system

Abstract

We provide the system. [Solution] Means of collecting competition information, The means for processing the aforementioned collected competition information and constructing a predictive model, A means of generating personalized ads by analyzing the user's behavior history, The means for distributing the generated advertisement, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In public competitions, in order to improve the predictability and entertainment value of the competition itself, it is required to provide insightful data analysis and prediction in real time. However, with current methods, it is difficult to efficiently collect and process large-scale data related to the competition and perform accurate prediction and customized advertisement delivery based on it. For this reason, there is a problem that it is impossible to attract users' interest and maximize the user experience.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system comprising means for collecting competition information, means for processing the collected competition information and building a predictive model, means for analyzing users' behavioral history and generating individual advertisements, and means for distributing the generated advertisements. This system trains a model based on past competition data and analyzes each user's interests to predict public sports events and provides personalized advertisements and information in a timely manner. This makes it possible to increase the appeal of public sports events and improve user satisfaction.

[0006] "Competition information" is a general term for data related to publicly run sports, including information such as past match results, attributes of competitors and sports animals, and weather conditions.

[0007] "Means of collection" refers to devices or programs for automatically acquiring publicly known public sports betting data and real-time information.

[0008] "Means for processing and building predictive models" refers to devices or programs that perform mathematical or algorithmic methods to analyze acquired data and predict outcomes or specific phenomena.

[0009] "User activity history" refers to data such as past operation logs, purchase history, and access patterns that users leave behind when using the system.

[0010] "Generating means" refers to a program or device that creates new information or advertisements based on data analysis and optimizes them for the user.

[0011] "Means of distribution" refers to a network system or protocol used to transmit and display generated information or advertisements to specific users or groups of users. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface that includes a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is implemented as a system for improving the user experience in public sports betting by effectively utilizing competition information. First, as a means of collecting competition information, the server acquires data from external databases and sensors. This data includes past match results, the health status of athletes and competition animals, and weather information. This data is collected in real time and stored on the server.

[0034] Next, the collected data is processed by the server through data cleaning and feature engineering processes. Here, missing values ​​are imputed, outliers are removed, and the data is converted into a format suitable for analysis. This process prepares the data for building predictive models.

[0035] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. Specific algorithms used include regression analysis, classification, and clustering. This model aims to accurately predict the outcome of upcoming matches and specific athletic phenomena.

[0036] The device tracks the user's behavior history and analyzes their preferences based on the collected data. Based on this analysis, the server generates personalized advertisements and recommendations. These advertisements are customized to attract the user's interest.

[0037] Ultimately, the server delivers the generated advertisements and recommendations to the user's device. This delivery is scheduled based on the user's daily usage and past interests, and is performed at the optimal time. It is expected that the information provided through this process will pique the user's interest and increase their willingness to participate in public gambling.

[0038] As a concrete example, if a user is interested in a particular athlete, the system predicts the athlete's performance in matches and delivers advertisements containing that information. It also provides relevant event and reward information based on the user's past behavior, encouraging participation in the competitions. This maximizes the appeal of public sports betting and improves the user experience.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server collects data from external competition databases and sensors. This data includes past match results, biometric information of athletes and competition animals, and weather data. The collected data is updated in real time and stored in the database.

[0042] Step 2:

[0043] The server prepares the collected data through a data cleaning process. Missing values ​​are filled in using means or statistical imputation, and outliers are detected, corrected, or removed through outlier analysis. This cleaned data is then used for feature engineering for predictive models.

[0044] Step 3:

[0045] The server extracts features using the organized data. Here, performance indicators for athletes and competitive animals, characteristics of the competitive environment, and past results are quantified. These features are then used as a dataset for model construction.

[0046] Step 4:

[0047] The server trains a machine learning model based on feature data. Training algorithms include, for example, linear regression, decision trees, and neural networks. The model is then split into training and test datasets, and its accuracy is evaluated.

[0048] Step 5:

[0049] The device analyzes the user's preferences based on their past behavior history. This includes the user's browsing history, purchase history, and click behavior. This data is analyzed to understand the user's interests.

[0050] Step 6:

[0051] The server generates personalized advertisements and recommendations based on user analysis results. This includes information on sports and special offers that users are likely to be interested in. The content of the advertisements is dynamically customized and optimized.

[0052] Step 7:

[0053] The server delivers generated advertisements and recommendations to the user's device. This delivery is timed to take into account the user's activity patterns and is designed to be more effective.

[0054] Step 8:

[0055] Users participate in or place bets on public sports betting based on the advertisements and recommendations they receive. User feedback is used to improve subsequent advertisements and recommendations, contributing to the continuous optimization of the system.

[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 public gambling, there is a need to effectively provide users with appropriate and interesting information. However, conventional systems do not adequately reflect the preferences of individual users, making it difficult to increase user motivation to participate.

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

[0060] In this invention, the server includes means for collecting competition-related information from external data sources, means for processing the collected information through data cleaning and feature engineering to build a statistical model for prediction, and means for analyzing user activity data to generate personalized suggestions tailored to user interests. This makes it possible to provide information that will interest the user.

[0061] "External data sources" refer to external information systems or databases that provide information related to public gambling.

[0062] "Data cleaning" is the process of removing outliers and missing values ​​from collected data and preparing it in a format that can be used for analysis.

[0063] Feature engineering is a technique for generating effective features in machine learning and data analysis, and for optimizing datasets.

[0064] A "statistical model" is a mathematical model used to analyze data and reveal specific patterns or laws.

[0065] "User activity data" refers to data that records a user's operation history and behavioral patterns.

[0066] "Personalized suggestions tailored to user interests" refers to information provision and recommended content customized based on the user's hobbies and preferences.

[0067] This invention is configured as a system that collects diverse information in public gambling events, builds predictive models, and provides customized information to users. Specifically, the server acquires information related to the gambling events from external data sources. Established APIs and database connections can be used for data collection.

[0068] The server cleans the collected data using the Python pandas library to remove missing and outlier values. Furthermore, it performs feature engineering and generates effective features using the scikit-learn library. At this stage, the data is prepared in a format suitable for analysis.

[0069] Once the data is ready, the server trains a statistical model using machine learning algorithms. This process utilizes scikit-learn's Random Forest and Support Vector Machine (SVM) models. This model is built to accurately predict the outcome of the competition.

[0070] Meanwhile, the device collects user activity data and analyzes users based on specific interests and behaviors. Here, past operation history and click data are monitored to reveal which sports the user is interested in.

[0071] The server uses a specific generative AI model to generate personalized suggestions based on the user's interests. An example of a prompt is: "Predict the results of this week's matches featuring a specific athlete, and generate customized advertisements tailored to the user's interests, along with relevant event information."

[0072] Ultimately, the server delivers this customized information to the user's device at the appropriate time. This makes it easier for users to receive information that interests them, and as a result, it is expected that their willingness to participate in public gambling will increase.

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

[0074] Step 1:

[0075] The server collects competition-related information from external data sources. This information includes past match results and weather data obtained using API calls. The server stores this data in preparation for subsequent processing.

[0076] Step 2:

[0077] The server performs data cleaning on the collected data. For the raw data received as input, it imputes missing values ​​with the mean and filters out outliers. This process ensures data consistency and generates output data suitable for useful analysis.

[0078] Step 3:

[0079] The server performs feature engineering on the cleaned data. Using the clean input data, it extracts new features based on the athlete's past performance and weather data to generate output data. Specifically, it calculates the athlete's form performance score.

[0080] Step 4:

[0081] The server trains a machine learning model using the data after feature engineering has been completed. The prepared features are input to the random forest algorithm of the scikit-learn library, and a win / loss prediction model is output. At this stage, hyperparameters are adjusted to improve the accuracy of the model.

[0082] Step 5:

[0083] The device monitors user activity data and extracts specific interests. It collects the user's past operation history and access frequency as input, analyzes this data, and generates output data. This analysis identifies which sports the user is interested in.

[0084] Step 6:

[0085] The server translates user interests into specific prompts and generates personalized suggestions using an AI model. Using the prompt "Predict the results of this week's matches for a specific athlete and create an ad with related information" as an example, the server outputs engaging ad content.

[0086] Step 7:

[0087] The server delivers generated advertisements and suggested information to the device. It references the user's activity time and location information as input and outputs information at the optimal time. For example, by sending notifications during commuting hours, it maximizes the user response rate.

[0088] (Application Example 1)

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

[0090] In publicly run gambling events, there is a challenge in that it is difficult for users to easily obtain the latest information on the events and maintain their interest. Furthermore, there is a problem in that individualized information based on users' interests and behavior is not appropriately provided, which leads to a decrease in their motivation to participate in the events. The objective of this invention is to solve this problem.

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

[0092] In this invention, the server includes means for collecting competition information, means for processing the collected competition information to build a predictive model, means for analyzing the user's behavior history to generate individual information, means for dynamically displaying the information using a visual device, and means for distributing the generated information. As a result, users can always receive the latest competition information visually and intuitively, which can increase their motivation to participate in competitions.

[0093] "Means of collecting competition information" refers to the process of collecting all data related to public sports betting using external databases and sensors in order to maintain user interest.

[0094] "Methods for constructing predictive models" refer to techniques that use collected data to employ machine learning algorithms to accurately predict the results of upcoming matches or phenomena related to the sport.

[0095] "Methods for analyzing user behavior history to generate individualized information" refers to the process of analyzing a user's past behavior patterns and creating customized information and advertisements based on their interests and preferences.

[0096] "Means of dynamically displaying information using visual devices" refers to technologies that use smart glasses or other display devices to visually deliver real-time competition information to the user's field of view.

[0097] "Means of distributing generated information" refers to the process of providing users with customized information at the appropriate time based on their behavioral history and interests.

[0098] One embodiment of this invention is to build a system that improves the user experience by utilizing competition information. The server collects competition-related data from external databases and sensors. This data includes match results, participant health status, and weather information. The server processes the collected data through data cleaning and feature engineering. Specifically, it uses Python to impute missing values ​​and remove outliers, and formats the data into a format suitable for predictive models.

[0099] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. This process involves using Scikit-learn for regression analysis and clustering. The resulting model is then used to predict match results and specific athletic phenomena.

[0100] The user's device tracks their behavior history, and the server analyzes the user's preferences based on that data. Based on this analysis, the server generates personalized advertisements and recommendations. The generated information is displayed to the user dynamically in real time through visual devices such as smart glasses.

[0101] For example, if a user is interested in a particular competitor, the server predicts that competitor's performance and projects that information onto smart glasses. Furthermore, it encourages the user to participate in competitions by notifying them of relevant event benefits.

[0102] An example of a prompt for a generative AI model is: "Predict the performance of athlete A in the following public sports competition. Also, generate relevant advertisements and customize them to suit user B's preferences."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects competition information from external databases and sensors. Inputs include match results, participant health status, and weather information, and the server collects this data in real time and stores it in local storage.

[0106] Step 2:

[0107] The server performs data cleaning and feature engineering processes on the collected data. The input is the saved data from step 1, and the server uses Python to impute missing values ​​and remove outliers. This data processing transforms the data into a format suitable for analysis and outputs it.

[0108] Step 3:

[0109] The server trains a machine learning model based on the formatted data. Here, Scikit-learn is used to perform predictive analysis on the input data and build a model that predicts match results and athletic phenomena. The output is the trained model.

[0110] Step 4:

[0111] The terminal collects the user's activity history and sends it to the server. The input here is the user's past operation log, and the output is an analysis result showing their preferences. The server analyzes the user's preferences based on the received data and generates prompt messages.

[0112] Step 5:

[0113] The server generates individual advertisements and information based on the analysis results and generated prompt messages. The input consists of the analysis results of the behavioral history and prompt messages. The server utilizes a generation AI model to create user-optimized advertisements and information. The output is customized information displayed to the user.

[0114] Step 6:

[0115] The terminal displays generated information to the user through a visual device. The input is customized information, and the visual device, such as smart glasses, transmits this information to the user's field of vision in real time. The output is the visual experience the user receives.

[0116] 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.

[0117] This invention provides a system that takes into account the user's emotional state to enable more precise and personalized information delivery and advertising distribution. By incorporating an emotion engine, it is possible to make public sports betting more attractive and improve the user experience.

[0118] First, the server collects traditional competition data from external databases and sensors, and in addition, it collects emotional data from the user's device. This emotional data may be obtained from multiple sources, such as voice tone, facial expressions, and text message analysis. This data is transmitted to the server in real time and stored in the database.

[0119] Next, the server analyzes this emotional data using an emotion engine. This analysis employs machine learning algorithms and natural language processing to identify the emotions the user is experiencing. The results capture subtle emotional fluctuations, revealing what interests the user or what is causing them stress.

[0120] Furthermore, the device cross-references the user's past behavioral history and analyzes it together with emotional data. This combined approach generates advertisements and recommendations that are linked to the user's specific emotional state. For example, if a user feels joy at a particular athlete's victory, information about that athlete's rewards and upcoming matches will be highlighted.

[0121] The generated advertisements and information are delivered from the server to the device. By incorporating empathetic elements and content that resonates with the user's emotions, the information becomes more readily accepted. This effectively increases the user's interest and willingness to purchase.

[0122] For example, if the emotion engine analyzes that a user is excited about a competition, the server will immediately deliver advertisements or reward information that will maintain that feeling of exhilaration. Conversely, if disappointment is detected, the server will provide content that encourages the user or information that builds anticipation for the next time. In this way, by utilizing emotion data, the quality of the user experience can be improved and the overall effectiveness of the system can be enhanced.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The device collects data to capture the user's emotions. This data collection utilizes facial expression recognition via the camera, voice tone analysis via the microphone, and natural language processing from text messages. The collected data is used as information to determine the emotional state.

[0126] Step 2:

[0127] The device transmits collected emotional data to the server in real time. Secure and rapid protocols are used for data transmission, ensuring data accuracy while protecting user privacy.

[0128] Step 3:

[0129] The server inputs the received emotional data into the emotion engine, which then analyzes the data. The emotion engine uses machine learning algorithms to identify the emotions the user is experiencing. For example, it may detect emotions such as joy, excitement, sadness, and interest.

[0130] Step 4:

[0131] The server combines the analyzed sentiment data with the user's past behavioral history. This allows it to understand the characteristics linked to the user's current emotional state and prepares it to generate more sophisticated advertisements and information.

[0132] Step 5:

[0133] The server generates personalized ads and recommendations based on an analysis of the user's emotions and behavioral history. These ads are designed to resonate with the user's emotions and are more likely to capture their interest.

[0134] Step 6:

[0135] The server delivers the generated advertisements and information to the device. The delivery timing takes into account the user's activity patterns and emotional state, and is performed at the time when the user is most likely to receive the information.

[0136] Step 7:

[0137] Users review the advertisements and information they receive and, if necessary, participate in public gambling events or purchase related products. Furthermore, user responses are used as feedback for future ad delivery, helping to further optimize the system.

[0138] (Example 2)

[0139] 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".

[0140] In recent years, there has been a growing demand for personalized information to enhance the user experience in the fields of sports viewing and entertainment. However, traditional methods are limited to providing information based solely on the user's behavioral history, making it difficult to provide individualized responses that reflect real-time emotional states. In particular, the lack of a system that can quickly capture changes in a user's emotions and provide appropriate content accordingly means that it is difficult to maximize user interest and purchasing intent.

[0141] 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.

[0142] In this invention, the server includes means for collecting competition information, means for identifying the user's emotional state using an emotion analysis engine, and means for analyzing the user's behavioral history and emotional data together to generate individual advertisements and information. This makes it possible to provide personalized information that takes into account the user's real-time emotional state.

[0143] "Competition information" refers to data related to sports and events, including match results, athlete performance, and schedules.

[0144] "Emotional data" refers to data obtained from a user's voice, facial expressions, and text messages, and provides information that indicates the user's emotional state and psychological tendencies.

[0145] A "sentiment analysis engine" is a system component that uses machine learning algorithms and natural language processing techniques to analyze a user's emotional data and identify their emotional state.

[0146] "Behavioral history" refers to data on a user's past activities, including browsing history, purchase history, search data, and other related information.

[0147] "Advertisements and information" refers to messages and content generated based on users' interests and emotions, and includes promotional and recommendation information.

[0148] "Personalized information delivery" refers to the process of providing information in a customized form according to each user's individual characteristics and emotional state.

[0149] This invention is a system for providing personalized information and delivering advertisements while taking into account the user's emotional state. By including an emotion analysis engine, this system can improve the user experience.

[0150] The server first collects competition information using external databases and sensors. This competition information includes match results, player performance, and schedules. The collected information is securely stored in cloud-based data storage. Next, the server obtains sentiment data from the user's device. This data is collected using voice analysis software, facial recognition cameras, and natural language processing algorithms.

[0151] The device also sends the user's behavioral history to the server. This allows the system to review past browsing and purchase history. The server uses a sentiment analysis engine to analyze sentiment data and behavioral history data in real time. Machine learning algorithms and natural language processing techniques are applied in this analysis.

[0152] Based on the analysis results, the server uses a generative AI model to generate advertisements and information that match the user's current emotional state. This generated information is delivered to the device as a message containing empathetic elements. If the user is excited about the results of a particular sport, promotional information that helps maintain that excitement will be emphasized.

[0153] For example, if a user feels joy at a particular team's victory in a match, content that maintains that feeling of exhilaration will be immediately provided. Another example of a prompt message could be a command sent to the generating AI model such as, "Suggest a new offer based on the user's current emotional state."

[0154] This invention captures users' emotions in detail and provides personalized information accordingly, thereby improving the quality of the user experience.

[0155] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0156] Step 1:

[0157] The server collects competition information through external databases and sensors. Match results and player performance data are obtained via API as input data. This data is then stored in a cloud database and prepared for analysis.

[0158] Step 2:

[0159] The device collects user emotional data. Inputs include voice tone, facial expressions, and text messages. This data is analyzed in real time using voice analysis software, facial recognition technology, and natural language processing algorithms. The resulting output is an indicator of the user's emotional state.

[0160] Step 3:

[0161] The server compares collected sentiment data with behavioral history obtained from the device. Input data includes past browsing and purchase history, as well as sentiment data. Using data mining techniques, it identifies user interests and preferences, and then generates an analytical report based on these results. The output is an updated user profile.

[0162] Step 4:

[0163] The server utilizes an emotion analysis engine and a generative AI model to generate advertisements and information tailored to the user. Input includes analyzed emotion data and behavioral history data. Based on this data, a machine learning model operates to generate personalized content. The output is sent to the device as advertisements and recommendations.

[0164] Step 5:

[0165] The device delivers advertisements and information sent from the server to the user. The input is content generated by the server. It utilizes notification functions to deliver information to the user in real time. The output is the advertisements and information displayed on the user's screen.

[0166] Step 6:

[0167] Users respond to advertisements and information displayed on their devices. Input is obtained through biometric authentication and click actions within the provided content. The feedback data sent to the server is used to train the sentiment analysis engine to improve its accuracy. This cycle improves the overall system performance.

[0168] (Application Example 2)

[0169] 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".

[0170] Modern advertising systems fail to adequately consider users' emotional states and lack sufficient personalization of the user experience. As a result, it is difficult to provide users with the most relevant information in specific situations, leading to reduced advertising effectiveness. Furthermore, there is a need for technological means to perform real-time sentiment analysis and deliver advertisements appropriate to the user's context.

[0171] 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.

[0172] In this invention, the server includes means for collecting competition information, means for processing the collected competition information and constructing a predictive model, means for analyzing the user's emotional state and generating advertisements, means for delivering the generated advertisements to the user's mobile device, means for collecting emotional data in real time using a device worn by the user, and means for analyzing the collected emotional data and displaying advertisements that increase the user's level of excitement. This enables the delivery of advertisements that are tailored to the user's emotions, resulting in more effective information transmission.

[0173] "Competition information" refers to all data and circumstances related to a particular sport or event, including match results, athlete data, and historical statistics.

[0174] A "predictive model" is a mathematical or machine learning-based algorithm that uses collected data to predict specific future events or outcomes.

[0175] "User emotional state" refers to an indicator that shows the user's inner mood and feelings, analyzed from sources such as tone of voice, facial expressions, or text messages.

[0176] "Means of generating advertisements" refers to the technical process of creating advertising content tailored to a user based on data such as the user's emotional state and behavioral history.

[0177] A "portable information terminal" is an electronic device, such as a mobile phone or tablet, that a user can carry with them at all times to receive and display information.

[0178] "User-worn devices" refer to equipment that users can wear to obtain or display information from the outside world, such as smart glasses or head-mounted displays.

[0179] "Means of collecting emotional data in real time" refers to the process of identifying a user's current emotions through the immediate analysis of audio, visual, and text data.

[0180] "Excitement-enhancing ads" are advertising content designed to consciously stimulate and energize users' interest and emotions based on analyzed emotional data.

[0181] The system of this invention optimizes ad delivery by collecting competition information, building predictive models based on that information, analyzing the user's emotional state, and personalizing ads. The server first collects the user's emotional state in real time through the analysis of voice tone, facial expressions, and text messages. This is done using a device worn by the user, such as smart glasses. The emotional data obtained is then analyzed using software such as EmotionAnalyzer. Based on the analysis results, AdRecommender is used to generate ads suitable for the user. These ads are delivered to the user's mobile device and displayed using SmartGlass® esDisplay.

[0182] As a concrete example, if a user is at a sporting event, the system can sense their excitement level and display the latest merchandise from the relevant team or information about upcoming matches in real time. This helps maintain the user's excitement and maximizes the effectiveness of advertising. An example of a prompt in this system would be: "Generate advertising content to serve when the user is excited. For example, digital merchandise at a sporting event or ticket information for the next match."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The server collects competition information from external databases and sensors. The collected data includes match progress and player performance data. Based on this input competition information, data preprocessing is performed to build a predictive model. Preprocessing involves normalizing the data and removing unnecessary data, preparing it for model construction.

[0186] Step 2:

[0187] The server collects emotional data in real time from the smart glasses or mobile device worn by the user. This includes analyzing voice, facial expressions, and text messages through EmotionAnalyzer software. Based on the input emotional data, the emotional state is analyzed in real time to identify the user's current mood and level of excitement, and an interpretation is generated.

[0188] Step 3:

[0189] The server combines analyzed emotional states, collected competition information, and the user's past behavioral history to generate personalized ads using AdRecommender. It creates ad content optimized for the user's interests and emotions based on the input data, and as a result, a specific ad is determined.

[0190] Step 4:

[0191] The server delivers the generated advertisements to the user's mobile device. The delivered advertisements are then displayed to the user for the first time through SmartGlassesDisplay. The displayed advertisements reflect the user's current emotional state and are designed to maintain excitement or pique new interests.

[0192] Step 5:

[0193] Users can react to the ads they see, and their feedback is used to inform future data collection. User interactions are recorded by the server and stored as data to help generate future ads. This feedback loop allows the system to continuously improve the effectiveness of its ads.

[0194] 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.

[0195] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] This invention is implemented as a system for improving the user experience in public sports betting by effectively utilizing competition information. First, as a means of collecting competition information, the server acquires data from external databases and sensors. This data includes past match results, the health status of athletes and competition animals, and weather information. This data is collected in real time and stored on the server.

[0211] Next, the collected data is processed by the server through data cleaning and feature engineering processes. Here, missing values ​​are imputed, outliers are removed, and the data is converted into a format suitable for analysis. This process prepares the data for building predictive models.

[0212] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. Specific algorithms used include regression analysis, classification, and clustering. This model aims to accurately predict the outcome of upcoming matches and specific athletic phenomena.

[0213] The device tracks the user's behavior history and analyzes their preferences based on the collected data. Based on this analysis, the server generates personalized advertisements and recommendations. These advertisements are customized to attract the user's interest.

[0214] Ultimately, the server delivers the generated advertisements and recommendations to the user's device. This delivery is scheduled based on the user's daily usage and past interests, and is performed at the optimal time. It is expected that the information provided through this process will pique the user's interest and increase their willingness to participate in public gambling.

[0215] As a concrete example, if a user is interested in a particular athlete, the system predicts the athlete's performance in matches and delivers advertisements containing that information. It also provides relevant event and reward information based on the user's past behavior, encouraging participation in the competitions. This maximizes the appeal of public sports betting and improves the user experience.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The server collects data from external competition databases and sensors. This data includes past match results, biometric information of athletes and competition animals, and weather data. The collected data is updated in real time and stored in the database.

[0219] Step 2:

[0220] The server prepares the collected data through a data cleaning process. Missing values ​​are filled in using means or statistical imputation, and outliers are detected, corrected, or removed through outlier analysis. This cleaned data is then used for feature engineering for predictive models.

[0221] Step 3:

[0222] The server extracts features using the organized data. Here, performance indicators for athletes and competitive animals, characteristics of the competitive environment, and past results are quantified. These features are then used as a dataset for model construction.

[0223] Step 4:

[0224] The server trains a machine learning model based on feature data. Training algorithms include, for example, linear regression, decision trees, and neural networks. The model is then split into training and test datasets, and its accuracy is evaluated.

[0225] Step 5:

[0226] The device analyzes the user's preferences based on their past behavior history. This includes the user's browsing history, purchase history, and click behavior. This data is analyzed to understand the user's interests.

[0227] Step 6:

[0228] The server generates personalized advertisements and recommendations based on user analysis results. This includes information on sports and special offers that users are likely to be interested in. The content of the advertisements is dynamically customized and optimized.

[0229] Step 7:

[0230] The server delivers generated advertisements and recommendations to the user's device. This delivery is timed to take into account the user's activity patterns and is designed to be more effective.

[0231] Step 8:

[0232] Users participate in or place bets on public sports betting based on the advertisements and recommendations they receive. User feedback is used to improve subsequent advertisements and recommendations, contributing to the continuous optimization of the system.

[0233] (Example 1)

[0234] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0235] In public gambling, there is a need to effectively provide users with appropriate and interesting information. However, conventional systems do not adequately reflect the preferences of individual users, making it difficult to increase user motivation to participate.

[0236] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0237] In this invention, the server includes means for collecting competition-related information from external data sources, means for processing the collected information through data cleaning and feature engineering to build a statistical model for prediction, and means for analyzing user activity data to generate personalized suggestions tailored to user interests. This makes it possible to provide information that will interest the user.

[0238] "External data sources" refer to external information systems or databases that provide information related to public gambling.

[0239] "Data cleaning" is the process of removing outliers and missing values ​​from collected data and preparing it in a format that can be used for analysis.

[0240] Feature engineering is a technique for generating effective features in machine learning and data analysis, and for optimizing datasets.

[0241] A "statistical model" is a mathematical model used to analyze data and reveal specific patterns or laws.

[0242] "User activity data" refers to data that records a user's operation history and behavioral patterns.

[0243] "Personalized suggestions tailored to user interests" refers to information provision and recommended content customized based on the user's hobbies and preferences.

[0244] This invention is configured as a system that collects diverse information in public gambling events, builds predictive models, and provides customized information to users. Specifically, the server acquires information related to the gambling events from external data sources. Established APIs and database connections can be used for data collection.

[0245] The server cleans the collected data using the Python pandas library to remove missing and outlier values. Furthermore, it performs feature engineering and generates effective features using the scikit-learn library. At this stage, the data is prepared in a format suitable for analysis.

[0246] Once the data is ready, the server trains a statistical model using machine learning algorithms. This process utilizes scikit-learn's Random Forest and Support Vector Machine (SVM) models. This model is built to accurately predict the outcome of the competition.

[0247] Meanwhile, the device collects user activity data and analyzes users based on specific interests and behaviors. Here, past operation history and click data are monitored to reveal which sports the user is interested in.

[0248] The server uses a specific generative AI model to generate personalized suggestions based on the user's interests. An example of a prompt is: "Predict the results of this week's matches featuring a specific athlete, and generate customized advertisements tailored to the user's interests, along with relevant event information."

[0249] Ultimately, the server delivers this customized information to the user's device at the appropriate time. This makes it easier for users to receive information that interests them, and as a result, it is expected that their willingness to participate in public gambling will increase.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The server collects competition-related information from external data sources. This information includes past match results and weather data obtained using API calls. The server stores this data in preparation for subsequent processing.

[0253] Step 2:

[0254] The server performs data cleaning on the collected data. For the raw data received as input, it imputes missing values ​​with the mean and filters out outliers. This process ensures data consistency and generates output data suitable for useful analysis.

[0255] Step 3:

[0256] The server performs feature engineering on the cleaned data. Using the clean input data, it extracts new features based on the athlete's past performance and weather data to generate output data. Specifically, it calculates the athlete's form performance score.

[0257] Step 4:

[0258] The server trains a machine learning model using the data after feature engineering has been completed. The prepared features are input to the random forest algorithm of the scikit-learn library, and a win / loss prediction model is output. At this stage, hyperparameters are adjusted to improve the accuracy of the model.

[0259] Step 5:

[0260] The device monitors user activity data and extracts specific interests. It collects the user's past operation history and access frequency as input, analyzes this data, and generates output data. This analysis identifies which sports the user is interested in.

[0261] Step 6:

[0262] The server translates user interests into specific prompts and generates personalized suggestions using an AI model. Using the prompt "Predict the results of this week's matches for a specific athlete and create an ad with related information" as an example, the server outputs engaging ad content.

[0263] Step 7:

[0264] The server delivers generated advertisements and suggested information to the device. It references the user's activity time and location information as input and outputs information at the optimal time. For example, by sending notifications during commuting hours, it maximizes the user response rate.

[0265] (Application Example 1)

[0266] 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."

[0267] In publicly run gambling events, there is a challenge in that it is difficult for users to easily obtain the latest information on the events and maintain their interest. Furthermore, there is a problem in that individualized information based on users' interests and behavior is not appropriately provided, which leads to a decrease in their motivation to participate in the events. The objective of this invention is to solve this problem.

[0268] 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.

[0269] In this invention, the server includes means for collecting competition information, means for processing the collected competition information to build a predictive model, means for analyzing the user's behavior history to generate individual information, means for dynamically displaying the information using a visual device, and means for distributing the generated information. As a result, users can always receive the latest competition information visually and intuitively, which can increase their motivation to participate in competitions.

[0270] "Means of collecting competition information" refers to the process of collecting all data related to public sports betting using external databases and sensors in order to maintain user interest.

[0271] "Methods for constructing predictive models" refer to techniques that use collected data to employ machine learning algorithms to accurately predict the results of upcoming matches or phenomena related to the sport.

[0272] "Methods for analyzing user behavior history to generate individualized information" refers to the process of analyzing a user's past behavior patterns and creating customized information and advertisements based on their interests and preferences.

[0273] "Means of dynamically displaying information using visual devices" refers to technologies that use smart glasses or other display devices to visually deliver real-time competition information to the user's field of view.

[0274] "Means of distributing generated information" refers to the process of providing users with customized information at the appropriate time based on their behavioral history and interests.

[0275] One embodiment of this invention is to build a system that improves the user experience by utilizing competition information. The server collects competition-related data from external databases and sensors. This data includes match results, participant health status, and weather information. The server processes the collected data through data cleaning and feature engineering. Specifically, it uses Python to impute missing values ​​and remove outliers, and formats the data into a format suitable for predictive models.

[0276] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. This process involves using Scikit-learn for regression analysis and clustering. The resulting model is then used to predict match results and specific athletic phenomena.

[0277] The user's terminal tracks the user's behavior history, and the server analyzes the user's preferences based on this data. Based on this analysis, the server generates personalized advertisements and recommendation information. The generated information is dynamically displayed to the user in real time through a visual device such as smart glasses.

[0278] As a specific example, if a user is interested in a particular competitor, the server predicts the competitor's performance and projects that information onto the smart glasses. Additionally, by notifying the user of special offers related to relevant events, the server promotes the user's willingness to participate in the competition.

[0279] An example of a prompt sentence for the generation AI model is: "Please predict the performance of Player A in the next public competition. Also, generate relevant advertisements and customize them according to the preferences of User B."

[0280] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0281] Step 1:

[0282] The server collects competition information from external databases and sensors. The inputs are match results, the health status of participants, and weather information. The server collects this data in real time and stores it in local storage.

[0283] Step 2:

[0284] The server executes the processes of data cleaning and feature engineering on the collected data. The input is the saved data from Step 1. The server uses Python to complement missing values and eliminate outliers. Through this data processing, data converted into a form suitable for analysis is output.

[0285] Step 3:

[0286] The server trains a machine learning model based on the formatted data. Here, Scikit-learn is used to perform predictive analysis on the input data and build a model that predicts the results of matches and competitive phenomena. The output is a trained model.

[0287] Step 4:

[0288] The terminal collects the user's behavioral history and sends it to the server. The input here is the user's past operation logs, and the output is the analysis result indicating preferences. The server analyzes the user's preferences based on the sent data and generates prompt sentences.

[0289] Step 5:

[0290] The server generates individual advertisements and information according to the analysis results and the generated prompt sentences. The input is the analysis result of the behavioral history and the prompt sentences. The server utilizes the generation AI model to create advertisements and information optimized for the user. The output is customized information for display to the user.

[0291] Step 6:

[0292] The terminal displays the generated information to the user through a visual device. The input is the customized information. The visual device uses smart glasses or the like to transmit this information to the user's field of vision in real time. The output is the visual experience received by the user.

[0293] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0294] The present invention provides a system that takes into account the user's emotional state and realizes more refined and personalized information provision and advertisement distribution. By incorporating an emotion engine, it is possible to make public competitions more attractive and improve the user experience.

[0295] First, the server collects traditional competition data from external databases and sensors, and in addition, it collects emotional data from the user's device. This emotional data may be obtained from multiple sources, such as voice tone, facial expressions, and text message analysis. This data is transmitted to the server in real time and stored in the database.

[0296] Next, the server analyzes this emotional data using an emotion engine. This analysis employs machine learning algorithms and natural language processing to identify the emotions the user is experiencing. The results capture subtle emotional fluctuations, revealing what interests the user or what is causing them stress.

[0297] Furthermore, the device cross-references the user's past behavioral history and analyzes it together with emotional data. This combined approach generates advertisements and recommendations that are linked to the user's specific emotional state. For example, if a user feels joy at a particular athlete's victory, information about that athlete's rewards and upcoming matches will be highlighted.

[0298] The generated advertisements and information are delivered from the server to the device. By incorporating empathetic elements and content that resonates with the user's emotions, the information becomes more readily accepted. This effectively increases the user's interest and willingness to purchase.

[0299] For example, if the emotion engine analyzes that a user is excited about a competition, the server will immediately deliver advertisements or reward information that will maintain that feeling of exhilaration. Conversely, if disappointment is detected, the server will provide content that encourages the user or information that builds anticipation for the next time. In this way, by utilizing emotion data, the quality of the user experience can be improved and the overall effectiveness of the system can be enhanced.

[0300] The following describes the processing flow.

[0301] Step 1:

[0302] The terminal collects data for capturing the user's emotions. This data collection utilizes facial expression recognition with a camera, voice tone analysis with a microphone, and natural language processing from text messages. The collected data is used as information for determining the emotional state.

[0303] Step 2:

[0304] The terminal transmits the collected emotion data to the server in real time. A secure and fast protocol is used for data transmission to ensure the accuracy of the data while protecting the user's privacy.

[0305] Step 3:

[0306] The server inputs the received emotion data into an emotion engine and analyzes the data. The emotion engine uses machine learning algorithms to identify the emotions held by the user. For example, joy, excitement, sadness, interest, etc. are detected.

[0307] [[ID=二十四]]Step 4:

[0308] The server combines the analyzed emotion data with the user's past behavior history. This enables understanding the characteristics associated with the user's current emotional state and prepares to generate more refined advertisements and information.

[0309] Step 5:

[0310] Based on the analysis results of emotions and behavior history, the server generates advertisements and recommendation information optimized for the user. This advertisement has content that conforms to the user's emotions and is likely to attract interest.

[0311] Step 6:

[0312] The server delivers the generated advertisements and information to the device. The delivery timing takes into account the user's activity patterns and emotional state, and is performed at the time when the user is most likely to receive the information.

[0313] Step 7:

[0314] Users review the advertisements and information they receive and, if necessary, participate in public gambling events or purchase related products. Furthermore, user responses are used as feedback for future ad delivery, helping to further optimize the system.

[0315] (Example 2)

[0316] 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".

[0317] In recent years, there has been a growing demand for personalized information to enhance the user experience in the fields of sports viewing and entertainment. However, traditional methods are limited to providing information based solely on the user's behavioral history, making it difficult to provide individualized responses that reflect real-time emotional states. In particular, the lack of a system that can quickly capture changes in a user's emotions and provide appropriate content accordingly means that it is difficult to maximize user interest and purchasing intent.

[0318] 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.

[0319] In this invention, the server includes means for collecting competition information, means for identifying the user's emotional state using an emotion analysis engine, and means for analyzing the user's behavioral history and emotional data together to generate individual advertisements and information. This makes it possible to provide personalized information that takes into account the user's real-time emotional state.

[0320] "Competition information" refers to data related to sports and events, including match results, athlete performance, and schedules.

[0321] "Emotional data" refers to data obtained from a user's voice, facial expressions, and text messages, and provides information that indicates the user's emotional state and psychological tendencies.

[0322] A "sentiment analysis engine" is a system component that uses machine learning algorithms and natural language processing techniques to analyze a user's emotional data and identify their emotional state.

[0323] "Behavioral history" refers to data on a user's past activities, including browsing history, purchase history, search data, and other related information.

[0324] "Advertisements and information" refers to messages and content generated based on users' interests and emotions, and includes promotional and recommendation information.

[0325] "Personalized information delivery" refers to the process of providing information in a customized form according to each user's individual characteristics and emotional state.

[0326] This invention is a system for providing personalized information and delivering advertisements while taking into account the user's emotional state. By including an emotion analysis engine, this system can improve the user experience.

[0327] The server first collects competition information using external databases and sensors. This competition information includes match results, player performance, and schedules. The collected information is securely stored in cloud-based data storage. Next, the server obtains sentiment data from the user's device. This data is collected using voice analysis software, facial recognition cameras, and natural language processing algorithms.

[0328] The device also sends the user's behavioral history to the server. This allows the system to review past browsing and purchase history. The server uses a sentiment analysis engine to analyze sentiment data and behavioral history data in real time. Machine learning algorithms and natural language processing techniques are applied in this analysis.

[0329] Based on the analysis results, the server uses a generative AI model to generate advertisements and information that match the user's current emotional state. This generated information is delivered to the device as a message containing empathetic elements. If the user is excited about the results of a particular sport, promotional information that helps maintain that excitement will be emphasized.

[0330] For example, if a user feels joy at a particular team's victory in a match, content that maintains that feeling of exhilaration will be immediately provided. Another example of a prompt message could be a command sent to the generating AI model such as, "Suggest a new offer based on the user's current emotional state."

[0331] This invention captures users' emotions in detail and provides personalized information accordingly, thereby improving the quality of the user experience.

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The server collects competition information through external databases and sensors. Match results and player performance data are obtained via API as input data. This data is then stored in a cloud database and prepared for analysis.

[0335] Step 2:

[0336] The device collects user emotional data. Inputs include voice tone, facial expressions, and text messages. This data is analyzed in real time using voice analysis software, facial recognition technology, and natural language processing algorithms. The resulting output is an indicator of the user's emotional state.

[0337] Step 3:

[0338] The server compares collected sentiment data with behavioral history obtained from the device. Input data includes past browsing and purchase history, as well as sentiment data. Using data mining techniques, it identifies user interests and preferences, and then generates an analytical report based on these results. The output is an updated user profile.

[0339] Step 4:

[0340] The server utilizes an emotion analysis engine and a generative AI model to generate advertisements and information tailored to the user. Input includes analyzed emotion data and behavioral history data. Based on this data, a machine learning model operates to generate personalized content. The output is sent to the device as advertisements and recommendations.

[0341] Step 5:

[0342] The device delivers advertisements and information sent from the server to the user. The input is content generated by the server. It utilizes notification functions to deliver information to the user in real time. The output is the advertisements and information displayed on the user's screen.

[0343] Step 6:

[0344] Users respond to advertisements and information displayed on their devices. Input is obtained through biometric authentication and click actions within the provided content. The feedback data sent to the server is used to train the sentiment analysis engine to improve its accuracy. This cycle improves the overall system performance.

[0345] (Application Example 2)

[0346] 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."

[0347] Modern advertising systems fail to adequately consider users' emotional states and lack sufficient personalization of the user experience. As a result, it is difficult to provide users with the most relevant information in specific situations, leading to reduced advertising effectiveness. Furthermore, there is a need for technological means to perform real-time sentiment analysis and deliver advertisements appropriate to the user's context.

[0348] 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.

[0349] In this invention, the server includes means for collecting competition information, means for processing the collected competition information and constructing a predictive model, means for analyzing the user's emotional state and generating advertisements, means for delivering the generated advertisements to the user's mobile device, means for collecting emotional data in real time using a device worn by the user, and means for analyzing the collected emotional data and displaying advertisements that increase the user's level of excitement. This enables the delivery of advertisements that are tailored to the user's emotions, resulting in more effective information transmission.

[0350] "Competition information" refers to all data and circumstances related to a particular sport or event, including match results, athlete data, and historical statistics.

[0351] A "predictive model" is a mathematical or machine learning-based algorithm that uses collected data to predict specific future events or outcomes.

[0352] "User emotional state" refers to an indicator that shows the user's inner mood and feelings, analyzed from sources such as tone of voice, facial expressions, or text messages.

[0353] "Means of generating advertisements" refers to the technical process of creating advertising content tailored to a user based on data such as the user's emotional state and behavioral history.

[0354] A "portable information terminal" is an electronic device, such as a mobile phone or tablet, that a user can carry with them at all times to receive and display information.

[0355] "User-worn devices" refer to equipment that users can wear to obtain or display information from the outside world, such as smart glasses or head-mounted displays.

[0356] "Means of collecting emotional data in real time" refers to the process of identifying a user's current emotions through the immediate analysis of audio, visual, and text data.

[0357] "Excitement-enhancing ads" are advertising content designed to consciously stimulate and energize users' interest and emotions based on analyzed emotional data.

[0358] The system of this invention optimizes ad delivery by collecting competition information, building predictive models based on that information, analyzing the user's emotional state, and personalizing ads. The server first collects the user's emotional state in real time through the analysis of voice tone, facial expressions, and text messages. This is done using a device worn by the user, such as smart glasses. The emotional data obtained is then analyzed using software such as EmotionAnalyzer. Based on the analysis results, AdRecommender is used to generate ads suitable for the user. These ads are delivered to the user's mobile device and displayed using SmartGlassesDisplay.

[0359] As a concrete example, if a user is at a sporting event, the system can sense their excitement level and display the latest merchandise from the relevant team or information about upcoming matches in real time. This helps maintain the user's excitement and maximizes the effectiveness of advertising. An example of a prompt in this system would be: "Generate advertising content to serve when the user is excited. For example, digital merchandise at a sporting event or ticket information for the next match."

[0360] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0361] Step 1:

[0362] The server collects competition information from external databases and sensors. The collected data includes match progress and player performance data. Based on this input competition information, data preprocessing is performed to build a predictive model. Preprocessing involves normalizing the data and removing unnecessary data, preparing it for model construction.

[0363] Step 2:

[0364] The server collects emotional data in real time from the smart glasses or mobile device worn by the user. This includes analyzing voice, facial expressions, and text messages through EmotionAnalyzer software. Based on the input emotional data, the emotional state is analyzed in real time to identify the user's current mood and level of excitement, and an interpretation is generated.

[0365] Step 3:

[0366] The server combines analyzed emotional states, collected competition information, and the user's past behavioral history to generate personalized ads using AdRecommender. It creates ad content optimized for the user's interests and emotions based on the input data, and as a result, a specific ad is determined.

[0367] Step 4:

[0368] The server delivers the generated advertisements to the user's mobile device. The delivered advertisements are then displayed to the user for the first time through SmartGlassesDisplay. The displayed advertisements reflect the user's current emotional state and are designed to maintain excitement or pique new interests.

[0369] Step 5:

[0370] Users can react to the ads they see, and their feedback is used to inform future data collection. User interactions are recorded by the server and stored as data to help generate future ads. This feedback loop allows the system to continuously improve the effectiveness of its ads.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] [Third Embodiment]

[0375] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0376] 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.

[0377] 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).

[0378] 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.

[0379] 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.

[0380] 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).

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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".

[0387] This invention is implemented as a system for improving the user experience in public sports betting by effectively utilizing competition information. First, as a means of collecting competition information, the server acquires data from external databases and sensors. This data includes past match results, the health status of athletes and competition animals, and weather information. This data is collected in real time and stored on the server.

[0388] Next, the collected data is processed by the server through data cleaning and feature engineering processes. Here, missing values ​​are imputed, outliers are removed, and the data is converted into a format suitable for analysis. This process prepares the data for building predictive models.

[0389] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. Specific algorithms used include regression analysis, classification, and clustering. This model aims to accurately predict the outcome of upcoming matches and specific athletic phenomena.

[0390] The device tracks the user's behavior history and analyzes their preferences based on the collected data. Based on this analysis, the server generates personalized advertisements and recommendations. These advertisements are customized to attract the user's interest.

[0391] Ultimately, the server delivers the generated advertisements and recommendations to the user's device. This delivery is scheduled based on the user's daily usage and past interests, and is performed at the optimal time. It is expected that the information provided through this process will pique the user's interest and increase their willingness to participate in public gambling.

[0392] As a concrete example, if a user is interested in a particular athlete, the system predicts the athlete's performance in matches and delivers advertisements containing that information. It also provides relevant event and reward information based on the user's past behavior, encouraging participation in the competitions. This maximizes the appeal of public sports betting and improves the user experience.

[0393] The following describes the processing flow.

[0394] Step 1:

[0395] The server collects data from external competition databases and sensors. This data includes past match results, biometric information of athletes and competition animals, and weather data. The collected data is updated in real time and stored in the database.

[0396] Step 2:

[0397] The server prepares the collected data through a data cleaning process. Missing values ​​are filled in using means or statistical imputation, and outliers are detected, corrected, or removed through outlier analysis. This cleaned data is then used for feature engineering for predictive models.

[0398] Step 3:

[0399] The server extracts features using the organized data. Here, performance indicators for athletes and competitive animals, characteristics of the competitive environment, and past results are quantified. These features are then used as a dataset for model construction.

[0400] Step 4:

[0401] The server trains a machine learning model based on feature data. Training algorithms include, for example, linear regression, decision trees, and neural networks. The model is then split into training and test datasets, and its accuracy is evaluated.

[0402] Step 5:

[0403] The device analyzes the user's preferences based on their past behavior history. This includes the user's browsing history, purchase history, and click behavior. This data is analyzed to understand the user's interests.

[0404] Step 6:

[0405] The server generates personalized advertisements and recommendations based on user analysis results. This includes information on sports and special offers that users are likely to be interested in. The content of the advertisements is dynamically customized and optimized.

[0406] Step 7:

[0407] The server delivers generated advertisements and recommendations to the user's device. This delivery is timed to take into account the user's activity patterns and is designed to be more effective.

[0408] Step 8:

[0409] Users participate in or place bets on public sports betting based on the advertisements and recommendations they receive. User feedback is used to improve subsequent advertisements and recommendations, contributing to the continuous optimization of the system.

[0410] (Example 1)

[0411] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0412] In public gambling, there is a need to effectively provide users with appropriate and interesting information. However, conventional systems do not adequately reflect the preferences of individual users, making it difficult to increase user motivation to participate.

[0413] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0414] In this invention, the server includes means for collecting competition-related information from external data sources, means for processing the collected information through data cleaning and feature engineering to build a statistical model for prediction, and means for analyzing user activity data to generate personalized suggestions tailored to user interests. This makes it possible to provide information that will interest the user.

[0415] "External data sources" refer to external information systems or databases that provide information related to public gambling.

[0416] "Data cleaning" is the process of removing outliers and missing values ​​from collected data and preparing it in a format that can be used for analysis.

[0417] Feature engineering is a technique for generating effective features in machine learning and data analysis, and for optimizing datasets.

[0418] A "statistical model" is a mathematical model used to analyze data and reveal specific patterns or laws.

[0419] "User activity data" refers to data that records a user's operation history and behavioral patterns.

[0420] "Personalized suggestions tailored to user interests" refers to information provision and recommended content customized based on the user's hobbies and preferences.

[0421] This invention is configured as a system that collects diverse information in public gambling events, builds predictive models, and provides customized information to users. Specifically, the server acquires information related to the gambling events from external data sources. Established APIs and database connections can be used for data collection.

[0422] The server cleans the collected data using the Python pandas library to remove missing and outlier values. Furthermore, it performs feature engineering and generates effective features using the scikit-learn library. At this stage, the data is prepared in a format suitable for analysis.

[0423] Once the data is ready, the server trains a statistical model using machine learning algorithms. This process utilizes scikit-learn's Random Forest and Support Vector Machine (SVM) models. This model is built to accurately predict the outcome of the competition.

[0424] Meanwhile, the device collects user activity data and analyzes users based on specific interests and behaviors. Here, past operation history and click data are monitored to reveal which sports the user is interested in.

[0425] The server uses a specific generative AI model to generate personalized suggestions based on the user's interests. An example of a prompt is: "Predict the results of this week's matches featuring a specific athlete, and generate customized advertisements tailored to the user's interests, along with relevant event information."

[0426] Ultimately, the server delivers this customized information to the user's device at the appropriate time. This makes it easier for users to receive information that interests them, and as a result, it is expected that their willingness to participate in public gambling will increase.

[0427] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0428] Step 1:

[0429] The server collects competition-related information from external data sources. This information includes past match results and weather data obtained using API calls. The server stores this data in preparation for subsequent processing.

[0430] Step 2:

[0431] The server performs data cleaning on the collected data. For the raw data received as input, it imputes missing values ​​with the mean and filters out outliers. This process ensures data consistency and generates output data suitable for useful analysis.

[0432] Step 3:

[0433] The server performs feature engineering on the cleaned data. Using the clean input data, it extracts new features based on the athlete's past performance and weather data to generate output data. Specifically, it calculates the athlete's form performance score.

[0434] Step 4:

[0435] The server trains a machine learning model using the data after feature engineering has been completed. The prepared features are input to the random forest algorithm of the scikit-learn library, and a win / loss prediction model is output. At this stage, hyperparameters are adjusted to improve the accuracy of the model.

[0436] Step 5:

[0437] The device monitors user activity data and extracts specific interests. It collects the user's past operation history and access frequency as input, analyzes this data, and generates output data. This analysis identifies which sports the user is interested in.

[0438] Step 6:

[0439] The server translates user interests into specific prompts and generates personalized suggestions using an AI model. Using the prompt "Predict the results of this week's matches for a specific athlete and create an ad with related information" as an example, the server outputs engaging ad content.

[0440] Step 7:

[0441] The server delivers generated advertisements and suggested information to the device. It references the user's activity time and location information as input and outputs information at the optimal time. For example, by sending notifications during commuting hours, it maximizes the user response rate.

[0442] (Application Example 1)

[0443] 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."

[0444] In publicly run gambling events, there is a challenge in that it is difficult for users to easily obtain the latest information on the events and maintain their interest. Furthermore, there is a problem in that individualized information based on users' interests and behavior is not appropriately provided, which leads to a decrease in their motivation to participate in the events. The objective of this invention is to solve this problem.

[0445] 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.

[0446] In this invention, the server includes means for collecting competition information, means for processing the collected competition information to build a predictive model, means for analyzing the user's behavior history to generate individual information, means for dynamically displaying the information using a visual device, and means for distributing the generated information. As a result, users can always receive the latest competition information visually and intuitively, which can increase their motivation to participate in competitions.

[0447] "Means of collecting competition information" refers to the process of collecting all data related to public sports betting using external databases and sensors in order to maintain user interest.

[0448] "Methods for constructing predictive models" refer to techniques that use collected data to employ machine learning algorithms to accurately predict the results of upcoming matches or phenomena related to the sport.

[0449] "Methods for analyzing user behavior history to generate individualized information" refers to the process of analyzing a user's past behavior patterns and creating customized information and advertisements based on their interests and preferences.

[0450] "Means of dynamically displaying information using visual devices" refers to technologies that use smart glasses or other display devices to visually deliver real-time competition information to the user's field of view.

[0451] "Means of distributing generated information" refers to the process of providing users with customized information at the appropriate time based on their behavioral history and interests.

[0452] One embodiment of this invention is to build a system that improves the user experience by utilizing competition information. The server collects competition-related data from external databases and sensors. This data includes match results, participant health status, and weather information. The server processes the collected data through data cleaning and feature engineering. Specifically, it uses Python to impute missing values ​​and remove outliers, and formats the data into a format suitable for predictive models.

[0453] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. This process involves using Scikit-learn for regression analysis and clustering. The resulting model is then used to predict match results and specific athletic phenomena.

[0454] The user's device tracks their behavior history, and the server analyzes the user's preferences based on that data. Based on this analysis, the server generates personalized advertisements and recommendations. The generated information is displayed to the user dynamically in real time through visual devices such as smart glasses.

[0455] For example, if a user is interested in a particular competitor, the server predicts that competitor's performance and projects that information onto smart glasses. Furthermore, it encourages the user to participate in competitions by notifying them of relevant event benefits.

[0456] An example of a prompt for a generative AI model is: "Predict the performance of athlete A in the following public sports competition. Also, generate relevant advertisements and customize them to suit user B's preferences."

[0457] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0458] Step 1:

[0459] The server collects competition information from external databases and sensors. Inputs include match results, participant health status, and weather information, and the server collects this data in real time and stores it in local storage.

[0460] Step 2:

[0461] The server performs data cleaning and feature engineering processes on the collected data. The input is the saved data from step 1, and the server uses Python to impute missing values ​​and remove outliers. This data processing transforms the data into a format suitable for analysis and outputs it.

[0462] Step 3:

[0463] The server trains a machine learning model based on the formatted data. Here, Scikit-learn is used to perform predictive analysis on the input data and build a model that predicts match results and athletic phenomena. The output is the trained model.

[0464] Step 4:

[0465] The terminal collects the user's activity history and sends it to the server. The input here is the user's past operation log, and the output is an analysis result showing their preferences. The server analyzes the user's preferences based on the received data and generates prompt messages.

[0466] Step 5:

[0467] The server generates individual advertisements and information based on the analysis results and generated prompt messages. The input consists of the analysis results of the behavioral history and prompt messages. The server utilizes a generation AI model to create user-optimized advertisements and information. The output is customized information displayed to the user.

[0468] Step 6:

[0469] The terminal displays generated information to the user through a visual device. The input is customized information, and the visual device, such as smart glasses, transmits this information to the user's field of vision in real time. The output is the visual experience the user receives.

[0470] 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.

[0471] This invention provides a system that takes into account the user's emotional state to enable more precise and personalized information delivery and advertising distribution. By incorporating an emotion engine, it is possible to make public sports betting more attractive and improve the user experience.

[0472] First, the server collects traditional competition data from external databases and sensors, and in addition, it collects emotional data from the user's device. This emotional data may be obtained from multiple sources, such as voice tone, facial expressions, and text message analysis. This data is transmitted to the server in real time and stored in the database.

[0473] Next, the server analyzes this emotional data using an emotion engine. This analysis employs machine learning algorithms and natural language processing to identify the emotions the user is experiencing. The results capture subtle emotional fluctuations, revealing what interests the user or what is causing them stress.

[0474] Furthermore, the device cross-references the user's past behavioral history and analyzes it together with emotional data. This combined approach generates advertisements and recommendations that are linked to the user's specific emotional state. For example, if a user feels joy at a particular athlete's victory, information about that athlete's rewards and upcoming matches will be highlighted.

[0475] The generated advertisements and information are delivered from the server to the device. By incorporating empathetic elements and content that resonates with the user's emotions, the information becomes more readily accepted. This effectively increases the user's interest and willingness to purchase.

[0476] For example, if the emotion engine analyzes that a user is excited about a competition, the server will immediately deliver advertisements or reward information that will maintain that feeling of exhilaration. Conversely, if disappointment is detected, the server will provide content that encourages the user or information that builds anticipation for the next time. In this way, by utilizing emotion data, the quality of the user experience can be improved and the overall effectiveness of the system can be enhanced.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The device collects data to capture the user's emotions. This data collection utilizes facial expression recognition via the camera, voice tone analysis via the microphone, and natural language processing from text messages. The collected data is used as information to determine the emotional state.

[0480] Step 2:

[0481] The device transmits collected emotional data to the server in real time. Secure and rapid protocols are used for data transmission, ensuring data accuracy while protecting user privacy.

[0482] Step 3:

[0483] The server inputs the received emotional data into the emotion engine, which then analyzes the data. The emotion engine uses machine learning algorithms to identify the emotions the user is experiencing. For example, it may detect emotions such as joy, excitement, sadness, and interest.

[0484] Step 4:

[0485] The server combines the analyzed sentiment data with the user's past behavioral history. This allows it to understand the characteristics linked to the user's current emotional state and prepares it to generate more sophisticated advertisements and information.

[0486] Step 5:

[0487] The server generates personalized ads and recommendations based on an analysis of the user's emotions and behavioral history. These ads are designed to resonate with the user's emotions and are more likely to capture their interest.

[0488] Step 6:

[0489] The server delivers the generated advertisements and information to the device. The delivery timing takes into account the user's activity patterns and emotional state, and is performed at the time when the user is most likely to receive the information.

[0490] Step 7:

[0491] Users review the advertisements and information they receive and, if necessary, participate in public gambling events or purchase related products. Furthermore, user responses are used as feedback for future ad delivery, helping to further optimize the system.

[0492] (Example 2)

[0493] 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."

[0494] In recent years, there has been a growing demand for personalized information to enhance the user experience in the fields of sports viewing and entertainment. However, traditional methods are limited to providing information based solely on the user's behavioral history, making it difficult to provide individualized responses that reflect real-time emotional states. In particular, the lack of a system that can quickly capture changes in a user's emotions and provide appropriate content accordingly means that it is difficult to maximize user interest and purchasing intent.

[0495] 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.

[0496] In this invention, the server includes means for collecting competition information, means for identifying the user's emotional state using an emotion analysis engine, and means for analyzing the user's behavioral history and emotional data together to generate individual advertisements and information. This makes it possible to provide personalized information that takes into account the user's real-time emotional state.

[0497] "Competition information" refers to data related to sports and events, including match results, athlete performance, and schedules.

[0498] "Emotional data" refers to data obtained from a user's voice, facial expressions, and text messages, and provides information that indicates the user's emotional state and psychological tendencies.

[0499] A "sentiment analysis engine" is a system component that uses machine learning algorithms and natural language processing techniques to analyze a user's emotional data and identify their emotional state.

[0500] "Behavioral history" refers to data on a user's past activities, including browsing history, purchase history, search data, and other related information.

[0501] "Advertisements and information" refers to messages and content generated based on users' interests and emotions, and includes promotional and recommendation information.

[0502] "Personalized information delivery" refers to the process of providing information in a customized form according to each user's individual characteristics and emotional state.

[0503] This invention is a system for providing personalized information and delivering advertisements while taking into account the user's emotional state. By including an emotion analysis engine, this system can improve the user experience.

[0504] The server first collects competition information using external databases and sensors. This competition information includes match results, player performance, and schedules. The collected information is securely stored in cloud-based data storage. Next, the server obtains sentiment data from the user's device. This data is collected using voice analysis software, facial recognition cameras, and natural language processing algorithms.

[0505] The device also sends the user's behavioral history to the server. This allows the system to review past browsing and purchase history. The server uses a sentiment analysis engine to analyze sentiment data and behavioral history data in real time. Machine learning algorithms and natural language processing techniques are applied in this analysis.

[0506] Based on the analysis results, the server uses a generative AI model to generate advertisements and information that match the user's current emotional state. This generated information is delivered to the device as a message containing empathetic elements. If the user is excited about the results of a particular sport, promotional information that helps maintain that excitement will be emphasized.

[0507] For example, if a user feels joy at a particular team's victory in a match, content that maintains that feeling of exhilaration will be immediately provided. Another example of a prompt message could be a command sent to the generating AI model such as, "Suggest a new offer based on the user's current emotional state."

[0508] This invention captures users' emotions in detail and provides personalized information accordingly, thereby improving the quality of the user experience.

[0509] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0510] Step 1:

[0511] The server collects competition information through external databases and sensors. Match results and player performance data are obtained via API as input data. This data is then stored in a cloud database and prepared for analysis.

[0512] Step 2:

[0513] The device collects user emotional data. Inputs include voice tone, facial expressions, and text messages. This data is analyzed in real time using voice analysis software, facial recognition technology, and natural language processing algorithms. The resulting output is an indicator of the user's emotional state.

[0514] Step 3:

[0515] The server compares collected sentiment data with behavioral history obtained from the device. Input data includes past browsing and purchase history, as well as sentiment data. Using data mining techniques, it identifies user interests and preferences, and then generates an analytical report based on these results. The output is an updated user profile.

[0516] Step 4:

[0517] The server utilizes an emotion analysis engine and a generative AI model to generate advertisements and information tailored to the user. Input includes analyzed emotion data and behavioral history data. Based on this data, a machine learning model operates to generate personalized content. The output is sent to the device as advertisements and recommendations.

[0518] Step 5:

[0519] The device delivers advertisements and information sent from the server to the user. The input is content generated by the server. It utilizes notification functions to deliver information to the user in real time. The output is the advertisements and information displayed on the user's screen.

[0520] Step 6:

[0521] Users respond to advertisements and information displayed on their devices. Input is obtained through biometric authentication and click actions within the provided content. The feedback data sent to the server is used to train the sentiment analysis engine to improve its accuracy. This cycle improves the overall system performance.

[0522] (Application Example 2)

[0523] 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."

[0524] Modern advertising systems fail to adequately consider users' emotional states and lack sufficient personalization of the user experience. As a result, it is difficult to provide users with the most relevant information in specific situations, leading to reduced advertising effectiveness. Furthermore, there is a need for technological means to perform real-time sentiment analysis and deliver advertisements appropriate to the user's context.

[0525] 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.

[0526] In this invention, the server includes means for collecting competition information, means for processing the collected competition information and constructing a predictive model, means for analyzing the user's emotional state and generating advertisements, means for delivering the generated advertisements to the user's mobile device, means for collecting emotional data in real time using a device worn by the user, and means for analyzing the collected emotional data and displaying advertisements that increase the user's level of excitement. This enables the delivery of advertisements that are tailored to the user's emotions, resulting in more effective information transmission.

[0527] "Competition information" refers to all data and circumstances related to a particular sport or event, including match results, athlete data, and historical statistics.

[0528] A "predictive model" is a mathematical or machine learning-based algorithm that uses collected data to predict specific future events or outcomes.

[0529] "User emotional state" refers to an indicator that shows the user's inner mood and feelings, analyzed from sources such as tone of voice, facial expressions, or text messages.

[0530] "Means of generating advertisements" refers to the technical process of creating advertising content tailored to a user based on data such as the user's emotional state and behavioral history.

[0531] A "portable information terminal" is an electronic device, such as a mobile phone or tablet, that a user can carry with them at all times to receive and display information.

[0532] "User-worn devices" refer to equipment that users can wear to obtain or display information from the outside world, such as smart glasses or head-mounted displays.

[0533] "Means of collecting emotional data in real time" refers to the process of identifying a user's current emotions through the immediate analysis of audio, visual, and text data.

[0534] "Excitement-enhancing ads" are advertising content designed to consciously stimulate and energize users' interest and emotions based on analyzed emotional data.

[0535] The system of this invention optimizes ad delivery by collecting competition information, building predictive models based on that information, analyzing the user's emotional state, and personalizing ads. The server first collects the user's emotional state in real time through the analysis of voice tone, facial expressions, and text messages. This is done using a device worn by the user, such as smart glasses. The emotional data obtained is then analyzed using software such as EmotionAnalyzer. Based on the analysis results, AdRecommender is used to generate ads suitable for the user. These ads are delivered to the user's mobile device and displayed using SmartGlassesDisplay.

[0536] As a concrete example, if a user is at a sporting event, the system can sense their excitement level and display the latest merchandise from the relevant team or information about upcoming matches in real time. This helps maintain the user's excitement and maximizes the effectiveness of advertising. An example of a prompt in this system would be: "Generate advertising content to serve when the user is excited. For example, digital merchandise at a sporting event or ticket information for the next match."

[0537] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0538] Step 1:

[0539] The server collects competition information from external databases and sensors. The collected data includes match progress and player performance data. Based on this input competition information, data preprocessing is performed to build a predictive model. Preprocessing involves normalizing the data and removing unnecessary data, preparing it for model construction.

[0540] Step 2:

[0541] The server collects emotional data in real time from the smart glasses or mobile device worn by the user. This includes analyzing voice, facial expressions, and text messages through EmotionAnalyzer software. Based on the input emotional data, the emotional state is analyzed in real time to identify the user's current mood and level of excitement, and an interpretation is generated.

[0542] Step 3:

[0543] The server combines analyzed emotional states, collected competition information, and the user's past behavioral history to generate personalized ads using AdRecommender. It creates ad content optimized for the user's interests and emotions based on the input data, and as a result, a specific ad is determined.

[0544] Step 4:

[0545] The server delivers the generated advertisements to the user's mobile device. The delivered advertisements are then displayed to the user for the first time through SmartGlassesDisplay. The displayed advertisements reflect the user's current emotional state and are designed to maintain excitement or pique new interests.

[0546] Step 5:

[0547] Users can react to the ads they see, and their feedback is used to inform future data collection. User interactions are recorded by the server and stored as data to help generate future ads. This feedback loop allows the system to continuously improve the effectiveness of its ads.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] [Fourth Embodiment]

[0552] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0553] 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.

[0554] 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).

[0555] 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.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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".

[0565] This invention is implemented as a system for improving the user experience in public sports betting by effectively utilizing competition information. First, as a means of collecting competition information, the server acquires data from external databases and sensors. This data includes past match results, the health status of athletes and competition animals, and weather information. This data is collected in real time and stored on the server.

[0566] Next, the collected data is processed by the server through data cleaning and feature engineering processes. Here, missing values ​​are imputed, outliers are removed, and the data is converted into a format suitable for analysis. This process prepares the data for building predictive models.

[0567] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. Specific algorithms used include regression analysis, classification, and clustering. This model aims to accurately predict the outcome of upcoming matches and specific athletic phenomena.

[0568] The device tracks the user's behavior history and analyzes their preferences based on the collected data. Based on this analysis, the server generates personalized advertisements and recommendations. These advertisements are customized to attract the user's interest.

[0569] Ultimately, the server delivers the generated advertisements and recommendations to the user's device. This delivery is scheduled based on the user's daily usage and past interests, and is performed at the optimal time. It is expected that the information provided through this process will pique the user's interest and increase their willingness to participate in public gambling.

[0570] As a concrete example, if a user is interested in a particular athlete, the system predicts the athlete's performance in matches and delivers advertisements containing that information. It also provides relevant event and reward information based on the user's past behavior, encouraging participation in the competitions. This maximizes the appeal of public sports betting and improves the user experience.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The server collects data from external competition databases and sensors. This data includes past match results, biometric information of athletes and competition animals, and weather data. The collected data is updated in real time and stored in the database.

[0574] Step 2:

[0575] The server prepares the collected data through a data cleaning process. Missing values ​​are filled in using means or statistical imputation, and outliers are detected, corrected, or removed through outlier analysis. This cleaned data is then used for feature engineering for predictive models.

[0576] Step 3:

[0577] The server extracts features using the organized data. Here, performance indicators for athletes and competitive animals, characteristics of the competitive environment, and past results are quantified. These features are then used as a dataset for model construction.

[0578] Step 4:

[0579] The server trains a machine learning model based on feature data. Training algorithms include, for example, linear regression, decision trees, and neural networks. The model is then split into training and test datasets, and its accuracy is evaluated.

[0580] Step 5:

[0581] The device analyzes the user's preferences based on their past behavior history. This includes the user's browsing history, purchase history, and click behavior. This data is analyzed to understand the user's interests.

[0582] Step 6:

[0583] The server generates personalized advertisements and recommendations based on user analysis results. This includes information on sports and special offers that users are likely to be interested in. The content of the advertisements is dynamically customized and optimized.

[0584] Step 7:

[0585] The server delivers generated advertisements and recommendations to the user's device. This delivery is timed to take into account the user's activity patterns and is designed to be more effective.

[0586] Step 8:

[0587] Users participate in or place bets on public sports betting based on the advertisements and recommendations they receive. User feedback is used to improve subsequent advertisements and recommendations, contributing to the continuous optimization of the system.

[0588] (Example 1)

[0589] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0590] In public gambling, there is a need to effectively provide users with appropriate and interesting information. However, conventional systems do not adequately reflect the preferences of individual users, making it difficult to increase user motivation to participate.

[0591] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0592] In this invention, the server includes means for collecting competition-related information from external data sources, means for processing the collected information through data cleaning and feature engineering to build a statistical model for prediction, and means for analyzing user activity data to generate personalized suggestions tailored to user interests. This makes it possible to provide information that will interest the user.

[0593] "External data sources" refer to external information systems or databases that provide information related to public gambling.

[0594] "Data cleaning" is the process of removing outliers and missing values ​​from collected data and preparing it in a format that can be used for analysis.

[0595] Feature engineering is a technique for generating effective features in machine learning and data analysis, and for optimizing datasets.

[0596] A "statistical model" is a mathematical model used to analyze data and reveal specific patterns or laws.

[0597] "User activity data" refers to data that records a user's operation history and behavioral patterns.

[0598] "Personalized suggestions tailored to user interests" refers to information provision and recommended content customized based on the user's hobbies and preferences.

[0599] This invention is configured as a system that collects diverse information in public gambling events, builds predictive models, and provides customized information to users. Specifically, the server acquires information related to the gambling events from external data sources. Established APIs and database connections can be used for data collection.

[0600] The server cleans the collected data using the Python pandas library to remove missing and outlier values. Furthermore, it performs feature engineering and generates effective features using the scikit-learn library. At this stage, the data is prepared in a format suitable for analysis.

[0601] Once the data is ready, the server trains a statistical model using machine learning algorithms. This process utilizes scikit-learn's Random Forest and Support Vector Machine (SVM) models. This model is built to accurately predict the outcome of the competition.

[0602] Meanwhile, the device collects user activity data and analyzes users based on specific interests and behaviors. Here, past operation history and click data are monitored to reveal which sports the user is interested in.

[0603] The server uses a specific generative AI model to generate personalized suggestions based on the user's interests. An example of a prompt is: "Predict the results of this week's matches featuring a specific athlete, and generate customized advertisements tailored to the user's interests, along with relevant event information."

[0604] Ultimately, the server delivers this customized information to the user's device at the appropriate time. This makes it easier for users to receive information that interests them, and as a result, it is expected that their willingness to participate in public gambling will increase.

[0605] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0606] Step 1:

[0607] The server collects competition-related information from external data sources. This information includes past match results and weather data obtained using API calls. The server stores this data in preparation for subsequent processing.

[0608] Step 2:

[0609] The server performs data cleaning on the collected data. For the raw data received as input, it imputes missing values ​​with the mean and filters out outliers. This process ensures data consistency and generates output data suitable for useful analysis.

[0610] Step 3:

[0611] The server performs feature engineering on the cleaned data. Using the clean input data, it extracts new features based on the athlete's past performance and weather data to generate output data. Specifically, it calculates the athlete's form performance score.

[0612] Step 4:

[0613] The server trains a machine learning model using the data after feature engineering has been completed. The prepared features are input to the random forest algorithm of the scikit-learn library, and a win / loss prediction model is output. At this stage, hyperparameters are adjusted to improve the accuracy of the model.

[0614] Step 5:

[0615] The device monitors user activity data and extracts specific interests. It collects the user's past operation history and access frequency as input, analyzes this data, and generates output data. This analysis identifies which sports the user is interested in.

[0616] Step 6:

[0617] The server translates user interests into specific prompts and generates personalized suggestions using an AI model. Using the prompt "Predict the results of this week's matches for a specific athlete and create an ad with related information" as an example, the server outputs engaging ad content.

[0618] Step 7:

[0619] The server delivers generated advertisements and suggested information to the device. It references the user's activity time and location information as input and outputs information at the optimal time. For example, by sending notifications during commuting hours, it maximizes the user response rate.

[0620] (Application Example 1)

[0621] 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".

[0622] In publicly run gambling events, there is a challenge in that it is difficult for users to easily obtain the latest information on the events and maintain their interest. Furthermore, there is a problem in that individualized information based on users' interests and behavior is not appropriately provided, which leads to a decrease in their motivation to participate in the events. The objective of this invention is to solve this problem.

[0623] 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.

[0624] In this invention, the server includes means for collecting competition information, means for processing the collected competition information to build a predictive model, means for analyzing the user's behavior history to generate individual information, means for dynamically displaying the information using a visual device, and means for distributing the generated information. As a result, users can always receive the latest competition information visually and intuitively, which can increase their motivation to participate in competitions.

[0625] "Means of collecting competition information" refers to the process of collecting all data related to public sports betting using external databases and sensors in order to maintain user interest.

[0626] "Methods for constructing predictive models" refer to techniques that use collected data to employ machine learning algorithms to accurately predict the results of upcoming matches or phenomena related to the sport.

[0627] "Methods for analyzing user behavior history to generate individualized information" refers to the process of analyzing a user's past behavior patterns and creating customized information and advertisements based on their interests and preferences.

[0628] "Means of dynamically displaying information using visual devices" refers to technologies that use smart glasses or other display devices to visually deliver real-time competition information to the user's field of view.

[0629] "Means of distributing generated information" refers to the process of providing users with customized information at the appropriate time based on their behavioral history and interests.

[0630] One embodiment of this invention is to build a system that improves the user experience by utilizing competition information. The server collects competition-related data from external databases and sensors. This data includes match results, participant health status, and weather information. The server processes the collected data through data cleaning and feature engineering. Specifically, it uses Python to impute missing values ​​and remove outliers, and formats the data into a format suitable for predictive models.

[0631] Subsequently, the server uses machine learning algorithms to train a predictive model based on the processed data. This process involves using Scikit-learn for regression analysis and clustering. The resulting model is then used to predict match results and specific athletic phenomena.

[0632] The user's device tracks their behavior history, and the server analyzes the user's preferences based on that data. Based on this analysis, the server generates personalized advertisements and recommendations. The generated information is displayed to the user dynamically in real time through visual devices such as smart glasses.

[0633] For example, if a user is interested in a particular competitor, the server predicts that competitor's performance and projects that information onto smart glasses. Furthermore, it encourages the user to participate in competitions by notifying them of relevant event benefits.

[0634] An example of a prompt for a generative AI model is: "Predict the performance of athlete A in the following public sports competition. Also, generate relevant advertisements and customize them to suit user B's preferences."

[0635] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0636] Step 1:

[0637] The server collects competition information from external databases and sensors. Inputs include match results, participant health status, and weather information, and the server collects this data in real time and stores it in local storage.

[0638] Step 2:

[0639] The server performs data cleaning and feature engineering processes on the collected data. The input is the saved data from step 1, and the server uses Python to impute missing values ​​and remove outliers. This data processing transforms the data into a format suitable for analysis and outputs it.

[0640] Step 3:

[0641] The server trains a machine learning model based on the formatted data. Here, Scikit-learn is used to perform predictive analysis on the input data and build a model that predicts match results and athletic phenomena. The output is the trained model.

[0642] Step 4:

[0643] The terminal collects the user's activity history and sends it to the server. The input here is the user's past operation log, and the output is an analysis result showing their preferences. The server analyzes the user's preferences based on the received data and generates prompt messages.

[0644] Step 5:

[0645] The server generates individual advertisements and information based on the analysis results and generated prompt messages. The input consists of the analysis results of the behavioral history and prompt messages. The server utilizes a generation AI model to create user-optimized advertisements and information. The output is customized information displayed to the user.

[0646] Step 6:

[0647] The terminal displays generated information to the user through a visual device. The input is customized information, and the visual device, such as smart glasses, transmits this information to the user's field of vision in real time. The output is the visual experience the user receives.

[0648] 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.

[0649] This invention provides a system that takes into account the user's emotional state to enable more precise and personalized information delivery and advertising distribution. By incorporating an emotion engine, it is possible to make public sports betting more attractive and improve the user experience.

[0650] First, the server collects traditional competition data from external databases and sensors, and in addition, it collects emotional data from the user's device. This emotional data may be obtained from multiple sources, such as voice tone, facial expressions, and text message analysis. This data is transmitted to the server in real time and stored in the database.

[0651] Next, the server analyzes this emotional data using an emotion engine. This analysis employs machine learning algorithms and natural language processing to identify the emotions the user is experiencing. The results capture subtle emotional fluctuations, revealing what interests the user or what is causing them stress.

[0652] Furthermore, the device cross-references the user's past behavioral history and analyzes it together with emotional data. This combined approach generates advertisements and recommendations that are linked to the user's specific emotional state. For example, if a user feels joy at a particular athlete's victory, information about that athlete's rewards and upcoming matches will be highlighted.

[0653] The generated advertisements and information are delivered from the server to the device. By incorporating empathetic elements and content that resonates with the user's emotions, the information becomes more readily accepted. This effectively increases the user's interest and willingness to purchase.

[0654] For example, if the emotion engine analyzes that a user is excited about a competition, the server will immediately deliver advertisements or reward information that will maintain that feeling of exhilaration. Conversely, if disappointment is detected, the server will provide content that encourages the user or information that builds anticipation for the next time. In this way, by utilizing emotion data, the quality of the user experience can be improved and the overall effectiveness of the system can be enhanced.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] The device collects data to capture the user's emotions. This data collection utilizes facial expression recognition via the camera, voice tone analysis via the microphone, and natural language processing from text messages. The collected data is used as information to determine the emotional state.

[0658] Step 2:

[0659] The device transmits collected emotional data to the server in real time. Secure and rapid protocols are used for data transmission, ensuring data accuracy while protecting user privacy.

[0660] Step 3:

[0661] The server inputs the received emotional data into the emotion engine, which then analyzes the data. The emotion engine uses machine learning algorithms to identify the emotions the user is experiencing. For example, it may detect emotions such as joy, excitement, sadness, and interest.

[0662] Step 4:

[0663] The server combines the analyzed sentiment data with the user's past behavioral history. This allows it to understand the characteristics linked to the user's current emotional state and prepares it to generate more sophisticated advertisements and information.

[0664] Step 5:

[0665] The server generates personalized ads and recommendations based on an analysis of the user's emotions and behavioral history. These ads are designed to resonate with the user's emotions and are more likely to capture their interest.

[0666] Step 6:

[0667] The server delivers the generated advertisements and information to the device. The delivery timing takes into account the user's activity patterns and emotional state, and is performed at the time when the user is most likely to receive the information.

[0668] Step 7:

[0669] Users review the advertisements and information they receive and, if necessary, participate in public gambling events or purchase related products. Furthermore, user responses are used as feedback for future ad delivery, helping to further optimize the system.

[0670] (Example 2)

[0671] 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".

[0672] In recent years, there has been a growing demand for personalized information to enhance the user experience in the fields of sports viewing and entertainment. However, traditional methods are limited to providing information based solely on the user's behavioral history, making it difficult to provide individualized responses that reflect real-time emotional states. In particular, the lack of a system that can quickly capture changes in a user's emotions and provide appropriate content accordingly means that it is difficult to maximize user interest and purchasing intent.

[0673] 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.

[0674] In this invention, the server includes means for collecting competition information, means for identifying the user's emotional state using an emotion analysis engine, and means for analyzing the user's behavioral history and emotional data together to generate individual advertisements and information. This makes it possible to provide personalized information that takes into account the user's real-time emotional state.

[0675] "Competition information" refers to data related to sports and events, including match results, athlete performance, and schedules.

[0676] "Emotional data" refers to data obtained from a user's voice, facial expressions, and text messages, and provides information that indicates the user's emotional state and psychological tendencies.

[0677] A "sentiment analysis engine" is a system component that uses machine learning algorithms and natural language processing techniques to analyze a user's emotional data and identify their emotional state.

[0678] "Behavioral history" refers to data on a user's past activities, including browsing history, purchase history, search data, and other related information.

[0679] "Advertisements and information" refers to messages and content generated based on users' interests and emotions, and includes promotional and recommendation information.

[0680] "Personalized information delivery" refers to the process of providing information in a customized form according to each user's individual characteristics and emotional state.

[0681] This invention is a system for providing personalized information and delivering advertisements while taking into account the user's emotional state. By including an emotion analysis engine, this system can improve the user experience.

[0682] The server first collects competition information using external databases and sensors. This competition information includes match results, player performance, and schedules. The collected information is securely stored in cloud-based data storage. Next, the server obtains sentiment data from the user's device. This data is collected using voice analysis software, facial recognition cameras, and natural language processing algorithms.

[0683] The device also sends the user's behavioral history to the server. This allows the system to review past browsing and purchase history. The server uses a sentiment analysis engine to analyze sentiment data and behavioral history data in real time. Machine learning algorithms and natural language processing techniques are applied in this analysis.

[0684] Based on the analysis results, the server uses a generative AI model to generate advertisements and information that match the user's current emotional state. This generated information is delivered to the device as a message containing empathetic elements. If the user is excited about the results of a particular sport, promotional information that helps maintain that excitement will be emphasized.

[0685] For example, if a user feels joy at a particular team's victory in a match, content that maintains that feeling of exhilaration will be immediately provided. Another example of a prompt message could be a command sent to the generating AI model such as, "Suggest a new offer based on the user's current emotional state."

[0686] This invention captures users' emotions in detail and provides personalized information accordingly, thereby improving the quality of the user experience.

[0687] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0688] Step 1:

[0689] The server collects competition information through external databases and sensors. Match results and player performance data are obtained via API as input data. This data is then stored in a cloud database and prepared for analysis.

[0690] Step 2:

[0691] The device collects user emotional data. Inputs include voice tone, facial expressions, and text messages. This data is analyzed in real time using voice analysis software, facial recognition technology, and natural language processing algorithms. The resulting output is an indicator of the user's emotional state.

[0692] Step 3:

[0693] The server compares collected sentiment data with behavioral history obtained from the device. Input data includes past browsing and purchase history, as well as sentiment data. Using data mining techniques, it identifies user interests and preferences, and then generates an analytical report based on these results. The output is an updated user profile.

[0694] Step 4:

[0695] The server utilizes an emotion analysis engine and a generative AI model to generate advertisements and information tailored to the user. Input includes analyzed emotion data and behavioral history data. Based on this data, a machine learning model operates to generate personalized content. The output is sent to the device as advertisements and recommendations.

[0696] Step 5:

[0697] The device delivers advertisements and information sent from the server to the user. The input is content generated by the server. It utilizes notification functions to deliver information to the user in real time. The output is the advertisements and information displayed on the user's screen.

[0698] Step 6:

[0699] Users respond to advertisements and information displayed on their devices. Input is obtained through biometric authentication and click actions within the provided content. The feedback data sent to the server is used to train the sentiment analysis engine to improve its accuracy. This cycle improves the overall system performance.

[0700] (Application Example 2)

[0701] 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".

[0702] Modern advertising systems fail to adequately consider users' emotional states and lack sufficient personalization of the user experience. As a result, it is difficult to provide users with the most relevant information in specific situations, leading to reduced advertising effectiveness. Furthermore, there is a need for technological means to perform real-time sentiment analysis and deliver advertisements appropriate to the user's context.

[0703] 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.

[0704] In this invention, the server includes means for collecting competition information, means for processing the collected competition information and constructing a predictive model, means for analyzing the user's emotional state and generating advertisements, means for delivering the generated advertisements to the user's mobile device, means for collecting emotional data in real time using a device worn by the user, and means for analyzing the collected emotional data and displaying advertisements that increase the user's level of excitement. This enables the delivery of advertisements that are tailored to the user's emotions, resulting in more effective information transmission.

[0705] "Competition information" refers to all data and circumstances related to a particular sport or event, including match results, athlete data, and historical statistics.

[0706] A "predictive model" is a mathematical or machine learning-based algorithm that uses collected data to predict specific future events or outcomes.

[0707] "User emotional state" refers to an indicator that shows the user's inner mood and feelings, analyzed from sources such as tone of voice, facial expressions, or text messages.

[0708] "Means of generating advertisements" refers to the technical process of creating advertising content tailored to a user based on data such as the user's emotional state and behavioral history.

[0709] A "portable information terminal" is an electronic device, such as a mobile phone or tablet, that a user can carry with them at all times to receive and display information.

[0710] "User-worn devices" refer to equipment that users can wear to obtain or display information from the outside world, such as smart glasses or head-mounted displays.

[0711] "Means of collecting emotional data in real time" refers to the process of identifying a user's current emotions through the immediate analysis of audio, visual, and text data.

[0712] "Excitement-enhancing ads" are advertising content designed to consciously stimulate and energize users' interest and emotions based on analyzed emotional data.

[0713] The system of this invention optimizes ad delivery by collecting competition information, building predictive models based on that information, analyzing the user's emotional state, and personalizing ads. The server first collects the user's emotional state in real time through the analysis of voice tone, facial expressions, and text messages. This is done using a device worn by the user, such as smart glasses. The emotional data obtained is then analyzed using software such as EmotionAnalyzer. Based on the analysis results, AdRecommender is used to generate ads suitable for the user. These ads are delivered to the user's mobile device and displayed using SmartGlassesDisplay.

[0714] As a concrete example, if a user is at a sporting event, the system can sense their excitement level and display the latest merchandise from the relevant team or information about upcoming matches in real time. This helps maintain the user's excitement and maximizes the effectiveness of advertising. An example of a prompt in this system would be: "Generate advertising content to serve when the user is excited. For example, digital merchandise at a sporting event or ticket information for the next match."

[0715] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0716] Step 1:

[0717] The server collects competition information from external databases and sensors. The collected data includes match progress and player performance data. Based on this input competition information, data preprocessing is performed to build a predictive model. Preprocessing involves normalizing the data and removing unnecessary data, preparing it for model construction.

[0718] Step 2:

[0719] The server collects emotional data in real time from the smart glasses or mobile device worn by the user. This includes analyzing voice, facial expressions, and text messages through EmotionAnalyzer software. Based on the input emotional data, the emotional state is analyzed in real time to identify the user's current mood and level of excitement, and an interpretation is generated.

[0720] Step 3:

[0721] The server combines analyzed emotional states, collected competition information, and the user's past behavioral history to generate personalized ads using AdRecommender. It creates ad content optimized for the user's interests and emotions based on the input data, and as a result, a specific ad is determined.

[0722] Step 4:

[0723] The server delivers the generated advertisements to the user's mobile device. The delivered advertisements are then displayed to the user for the first time through SmartGlassesDisplay. The displayed advertisements reflect the user's current emotional state and are designed to maintain excitement or pique new interests.

[0724] Step 5:

[0725] Users can react to the ads they see, and their feedback is used to inform future data collection. User interactions are recorded by the server and stored as data to help generate future ads. This feedback loop allows the system to continuously improve the effectiveness of its ads.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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."

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] The following is further disclosed regarding the embodiments described above.

[0748] (Claim 1)

[0749] Means of collecting competition information,

[0750] The means for processing the aforementioned collected competition information and constructing a predictive model,

[0751] A means of generating personalized ads by analyzing the user's behavior history,

[0752] The means for distributing the generated advertisement,

[0753] A system that includes this.

[0754] (Claim 2)

[0755] The system according to claim 1, further comprising means for predicting the outcome of a match based on collected competition information.

[0756] (Claim 3)

[0757] The system according to claim 1, further comprising means for processing collected biological information and analyzing the condition of a particular athlete or athletic animal.

[0758] "Example 1"

[0759] (Claim 1)

[0760] Means for collecting information related to the competition from external data sources,

[0761] The collected information is processed through data cleaning and feature engineering, and means for constructing a statistical model for prediction.

[0762] A means of analyzing user activity data to generate personalized suggestions tailored to their interests,

[0763] A means for distributing the generated proposal to the user device,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, further comprising means for predicting the outcome of a win or loss using a machine learning algorithm based on processed data.

[0767] (Claim 3)

[0768] The system according to claim 1, further comprising means for processing collected physical information and for evaluating the condition of a particular participant or competition animal.

[0769] "Application Example 1"

[0770] (Claim 1)

[0771] Means of collecting competition information,

[0772] A means for processing the collected competition information and constructing a predictive model,

[0773] A means of analyzing a user's behavioral history to generate individual information,

[0774] A means of dynamically displaying information using a visual device,

[0775] means for distributing the generated information,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, further comprising means for predicting the outcome of a match based on collected competition information.

[0779] (Claim 3)

[0780] The system according to claim 1, further comprising means for processing collected biometric data and analyzing the condition of a particular participant or competition animal.

[0781] "Example 2 of combining an emotion engine"

[0782] (Claim 1)

[0783] Means of collecting competition information,

[0784] The means for processing the collected competition information and multiple emotion data obtained from external sources,

[0785] A means of identifying a user's emotional state using an emotion analysis engine,

[0786] A method for generating personalized advertisements and information by analyzing user behavior history and sentiment data together,

[0787] A means of delivering the generated advertisements and information in a way that is sensitive to the user's emotional state,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, further comprising means for predicting results based on collected competition information and user sentiment data, and for providing information that influences the user.

[0791] (Claim 3)

[0792] The system according to claim 1, further comprising means for processing collected biometric data and user emotional data to analyze the state of a particular competitor or competition animal.

[0793] "Application example 2 when combining with an emotional engine"

[0794] (Claim 1)

[0795] Means of collecting competition information,

[0796] The means for processing the aforementioned collected competition information and constructing a predictive model,

[0797] A method for generating advertisements by analyzing the emotional state of users,

[0798] A means of delivering the generated advertisement to the user's mobile device,

[0799] A means of collecting emotional data in real time using a device worn by the user,

[0800] A means of analyzing collected emotional data and displaying advertisements that increase the user's level of excitement,

[0801] ...

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, further comprising means for predicting the outcome of a match based on collected competition information.

[0805] (Claim 3)

[0806] The system according to claim 1, further comprising means for processing collected biological information and analyzing the condition of a particular athlete or athletic animal. [Explanation of Symbols]

[0807] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting competition information, The means for processing the aforementioned collected competition information and constructing a predictive model, A means of generating personalized ads by analyzing the user's behavior history, The means for distributing the generated advertisement, A system that includes this.

2. The system according to claim 1, further comprising means for predicting the outcome of a match based on collected competition information.

3. The system according to claim 1, further comprising means for processing collected biological information and analyzing the condition of a specific athlete or athletic animal.

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