system

Abstract

We provide the system. [Solution] A means of automatically collecting event information, A means for analyzing collected event information and evaluating the scale and impact of events, A means for determining the arrangement of mobile communication devices or the configuration of communication devices based on the evaluation results, A means of notifying the user of the determined countermeasures, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, 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] Among the increasing number of events, there are problems that the conventional manual event detection and countermeasure planning are inefficient and require a great deal of time and labor. In addition, since the prediction of event scale and the determination of appropriate communication countermeasures depend on subjectivity, there is a lack of accuracy and it is difficult to maintain consistent quality.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects event information, analyzes the obtained information, and evaluates the scale and impact of events. The system includes means for determining appropriate communication countermeasures based on the evaluation results and notifying the user of the determination. Furthermore, it includes a function to receive user feedback, and the analysis algorithm improves the accuracy of countermeasures by comparing past and current event data.

[0006] "Event information" refers to detailed information about a gathering or event that will take place at a specific date, time, and location, including, for example, the event name, date, location, and estimated number of attendees.

[0007] "Analysis" is the process of evaluating the scale and impact of an event based on acquired data, and it is a process of organizing data to derive its relationships and characteristics.

[0008] "Evaluation" is the act of quantitatively estimating the impact of an event using numerical values ​​and indicators obtained through analysis, and is a process that provides information necessary for subsequent decision-making.

[0009] "Mobile communication equipment" refers to communication facilities that are temporarily deployed during an event, and are a means of supplementing communication demand in areas that cannot be handled by regular base stations.

[0010] "Changing the settings of communication equipment" refers to the act of adjusting parameters of existing communication equipment to suit the holding of an event, and is a technical means of optimizing communication quality and capacity.

[0011] "Decision-making" is the process of determining what measures are necessary based on the results of analysis and evaluation, and it provides guidance for carrying out actual operations.

[0012] A "user" is an individual or group that operates or utilizes the system and is responsible for implementing communication countermeasures based on the system's output.

[0013] "Feedback" refers to user evaluations and opinions on system output and proposed countermeasures, and is important information for improving the accuracy and effectiveness of the system.

[0014] An "algorithm" is a set of computational procedures and rules used to perform specific processing based on data and derive a desired result; it is a fundamental mechanism for achieving analysis and evaluation. [Brief explanation of the drawing]

[0015] [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]It 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 Example 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.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0019] 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.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of the present invention consists of a server, a terminal, and a user. The server automatically collects data from various event information sites via the internet. The collected data includes attribute data such as the type of event, location, date and time, and expected number of participants, and the scale and impact of the event are analyzed based on this data.

[0037] The server evaluates the characteristics of the event based on the analyzed data and calculates the necessary communication measures based on the number of participants and past data. At this time, the analysis algorithm predicts the scale of the event and the communication needs of participants by referring to data from similar past events. If necessary, the placement of mobile communication equipment or modification of existing communication equipment settings will be recommended.

[0038] The analysis and evaluation results are sent from the server to the terminal, and the user is shown information related to the event and recommended countermeasures. The user makes a decision based on this information and implements appropriate countermeasures. After the event ends, the user provides feedback on the effectiveness of the implemented countermeasures, and this information is sent to the server.

[0039] The feedback information is stored as training data in the AI ​​model on the server and used to improve the accuracy of future analyses and countermeasures. This enables the system to continuously improve its accuracy.

[0040] As a concrete example, consider a large-scale concert event held in an urban area. In this case, the server collects event information in advance and predicts that the number of participants will exceed 20,000. Based on data from past events of similar size, the server predicts an increase in communication demand and recommends the deployment of mobile communication equipment. The user receives this information, arranges for mobile communication equipment, and takes measures to increase the capacity of the communication network. As a result, communication quality is maintained during the event, and participant satisfaction improves. Finally, the user evaluates the effectiveness of these measures and sends the results to the server as feedback.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server automatically collects event data from various event information websites via the internet using specific APIs or scraping techniques. This data includes event name, date and time, location, and estimated number of attendees.

[0044] Step 2:

[0045] The server cleanses the collected event data, removing noise and missing data to format it. It checks for duplicate data and misinformation, and organizes the dataset.

[0046] Step 3:

[0047] Based on pre-processed data, the server uses AI analysis algorithms to assess the scale of the event and predict the number of participants and communication demand. This includes comparing with past event data and learning from similar events.

[0048] Step 4:

[0049] Based on the analysis results, the server determines the need for countermeasures by comparing them against pre-set thresholds. For example, if communication demand is expected during a large-scale event, it determines the placement of mobile communication equipment.

[0050] Step 5:

[0051] The server sends the analysis results and determined countermeasures to the terminal, which then displays them to the user. The user then checks the detailed event information and recommended communication countermeasures on the terminal.

[0052] Step 6:

[0053] Based on the displayed information, users will arrange mobile communication devices and modify the settings of existing communication devices. Users will also instruct on-site staff as needed to implement specific countermeasures.

[0054] Step 7:

[0055] After the event concludes, users will input feedback via their devices regarding the effectiveness of the countermeasures and communication status, and send this feedback to the server. This feedback will be used as training data for the AI ​​model, helping to improve the accuracy of the analysis.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern society, numerous large-scale events are held, often placing a heavy load on communication networks. Therefore, accurately predicting the scale of events and communication demands in advance, and implementing appropriate communication countermeasures, is essential. However, current systems suffer from insufficient accuracy in collecting and analyzing event information, limiting the effectiveness of communication countermeasures. Furthermore, the lack of mechanisms for utilizing the effectiveness of implemented countermeasures as feedback leads to a tendency for similar problems to recur.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for automatically collecting event information via a network, means for analyzing the scale of an event and the communication demand by referring to a database of similar past events using the collected event information, and means for calculating measures to recommend the placement of mobile communication devices or changes to the settings of existing communication devices based on the analysis results. This makes it possible to predict the communication demand for an event with high accuracy and to quickly provide appropriate communication measures. Furthermore, it is possible to learn from feedback on the effectiveness of the measures and continuously improve accuracy.

[0061] "Event information" refers to attribute data related to an event, such as its type, location, date and time, and estimated number of participants.

[0062] "Network" refers to the infrastructure that enables information and communication, including the internet, and is the foundation for sending and receiving data.

[0063] A "similar event database" refers to a collection of data that accumulates information on past events and is used to compare and analyze it with current event information.

[0064] "Communication demand" refers to the predicted communication capacity and connection speed that participants will require from the communication network during a specific event.

[0065] "Mobile communication equipment" refers to portable communication devices deployed to provide additional communication infrastructure at event venues.

[0066] "Existing communication equipment" refers to communication infrastructure equipment that is pre-installed at the event venue, and includes communication devices whose settings can be changed.

[0067] "Feedback" refers to information obtained from users regarding the effectiveness of implemented measures, and this data is used to improve the accuracy of system analysis.

[0068] To implement this system, the server, terminals, and users each play specific roles. The server is the main processing component, responsible for advanced data processing and calculations for communication countermeasures. The server utilizes web scraping techniques to collect various event information via the internet and analyzes the event data using programming languages ​​such as Python. The analysis algorithm accurately predicts the scale of events and communication demands by referencing similar past event data and utilizing a generative AI model.

[0069] The terminal is a device that displays analysis results and proposed countermeasures to the user. A dedicated application runs on the terminal and displays information received from the server via an encrypted, secure protocol. The user uses the terminal to review the proposed communication countermeasures and, if necessary, arrange for mobile communication equipment or modify the settings of existing communication equipment.

[0070] As a concrete example, consider a scenario where a large-scale concert is held in an urban area. The server obtains and analyzes the expected number of attendees and venue information in advance to predict communication needs based on past data. Based on the prediction, the server recommends the placement of additional mobile communication equipment and sends the results to the terminal. The user uses this information to arrange equipment and take measures to optimize the communication environment on the day of the event. This process makes it possible to reduce the load on the communication network and improve participant satisfaction.

[0071] Examples of prompts for the generating AI model include: "Please explain the data processing steps for forecasting communication demand for a large-scale concert event," and "Please explain how to use historical data to predict communication measures for an urban event expected to have 20,000 participants."

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

[0073] Step 1:

[0074] The server automatically collects data from various event information websites via the internet. Specifically, it uses web scraping techniques to obtain data such as the type of event, location, date and time, and estimated number of attendees. The input is the URL of the event information website, and the output is a structured set of event information.

[0075] Step 2:

[0076] The server uses collected event data to perform analysis, leveraging generative AI models. Here, it retrieves historical similar event data from a database and compares it with current event information to predict communication demand. Input includes current event information and historical similar event data, while output includes analysis results regarding predicted communication demand and event scale. Machine learning models built in Python or R are used for this analysis.

[0077] Step 3:

[0078] The server calculates the placement of mobile communication equipment or changes to the configuration of existing communication equipment based on the analysis results, and generates proposed countermeasures. The input is predicted communication demand, which is used to calculate the required communication equipment placement plan. The output is specific countermeasures, such as proposals for arranging additional communication equipment or modifications to existing equipment.

[0079] Step 4:

[0080] The server sends the calculated proposed countermeasures to the terminal. The terminal receives the data from the server using an encrypted communication protocol, and a dedicated application displays the proposed countermeasures to the user. The input is the proposed countermeasures sent from the server, and the output is the proposed countermeasures displayed on the user's screen.

[0081] Step 5:

[0082] The user makes decisions and implements communication countermeasures based on the proposed solutions displayed on the terminal. Here, appropriate arrangements and configuration changes are made via the terminal. The input is the proposed solutions displayed on the terminal, and the output is the communication countermeasures actually implemented. Specifically, this could involve reserving or rearranging mobile communication equipment.

[0083] Step 6:

[0084] After the event, users provide feedback on the effectiveness of the countermeasures and send it to the server. The input consists of user evaluation data regarding communication quality and the number of participants during the event, and the output is stored on the server as training data for the generated AI model, thereby improving the accuracy of future predictions.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern society, large-scale gatherings and events often lead to a sharp increase in communication demand from participants. This frequently results in a decline in communication quality and diminishes user satisfaction. A system is needed to prevent such situations and effectively optimize the communication infrastructure.

[0088] 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.

[0089] In this invention, the server includes means for automatically collecting and analyzing event information, means for determining the placement and configuration changes of mobile communication devices and communication devices based on the analysis results and optimizing communication demand, and means for notifying users of the determined countermeasures and recommending the installation of additional communication equipment. This makes it possible to prevent a deterioration in communication quality during events and improve the communication experience for users.

[0090] "Event information" refers to data about various events and gatherings, including attributes such as location, date and time, and estimated number of participants.

[0091] The "analysis means" refers to a function that uses collected event information to evaluate the scale and impact of events and to carry out a process for predicting communication demand.

[0092] A "mobile communication device" is a device that, when deployed as needed, is used to supplement the capacity of a communication network in a specific area.

[0093] "Configuration changes" refer to the act of adjusting the parameters and operation of existing communication equipment in order to optimize communication.

[0094] A "user" is an individual or organization that receives information and recommendations from the system and is responsible for implementing communication countermeasures.

[0095] "Additional communication equipment" refers to communication means or devices newly introduced or deployed to meet the communication demands of an event.

[0096] The system of this invention revolves around a server. The server has the function of automatically collecting event information from the internet and uses a web crawler to obtain data from various event information sites. The collected data includes the type of event, location, date and time, and estimated number of participants.

[0097] The collected event information is analyzed on the server. Here, data analysis software such as Pandas and Scikit-learn is used to evaluate the scale and impact of events and predict the next required communication demand. This analysis employs an algorithm that compares past event data with current data to determine the optimal communication measures. This algorithm is implemented using machine learning frameworks such as TENSORFLOW®.

[0098] Based on the analysis results, the server determines the placement of mobile communication devices and changes to the configuration of existing communication devices. The determined countermeasures are sent to the device as a push notification via Firebase Cloud Messaging. The user receives this notification and takes the necessary steps to install additional communication equipment.

[0099] After the event, feedback will be collected from users regarding the effectiveness of the measures they took. This feedback information will be sent to the server and stored as training data for the AI ​​model on the server. Through this continuous learning, the system will be able to improve the accuracy of future analyses and countermeasures.

[0100] As a concrete example, let's consider a large-scale fireworks display held in an urban area in 2025. In this scenario, the server can analyze event information in advance, predict the increase in communication demand due to the increase in the number of participants, and propose the installation of 5G mobile communication equipment. As a result, communication quality will improve, and the event experience for participants will be enhanced. An example of a prompt for the generating AI model would be, "Predict the communication demand for a large-scale fireworks display and propose necessary countermeasures."

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

[0102] Step 1:

[0103] The server automatically collects data from event information websites on the internet using a web crawler. The input is the URL of the event information website, and the output is attribute data such as the type of event, location, date and time, and estimated number of attendees. This process extracts the information using scraping techniques and stores it in a database.

[0104] Step 2:

[0105] The server formats and cleanses the collected event data using Pandas. The input is the raw data obtained in step 1, and the output is structured data in a parseable format. At this stage, data preprocessing such as removing missing values ​​and standardizing the format is performed.

[0106] Step 3:

[0107] The server uses Scikit-learn and TensorFlow to compare past and current event data and predict communication demand. The input is formatted event data, and the output is the predicted communication demand and the optimal communication device placement proposal. Here, a machine learning algorithm is used to apply a predictive model.

[0108] Step 4:

[0109] The server uses Firebase Cloud Messaging to send analysis results to the device. The input is a communication demand assessment and deployment plan based on the analysis results, and the output is a push notification to the user. This notification includes specific details of communication countermeasures.

[0110] Step 5:

[0111] The user receives a notification from their device and implements the suggested communication countermeasures. The input is the notification content from the server, and the output is feedback on the implemented communication countermeasures. The user sends this feedback from their device to the server.

[0112] Step 6:

[0113] The server receives feedback from users and stores it as training data for the generated AI model. The input is the user's feedback on countermeasures, and the output is the improvement in future analysis accuracy through model learning. Through this process, the system is continuously improved.

[0114] 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.

[0115] The system of the present invention consists of a server, a terminal, a user, and an emotion engine. In addition to the conventional functions of collecting and analyzing event information and notifying the user of appropriate communication measures, this system is characterized by recognizing the user's emotions and taking appropriate actions based on them.

[0116] The server automatically collects event data from various event information websites and organizes information such as the event's content, scale, and expected number of participants. Then, it uses an AI-based analysis algorithm to evaluate the impact of the event. Based on this evaluation, it determines whether the deployment of mobile communication equipment or the configuration of existing communication equipment needs to be changed.

[0117] Analysis results and proposed solutions are sent from the server to the terminal. The terminal provides this information to the user and recognizes the user's emotional state through the emotion engine. The emotion engine analyzes user feedback and responses to notifications, capturing changes in emotion through voice and text.

[0118] Based on this emotion recognition, the server adjusts the tone and expression of subsequent notifications to provide information in a way that is optimal for the user's emotional state. Furthermore, the user's emotional state is recorded and stored as data that directly impacts the effectiveness of countermeasures. For example, if the user expresses dissatisfaction, the system can generate a detailed error report to suggest further improvements.

[0119] As a concrete example, suppose a user is instructed to handle a large-scale sporting event. The server analyzes the event information and determines the necessary communication measures. The information is then sent to the user's terminal, and the user confirms the provided measures. If the emotion engine analyzes the user's emotions and determines that the user is at ease, normal notifications are sent to maintain that state. However, if the user expresses concern, the emotion engine uses this emotion to provide options for requesting additional information or detailed explanations, helping to deepen their understanding.

[0120] This system configuration enables responses that are sensitive to the user's emotions, providing the emotional insights necessary to maximize the effectiveness of countermeasures.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The server automatically collects event data from various event information websites using APIs and web scraping techniques. The collected data includes event name, date and time, location, and estimated number of attendees.

[0124] Step 2:

[0125] The server cleanses the collected data, removing unnecessary and duplicate information and organizing it. It checks the accuracy of the data and formats the event information dataset in preparation for analysis.

[0126] Step 3:

[0127] The server uses pre-processed data to evaluate the scale and impact of the event using an AI analysis algorithm. By comparing it with data from similar past events, it determines appropriate communication measures based on the expected communication demand and number of participants.

[0128] Step 4:

[0129] The analysis results obtained from the server and the determined countermeasures are sent to the terminal. These include specific countermeasures such as the necessity of deploying mobile communication equipment and the effectiveness of changing settings.

[0130] Step 5:

[0131] The device visually displays this information to the user. The user can review the notified event information and recommended actions.

[0132] Step 6:

[0133] The emotion engine recognizes the user's emotional state through the device. It analyzes the user's mood and reactions from voice and text input to determine emotions such as reassurance, concern, and dissatisfaction.

[0134] Step 7:

[0135] Based on the user's emotional state, the server adjusts the content and wording of subsequent notifications. For example, if the user is showing signs of anxiety, it will provide detailed explanations and additional information to reassure them.

[0136] Step 8:

[0137] Based on the information confirmed on their devices, users will rearrange or change the settings of their mobile communication equipment. They will then instruct on-site staff on the decided measures and procure the necessary resources.

[0138] Step 9:

[0139] After the event, users input feedback on the effectiveness of the countermeasures on their devices. The emotion engine analyzes this feedback along with the user's facial expressions and reactions at the time, and sends the results to the server. This information contributes to the continuous learning of the AI ​​model and is used to improve the accuracy of future analyses.

[0140] (Example 2)

[0141] 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".

[0142] While it is important to properly assess the impact of an event and take necessary communication countermeasures, conventional systems lack the ability to consider users' emotional states. Therefore, there is a need for a system that can alleviate users' anxiety and concerns and implement efficient and acceptable countermeasures.

[0143] 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.

[0144] In this invention, the server includes means for automatically collecting event data, means for analyzing the collected data and evaluating its scale and impact, and means for recognizing the user's emotional state. This enables communication countermeasures that are sensitive to the user's emotions, thereby improving the effectiveness of the countermeasures.

[0145] "Event data" refers to information about an event, including its content, scale, and estimated number of participants.

[0146] A "mobile communication device" is a portable communication device installed for communication purposes, whose placement can be changed as needed.

[0147] A "user" refers to an individual or organization that uses the system and is the recipient of notifications.

[0148] "Emotional state" refers to the psychological situation a user is experiencing, and includes emotions such as reassurance and concern.

[0149] "Analysis means" refers to methods or algorithms for processing data and understanding its meaning, and is used to evaluate the impact of an event.

[0150] "Notification methods" refer to ways of providing information to users, such as screen displays and audio alerts.

[0151] This system consists of a server, terminals, users, and an emotion engine. The server is responsible for automatically collecting event data, obtaining this data from various sources on the internet. Web scraping techniques, such as Python libraries like Beautiful Soup and Scrapy, can be used here. The collected data is stored in a database management system (e.g., MySQL® or PostgreSQL).

[0152] The server processes the collected event data using AI-based analysis algorithms to evaluate the impact of the events. Machine learning libraries such as TensorFlow and PyTorch can be used for this process. Based on the analysis results, necessary communication countermeasures are determined.

[0153] The analysis results and proposed countermeasures are sent from the server to the terminal. The terminal notifies the user of this information, and the user confirms it. Screen displays and push notifications are used as notification methods.

[0154] Furthermore, the device integrates an emotion engine that recognizes the user's emotional state in real time. The emotion engine utilizes emotion analysis APIs such as IBM Watson® Tone Analyzer and Microsoft® Azure® Text Analytics to analyze user feedback and responses.

[0155] Based on this emotional information, the server dynamically adjusts the notification content to provide information best suited to the user's emotional state. For example, if a user expresses anxiety, additional detailed information may be provided to encourage a sense of reassurance.

[0156] As a concrete example, during a large-scale sporting event, the server analyzes event information and predicts a significant increase in communication demand, determining that additional Wi-Fi hotspots are necessary. This information is sent to the terminal, the user confirms it, and the emotion engine analyzes the user's reaction. If anxiety is indicated, the system provides additional detailed explanations to support the user's understanding.

[0157] An example of a prompt for a generative AI model is, "Explain the role of the server and provide specific use cases for how the emotion engine is used to analyze user responses." This prompt provides information about the system's functionality and specific applications of emotion analysis.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server collects event data from event information websites. The input consists of various web pages, and it retrieves information about specific events. Using web scraping techniques, such as Beautiful Soup and Scrapy, it extracts data about event content, dates, and scale in text format. The output is formatted event data.

[0161] Step 2:

[0162] The server stores the collected event data in a database. The input is pre-formatted event data. Storing this data as structured data in MySQL or PostgreSQL enables efficient access and manipulation. The output is the data stored in the database.

[0163] Step 3:

[0164] The server processes stored event data using an AI-based analysis algorithm. The input is event data in a database. TensorFlow and PyTorch are used to calculate the expected impact of events and predict communication demand. The output is an evaluation of the event's scale and deployment to communication destinations.

[0165] Step 4:

[0166] The server generates specific communication countermeasures based on the analysis results. The input is the evaluation results. Using these results, it proposes optimizations for the placement and configuration adjustments of communication equipment. The output is the proposed communication countermeasures.

[0167] Step 5:

[0168] The server sends a proposed communication countermeasure to the terminal. The input is the generated proposed communication countermeasure. This information is securely transmitted to the terminal using the HTTPS protocol. The output is the countermeasure information received by the terminal.

[0169] Step 6:

[0170] The terminal notifies the user of information received from the server. The input is the received countermeasure information. The proposed countermeasures are displayed to the user using on-screen pop-ups or push notifications. The output is the notification to the user.

[0171] Step 7:

[0172] The device uses an emotion engine to recognize the user's emotional state. Input is the user's responses and feedback. IBM Watson Tone Analyzer and Microsoft Azure Text Analytics are used to analyze the emotions the user expresses. Output is the analyzed emotion data.

[0173] Step 8:

[0174] The server adjusts notification content based on the user's emotional state. The input is emotional data. Based on the emotional analysis results, it provides additional details or changes the notification tone. The output is the adjusted notification content.

[0175] (Application Example 2)

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

[0177] Conventional communication security systems fail to consider user emotions, resulting in a lack of maximization of user satisfaction and system effectiveness. Furthermore, conventional measures may be insufficient in irregular situations. Therefore, there is a need for flexible communication security measures that take user emotions into account.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for automatically collecting event information, means for analyzing the collected event information and evaluating the scale and impact of the event, and means for recognizing the user's emotional state and determining an emotionally-based response. This enables the provision of appropriate information and optimization of communication measures tailored to the user's emotional state.

[0180] "Event information" refers to data related to various events, including information such as the event's content, scale, and expected number of participants.

[0181] "Analysis" is the process of using collected data, understanding its content, and evaluating it based on specific criteria.

[0182] "Evaluation" is the process of determining the impact and importance of information that has been collected and analyzed.

[0183] A "mobile communication device" is a portable device used to transmit information via various communication networks.

[0184] "Changing the settings of communication equipment" refers to the process of making adjustments to existing communication equipment in order to optimize its functions and performance.

[0185] "Emotional state recognition" is the process of acquiring emotions from the user's voice, text, etc., and determining their state.

[0186] "Deciding on a course of action" means selecting the appropriate measures or responses based on specific conditions.

[0187] A "system" is a collection of multiple means or devices that work together to achieve a specific purpose.

[0188] In this embodiment, a system is provided in which a server takes the lead in effectively processing event information and optimizing communication based on user sentiment. The server automatically collects data from various event information sites via the internet or other networks. This data includes the content and scale of the event, the expected number of participants, etc. The collected information is analyzed using an algorithm based on Python to evaluate the impact of the event.

[0189] Furthermore, the server recognizes the user's emotional state through sensors connected to a computer such as a Raspberry Pi. Emotional analysis uses emotion recognition libraries like OpenCV or PyTorch to generate emotion labels from the user's voice and text. These labels are then used to adjust the tone and content of subsequent communications.

[0190] Users review the information received through their devices, and the system adjusts further information based on feedback provided by the emotion engine. This enables optimal communication tailored to the user, thereby increasing the effectiveness of countermeasures.

[0191] A concrete example is a home conversational robot that, upon recognizing a user's stress levels, suggests content related to relaxation techniques. This system utilizes a generative AI model to assess the user's emotions in real time and dynamically provide appropriate information.

[0192] An example of a prompt message would be: "If the user's emotion is recognized as 'stress,' activate function A to suggest relaxation techniques and explain them to the user in a caring manner."

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server automatically collects event data from various event information websites. Inputs include access information such as URL lists and API keys, and output includes data on event content, scale, and number of participants. This allows the server to prepare the basic data for the next analysis step.

[0196] Step 2:

[0197] The server analyzes the collected event data and uses an AI-based algorithm to evaluate the impact of the events. The input is the event data collected in step 1, and the output is an impact assessment score and an overview of the necessary communication countermeasures. Data processing includes text mining and numerical analysis.

[0198] Step 3:

[0199] The server determines whether it is necessary to deploy mobile communication equipment or modify the settings of existing communication equipment. Using the evaluation results from Step 2 as input, the output becomes a plan for specific communication measures. For example, it might decide to deploy additional communication equipment in densely populated areas.

[0200] Step 4:

[0201] The server notifies the terminal of the determined countermeasures. The input is the proposed communication countermeasures obtained in step 3, and the output is the notification message sent to the user. A messaging protocol is used for the actual communication.

[0202] Step 5:

[0203] The device recognizes the user's emotional state through an emotion engine. Input is the user's voice or text, and output is an emotion label. Specifically, it uses speech recognition technology to convert speech to text and then analyzes the emotion using an emotion recognition library.

[0204] Step 6:

[0205] The server adjusts the notification content and tone based on the user's emotional state. The input is the emotional label obtained in step 5, and the output is the adjusted notification message. This process uses a generative AI model to optimize the content by applying prompt sentences.

[0206] 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.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] The system of the present invention consists of a server, a terminal, and a user. The server automatically collects data from various event information sites via the internet. The collected data includes attribute data such as the type of event, location, date and time, and expected number of participants, and the scale and impact of the event are analyzed based on this data.

[0223] The server evaluates the characteristics of the event based on the analyzed data and calculates the necessary communication measures based on the number of participants and past data. At this time, the analysis algorithm predicts the scale of the event and the communication needs of participants by referring to data from similar past events. If necessary, the placement of mobile communication equipment or modification of existing communication equipment settings will be recommended.

[0224] The analysis and evaluation results are sent from the server to the terminal, and the user is shown information related to the event and recommended countermeasures. The user makes a decision based on this information and implements appropriate countermeasures. After the event ends, the user provides feedback on the effectiveness of the implemented countermeasures, and this information is sent to the server.

[0225] The feedback information is stored as training data in the AI ​​model on the server and used to improve the accuracy of future analyses and countermeasures. This enables the system to continuously improve its accuracy.

[0226] As a concrete example, consider a large-scale concert event held in an urban area. In this case, the server collects event information in advance and predicts that the number of participants will exceed 20,000. Based on data from past events of similar size, the server predicts an increase in communication demand and recommends the deployment of mobile communication equipment. The user receives this information, arranges for mobile communication equipment, and takes measures to increase the capacity of the communication network. As a result, communication quality is maintained during the event, and participant satisfaction improves. Finally, the user evaluates the effectiveness of these measures and sends the results to the server as feedback.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The server automatically collects event data from various event information websites via the internet using specific APIs or scraping techniques. This data includes event name, date and time, location, and estimated number of attendees.

[0230] Step 2:

[0231] The server cleanses the collected event data, removing noise and missing data to format it. It checks for duplicate data and misinformation, and organizes the dataset.

[0232] Step 3:

[0233] Based on pre-processed data, the server uses AI analysis algorithms to assess the scale of the event and predict the number of participants and communication demand. This includes comparing with past event data and learning from similar events.

[0234] Step 4:

[0235] Based on the analysis results, the server determines the need for countermeasures by comparing them against pre-set thresholds. For example, if communication demand is expected during a large-scale event, it determines the placement of mobile communication equipment.

[0236] Step 5:

[0237] The server sends the analysis results and determined countermeasures to the terminal, which then displays them to the user. The user then checks the detailed event information and recommended communication countermeasures on the terminal.

[0238] Step 6:

[0239] Based on the displayed information, users will arrange mobile communication devices and modify the settings of existing communication devices. Users will also instruct on-site staff as needed to implement specific countermeasures.

[0240] Step 7:

[0241] After the event concludes, users will input feedback via their devices regarding the effectiveness of the countermeasures and communication status, and send this feedback to the server. This feedback will be used as training data for the AI ​​model, helping to improve the accuracy of the analysis.

[0242] (Example 1)

[0243] 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."

[0244] In modern society, numerous large-scale events are held, often placing a heavy load on communication networks. Therefore, accurately predicting the scale of events and communication demands in advance, and implementing appropriate communication countermeasures, is essential. However, current systems suffer from insufficient accuracy in collecting and analyzing event information, limiting the effectiveness of communication countermeasures. Furthermore, the lack of mechanisms for utilizing the effectiveness of implemented countermeasures as feedback leads to a tendency for similar problems to recur.

[0245] 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.

[0246] In this invention, the server includes means for automatically collecting event information via a network, means for analyzing the scale of an event and the communication demand by referring to a database of similar past events using the collected event information, and means for calculating measures to recommend the placement of mobile communication devices or changes to the settings of existing communication devices based on the analysis results. This makes it possible to predict the communication demand for an event with high accuracy and to quickly provide appropriate communication measures. Furthermore, it is possible to learn from feedback on the effectiveness of the measures and continuously improve accuracy.

[0247] "Event information" refers to attribute data related to an event, such as its type, location, date and time, and estimated number of participants.

[0248] "Network" refers to the infrastructure that enables information and communication, including the internet, and is the foundation for sending and receiving data.

[0249] A "similar event database" refers to a collection of data that accumulates information on past events and is used to compare and analyze it with current event information.

[0250] "Communication demand" refers to the predicted communication capacity and connection speed that participants will require from the communication network during a specific event.

[0251] "Mobile communication equipment" refers to portable communication devices deployed to provide additional communication infrastructure at event venues.

[0252] "Existing communication equipment" refers to communication infrastructure equipment that is pre-installed at the event venue, and includes communication devices whose settings can be changed.

[0253] "Feedback" refers to information obtained from users regarding the effectiveness of implemented measures, and this data is used to improve the accuracy of system analysis.

[0254] To implement this system, the server, terminals, and users each play specific roles. The server is the main processing component, responsible for advanced data processing and calculations for communication countermeasures. The server utilizes web scraping techniques to collect various event information via the internet and analyzes the event data using programming languages ​​such as Python. The analysis algorithm accurately predicts the scale of events and communication demands by referencing similar past event data and utilizing a generative AI model.

[0255] The terminal is a device that displays analysis results and proposed countermeasures to the user. A dedicated application runs on the terminal and displays information received from the server via an encrypted, secure protocol. The user uses the terminal to review the proposed communication countermeasures and, if necessary, arrange for mobile communication equipment or modify the settings of existing communication equipment.

[0256] As a concrete example, consider a scenario where a large-scale concert is held in an urban area. The server obtains and analyzes the expected number of attendees and venue information in advance to predict communication needs based on past data. Based on the prediction, the server recommends the placement of additional mobile communication equipment and sends the results to the terminal. The user uses this information to arrange equipment and take measures to optimize the communication environment on the day of the event. This process makes it possible to reduce the load on the communication network and improve participant satisfaction.

[0257] Examples of prompts for the generating AI model include: "Please explain the data processing steps for forecasting communication demand for a large-scale concert event," and "Please explain how to use historical data to predict communication measures for an urban event expected to have 20,000 participants."

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server automatically collects data from various event information websites via the internet. Specifically, it uses web scraping techniques to obtain data such as the type of event, location, date and time, and estimated number of attendees. The input is the URL of the event information website, and the output is a structured set of event information.

[0261] Step 2:

[0262] The server uses collected event data to perform analysis, leveraging generative AI models. Here, it retrieves historical similar event data from a database and compares it with current event information to predict communication demand. Input includes current event information and historical similar event data, while output includes analysis results regarding predicted communication demand and event scale. Machine learning models built in Python or R are used for this analysis.

[0263] Step 3:

[0264] The server calculates the placement of mobile communication equipment or changes to the configuration of existing communication equipment based on the analysis results, and generates proposed countermeasures. The input is predicted communication demand, which is used to calculate the required communication equipment placement plan. The output is specific countermeasures, such as proposals for arranging additional communication equipment or modifications to existing equipment.

[0265] Step 4:

[0266] The server sends the calculated proposed countermeasures to the terminal. The terminal receives the data from the server using an encrypted communication protocol, and a dedicated application displays the proposed countermeasures to the user. The input is the proposed countermeasures sent from the server, and the output is the proposed countermeasures displayed on the user's screen.

[0267] Step 5:

[0268] The user makes decisions and implements communication countermeasures based on the proposed solutions displayed on the terminal. Here, appropriate arrangements and configuration changes are made via the terminal. The input is the proposed solutions displayed on the terminal, and the output is the communication countermeasures actually implemented. Specifically, this could involve reserving or rearranging mobile communication equipment.

[0269] Step 6:

[0270] After the event, users provide feedback on the effectiveness of the countermeasures and send it to the server. The input consists of user evaluation data regarding communication quality and the number of participants during the event, and the output is stored on the server as training data for the generated AI model, thereby improving the accuracy of future predictions.

[0271] (Application Example 1)

[0272] 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."

[0273] In modern society, large-scale gatherings and events often lead to a sharp increase in communication demand from participants. This frequently results in a decline in communication quality and diminishes user satisfaction. A system is needed to prevent such situations and effectively optimize the communication infrastructure.

[0274] 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.

[0275] In this invention, the server includes means for automatically collecting and analyzing event information, means for determining the placement and configuration changes of mobile communication devices and communication devices based on the analysis results and optimizing communication demand, and means for notifying users of the determined countermeasures and recommending the installation of additional communication equipment. This makes it possible to prevent a deterioration in communication quality during events and improve the communication experience for users.

[0276] "Event information" refers to data about various events and gatherings, including attributes such as location, date and time, and estimated number of participants.

[0277] The "analysis means" refers to a function that uses collected event information to evaluate the scale and impact of events and to carry out a process for predicting communication demand.

[0278] A "mobile communication device" is a device that, when placed as needed, reinforces the capacity of a communication network in a specific area.

[0279] "Setting change" refers to the act of adjusting the parameters and operations of existing communication devices to optimize communication.

[0280] A "user" is an individual or group that receives information and recommendations from the system and has the responsibility to implement communication countermeasures.

[0281] "Additional communication equipment" refers to communication means and devices newly introduced or arranged to meet the communication demand of an event.

[0282] The system of the present invention has the server playing a central role. The server has the function of automatically collecting event information from the Internet and acquiring data from various event information sites using a web crawler. The collected data includes the type of event, the venue, the date and time of the event, the expected number of participants, etc.

[0283] The collected event information is analyzed on the server. Here, by using data analysis software such as Pandas and Scikit-learn, the scale and impact of the event are evaluated, and then the necessary communication demand is predicted. For this analysis, an algorithm that compares past event data with current data to determine the optimal communication countermeasure is used. This algorithm is implemented using a machine learning framework such as TensorFlow.

[0284] Based on the analysis results, the server determines the placement of mobile communication devices and the setting changes of existing communication devices. The determined countermeasures are sent as push notifications to the terminals via Firebase Cloud Messaging. The user receives this notification and proceeds with the procedure of installing additional communication equipment as needed.

[0285] After the event ends, feedback is received on the effectiveness of the countermeasures taken by the user. This feedback information is sent to the server and accumulated as learning data in the generation AI model of the AI model in the server. Through this continuous learning, the system can improve future analysis accuracy and the accuracy of countermeasures.

[0286] As a specific example, assume a large-scale fireworks festival held in an urban area in 2025. At this time, the server can analyze the event information in advance, predict the increase in communication demand due to the increase in the number of participants, and propose to install 5G mobile communication devices. As a result, the communication quality is improved and the event experience of the participants is enhanced. Examples of prompts for the generation AI model include "Predict the communication demand when assuming a large-scale fireworks festival and propose necessary countermeasure plans."

[0287] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server automatically collects data from event information sites on the Internet using a web crawler. The input is the URL of the event information site, and the output is attribute data such as the type of event, the location of the event, the date and time of the event, and the expected number of participants. In this process, scraping technology is used to extract information and store it in a database.

[0290] Step 2:

[0291] The server formats and cleans the collected event data using Pandas. The input is the raw data obtained in Step 1, and the output is structured data in an analyzable format. At this stage, data preprocessing such as removing missing values and unifying formats is performed.

[0292] Step 3:

[0293] The server uses Scikit-learn and TensorFlow to compare past and current event data and predict communication demand. The input is formatted event data, and the output is the predicted communication demand and the optimal communication device placement proposal. Here, a machine learning algorithm is used to apply a predictive model.

[0294] Step 4:

[0295] The server uses Firebase Cloud Messaging to send analysis results to the device. The input is a communication demand assessment and deployment plan based on the analysis results, and the output is a push notification to the user. This notification includes specific details of communication countermeasures.

[0296] Step 5:

[0297] The user receives a notification from their device and implements the suggested communication countermeasures. The input is the notification content from the server, and the output is feedback on the implemented communication countermeasures. The user sends this feedback from their device to the server.

[0298] Step 6:

[0299] The server receives feedback from users and stores it as training data for the generated AI model. The input is the user's feedback on countermeasures, and the output is the improvement in future analysis accuracy through model learning. Through this process, the system is continuously improved.

[0300] 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.

[0301] The system of the present invention consists of a server, a terminal, a user, and an emotion engine. In addition to the conventional functions of collecting and analyzing event information and notifying the user of appropriate communication measures, this system is characterized by recognizing the user's emotions and taking appropriate actions based on them.

[0302] The server automatically collects event data from various event information websites and organizes information such as the event's content, scale, and expected number of participants. Then, it uses an AI-based analysis algorithm to evaluate the impact of the event. Based on this evaluation, it determines whether the deployment of mobile communication equipment or the configuration of existing communication equipment needs to be changed.

[0303] Analysis results and proposed solutions are sent from the server to the terminal. The terminal provides this information to the user and recognizes the user's emotional state through the emotion engine. The emotion engine analyzes user feedback and responses to notifications, capturing changes in emotion through voice and text.

[0304] Based on this emotion recognition, the server adjusts the tone and expression of subsequent notifications to provide information in a way that is optimal for the user's emotional state. Furthermore, the user's emotional state is recorded and stored as data that directly impacts the effectiveness of countermeasures. For example, if the user expresses dissatisfaction, the system can generate a detailed error report to suggest further improvements.

[0305] As a concrete example, suppose a user is instructed to handle a large-scale sporting event. The server analyzes the event information and determines the necessary communication measures. The information is then sent to the user's terminal, and the user confirms the provided measures. If the emotion engine analyzes the user's emotions and determines that the user is at ease, normal notifications are sent to maintain that state. However, if the user expresses concern, the emotion engine uses this emotion to provide options for requesting additional information or detailed explanations, helping to deepen their understanding.

[0306] With such a system configuration, it becomes possible to respond according to the user's emotions and provide the emotional insights necessary to maximize the effectiveness of the countermeasures.

[0307] The processing flow will be described below.

[0308] Step 1:

[0309] The server automatically collects event data from various event information sites using APIs and web scraping technologies. The data to be collected includes event names, opening dates and times, locations, expected number of participants, etc.

[0310] Step 2:

[0311] The server cleans the collected data, deletes unnecessary information and duplicate data, and organizes it. Check the accuracy of the data, format the event information dataset, and prepare it for analysis.

[0312] Step 3:

[0313] Using the preprocessed data, the server evaluates the scale and impact of the event with AI analysis algorithms. Compare with data of past similar events and determine appropriate communication countermeasures from the expected communication demand and number of participants.

[0314] Step 4:

[0315] The analysis results obtained from the server and the determined countermeasure plans are sent to the terminal. This includes specific countermeasure plans such as the necessity of the arrangement of mobile communication devices and the effectiveness of setting changes.

[0316] Step 5:

[0317] The terminal visually displays this information to the user. The user can check the notified event information and the recommended countermeasure content.

[0318] Step 6:

[0319] The emotion engine recognizes the user's emotional state through the device. It analyzes the user's mood and reactions from voice and text input to determine emotions such as reassurance, concern, and dissatisfaction.

[0320] Step 7:

[0321] Based on the user's emotional state, the server adjusts the content and wording of subsequent notifications. For example, if the user is showing signs of anxiety, it will provide detailed explanations and additional information to reassure them.

[0322] Step 8:

[0323] Based on the information confirmed on their devices, users will rearrange or change the settings of their mobile communication equipment. They will then instruct on-site staff on the decided measures and procure the necessary resources.

[0324] Step 9:

[0325] After the event, users input feedback on the effectiveness of the countermeasures on their devices. The emotion engine analyzes this feedback along with the user's facial expressions and reactions at the time, and sends the results to the server. This information contributes to the continuous learning of the AI ​​model and is used to improve the accuracy of future analyses.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] While it is important to properly assess the impact of an event and take necessary communication countermeasures, conventional systems lack the ability to consider users' emotional states. Therefore, there is a need for a system that can alleviate users' anxiety and concerns and implement efficient and acceptable countermeasures.

[0329] 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.

[0330] In this invention, the server includes means for automatically collecting event data, means for analyzing the collected data and evaluating its scale and impact, and means for recognizing the user's emotional state. This enables communication countermeasures that are sensitive to the user's emotions, thereby improving the effectiveness of the countermeasures.

[0331] "Event data" refers to information about an event, including its content, scale, and estimated number of participants.

[0332] A "mobile communication device" is a portable communication device installed for communication purposes, whose placement can be changed as needed.

[0333] A "user" refers to an individual or organization that uses the system and is the recipient of notifications.

[0334] "Emotional state" refers to the psychological situation a user is experiencing, and includes emotions such as reassurance and concern.

[0335] "Analysis means" refers to methods or algorithms for processing data and understanding its meaning, and is used to evaluate the impact of an event.

[0336] "Notification methods" refer to ways of providing information to users, such as screen displays and audio alerts.

[0337] This system consists of a server, terminals, users, and an emotion engine. The server is responsible for automatically collecting event data, obtaining this data from various sources on the internet. Web scraping techniques, such as Python libraries like Beautiful Soup and Scrapy, can be used here. The collected data is stored in a database management system (e.g., MySQL or PostgreSQL).

[0338] The server processes the collected event data using AI-based analysis algorithms to evaluate the impact of the events. Machine learning libraries such as TensorFlow and PyTorch can be used for this process. Based on the analysis results, necessary communication countermeasures are determined.

[0339] The analysis results and proposed countermeasures are sent from the server to the terminal. The terminal notifies the user of this information, and the user confirms it. Screen displays and push notifications are used as notification methods.

[0340] Furthermore, the device integrates an emotion engine that recognizes the user's emotional state in real time. The emotion engine utilizes emotion analysis APIs such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics to analyze user feedback and reactions.

[0341] Based on this emotional information, the server dynamically adjusts the notification content to provide information best suited to the user's emotional state. For example, if a user expresses anxiety, additional detailed information may be provided to encourage a sense of reassurance.

[0342] As a concrete example, during a large-scale sporting event, the server analyzes event information and predicts a significant increase in communication demand, determining that additional Wi-Fi hotspots are necessary. This information is sent to the terminal, the user confirms it, and the emotion engine analyzes the user's reaction. If anxiety is indicated, the system provides additional detailed explanations to support the user's understanding.

[0343] An example of a prompt for a generative AI model is, "Explain the role of the server and provide specific use cases for how the emotion engine is used to analyze user responses." This prompt provides information about the system's functionality and specific applications of emotion analysis.

[0344] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0345] Step 1:

[0346] The server collects event data from event information websites. The input consists of various web pages, and it retrieves information about specific events. Using web scraping techniques, such as Beautiful Soup and Scrapy, it extracts data about event content, dates, and scale in text format. The output is formatted event data.

[0347] Step 2:

[0348] The server stores the collected event data in a database. The input is pre-formatted event data. Storing this data as structured data in MySQL or PostgreSQL enables efficient access and manipulation. The output is the data stored in the database.

[0349] Step 3:

[0350] The server processes stored event data using an AI-based analysis algorithm. The input is event data in a database. TensorFlow and PyTorch are used to calculate the expected impact of events and predict communication demand. The output is an evaluation of the event's scale and deployment to communication destinations.

[0351] Step 4:

[0352] The server generates specific communication countermeasures based on the analysis results. The input is the evaluation results. Using these results, it proposes optimizations for the placement and configuration adjustments of communication equipment. The output is the proposed communication countermeasures.

[0353] Step 5:

[0354] The server sends a proposed communication countermeasure to the terminal. The input is the generated proposed communication countermeasure. This information is securely transmitted to the terminal using the HTTPS protocol. The output is the countermeasure information received by the terminal.

[0355] Step 6:

[0356] The terminal notifies the user of information received from the server. The input is the received countermeasure information. The proposed countermeasures are displayed to the user using on-screen pop-ups or push notifications. The output is the notification to the user.

[0357] Step 7:

[0358] The device uses an emotion engine to recognize the user's emotional state. Input is the user's responses and feedback. IBM Watson Tone Analyzer and Microsoft Azure Text Analytics are used to analyze the emotions the user expresses. Output is the analyzed emotion data.

[0359] Step 8:

[0360] The server adjusts notification content based on the user's emotional state. The input is emotional data. Based on the emotional analysis results, it provides additional details or changes the notification tone. The output is the adjusted notification content.

[0361] (Application Example 2)

[0362] 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."

[0363] Conventional communication security systems fail to consider user emotions, resulting in a lack of maximization of user satisfaction and system effectiveness. Furthermore, conventional measures may be insufficient in irregular situations. Therefore, there is a need for flexible communication security measures that take user emotions into account.

[0364] 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.

[0365] In this invention, the server includes means for automatically collecting event information, means for analyzing the collected event information and evaluating the scale and impact of the event, and means for recognizing the user's emotional state and determining an emotionally-based response. This enables the provision of appropriate information and optimization of communication measures tailored to the user's emotional state.

[0366] "Event information" refers to data related to various events, including information such as the event's content, scale, and expected number of participants.

[0367] "Analysis" is the process of using collected data, understanding its content, and evaluating it based on specific criteria.

[0368] "Evaluation" is the process of determining the impact and importance of information that has been collected and analyzed.

[0369] A "mobile communication device" is a portable device used to transmit information via various communication networks.

[0370] "Changing the settings of communication equipment" refers to the process of making adjustments to existing communication equipment in order to optimize its functions and performance.

[0371] "Emotional state recognition" is the process of acquiring emotions from the user's voice, text, etc., and determining their state.

[0372] "Deciding on a course of action" means selecting the appropriate measures or responses based on specific conditions.

[0373] A "system" is a collection of multiple means or devices that work together to achieve a specific purpose.

[0374] In this embodiment, a system is provided in which a server takes the lead in effectively processing event information and optimizing communication based on user sentiment. The server automatically collects data from various event information sites via the internet or other networks. This data includes the content and scale of the event, the expected number of participants, etc. The collected information is analyzed using an algorithm based on Python to evaluate the impact of the event.

[0375] Furthermore, the server recognizes the user's emotional state through sensors connected to a computer such as a Raspberry Pi. Emotional analysis uses emotion recognition libraries like OpenCV or PyTorch to generate emotion labels from the user's voice and text. These labels are then used to adjust the tone and content of subsequent communications.

[0376] Users review the information received through their devices, and the system adjusts further information based on feedback provided by the emotion engine. This enables optimal communication tailored to the user, thereby increasing the effectiveness of countermeasures.

[0377] A concrete example is a home conversational robot that, upon recognizing a user's stress levels, suggests content related to relaxation techniques. This system utilizes a generative AI model to assess the user's emotions in real time and dynamically provide appropriate information.

[0378] An example of a prompt message would be: "If the user's emotion is recognized as 'stress,' activate function A to suggest relaxation techniques and explain them to the user in a caring manner."

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The server automatically collects event data from various event information websites. Inputs include access information such as URL lists and API keys, and output includes data on event content, scale, and number of participants. This allows the server to prepare the basic data for the next analysis step.

[0382] Step 2:

[0383] The server analyzes the collected event data and uses an AI-based algorithm to evaluate the impact of the events. The input is the event data collected in step 1, and the output is an impact assessment score and an overview of the necessary communication countermeasures. Data processing includes text mining and numerical analysis.

[0384] Step 3:

[0385] The server determines whether it is necessary to deploy mobile communication equipment or modify the settings of existing communication equipment. Using the evaluation results from Step 2 as input, the output becomes a plan for specific communication measures. For example, it might decide to deploy additional communication equipment in densely populated areas.

[0386] Step 4:

[0387] The server notifies the terminal of the determined countermeasures. The input is the proposed communication countermeasures obtained in step 3, and the output is the notification message sent to the user. A messaging protocol is used for the actual communication.

[0388] Step 5:

[0389] The device recognizes the user's emotional state through an emotion engine. Input is the user's voice or text, and output is an emotion label. Specifically, it uses speech recognition technology to convert speech to text and then analyzes the emotion using an emotion recognition library.

[0390] Step 6:

[0391] The server adjusts the notification content and tone based on the user's emotional state. The input is the emotional label obtained in step 5, and the output is the adjusted notification message. This process uses a generative AI model to optimize the content by applying prompt sentences.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] The system of the present invention consists of a server, a terminal, and a user. The server automatically collects data from various event information sites via the internet. The collected data includes attribute data such as the type of event, location, date and time, and expected number of participants, and the scale and impact of the event are analyzed based on this data.

[0409] The server evaluates the characteristics of the event based on the analyzed data and calculates the necessary communication measures based on the number of participants and past data. At this time, the analysis algorithm predicts the scale of the event and the communication needs of participants by referring to data from similar past events. If necessary, the placement of mobile communication equipment or modification of existing communication equipment settings will be recommended.

[0410] The analysis and evaluation results are sent from the server to the terminal, and the user is shown information related to the event and recommended countermeasures. The user makes a decision based on this information and implements appropriate countermeasures. After the event ends, the user provides feedback on the effectiveness of the implemented countermeasures, and this information is sent to the server.

[0411] The feedback information is stored as training data in the AI ​​model on the server and used to improve the accuracy of future analyses and countermeasures. This enables the system to continuously improve its accuracy.

[0412] As a concrete example, consider a large-scale concert event held in an urban area. In this case, the server collects event information in advance and predicts that the number of participants will exceed 20,000. Based on data from past events of similar size, the server predicts an increase in communication demand and recommends the deployment of mobile communication equipment. The user receives this information, arranges for mobile communication equipment, and takes measures to increase the capacity of the communication network. As a result, communication quality is maintained during the event, and participant satisfaction improves. Finally, the user evaluates the effectiveness of these measures and sends the results to the server as feedback.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The server automatically collects event data from various event information websites via the internet using specific APIs or scraping techniques. This data includes event name, date and time, location, and estimated number of attendees.

[0416] Step 2:

[0417] The server cleanses the collected event data, removing noise and missing data to format it. It checks for duplicate data and misinformation, and organizes the dataset.

[0418] Step 3:

[0419] Based on pre-processed data, the server uses AI analysis algorithms to assess the scale of the event and predict the number of participants and communication demand. This includes comparing with past event data and learning from similar events.

[0420] Step 4:

[0421] Based on the analysis results, the server determines the need for countermeasures by comparing them against pre-set thresholds. For example, if communication demand is expected during a large-scale event, it determines the placement of mobile communication equipment.

[0422] Step 5:

[0423] The server sends the analysis results and determined countermeasures to the terminal, which then displays them to the user. The user then checks the detailed event information and recommended communication countermeasures on the terminal.

[0424] Step 6:

[0425] Based on the displayed information, users will arrange mobile communication devices and modify the settings of existing communication devices. Users will also instruct on-site staff as needed to implement specific countermeasures.

[0426] Step 7:

[0427] After the event concludes, users will input feedback via their devices regarding the effectiveness of the countermeasures and communication status, and send this feedback to the server. This feedback will be used as training data for the AI ​​model, helping to improve the accuracy of the analysis.

[0428] (Example 1)

[0429] 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."

[0430] In modern society, numerous large-scale events are held, often placing a heavy load on communication networks. Therefore, accurately predicting the scale of events and communication demands in advance, and implementing appropriate communication countermeasures, is essential. However, current systems suffer from insufficient accuracy in collecting and analyzing event information, limiting the effectiveness of communication countermeasures. Furthermore, the lack of mechanisms for utilizing the effectiveness of implemented countermeasures as feedback leads to a tendency for similar problems to recur.

[0431] 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.

[0432] In this invention, the server includes means for automatically collecting event information via a network, means for analyzing the scale of an event and the communication demand by referring to a database of similar past events using the collected event information, and means for calculating measures to recommend the placement of mobile communication devices or changes to the settings of existing communication devices based on the analysis results. This makes it possible to predict the communication demand for an event with high accuracy and to quickly provide appropriate communication measures. Furthermore, it is possible to learn from feedback on the effectiveness of the measures and continuously improve accuracy.

[0433] "Event information" refers to attribute data related to an event, such as its type, location, date and time, and estimated number of participants.

[0434] "Network" refers to the infrastructure that enables information and communication, including the internet, and is the foundation for sending and receiving data.

[0435] A "similar event database" refers to a collection of data that accumulates information on past events and is used to compare and analyze it with current event information.

[0436] "Communication demand" refers to the predicted communication capacity and connection speed that participants will require from the communication network during a specific event.

[0437] "Mobile communication equipment" refers to portable communication devices deployed to provide additional communication infrastructure at event venues.

[0438] "Existing communication equipment" refers to communication infrastructure equipment that is pre-installed at the event venue, and includes communication devices whose settings can be changed.

[0439] "Feedback" refers to information obtained from users regarding the effectiveness of implemented measures, and this data is used to improve the accuracy of system analysis.

[0440] To implement this system, the server, terminals, and users each play specific roles. The server is the main processing component, responsible for advanced data processing and calculations for communication countermeasures. The server utilizes web scraping techniques to collect various event information via the internet and analyzes the event data using programming languages ​​such as Python. The analysis algorithm accurately predicts the scale of events and communication demands by referencing similar past event data and utilizing a generative AI model.

[0441] The terminal is a device that displays analysis results and proposed countermeasures to the user. A dedicated application runs on the terminal and displays information received from the server via an encrypted, secure protocol. The user uses the terminal to review the proposed communication countermeasures and, if necessary, arrange for mobile communication equipment or modify the settings of existing communication equipment.

[0442] As a concrete example, consider a scenario where a large-scale concert is held in an urban area. The server obtains and analyzes the expected number of attendees and venue information in advance to predict communication needs based on past data. Based on the prediction, the server recommends the placement of additional mobile communication equipment and sends the results to the terminal. The user uses this information to arrange equipment and take measures to optimize the communication environment on the day of the event. This process makes it possible to reduce the load on the communication network and improve participant satisfaction.

[0443] Examples of prompts for the generating AI model include: "Please explain the data processing steps for forecasting communication demand for a large-scale concert event," and "Please explain how to use historical data to predict communication measures for an urban event expected to have 20,000 participants."

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The server automatically collects data from various event information websites via the internet. Specifically, it uses web scraping techniques to obtain data such as the type of event, location, date and time, and estimated number of attendees. The input is the URL of the event information website, and the output is a structured set of event information.

[0447] Step 2:

[0448] The server uses collected event data to perform analysis, leveraging generative AI models. Here, it retrieves historical similar event data from a database and compares it with current event information to predict communication demand. Input includes current event information and historical similar event data, while output includes analysis results regarding predicted communication demand and event scale. Machine learning models built in Python or R are used for this analysis.

[0449] Step 3:

[0450] The server calculates the placement of mobile communication equipment or changes to the configuration of existing communication equipment based on the analysis results, and generates proposed countermeasures. The input is predicted communication demand, which is used to calculate the required communication equipment placement plan. The output is specific countermeasures, such as proposals for arranging additional communication equipment or modifications to existing equipment.

[0451] Step 4:

[0452] The server sends the calculated proposed countermeasures to the terminal. The terminal receives the data from the server using an encrypted communication protocol, and a dedicated application displays the proposed countermeasures to the user. The input is the proposed countermeasures sent from the server, and the output is the proposed countermeasures displayed on the user's screen.

[0453] Step 5:

[0454] The user makes decisions and implements communication countermeasures based on the proposed solutions displayed on the terminal. Here, appropriate arrangements and configuration changes are made via the terminal. The input is the proposed solutions displayed on the terminal, and the output is the communication countermeasures actually implemented. Specifically, this could involve reserving or rearranging mobile communication equipment.

[0455] Step 6:

[0456] After the event, users provide feedback on the effectiveness of the countermeasures and send it to the server. The input consists of user evaluation data regarding communication quality and the number of participants during the event, and the output is stored on the server as training data for the generated AI model, thereby improving the accuracy of future predictions.

[0457] (Application Example 1)

[0458] 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."

[0459] In modern society, large-scale gatherings and events often lead to a sharp increase in communication demand from participants. This frequently results in a decline in communication quality and diminishes user satisfaction. A system is needed to prevent such situations and effectively optimize the communication infrastructure.

[0460] 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.

[0461] In this invention, the server includes means for automatically collecting and analyzing event information, means for determining the placement and configuration changes of mobile communication devices and communication devices based on the analysis results and optimizing communication demand, and means for notifying users of the determined countermeasures and recommending the installation of additional communication equipment. This makes it possible to prevent a deterioration in communication quality during events and improve the communication experience for users.

[0462] "Event information" refers to data about various events and gatherings, including attributes such as location, date and time, and estimated number of participants.

[0463] The "analysis means" refers to a function that uses collected event information to evaluate the scale and impact of events and to carry out a process for predicting communication demand.

[0464] A "mobile communication device" is a device that, when deployed as needed, is used to supplement the capacity of a communication network in a specific area.

[0465] "Configuration changes" refer to the act of adjusting the parameters and operation of existing communication equipment in order to optimize communication.

[0466] A "user" is an individual or organization that receives information and recommendations from the system and is responsible for implementing communication countermeasures.

[0467] "Additional communication equipment" refers to communication means or devices newly introduced or deployed to meet the communication demands of an event.

[0468] The system of this invention revolves around a server. The server has the function of automatically collecting event information from the internet and uses a web crawler to obtain data from various event information sites. The collected data includes the type of event, location, date and time, and estimated number of participants.

[0469] The collected event information is analyzed on the server. Here, data analysis software such as Pandas and Scikit-learn is used to evaluate the scale and impact of events and predict the next required communication demand. This analysis employs an algorithm that compares past event data with current data to determine the optimal communication measures. This algorithm is implemented using machine learning frameworks such as TensorFlow.

[0470] Based on the analysis results, the server determines the placement of mobile communication devices and changes to the configuration of existing communication devices. The determined countermeasures are sent to the device as a push notification via Firebase Cloud Messaging. The user receives this notification and takes the necessary steps to install additional communication equipment.

[0471] After the event, feedback will be collected from users regarding the effectiveness of the measures they took. This feedback information will be sent to the server and stored as training data for the AI ​​model on the server. Through this continuous learning, the system will be able to improve the accuracy of future analyses and countermeasures.

[0472] As a concrete example, let's consider a large-scale fireworks display held in an urban area in 2025. In this scenario, the server can analyze event information in advance, predict the increase in communication demand due to the increase in the number of participants, and propose the installation of 5G mobile communication equipment. As a result, communication quality will improve, and the event experience for participants will be enhanced. An example of a prompt for the generating AI model would be, "Predict the communication demand for a large-scale fireworks display and propose necessary countermeasures."

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The server automatically collects data from event information websites on the internet using a web crawler. The input is the URL of the event information website, and the output is attribute data such as the type of event, location, date and time, and estimated number of attendees. This process extracts the information using scraping techniques and stores it in a database.

[0476] Step 2:

[0477] The server formats and cleanses the collected event data using Pandas. The input is the raw data obtained in step 1, and the output is structured data in a parseable format. At this stage, data preprocessing such as removing missing values ​​and standardizing the format is performed.

[0478] Step 3:

[0479] The server uses Scikit-learn and TensorFlow to compare past and current event data and predict communication demand. The input is formatted event data, and the output is the predicted communication demand and the optimal communication device placement proposal. Here, a machine learning algorithm is used to apply a predictive model.

[0480] Step 4:

[0481] The server uses Firebase Cloud Messaging to send analysis results to the device. The input is a communication demand assessment and deployment plan based on the analysis results, and the output is a push notification to the user. This notification includes specific details of communication countermeasures.

[0482] Step 5:

[0483] The user receives a notification from their device and implements the suggested communication countermeasures. The input is the notification content from the server, and the output is feedback on the implemented communication countermeasures. The user sends this feedback from their device to the server.

[0484] Step 6:

[0485] The server receives feedback from users and stores it as training data for the generated AI model. The input is the user's feedback on countermeasures, and the output is the improvement in future analysis accuracy through model learning. Through this process, the system is continuously improved.

[0486] 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.

[0487] The system of the present invention consists of a server, a terminal, a user, and an emotion engine. In addition to the conventional functions of collecting and analyzing event information and notifying the user of appropriate communication measures, this system is characterized by recognizing the user's emotions and taking appropriate actions based on them.

[0488] The server automatically collects event data from various event information websites and organizes information such as the event's content, scale, and expected number of participants. Then, it uses an AI-based analysis algorithm to evaluate the impact of the event. Based on this evaluation, it determines whether the deployment of mobile communication equipment or the configuration of existing communication equipment needs to be changed.

[0489] Analysis results and proposed solutions are sent from the server to the terminal. The terminal provides this information to the user and recognizes the user's emotional state through the emotion engine. The emotion engine analyzes user feedback and responses to notifications, capturing changes in emotion through voice and text.

[0490] Based on this emotion recognition, the server adjusts the tone and expression of subsequent notifications to provide information in a way that is optimal for the user's emotional state. Furthermore, the user's emotional state is recorded and stored as data that directly impacts the effectiveness of countermeasures. For example, if the user expresses dissatisfaction, the system can generate a detailed error report to suggest further improvements.

[0491] As a concrete example, suppose a user is instructed to handle a large-scale sporting event. The server analyzes the event information and determines the necessary communication measures. The information is then sent to the user's terminal, and the user confirms the provided measures. If the emotion engine analyzes the user's emotions and determines that the user is at ease, normal notifications are sent to maintain that state. However, if the user expresses concern, the emotion engine uses this emotion to provide options for requesting additional information or detailed explanations, helping to deepen their understanding.

[0492] This system configuration enables responses that are sensitive to the user's emotions, providing the emotional insights necessary to maximize the effectiveness of countermeasures.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The server automatically collects event data from various event information websites using APIs and web scraping techniques. The collected data includes event name, date and time, location, and estimated number of attendees.

[0496] Step 2:

[0497] The server cleanses the collected data, removing unnecessary and duplicate information and organizing it. It checks the accuracy of the data and formats the event information dataset in preparation for analysis.

[0498] Step 3:

[0499] The server uses pre-processed data to evaluate the scale and impact of the event using an AI analysis algorithm. By comparing it with data from similar past events, it determines appropriate communication measures based on the expected communication demand and number of participants.

[0500] Step 4:

[0501] The analysis results obtained from the server and the determined countermeasures are sent to the terminal. These include specific countermeasures such as the necessity of deploying mobile communication equipment and the effectiveness of changing settings.

[0502] Step 5:

[0503] The device visually displays this information to the user. The user can review the notified event information and recommended actions.

[0504] Step 6:

[0505] The emotion engine recognizes the user's emotional state through the device. It analyzes the user's mood and reactions from voice and text input to determine emotions such as reassurance, concern, and dissatisfaction.

[0506] Step 7:

[0507] Based on the user's emotional state, the server adjusts the content and wording of subsequent notifications. For example, if the user is showing signs of anxiety, it will provide detailed explanations and additional information to reassure them.

[0508] Step 8:

[0509] Based on the information confirmed on their devices, users will rearrange or change the settings of their mobile communication equipment. They will then instruct on-site staff on the decided measures and procure the necessary resources.

[0510] Step 9:

[0511] After the event, users input feedback on the effectiveness of the countermeasures on their devices. The emotion engine analyzes this feedback along with the user's facial expressions and reactions at the time, and sends the results to the server. This information contributes to the continuous learning of the AI ​​model and is used to improve the accuracy of future analyses.

[0512] (Example 2)

[0513] 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."

[0514] While it is important to properly assess the impact of an event and take necessary communication countermeasures, conventional systems lack the ability to consider users' emotional states. Therefore, there is a need for a system that can alleviate users' anxiety and concerns and implement efficient and acceptable countermeasures.

[0515] 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.

[0516] In this invention, the server includes means for automatically collecting event data, means for analyzing the collected data and evaluating its scale and impact, and means for recognizing the user's emotional state. This enables communication countermeasures that are sensitive to the user's emotions, thereby improving the effectiveness of the countermeasures.

[0517] "Event data" refers to information about an event, including its content, scale, and estimated number of participants.

[0518] A "mobile communication device" is a portable communication device installed for communication purposes, whose placement can be changed as needed.

[0519] A "user" refers to an individual or organization that uses the system and is the recipient of notifications.

[0520] "Emotional state" refers to the psychological situation a user is experiencing, and includes emotions such as reassurance and concern.

[0521] "Analysis means" refers to methods or algorithms for processing data and understanding its meaning, and is used to evaluate the impact of an event.

[0522] "Notification methods" refer to ways of providing information to users, such as screen displays and audio alerts.

[0523] This system consists of a server, terminals, users, and an emotion engine. The server is responsible for automatically collecting event data, obtaining this data from various sources on the internet. Web scraping techniques, such as Python libraries like Beautiful Soup and Scrapy, can be used here. The collected data is stored in a database management system (e.g., MySQL or PostgreSQL).

[0524] The server processes the collected event data using AI-based analysis algorithms to evaluate the impact of the events. Machine learning libraries such as TensorFlow and PyTorch can be used for this process. Based on the analysis results, necessary communication countermeasures are determined.

[0525] The analysis results and proposed countermeasures are sent from the server to the terminal. The terminal notifies the user of this information, and the user confirms it. Screen displays and push notifications are used as notification methods.

[0526] Furthermore, the device integrates an emotion engine that recognizes the user's emotional state in real time. The emotion engine utilizes emotion analysis APIs such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics to analyze user feedback and reactions.

[0527] Based on this emotional information, the server dynamically adjusts the notification content to provide information best suited to the user's emotional state. For example, if a user expresses anxiety, additional detailed information may be provided to encourage a sense of reassurance.

[0528] As a concrete example, during a large-scale sporting event, the server analyzes event information and predicts a significant increase in communication demand, determining that additional Wi-Fi hotspots are necessary. This information is sent to the terminal, the user confirms it, and the emotion engine analyzes the user's reaction. If anxiety is indicated, the system provides additional detailed explanations to support the user's understanding.

[0529] An example of a prompt for a generative AI model is, "Explain the role of the server and provide specific use cases for how the emotion engine is used to analyze user responses." This prompt provides information about the system's functionality and specific applications of emotion analysis.

[0530] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0531] Step 1:

[0532] The server collects event data from event information websites. The input consists of various web pages, and it retrieves information about specific events. Using web scraping techniques, such as Beautiful Soup and Scrapy, it extracts data about event content, dates, and scale in text format. The output is formatted event data.

[0533] Step 2:

[0534] The server stores the collected event data in a database. The input is pre-formatted event data. Storing this data as structured data in MySQL or PostgreSQL enables efficient access and manipulation. The output is the data stored in the database.

[0535] Step 3:

[0536] The server processes stored event data using an AI-based analysis algorithm. The input is event data in a database. TensorFlow and PyTorch are used to calculate the expected impact of events and predict communication demand. The output is an evaluation of the event's scale and deployment to communication destinations.

[0537] Step 4:

[0538] The server generates specific communication countermeasures based on the analysis results. The input is the evaluation results. Using these results, it proposes optimizations for the placement and configuration adjustments of communication equipment. The output is the proposed communication countermeasures.

[0539] Step 5:

[0540] The server sends a proposed communication countermeasure to the terminal. The input is the generated proposed communication countermeasure. This information is securely transmitted to the terminal using the HTTPS protocol. The output is the countermeasure information received by the terminal.

[0541] Step 6:

[0542] The terminal notifies the user of information received from the server. The input is the received countermeasure information. The proposed countermeasures are displayed to the user using on-screen pop-ups or push notifications. The output is the notification to the user.

[0543] Step 7:

[0544] The device uses an emotion engine to recognize the user's emotional state. Input is the user's responses and feedback. IBM Watson Tone Analyzer and Microsoft Azure Text Analytics are used to analyze the emotions the user expresses. Output is the analyzed emotion data.

[0545] Step 8:

[0546] The server adjusts notification content based on the user's emotional state. The input is emotional data. Based on the emotional analysis results, it provides additional details or changes the notification tone. The output is the adjusted notification content.

[0547] (Application Example 2)

[0548] 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."

[0549] Conventional communication security systems fail to consider user emotions, resulting in a lack of maximization of user satisfaction and system effectiveness. Furthermore, conventional measures may be insufficient in irregular situations. Therefore, there is a need for flexible communication security measures that take user emotions into account.

[0550] 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.

[0551] In this invention, the server includes means for automatically collecting event information, means for analyzing the collected event information and evaluating the scale and impact of the event, and means for recognizing the user's emotional state and determining an emotionally-based response. This enables the provision of appropriate information and optimization of communication measures tailored to the user's emotional state.

[0552] "Event information" refers to data related to various events, including information such as the event's content, scale, and expected number of participants.

[0553] "Analysis" is the process of using collected data, understanding its content, and evaluating it based on specific criteria.

[0554] "Evaluation" is the process of determining the impact and importance of information that has been collected and analyzed.

[0555] A "mobile communication device" is a portable device used to transmit information via various communication networks.

[0556] "Changing the settings of communication equipment" refers to the process of making adjustments to existing communication equipment in order to optimize its functions and performance.

[0557] "Emotional state recognition" is the process of acquiring emotions from the user's voice, text, etc., and determining their state.

[0558] "Deciding on a course of action" means selecting the appropriate measures or responses based on specific conditions.

[0559] A "system" is a collection of multiple means or devices that work together to achieve a specific purpose.

[0560] In this embodiment, a system is provided in which a server takes the lead in effectively processing event information and optimizing communication based on user sentiment. The server automatically collects data from various event information sites via the internet or other networks. This data includes the content and scale of the event, the expected number of participants, etc. The collected information is analyzed using an algorithm based on Python to evaluate the impact of the event.

[0561] Furthermore, the server recognizes the user's emotional state through sensors connected to a computer such as a Raspberry Pi. Emotional analysis uses emotion recognition libraries like OpenCV or PyTorch to generate emotion labels from the user's voice and text. These labels are then used to adjust the tone and content of subsequent communications.

[0562] Users review the information received through their devices, and the system adjusts further information based on feedback provided by the emotion engine. This enables optimal communication tailored to the user, thereby increasing the effectiveness of countermeasures.

[0563] A concrete example is a home conversational robot that, upon recognizing a user's stress levels, suggests content related to relaxation techniques. This system utilizes a generative AI model to assess the user's emotions in real time and dynamically provide appropriate information.

[0564] An example of a prompt message would be: "If the user's emotion is recognized as 'stress,' activate function A to suggest relaxation techniques and explain them to the user in a caring manner."

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The server automatically collects event data from various event information websites. Inputs include access information such as URL lists and API keys, and output includes data on event content, scale, and number of participants. This allows the server to prepare the basic data for the next analysis step.

[0568] Step 2:

[0569] The server analyzes the collected event data and uses an AI-based algorithm to evaluate the impact of the events. The input is the event data collected in step 1, and the output is an impact assessment score and an overview of the necessary communication countermeasures. Data processing includes text mining and numerical analysis.

[0570] Step 3:

[0571] The server determines whether it is necessary to deploy mobile communication equipment or modify the settings of existing communication equipment. Using the evaluation results from Step 2 as input, the output becomes a plan for specific communication measures. For example, it might decide to deploy additional communication equipment in densely populated areas.

[0572] Step 4:

[0573] The server notifies the terminal of the determined countermeasures. The input is the proposed communication countermeasures obtained in step 3, and the output is the notification message sent to the user. A messaging protocol is used for the actual communication.

[0574] Step 5:

[0575] The device recognizes the user's emotional state through an emotion engine. Input is the user's voice or text, and output is an emotion label. Specifically, it uses speech recognition technology to convert speech to text and then analyzes the emotion using an emotion recognition library.

[0576] Step 6:

[0577] The server adjusts the notification content and tone based on the user's emotional state. The input is the emotional label obtained in step 5, and the output is the adjusted notification message. This process uses a generative AI model to optimize the content by applying prompt sentences.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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".

[0595] The system of the present invention consists of a server, a terminal, and a user. The server automatically collects data from various event information sites via the internet. The collected data includes attribute data such as the type of event, location, date and time, and expected number of participants, and the scale and impact of the event are analyzed based on this data.

[0596] The server evaluates the characteristics of the event based on the analyzed data and calculates the necessary communication measures based on the number of participants and past data. At this time, the analysis algorithm predicts the scale of the event and the communication needs of participants by referring to data from similar past events. If necessary, the placement of mobile communication equipment or modification of existing communication equipment settings will be recommended.

[0597] The analysis and evaluation results are sent from the server to the terminal, and the user is shown information related to the event and recommended countermeasures. The user makes a decision based on this information and implements appropriate countermeasures. After the event ends, the user provides feedback on the effectiveness of the implemented countermeasures, and this information is sent to the server.

[0598] The feedback information is stored as training data in the AI ​​model on the server and used to improve the accuracy of future analyses and countermeasures. This enables the system to continuously improve its accuracy.

[0599] As a concrete example, consider a large-scale concert event held in an urban area. In this case, the server collects event information in advance and predicts that the number of participants will exceed 20,000. Based on data from past events of similar size, the server predicts an increase in communication demand and recommends the deployment of mobile communication equipment. The user receives this information, arranges for mobile communication equipment, and takes measures to increase the capacity of the communication network. As a result, communication quality is maintained during the event, and participant satisfaction improves. Finally, the user evaluates the effectiveness of these measures and sends the results to the server as feedback.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] The server automatically collects event data from various event information websites via the internet using specific APIs or scraping techniques. This data includes event name, date and time, location, and estimated number of attendees.

[0603] Step 2:

[0604] The server cleanses the collected event data, removing noise and missing data to format it. It checks for duplicate data and misinformation, and organizes the dataset.

[0605] Step 3:

[0606] Based on pre-processed data, the server uses AI analysis algorithms to assess the scale of the event and predict the number of participants and communication demand. This includes comparing with past event data and learning from similar events.

[0607] Step 4:

[0608] Based on the analysis results, the server determines the need for countermeasures by comparing them against pre-set thresholds. For example, if communication demand is expected during a large-scale event, it determines the placement of mobile communication equipment.

[0609] Step 5:

[0610] The server sends the analysis results and determined countermeasures to the terminal, which then displays them to the user. The user then checks the detailed event information and recommended communication countermeasures on the terminal.

[0611] Step 6:

[0612] Based on the displayed information, users will arrange mobile communication devices and modify the settings of existing communication devices. Users will also instruct on-site staff as needed to implement specific countermeasures.

[0613] Step 7:

[0614] After the event concludes, users will input feedback via their devices regarding the effectiveness of the countermeasures and communication status, and send this feedback to the server. This feedback will be used as training data for the AI ​​model, helping to improve the accuracy of the analysis.

[0615] (Example 1)

[0616] 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".

[0617] In modern society, numerous large-scale events are held, often placing a heavy load on communication networks. Therefore, accurately predicting the scale of events and communication demands in advance, and implementing appropriate communication countermeasures, is essential. However, current systems suffer from insufficient accuracy in collecting and analyzing event information, limiting the effectiveness of communication countermeasures. Furthermore, the lack of mechanisms for utilizing the effectiveness of implemented countermeasures as feedback leads to a tendency for similar problems to recur.

[0618] 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.

[0619] In this invention, the server includes means for automatically collecting event information via a network, means for analyzing the scale of an event and the communication demand by referring to a database of similar past events using the collected event information, and means for calculating measures to recommend the placement of mobile communication devices or changes to the settings of existing communication devices based on the analysis results. This makes it possible to predict the communication demand for an event with high accuracy and to quickly provide appropriate communication measures. Furthermore, it is possible to learn from feedback on the effectiveness of the measures and continuously improve accuracy.

[0620] "Event information" refers to attribute data related to an event, such as its type, location, date and time, and estimated number of participants.

[0621] "Network" refers to the infrastructure that enables information and communication, including the internet, and is the foundation for sending and receiving data.

[0622] A "similar event database" refers to a collection of data that accumulates information on past events and is used to compare and analyze it with current event information.

[0623] "Communication demand" refers to the predicted communication capacity and connection speed that participants will require from the communication network during a specific event.

[0624] "Mobile communication equipment" refers to portable communication devices deployed to provide additional communication infrastructure at event venues.

[0625] "Existing communication equipment" refers to communication infrastructure equipment that is pre-installed at the event venue, and includes communication devices whose settings can be changed.

[0626] "Feedback" refers to information obtained from users regarding the effectiveness of implemented measures, and this data is used to improve the accuracy of system analysis.

[0627] To implement this system, the server, terminals, and users each play specific roles. The server is the main processing component, responsible for advanced data processing and calculations for communication countermeasures. The server utilizes web scraping techniques to collect various event information via the internet and analyzes the event data using programming languages ​​such as Python. The analysis algorithm accurately predicts the scale of events and communication demands by referencing similar past event data and utilizing a generative AI model.

[0628] The terminal is a device that displays analysis results and proposed countermeasures to the user. A dedicated application runs on the terminal and displays information received from the server via an encrypted, secure protocol. The user uses the terminal to review the proposed communication countermeasures and, if necessary, arrange for mobile communication equipment or modify the settings of existing communication equipment.

[0629] As a concrete example, consider a scenario where a large-scale concert is held in an urban area. The server obtains and analyzes the expected number of attendees and venue information in advance to predict communication needs based on past data. Based on the prediction, the server recommends the placement of additional mobile communication equipment and sends the results to the terminal. The user uses this information to arrange equipment and take measures to optimize the communication environment on the day of the event. This process makes it possible to reduce the load on the communication network and improve participant satisfaction.

[0630] Examples of prompts for the generating AI model include: "Please explain the data processing steps for forecasting communication demand for a large-scale concert event," and "Please explain how to use historical data to predict communication measures for an urban event expected to have 20,000 participants."

[0631] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0632] Step 1:

[0633] The server automatically collects data from various event information websites via the internet. Specifically, it uses web scraping techniques to obtain data such as the type of event, location, date and time, and estimated number of attendees. The input is the URL of the event information website, and the output is a structured set of event information.

[0634] Step 2:

[0635] The server uses collected event data to perform analysis, leveraging generative AI models. Here, it retrieves historical similar event data from a database and compares it with current event information to predict communication demand. Input includes current event information and historical similar event data, while output includes analysis results regarding predicted communication demand and event scale. Machine learning models built in Python or R are used for this analysis.

[0636] Step 3:

[0637] The server calculates the placement of mobile communication equipment or changes to the configuration of existing communication equipment based on the analysis results, and generates proposed countermeasures. The input is predicted communication demand, which is used to calculate the required communication equipment placement plan. The output is specific countermeasures, such as proposals for arranging additional communication equipment or modifications to existing equipment.

[0638] Step 4:

[0639] The server sends the calculated proposed countermeasures to the terminal. The terminal receives the data from the server using an encrypted communication protocol, and a dedicated application displays the proposed countermeasures to the user. The input is the proposed countermeasures sent from the server, and the output is the proposed countermeasures displayed on the user's screen.

[0640] Step 5:

[0641] The user makes decisions and implements communication countermeasures based on the proposed solutions displayed on the terminal. Here, appropriate arrangements and configuration changes are made via the terminal. The input is the proposed solutions displayed on the terminal, and the output is the communication countermeasures actually implemented. Specifically, this could involve reserving or rearranging mobile communication equipment.

[0642] Step 6:

[0643] After the event, users provide feedback on the effectiveness of the countermeasures and send it to the server. The input consists of user evaluation data regarding communication quality and the number of participants during the event, and the output is stored on the server as training data for the generated AI model, thereby improving the accuracy of future predictions.

[0644] (Application Example 1)

[0645] 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".

[0646] In modern society, large-scale gatherings and events often lead to a sharp increase in communication demand from participants. This frequently results in a decline in communication quality and diminishes user satisfaction. A system is needed to prevent such situations and effectively optimize the communication infrastructure.

[0647] 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.

[0648] In this invention, the server includes means for automatically collecting and analyzing event information, means for determining the placement and configuration changes of mobile communication devices and communication devices based on the analysis results and optimizing communication demand, and means for notifying users of the determined countermeasures and recommending the installation of additional communication equipment. This makes it possible to prevent a deterioration in communication quality during events and improve the communication experience for users.

[0649] "Event information" refers to data about various events and gatherings, including attributes such as location, date and time, and estimated number of participants.

[0650] The "analysis means" refers to a function that uses collected event information to evaluate the scale and impact of events and to carry out a process for predicting communication demand.

[0651] A "mobile communication device" is a device that, when deployed as needed, is used to supplement the capacity of a communication network in a specific area.

[0652] "Configuration changes" refer to the act of adjusting the parameters and operation of existing communication equipment in order to optimize communication.

[0653] A "user" is an individual or organization that receives information and recommendations from the system and is responsible for implementing communication countermeasures.

[0654] "Additional communication equipment" refers to communication means or devices newly introduced or deployed to meet the communication demands of an event.

[0655] The system of this invention revolves around a server. The server has the function of automatically collecting event information from the internet and uses a web crawler to obtain data from various event information sites. The collected data includes the type of event, location, date and time, and estimated number of participants.

[0656] The collected event information is analyzed on the server. Here, data analysis software such as Pandas and Scikit-learn is used to evaluate the scale and impact of events and predict the next required communication demand. This analysis employs an algorithm that compares past event data with current data to determine the optimal communication measures. This algorithm is implemented using machine learning frameworks such as TensorFlow.

[0657] Based on the analysis results, the server determines the placement of mobile communication devices and changes to the configuration of existing communication devices. The determined countermeasures are sent to the device as a push notification via Firebase Cloud Messaging. The user receives this notification and takes the necessary steps to install additional communication equipment.

[0658] After the event, feedback will be collected from users regarding the effectiveness of the measures they took. This feedback information will be sent to the server and stored as training data for the AI ​​model on the server. Through this continuous learning, the system will be able to improve the accuracy of future analyses and countermeasures.

[0659] As a concrete example, let's consider a large-scale fireworks display held in an urban area in 2025. In this scenario, the server can analyze event information in advance, predict the increase in communication demand due to the increase in the number of participants, and propose the installation of 5G mobile communication equipment. As a result, communication quality will improve, and the event experience for participants will be enhanced. An example of a prompt for the generating AI model would be, "Predict the communication demand for a large-scale fireworks display and propose necessary countermeasures."

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] The server automatically collects data from event information websites on the internet using a web crawler. The input is the URL of the event information website, and the output is attribute data such as the type of event, location, date and time, and estimated number of attendees. This process extracts the information using scraping techniques and stores it in a database.

[0663] Step 2:

[0664] The server formats and cleanses the collected event data using Pandas. The input is the raw data obtained in step 1, and the output is structured data in a parseable format. At this stage, data preprocessing such as removing missing values ​​and standardizing the format is performed.

[0665] Step 3:

[0666] The server uses Scikit-learn and TensorFlow to compare past and current event data and predict communication demand. The input is formatted event data, and the output is the predicted communication demand and the optimal communication device placement proposal. Here, a machine learning algorithm is used to apply a predictive model.

[0667] Step 4:

[0668] The server uses Firebase Cloud Messaging to send analysis results to the device. The input is a communication demand assessment and deployment plan based on the analysis results, and the output is a push notification to the user. This notification includes specific details of communication countermeasures.

[0669] Step 5:

[0670] The user receives a notification from their device and implements the suggested communication countermeasures. The input is the notification content from the server, and the output is feedback on the implemented communication countermeasures. The user sends this feedback from their device to the server.

[0671] Step 6:

[0672] The server receives feedback from users and stores it as training data for the generated AI model. The input is the user's feedback on countermeasures, and the output is the improvement in future analysis accuracy through model learning. Through this process, the system is continuously improved.

[0673] 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.

[0674] The system of the present invention consists of a server, a terminal, a user, and an emotion engine. In addition to the conventional functions of collecting and analyzing event information and notifying the user of appropriate communication measures, this system is characterized by recognizing the user's emotions and taking appropriate actions based on them.

[0675] The server automatically collects event data from various event information websites and organizes information such as the event's content, scale, and expected number of participants. Then, it uses an AI-based analysis algorithm to evaluate the impact of the event. Based on this evaluation, it determines whether the deployment of mobile communication equipment or the configuration of existing communication equipment needs to be changed.

[0676] Analysis results and proposed solutions are sent from the server to the terminal. The terminal provides this information to the user and recognizes the user's emotional state through the emotion engine. The emotion engine analyzes user feedback and responses to notifications, capturing changes in emotion through voice and text.

[0677] Based on this emotion recognition, the server adjusts the tone and expression of subsequent notifications to provide information in a way that is optimal for the user's emotional state. Furthermore, the user's emotional state is recorded and stored as data that directly impacts the effectiveness of countermeasures. For example, if the user expresses dissatisfaction, the system can generate a detailed error report to suggest further improvements.

[0678] As a concrete example, suppose a user is instructed to handle a large-scale sporting event. The server analyzes the event information and determines the necessary communication measures. The information is then sent to the user's terminal, and the user confirms the provided measures. If the emotion engine analyzes the user's emotions and determines that the user is at ease, normal notifications are sent to maintain that state. However, if the user expresses concern, the emotion engine uses this emotion to provide options for requesting additional information or detailed explanations, helping to deepen their understanding.

[0679] This system configuration enables responses that are sensitive to the user's emotions, providing the emotional insights necessary to maximize the effectiveness of countermeasures.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The server automatically collects event data from various event information websites using APIs and web scraping techniques. The collected data includes event name, date and time, location, and estimated number of attendees.

[0683] Step 2:

[0684] The server cleanses the collected data, removing unnecessary and duplicate information and organizing it. It checks the accuracy of the data and formats the event information dataset in preparation for analysis.

[0685] Step 3:

[0686] The server uses pre-processed data to evaluate the scale and impact of the event using an AI analysis algorithm. By comparing it with data from similar past events, it determines appropriate communication measures based on the expected communication demand and number of participants.

[0687] Step 4:

[0688] The analysis results obtained from the server and the determined countermeasures are sent to the terminal. These include specific countermeasures such as the necessity of deploying mobile communication equipment and the effectiveness of changing settings.

[0689] Step 5:

[0690] The device visually displays this information to the user. The user can review the notified event information and recommended actions.

[0691] Step 6:

[0692] The emotion engine recognizes the user's emotional state through the device. It analyzes the user's mood and reactions from voice and text input to determine emotions such as reassurance, concern, and dissatisfaction.

[0693] Step 7:

[0694] Based on the user's emotional state, the server adjusts the content and wording of subsequent notifications. For example, if the user is showing signs of anxiety, it will provide detailed explanations and additional information to reassure them.

[0695] Step 8:

[0696] Based on the information confirmed on their devices, users will rearrange or change the settings of their mobile communication equipment. They will then instruct on-site staff on the decided measures and procure the necessary resources.

[0697] Step 9:

[0698] After the event, users input feedback on the effectiveness of the countermeasures on their devices. The emotion engine analyzes this feedback along with the user's facial expressions and reactions at the time, and sends the results to the server. This information contributes to the continuous learning of the AI ​​model and is used to improve the accuracy of future analyses.

[0699] (Example 2)

[0700] 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".

[0701] While it is important to properly assess the impact of an event and take necessary communication countermeasures, conventional systems lack the ability to consider users' emotional states. Therefore, there is a need for a system that can alleviate users' anxiety and concerns and implement efficient and acceptable countermeasures.

[0702] 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.

[0703] In this invention, the server includes means for automatically collecting event data, means for analyzing the collected data and evaluating its scale and impact, and means for recognizing the user's emotional state. This enables communication countermeasures that are sensitive to the user's emotions, thereby improving the effectiveness of the countermeasures.

[0704] "Event data" refers to information about an event, including its content, scale, and estimated number of participants.

[0705] A "mobile communication device" is a portable communication device installed for communication purposes, whose placement can be changed as needed.

[0706] A "user" refers to an individual or organization that uses the system and is the recipient of notifications.

[0707] "Emotional state" refers to the psychological situation a user is experiencing, and includes emotions such as reassurance and concern.

[0708] "Analysis means" refers to methods or algorithms for processing data and understanding its meaning, and is used to evaluate the impact of an event.

[0709] "Notification methods" refer to ways of providing information to users, such as screen displays and audio alerts.

[0710] This system consists of a server, terminals, users, and an emotion engine. The server is responsible for automatically collecting event data, obtaining this data from various sources on the internet. Web scraping techniques, such as Python libraries like Beautiful Soup and Scrapy, can be used here. The collected data is stored in a database management system (e.g., MySQL or PostgreSQL).

[0711] The server processes the collected event data using AI-based analysis algorithms to evaluate the impact of the events. Machine learning libraries such as TensorFlow and PyTorch can be used for this process. Based on the analysis results, necessary communication countermeasures are determined.

[0712] The analysis results and proposed countermeasures are sent from the server to the terminal. The terminal notifies the user of this information, and the user confirms it. Screen displays and push notifications are used as notification methods.

[0713] Furthermore, the device integrates an emotion engine that recognizes the user's emotional state in real time. The emotion engine utilizes emotion analysis APIs such as IBM Watson Tone Analyzer and Microsoft Azure Text Analytics to analyze user feedback and reactions.

[0714] Based on this emotional information, the server dynamically adjusts the notification content to provide information best suited to the user's emotional state. For example, if a user expresses anxiety, additional detailed information may be provided to encourage a sense of reassurance.

[0715] As a concrete example, during a large-scale sporting event, the server analyzes event information and predicts a significant increase in communication demand, determining that additional Wi-Fi hotspots are necessary. This information is sent to the terminal, the user confirms it, and the emotion engine analyzes the user's reaction. If anxiety is indicated, the system provides additional detailed explanations to support the user's understanding.

[0716] An example of a prompt for a generative AI model is, "Explain the role of the server and provide specific use cases for how the emotion engine is used to analyze user responses." This prompt provides information about the system's functionality and specific applications of emotion analysis.

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

[0718] Step 1:

[0719] The server collects event data from event information websites. The input consists of various web pages, and it retrieves information about specific events. Using web scraping techniques, such as Beautiful Soup and Scrapy, it extracts data about event content, dates, and scale in text format. The output is formatted event data.

[0720] Step 2:

[0721] The server stores the collected event data in a database. The input is pre-formatted event data. Storing this data as structured data in MySQL or PostgreSQL enables efficient access and manipulation. The output is the data stored in the database.

[0722] Step 3:

[0723] The server processes stored event data using an AI-based analysis algorithm. The input is event data in a database. TensorFlow and PyTorch are used to calculate the expected impact of events and predict communication demand. The output is an evaluation of the event's scale and deployment to communication destinations.

[0724] Step 4:

[0725] The server generates specific communication countermeasures based on the analysis results. The input is the evaluation results. Using these results, it proposes optimizations for the placement and configuration adjustments of communication equipment. The output is the proposed communication countermeasures.

[0726] Step 5:

[0727] The server sends a proposed communication countermeasure to the terminal. The input is the generated proposed communication countermeasure. This information is securely transmitted to the terminal using the HTTPS protocol. The output is the countermeasure information received by the terminal.

[0728] Step 6:

[0729] The terminal notifies the user of information received from the server. The input is the received countermeasure information. The proposed countermeasures are displayed to the user using on-screen pop-ups or push notifications. The output is the notification to the user.

[0730] Step 7:

[0731] The device uses an emotion engine to recognize the user's emotional state. Input is the user's responses and feedback. IBM Watson Tone Analyzer and Microsoft Azure Text Analytics are used to analyze the emotions the user expresses. Output is the analyzed emotion data.

[0732] Step 8:

[0733] The server adjusts notification content based on the user's emotional state. The input is emotional data. Based on the emotional analysis results, it provides additional details or changes the notification tone. The output is the adjusted notification content.

[0734] (Application Example 2)

[0735] 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".

[0736] Conventional communication security systems fail to consider user emotions, resulting in a lack of maximization of user satisfaction and system effectiveness. Furthermore, conventional measures may be insufficient in irregular situations. Therefore, there is a need for flexible communication security measures that take user emotions into account.

[0737] 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.

[0738] In this invention, the server includes means for automatically collecting event information, means for analyzing the collected event information and evaluating the scale and impact of the event, and means for recognizing the user's emotional state and determining an emotionally-based response. This enables the provision of appropriate information and optimization of communication measures tailored to the user's emotional state.

[0739] "Event information" refers to data related to various events, including information such as the event's content, scale, and expected number of participants.

[0740] "Analysis" is the process of using collected data, understanding its content, and evaluating it based on specific criteria.

[0741] "Evaluation" is the process of determining the impact and importance of information that has been collected and analyzed.

[0742] A "mobile communication device" is a portable device used to transmit information via various communication networks.

[0743] "Changing the settings of communication equipment" refers to the process of making adjustments to existing communication equipment in order to optimize its functions and performance.

[0744] "Emotional state recognition" is the process of acquiring emotions from the user's voice, text, etc., and determining their state.

[0745] "Deciding on a course of action" means selecting the appropriate measures or responses based on specific conditions.

[0746] A "system" is a collection of multiple means or devices that work together to achieve a specific purpose.

[0747] In this embodiment, a system is provided in which a server takes the lead in effectively processing event information and optimizing communication based on user sentiment. The server automatically collects data from various event information sites via the internet or other networks. This data includes the content and scale of the event, the expected number of participants, etc. The collected information is analyzed using an algorithm based on Python to evaluate the impact of the event.

[0748] Furthermore, the server recognizes the user's emotional state through sensors connected to a computer such as a Raspberry Pi. Emotional analysis uses emotion recognition libraries like OpenCV or PyTorch to generate emotion labels from the user's voice and text. These labels are then used to adjust the tone and content of subsequent communications.

[0749] Users review the information received through their devices, and the system adjusts further information based on feedback provided by the emotion engine. This enables optimal communication tailored to the user, thereby increasing the effectiveness of countermeasures.

[0750] A concrete example is a home conversational robot that, upon recognizing a user's stress levels, suggests content related to relaxation techniques. This system utilizes a generative AI model to assess the user's emotions in real time and dynamically provide appropriate information.

[0751] An example of a prompt message would be: "If the user's emotion is recognized as 'stress,' activate function A to suggest relaxation techniques and explain them to the user in a caring manner."

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The server automatically collects event data from various event information websites. Inputs include access information such as URL lists and API keys, and output includes data on event content, scale, and number of participants. This allows the server to prepare the basic data for the next analysis step.

[0755] Step 2:

[0756] The server analyzes the collected event data and uses an AI-based algorithm to evaluate the impact of the events. The input is the event data collected in step 1, and the output is an impact assessment score and an overview of the necessary communication countermeasures. Data processing includes text mining and numerical analysis.

[0757] Step 3:

[0758] The server determines whether it is necessary to deploy mobile communication equipment or modify the settings of existing communication equipment. Using the evaluation results from Step 2 as input, the output becomes a plan for specific communication measures. For example, it might decide to deploy additional communication equipment in densely populated areas.

[0759] Step 4:

[0760] The server notifies the terminal of the determined countermeasures. The input is the proposed communication countermeasures obtained in step 3, and the output is the notification message sent to the user. A messaging protocol is used for the actual communication.

[0761] Step 5:

[0762] The device recognizes the user's emotional state through an emotion engine. Input is the user's voice or text, and output is an emotion label. Specifically, it uses speech recognition technology to convert speech to text and then analyzes the emotion using an emotion recognition library.

[0763] Step 6:

[0764] The server adjusts the notification content and tone based on the user's emotional state. The input is the emotional label obtained in step 5, and the output is the adjusted notification message. This process uses a generative AI model to optimize the content by applying prompt sentences.

[0765] 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.

[0766] 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.

[0767] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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."

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] The following is further disclosed regarding the embodiments described above.

[0787] (Claim 1)

[0788] A means of automatically collecting event information,

[0789] A means for analyzing collected event information and evaluating the scale and impact of events,

[0790] A means for determining the arrangement of mobile communication devices or the configuration of communication devices based on the evaluation results,

[0791] A means of notifying the user of the determined countermeasures,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, further comprising means for receiving feedback from users on the effectiveness of recommended measures based on event information.

[0795] (Claim 3)

[0796] The system according to claim 1, wherein the analysis means comprises an algorithm that compares past event data with current event data to determine the necessity of countermeasures.

[0797] "Example 1"

[0798] (Claim 1)

[0799] A means of automatically collecting event information via a network,

[0800] A means of analyzing the scale of an event and the communication demand by referring to a database of similar past events using collected event information,

[0801] A means for calculating measures that recommend the placement of mobile communication devices or changes to the settings of existing communication devices based on the analysis results,

[0802] A means of sending the calculated countermeasures to the user's terminal for notification,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, further comprising means for receiving feedback from users on the effectiveness of the proposed countermeasures after their implementation and accumulating such feedback as learning data.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the analysis means comprises an algorithm that uses a generative AI model to compare past event data with current event data and predicts communication demand with high accuracy.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] A means of automatically collecting event information,

[0811] A means for analyzing collected event information and evaluating the scale and impact of events,

[0812] Based on the evaluation results, a means for determining the placement and configuration changes of mobile communication devices or communication devices and optimizing communication demand,

[0813] A means of notifying users of the determined countermeasures and recommending the installation of additional communication equipment,

[0814] An information processing system that includes this.

[0815] (Claim 2)

[0816] The information processing system according to claim 1, further comprising means for receiving feedback from users regarding the effectiveness of recommended measures based on event information and using that information for analysis.

[0817] (Claim 3)

[0818] The information processing system according to claim 1, wherein the analysis means comprises an algorithm that compares past response information with current event information to determine the optimality of communication countermeasures.

[0819] "Example 2 of combining an emotion engine"

[0820] (Claim 1)

[0821] A means of automatically collecting event data,

[0822] A means of analyzing the collected data and assessing its scale and impact,

[0823] A means for determining the arrangement of a mobile communication device or the configuration of a communication device based on the evaluation results,

[0824] A means of notifying the user of the determined countermeasures,

[0825] A means of recognizing the user's emotional state,

[0826] A means of adjusting notification content based on recognized emotional states,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, further comprising means for receiving feedback from users regarding the effectiveness of recommended measures based on event information.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the analysis means comprises an algorithm that compares past data with current data to determine the necessity of countermeasures.

[0832] "Application example 2 of combining emotional engines"

[0833] (Claim 1)

[0834] A means of automatically collecting event information,

[0835] A means for analyzing collected event information and evaluating the scale and impact of events,

[0836] A means for determining the arrangement of mobile communication devices or the configuration of communication devices based on the evaluation results,

[0837] A means of notifying the user of the determined countermeasures,

[0838] A means of recognizing the user's emotional state and determining an emotionally-based response,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, further comprising means for receiving feedback from users on the effectiveness of recommended measures based on event information.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the analysis means includes an algorithm that compares past event data with current event data to determine the need for countermeasures, and adjusts the notification content based on the user's emotional state. [Explanation of Symbols]

[0844] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically collecting event information, A means for analyzing collected event information and evaluating the scale and impact of events, A means for determining the arrangement of mobile communication devices or the configuration of communication devices based on the evaluation results, A means of notifying the user of the determined countermeasures, A system that includes this.

2. The system according to claim 1, further comprising means for receiving feedback from users on the effectiveness of recommended measures based on event information.

3. The system according to claim 1, wherein the analysis means comprises an algorithm that compares past event data with current event data to determine the necessity of countermeasures.

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