follows:

[0584] The server acquires data from various digital information sources to efficiently collect event information. Here, the server uses APIs and web scraping techniques. The acquired data is stored on the server's storage media, such as a database. This creates a system that continuously updates the latest event information from event booking sites, social media, local news sites, and other sources.

[0585] Next, the server analyzes the event information stored in the database. Using natural language processing (NLP) techniques, it extracts features from information such as event name, date, location, and expected number of participants. This analysis is essential for predicting communication demand.

[0586] Based on the extracted features, the server runs an algorithm to predict communication demand. The server is equipped with a prediction model that utilizes AI. This model is built on data from similar past events and communication logs, and provides accurate simulation results when event information is input.

[0587] As a concrete example, when a large-scale music festival is held, the server quickly collects and analyzes event information. Using predictive models, it identifies peak times and areas for communication demand and automatically generates a plan proposing the optimal placement of mobile communication equipment. This entire process enables efficient and rapid communication response.

[0588] An example of a prompt message is provided: "Predict communication demand based on the number of participants and location of the next event, and propose necessary countermeasures." This allows for the proposal of specific countermeasures using a synthetic AI model.

[0589] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0590] Step 1:

[0591] The server accesses digital information sources on the internet to retrieve event information. Input requires API and web scraping settings or URLs, and output is the raw data of the retrieved events. The server maintains information consistency by updating this information in real time and storing it in a storage medium.

[0592] Step 2:

[0593] The server analyzes event information stored on a storage medium. Its inputs are raw data and natural language processing (NLP) algorithms for analysis, while its output consists of extracted event features (e.g., event name, date, location, expected number of attendees). The server extracts each item in text format, efficiently organizes and stores them in a database.

[0594] Step 3:

[0595] The server predicts communication demand based on the characteristics of extracted events. Inputs include event characteristics, data from similar past events, and communication logs. A generative AI model is used as the prediction algorithm, and simulations are performed. The output is a detailed report of the expected communication demand. The server analyzes historical data and displays future demand numerically and graphically.

[0596] Step 4:

[0597] The server generates a plan for appropriate communication equipment placement or adjustment of existing facilities based on predicted communication demand. The input is a communication demand report, and the output is a detailed implementation plan. The server uses an algorithm for optimal placement to calculate and report on efficient equipment placement.

[0598] Step 5:

[0599] The server notifies the responsible parties of the generated plan. The input is the implementation plan document, and the output is a notification via email or dashboard. The server has the capability to automatically format the plan and quickly distribute it to the relevant parties.

[0600] (Application Example 1)

[0601] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] In recent years, the load on communication infrastructure has increased dramatically as a variety of events are held. In particular, large-scale events experience communication congestion, leading to a poor communication experience for participants. Therefore, there is a need for a system that can efficiently and quickly predict communication demand, and then appropriately allocate equipment and provide advance notification to participants.

[0603] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0604] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for generating an implementation plan and notifying users of communication infrastructure congestion information. This makes it possible to provide event participants with appropriate ways to use the communication infrastructure and ensure smooth communication.

[0605] "Event information" refers to detailed information about an event held at a specific date, time, and location, and includes data such as the number of participants, location, and scale.

[0606] "Communication demand" is an indicator that shows the predicted amount of usage and load on communication networks at a specific time and place.

[0607] "Mobile communication equipment" refers to mobile communication devices and infrastructure used to temporarily supplement the load on a communication network.

[0608] "Existing communication equipment" refers to currently installed, fixed communication-related infrastructure and devices.

[0609] An "implementation plan" is a document that specifically instructs the optimal placement and adjustment of necessary communication equipment based on predicted communication demand.

[0610] "Notification" refers to a means of transmitting specific information to specific recipients, and in this context, it refers to providing event participants with information regarding the use of communication infrastructure.

[0611] This invention efficiently manages event communication demands through a system in which a server plays a central role. The server first automatically retrieves event information from multiple digital platforms on the internet. In doing so, it connects with event reservation sites and social media via APIs and stores the latest event information in real time in a database. The server then analyzes the stored data to determine the expected number of participants and the venue for each event.

[0612] Next, the server uses machine learning algorithms to predict communication demand based on past event data and communication equipment logs. The scikit-learn library in Python is used for the prediction, and a highly accurate prediction model is built.

[0613] Subsequently, based on the prediction results, the server generates a specific implementation plan for deploying necessary mobile communication equipment and adjusting existing communication equipment. Once the plan is generated, the server notifies users of congestion information via a smartphone application. The notification uses an application created with a smartphone app development framework. Through the app, users can learn about the status of the communication infrastructure in advance before participating in an event.

[0614] As a concrete example, consider the case of a large-scale music festival. When the server retrieves event information and anticipates high communication demand, a notification message such as "Normal network traffic may be congested during the music festival, so we recommend using Wi-Fi" is sent to participants' smartphones. This allows participants to understand the communication situation in advance and take appropriate measures smoothly.

[0615] By using a generative AI model, an example prompt message could be, "Please tell me the expected state of the communication infrastructure for the event I plan to attend this weekend." This would allow event participants to use communication services with peace of mind.

[0616] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0617] Step 1:

[0618] The server automatically collects event information from multiple digital platforms on the internet. Specifically, it uses APIs to retrieve the latest event information from event booking sites and social media, and stores it in a database as structured data. The input is event data obtained from the APIs, and the output is event information stored in the database maintained by the server.

[0619] Step 2:

[0620] The server analyzes event information in the database and extracts attribute data such as the expected number of participants and the event location. The analysis results are compiled into a dataset necessary for predicting communication demand for each event. The input is event information stored in the database, and the output is analysis data including event attributes.

[0621] Step 3:

[0622] The server uses historical event data and base station communication logs to build a generative AI model and predict communication demand. The processing uses the Python scikit-learn library, and the prediction simulation is performed by inputting the analyzed data into the prediction model. The input is the analyzed event attribute data and communication logs, and the output is the result of the predicted communication demand.

[0623] Step 4:

[0624] The server determines the optimal placement of mobile communication equipment based on the prediction results and generates a specific implementation plan. The plan is compiled as specific placement instructions to mitigate the predicted communication load. The input is the predicted communication demand, and the output is the implementation plan.

[0625] Step 5:

[0626] The server notifies the user of the generated implementation plan via a smartphone application. The device receives real-time information on areas where congestion is expected, allowing the user to gain prior knowledge about the communication environment. The input is the implementation plan, and the output is a notification message sent to the user's device.

[0627] Step 6:

[0628] Based on notifications from the server, users take appropriate communication measures before participating in an event. For example, they might refer to prompts included in the notification, such as "Please tell me the expected communication infrastructure conditions for the event you plan to attend this weekend," and take measures such as using Wi-Fi in congested areas. The input is the notification message from the server, and the output is the user's countermeasures.

[0629] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0630] This invention aims to efficiently predict and manage communication demand related to events, while also optimizing the entire system by considering the emotional state of users. This system primarily operates around a server, collecting and analyzing various types of information and formulating implementation plans.

[0631] First, the server automatically collects event information from multiple digital platforms. This includes detailed information such as the event name, date, location, and expected number of attendees, which is then stored in a database.

[0632] Next, the server analyzes the event information and calculates communication demand using a predictive model. This predictive model is built on data from past events and base station communication logs, and has the ability to specifically simulate the data traffic on the day of the event.

[0633] Furthermore, this system incorporates an emotion engine to recognize user emotions. Based on user emotion data acquired from terminals and various devices, the server understands the emotional tendencies of event participants. This emotion data is not only used to improve the accuracy of communication demand predictions, but is also utilized as an important factor in determining whether or not to hold an event and adjusting its scale.

[0634] As a concrete example, consider an implementation at a music festival. In this festival, a server receives participant emotional data in real time and, based on this data, predicts an increase in communication demand when participants are highly excited. As a result, the server quickly decides to deploy additional mobile communication equipment, helping to ensure the smooth operation of the festival.

[0635] This series of processes provides a sense of security to both event organizers and participants, and improves the overall efficiency of the system. This form of invention visualizes and utilizes the invisible element of emotion, and is highly effective in optimizing communication infrastructure.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The server connects to multiple pre-registered digital platforms to collect event information. Using scraping and APIs, it retrieves information such as event name, date, location, and estimated number of attendees, and stores it in a database.

[0639] Step 2:

[0640] The server analyzes the stored event information. It compares it with data from similar past events to understand the characteristics and scale of the event, and prepares to set up the variables necessary for the communication demand forecasting model.

[0641] Step 3:

[0642] The server inputs the analyzed event information into a predictive model to forecast communication demand. This model is built on past event data and communication logs, and uses machine learning algorithms to simulate communication demand on the day of the event.

[0643] Step 4:

[0644] The device acquires emotional data from event participants in real time. This utilizes the participants' smartphone sensors and wearable devices. The device then transmits this data to an emotion engine.

[0645] Step 5:

[0646] The server analyzes the emotional data received from the emotion engine. It evaluates the user's level of excitement and stress, and integrates this data into a predictive model to forecast the impact on communication demand during an event.

[0647] Step 6:

[0648] Based on all analysis results, the server develops a plan for the placement of mobile communication equipment and the adjustment of existing communication equipment. If necessary, it instructs the responsible team to implement the new placement plan and outputs the plan as a report.

[0649] Step 7:

[0650] Users enjoy guaranteed communication quality while participating in the event. After the event, the server analyzes the difference between actual data traffic and predictions and uses this as feedback to improve the accuracy of the learning model.

[0651] (Example 2)

[0652] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] The challenge lies in preventing a decline in communication services due to the sudden increase in communication demand during events, and in providing a comfortable communication environment. Furthermore, it is necessary to adjust the communication infrastructure more appropriately by taking user emotions into consideration.

[0654] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0655] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for analyzing user sentiment data and understanding sentiment trends. This improves the accuracy of communication demand prediction and enables the rapid formulation of an optimal communication equipment deployment plan.

[0656] "Event information" refers to specific details about an event, such as the event name, date, location, and expected number of participants.

[0657] "Communication demand" refers to the predicted amount of data traffic at a specific time and place, and is a factor that directly affects the load on communication infrastructure.

[0658] "Mobile communication equipment" refers to additional communication devices and equipment installed to meet the increased communication needs during events.

[0659] "Emotional data" refers to information that indicates a user's emotional state, and is collected from data such as voice tone, heart rate, and social media content.

[0660] "Emotional tendencies" refer to the overall emotional trends and patterns obtained by analyzing users' emotional data.

[0661] An "implementation plan" refers to the specific procedures and plans for planning the placement and adjustment of communication equipment based on predicted communication demand and sentiment trends.

[0662] This invention provides a system that efficiently predicts communication demand related to events and optimizes the communication infrastructure while considering the emotional state of users. The system primarily operates around a server, which collects and analyzes necessary data and formulates implementation plans.

[0663] The server first automatically collects event information from multiple sources on the internet using APIs. This information includes the event name, date, location, and expected number of attendees, and is stored in a database. Social networking platforms and event announcement services are used as information sources.

[0664] Next, the server applies a predictive model built using libraries such as Python's scikit-learn to forecast communication demand based on collected event information. This model is based on historical event data and communication logs, and simulations can identify times and locations where communication is concentrated.

[0665] The server also acquires user sentiment data, such as voice tone, heart rate, and social media posts from terminals and devices. This data is analyzed, for example, through a sentiment analysis API using Microsoft Azure Cognitive Services. Based on the analysis results, the server understands the emotional tendencies of participants and improves the accuracy of communication demand predictions.

[0666] As a concrete example, consider its use in a music festival. The server receives participant emotional data in real time, allowing it to predict increased communication demand based on high levels of excitement. Based on these results, the server can quickly decide on the deployment of additional mobile communication equipment to support smooth event management.

[0667] An example of a prompt would be, "Predict communication demand at the next music festival and plan the optimal placement of mobile communication equipment based on participant sentiment data." This prompt allows the system to generate a flexible plan that responds to real-time conditions.

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

[0669] Step 1:

[0670] The server automatically collects event information from multiple sources on the internet via APIs. This input includes specific keywords and topics. Specifically, the server retrieves information such as event name, date, location, and expected number of attendees from social networking platforms and event announcement services, and stores it in a database. The output is a dataset containing the retrieved event information.

[0671] Step 2:

[0672] The server retrieves event information stored in a database and applies a predictive model using the Python scikit-learn library to forecast communication demand. The inputs are an event information dataset, historical event data, and communication logs. Specifically, the server trains the model based on historical data and simulates communication traffic on the day of the event. The output is an indicator of the predicted communication demand.

[0673] Step 3:

[0674] The terminal collects user emotion data in real time from smartphones and wearable devices. This data includes voice tone, heart rate, and social media posts. The server analyzes this data using emotion analysis APIs such as Microsoft Azure Cognitive Services. The input is the emotion data collected in real time. Specifically, it estimates emotion tendencies via the emotion analysis API. The output is a result showing the participant's emotion tendencies.

[0675] Step 4:

[0676] The server plans the optimal placement of mobile communication equipment based on predicted communication demand and analyzed sentiment trends. Inputs are communication demand indicators and sentiment trend results. Specifically, the server evaluates the communication load in each area and determines the necessary mobile communication equipment. Output is an implementation plan for the placement and adjustment of communication equipment.

[0677] Step 5:

[0678] The user manages the actual event based on the proposed communication equipment layout plan. The server monitors this process and makes real-time adjustments as needed. The input is the implementation plan provided by the server. Specifically, the user appropriately allocates communication resources and supports the smooth running of the event. The output is the event with a comfortable communication environment ensured.

[0679] (Application Example 2)

[0680] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0681] In modern cities, the surge in communication demand and the resulting congestion during events pose a significant challenge to urban management. In particular, there is a need to optimize communication infrastructure efficiently, taking into account the impact of participants' emotional states on communication demand. However, conventional systems have struggled to utilize emotional data for demand forecasting and to provide participants with appropriate guidance to avoid congestion.

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

[0683] In this invention, the server includes means for acquiring event information, means for predicting communication demand, and means for collecting and analyzing emotional information from user devices. This enables highly accurate prediction of communication demand that takes into account the emotional tendencies of event participants. It also makes it possible to predict congestion in urban environments and provide participants with guidance on the optimal travel route.

[0684] "Means for acquiring event information" refers to a system that automatically collects detailed information about an event from multiple digital platforms on the internet and stores that information in a database.

[0685] "Methods for predicting communication demand" refer to technologies that utilize predictive models built on past event data and communication logs, and input acquired event information to simulate communication traffic on a given day.

[0686] "Means for determining the need to deploy mobile communication equipment or adjust existing communication equipment" refers to an evaluation process for determining necessary adjustments or additions to communication infrastructure based on predicted communication demand.

[0687] "Means for generating implementation plans" refers to the function of formulating a plan for the specific placement and adjustment of communication infrastructure based on predictions and judgment results.

[0688] "Means for collecting and analyzing emotional information from user devices" refers to technologies that collect data on a user's emotions from smartphones and wearable devices, and then analyze that data to understand the user's emotional state.

[0689] "Methods for evaluating participants' emotional tendencies" refer to methods for analyzing and evaluating the overall emotional trends and tendencies of event participants based on collected emotional data.

[0690] "A guidance system that predicts congestion in urban environments and supports the optimization of travel routes" refers to a mechanism that predicts the flow of people within a city in real time and provides participants with optimized travel guidance.

[0691] The system for realizing this invention is centered around a server. This server collects event information from various digital platforms on the internet. Specifically, it automatically retrieves information such as the event name, date and time, location, and expected number of participants, and stores it in a database. The hardware used for this is typically server equipment provided by a cloud service provider.

[0692] Next, the server uses the collected event information to predict communication demand. A prediction model that utilizes past event data and base station communication logs is incorporated and used for data analysis. Machine learning libraries such as Scikit-learn are sometimes used. Furthermore, sentiment information is collected from the user's terminal and analyzed. For analysis, the natural language processing library NLTK is used to estimate the user's emotional state.

[0693] Users can learn about congestion levels in urban environments via their smartphones or smart glasses and receive guidance on optimal travel routes. Real-time guidance allows users to plan their travel to avoid congested areas, thus supporting a less stressful urban life.

[0694] As a concrete example, when a music festival is held, the server analyzes participants' emotional data in real time and predicts communication demand based on the results. Based on these results, appropriate communication equipment is deployed and adjusted, ensuring the smooth operation of the event. An example of a prompt message would be, "Please tell me the route to avoid congestion during the music festival." In this way, the invention brings peace of mind and efficiency to both event participants and organizers.

[0695] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0696] Step 1:

[0697] The server collects event information from multiple digital platforms. Specifically, it uses APIs to retrieve information such as the event name, date and time, location, and estimated number of attendees from the internet, and stores this information in a database. During this process, the server executes data collection scripts and saves the collected data as structured data.

[0698] Step 2:

[0699] The server predicts communication demand based on event information stored in the database. By inputting event information into a prediction model built using past event data and base station communication logs, it simulates communication traffic on the day of the event. This simulation is performed using machine learning libraries such as Scikit-learn.

[0700] Step 3:

[0701] The server collects emotional data from the user's terminal and analyzes it. It collects facial expression data and sensor data via the user's smart devices (smartphones and smart glasses). Based on this data, it performs natural language processing using the NLTK library to determine the emotional state.

[0702] Step 4:

[0703] Based on the analyzed emotional information, the server evaluates the emotional tendencies of participants and improves the accuracy of communication demand forecasting. Specifically, it predicts that communication demand will increase when users are highly agitated, and then incorporates this into the prediction model to run the simulation again.

[0704] Step 5:

[0705] Based on the prediction results, the server determines whether mobile communication equipment needs to be deployed or existing communication equipment needs to be adjusted. This allows it to formulate an implementation plan to adequately meet communication demand. After formulating the plan, it determines the necessary deployment adjustments and outputs a specific plan.

[0706] Step 6:

[0707] Users receive information on congestion levels within urban environments and guidance on optimal routes from a server via their smart devices. The server feeds data back to the user's device in real time, helping users avoid congestion. This includes UI displays and voice guidance in the device application.

[0708] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0709] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0710] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0711] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0712] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0713] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0714] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0715] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0716] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0717] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0718] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0719] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0720] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0721] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0722] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0723] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0724] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0725] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0726] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0727] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0728] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0729] The following is further disclosed regarding the embodiments described above.

[0730] (Claim 1)

[0731] Means of obtaining event information,

[0732] A means for predicting communication demand based on the aforementioned event information,

[0733] A means for determining the need to arrange mobile communication equipment or adjust existing communication equipment based on the predicted communication demand,

[0734] Means for generating an implementation plan based on the aforementioned judgment,

[0735] A system that includes this.

[0736] (Claim 2)

[0737] The system according to claim 1, characterized in that it includes means for collecting information from multiple digital platforms on the internet and storing it in a database in order to obtain event information.

[0738] (Claim 3)

[0739] The system according to claim 1, characterized in that it includes means for constructing a predictive model using past event data and base station communication logs in order to predict communication demand, and for inputting event information into the model to perform a simulation.

[0740] "Example 1"

[0741] (Claim 1)

[0742] A processing unit for acquiring event information,

[0743] A processing device that analyzes the aforementioned event information and extracts features,

[0744] A device equipped with an algorithm that predicts communication demand based on the extracted features,

[0745] A device that determines the need to arrange communication equipment or adjust existing facilities based on the predicted communication demand,

[0746] A control device that generates an operation plan based on the aforementioned determination,

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, characterized in that it includes a process for collecting data from a digital information source and storing it in a storage medium in order to obtain event information.

[0750] (Claim 3)

[0751] The system according to claim 1, characterized in that it includes a process for generating a prediction model by utilizing past event information and communication device usage records in order to predict communication demand, and for inputting event information into the model and executing a simulation.

[0752] "Application Example 1"

[0753] (Claim 1)

[0754] Means of obtaining event information,

[0755] A means for predicting communication demand based on the aforementioned event information,

[0756] A means for determining the need to arrange mobile communication equipment or adjust existing communication equipment based on the predicted communication demand,

[0757] A means for generating an implementation plan based on the aforementioned judgment and notifying users of congestion information on the communication infrastructure,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, characterized in that it includes means for obtaining event information by collecting information from multiple digital platforms on the internet, storing it in a database, and providing participants with a forecast of communication demand during the event.

[0761] (Claim 3)

[0762] The system according to claim 1, characterized in that it includes means for constructing a predictive model using past event data and communication logs of communication equipment to predict communication demand, inputting event information into the model to perform a simulation, and providing the results to a user.

[0763] "Example 2 of combining an emotion engine"

[0764] (Claim 1)

[0765] Means of obtaining event information,

[0766] A means for predicting communication demand based on the aforementioned event information,

[0767] A means for determining the need to arrange mobile communication equipment or adjust existing communication equipment based on the predicted communication demand,

[0768] A means of analyzing user emotional data and understanding emotional trends,

[0769] Means for generating an implementation plan based on the aforementioned judgment and emotional tendencies,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, characterized in that it includes means for collecting information from multiple sources on the internet and storing it in a database in order to obtain event information.

[0773] (Claim 3)

[0774] The system according to claim 1, characterized in that it includes means for constructing a predictive model using past event data and communication logs to predict communication demand, and inputting event information into the model to perform a simulation.

[0775] "Application example 2 when combining with an emotional engine"

[0776] (Claim 1)

[0777] Means of obtaining event information,

[0778] A means for predicting communication demand based on the aforementioned event information,

[0779] A means for determining the need to arrange mobile communication equipment or adjust existing communication equipment based on the predicted communication demand,

[0780] Means for generating an implementation plan based on the aforementioned judgment,

[0781] A means of collecting and analyzing emotional information from user devices,

[0782] A means for evaluating the emotional tendencies of event participants based on collected emotional information and improving the accuracy of predicting the aforementioned communication demand,

[0783] A guidance system that predicts congestion levels within urban environments and assists in optimizing travel routes,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, characterized by comprising means for collecting information from multiple digital platforms on the internet and storing it in a database.

[0787] (Claim 3)

[0788] The system according to claim 1, characterized by comprising means for constructing a predictive model using past event data and base station communication logs, and inputting event information into the model to perform a simulation. [Explanation of Symbols]

[0789] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining event information, A means for predicting communication demand based on the aforementioned event information, A means for determining the need to arrange mobile communication equipment or adjust existing communication equipment based on the predicted communication demand, A means for generating an implementation plan based on the aforementioned judgment and notifying users of congestion information on the communication infrastructure, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for obtaining event information by collecting information from multiple digital platforms on the internet, storing it in a database, and providing participants with a forecast of communication demand during the event.

3. The system according to claim 1, characterized in that it includes means for constructing a predictive model using past event data and communication logs of communication equipment to predict communication demand, inputting event information into the model to perform a simulation, and providing the results to a user.

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