system

Abstract

We provide the system. [Solution] A data acquisition method for collecting information from local facilities, An analytical means for predicting the operational optimization of a specific piece of equipment based on the aforementioned information, A notification means for providing the user with the aforementioned prediction results, A monitoring system that monitors the user's activity history and alerts them when excessive habits are detected, A means of providing guidance to recommend the optimization of facility utilization, 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In games, it is difficult to accurately evaluate the winning rate and settings of the game and determine the optimal playing conditions, and there is also a problem of the risk of excessive gambling. Game enthusiasts hope for efficient and healthy play, but often lack sufficient data and strategic approaches. This invention aims to solve these problems by helping players enjoy games based on data.

Means for Solving the Problems

[0005] This invention provides a system that includes an information acquisition means based on items collected from local amusement facilities. An analysis means predicts the setting values ​​of a specific amusement machine and notifies the user of the results through a notification means. Furthermore, a monitoring means continuously monitors the user's gaming history and issues a warning when excessive behavior is detected, thereby supporting the user in playing more safely and strategically. In addition, by providing a recommendation means that recommends the optimal gaming time and gaming machine based on the analysis results, and a management means that limits the budget and time related to gaming according to the user's settings, the system enables healthy gameplay for the user.

[0006] "Local amusement facilities" refer to pachinko parlors and slot machine shops located in a specific area, and are places where amusement machines are installed.

[0007] "Information acquisition means" refers to the technical elements and devices used to collect data such as machine information and user reviews from amusement facilities.

[0008] "Items" refer to all information collected as data related to gaming machines, such as setting values, payout data, and user reviews.

[0009] "Analysis means" refers to technical methods for analyzing collected data to predict the settings and winning percentages of a specific gaming machine.

[0010] "Notification means" refers to communication technologies and devices used to transmit analysis results and recommended information to users.

[0011] "Monitoring measures" refer to technical means for constantly checking a user's gaming history and detecting excessive gaming behavior.

[0012] "Recommended methods" refer to technical means for suggesting the optimal gaming time and gaming machine to the user based on the analysis results.

[0013] "Management measures" refer to technical methods or devices used to limit the budget and time spent playing games according to the user's settings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system for analyzing data from each facility that provides amusement machines and providing information based on that analysis to users. This system consists of a server, a terminal (the user's device), and users.

[0036] The server runs a program to collect machine information, payout data, and user reviews from local amusement facilities. This data is collected using techniques such as API access and web scraping and stored in a database. The server then performs analysis using the collected data. By applying machine learning algorithms, the server predicts how likely a particular gaming machine is to have high settings, or at what times of day the winning rate is higher.

[0037] Based on the analysis results, the server generates information recommending suitable gaming times and machines for the user. This information is configured to be sent to the user's device as a push notification or in-app notification as needed.

[0038] Users receive notifications from the server via their devices and make optimal gaming decisions based on that information. Specifically, they can choose to play during recommended times and on recommended game machines.

[0039] The server also monitors users' gameplay in real time and analyzes their game history. If the set time or budget limits are exceeded, the server sends an alert to the terminal, prompting the user to pause play or reset their budget.

[0040] As a concrete example, suppose the server notifies the user's smartphone of the optimal timing for setting up a gaming machine. The user then goes to the arcade at the designated time and starts playing on the recommended machine. If the play time is extended and exceeds the set limit, the server automatically sends a notification to the user's device to encourage them to maintain a healthy play time.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server retrieves data from local amusement facilities. Using API access and web scraping techniques, it collects machine information, past payout data, and user reviews. The collected data is stored in a database.

[0044] Step 2:

[0045] The server analyzes the data in the database. It applies machine learning algorithms to predict whether a particular gaming machine is likely to have high settings, or during which time periods the winning rate is higher. The analysis results are stored for use in subsequent processes.

[0046] Step 3:

[0047] The server generates recommendations to send to the user based on the analysis results. Specifically, it compiles information such as recommended play time and game models. This information is personalized according to the user's past play history and preferences.

[0048] Step 4:

[0049] The server sends the generated recommendations to the user's device. The information is configured to be sent as a push notification or in-app notification, ensuring that the user receives the information in a timely manner.

[0050] Step 5:

[0051] The user receives a notification on their device and reviews the recommendations. Based on the recommended time slot and device, the user visits the facility and begins playing.

[0052] Step 6:

[0053] The server monitors users' gaming history and play time in real time. If an anomaly is detected through analysis, an alert is generated triggered when the set limits (time or budget) are exceeded.

[0054] Step 7:

[0055] The server sends the generated alerts to the user's device. The user can check the alerts on their device and receive instructions to pause gameplay or review their budget as needed.

[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] One challenge is that users find it difficult to select the optimal game based on the different settings and conditions at each facility providing the gaming machines. Furthermore, managing the time and budget constraints imposed by excessive gaming is also difficult. This can lead to users wasting time and resources.

[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: information gathering means for collecting information from local amusement facilities; means for storing the collected information in a data storage device; data analysis means for applying machine learning techniques using the information to predict the likelihood that a particular amusement machine is set to a high setting; information generation means for recommending the optimal playing time and amusement machine to the user based on the prediction results; information notification means for transmitting the generated recommendation information to the user's device; and game monitoring means for monitoring the user's playing status and issuing a warning if the set time or budget limit is exceeded. This enables the user to effectively and efficiently select and manage their games.

[0061] "Information gathering means" refers to technology or mechanisms for automatically collecting necessary information from local amusement facilities.

[0062] A "data storage device" is a storage medium or database used to store and manage collected information.

[0063] "Data analysis means" refers to algorithms or software used to analyze collected information and estimate the likelihood that a particular gaming machine is set to a high payout setting.

[0064] "Information generation means" refers to a mechanism or program for determining the recommended gaming time and machine type for the user based on the analysis results.

[0065] "Information notification means" refers to a system or technology for sending generated recommendation information to the user's device.

[0066] "Game monitoring means" refers to a device or function that continuously checks the user's gameplay and issues a warning if the set conditions are exceeded.

[0067] A description of an embodiment for carrying out this invention will be provided. This system consists of a server, a terminal (user's device), and a user.

[0068] The server collects data from local amusement facilities. The information collected includes machine model information, payout data, and user reviews. This data is automatically retrieved using API access and web scraping techniques. For example, this includes web scraping using Python's BeautifulSoup and API access using HTTP requests. This data is stored in relational databases such as MySQL® and PostgreSQL.

[0069] After storing the data, the server uses machine learning techniques to analyze it. Specifically, it uses the Python scikit-learn library to create a machine learning model based on the collected data. This model is used to predict the likelihood that a particular gaming machine is set to a high payout rate, or the time of day when the winning rate is higher. For example, random forests or linear regression models can be applied to analyze important features.

[0070] Based on the analysis results, the server generates recommended gaming times and machines for the user using an information generation system. This recommendation information is sent to the user's device in the form of push notifications or in-app notifications. Firebase Cloud Messaging can be used to efficiently deliver these notifications.

[0071] Users can receive notifications from the server via their devices and plan their gaming activities based on the recommendations. For example, they can go to the arcade at a specified time and play the recommended machines.

[0072] The server monitors users' gaming activity in real time and issues warnings if they exceed their set time or budget limits. This helps users avoid excessive gaming and enjoy a safe and healthy gaming experience.

[0073] As a concrete example, the server sends a notification to the user's smartphone stating that "a specific machine may have high settings at 3 PM." The user then acts on this notification, visits the arcade at 3 PM, and starts playing on the recommended machine. If the play time exceeds the set limit, the server immediately issues a warning, encouraging the user to maintain a healthy playing time.

[0074] An example of a prompt for the generating AI model would be, "Based on payout data from local amusement facilities, predict the time when high-setting machines are most likely to be available." This would enable the provision of more accurate recommendations.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server uses API access and web scraping techniques to collect data from local amusement facilities. Inputs include URLs and API endpoints for each amusement facility, to which the server sends requests. Outputs include information on the amusement machine models, payout data, and user reviews. Specifically, the server uses the Python library BeautifulSoup to analyze web pages and extract the necessary information.

[0078] Step 2:

[0079] The server stores the collected data in a database. Inputs include gaming machine information and payout data collected by the server. Storing the data in a database enables efficient data retrieval and management. Outputs include organized and saved data recorded in the database. Specific operations include the process of inserting data into the database using SQL.

[0080] Step 3:

[0081] The server executes machine learning algorithms to analyze data in the database. The input includes gaming machine information and payout data retrieved from the database. The server analyzes this data to predict the likelihood of high settings and the time periods when the winning probability increases. The output is the analysis results. Specifically, it uses Python's scikit-learn to build a random forest model and select gaming machines with specific characteristics.

[0082] Step 4:

[0083] The server generates information on recommended gaming time and machine types for the user based on the analysis results. The input is the results of machine learning analysis. From this data, the server creates a message recommending a specific gaming machine and gaming time. The output is a notification message for the user. Specifically, a generation AI model is used to generate a notification message tailored to the recommendations.

[0084] Step 5:

[0085] The server sends the generated recommendation information to the user's device. The input is the generated notification message. The output is information sent to the user's device in the form of push notifications or in-app notifications. Specifically, this involves a process that utilizes the Firebase Cloud Messaging service to send notifications to the user's smartphone.

[0086] Step 6:

[0087] The user receives notifications from the server via their device and creates a gaming plan based on the recommendations. The input is the recommendations displayed on the device. The user then plays at the arcade based on this information. The output is the user's action plan, which is determined by the notification. Specifically, the user visits the arcade at the designated time and starts playing on a specific gaming machine.

[0088] Step 7:

[0089] The server monitors the user's gameplay in real time and issues a warning if the set time or budget limits are exceeded. Inputs include the user's play time and spending history. Based on this, the server determines whether the conditions have been exceeded. The output is a warning message sent to the user's device. Specifically, this involves a process of monitoring real-time data and issuing alerts based on the conditions.

[0090] (Application Example 1)

[0091] 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."

[0092] In smart cities, there is a need to provide optimal visiting schedules that allow users to efficiently utilize entertainment and tourist facilities. Furthermore, systems are needed to curb excessive user behavior and support healthy lifestyles.

[0093] 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.

[0094] In this invention, the server includes data acquisition means for collecting information from local facilities, analysis means for predicting the optimization of operation of specific equipment based on the information, and notification means for providing the prediction results to the user. This enables users to understand the optimal visiting time for each facility, allowing for comfortable and efficient use of the facilities.

[0095] A "data acquisition method" is a system for collecting various types of information from local facilities.

[0096] "Analysis tools" refer to functions that predict the optimal operation of specific equipment based on collected information.

[0097] A "notification method" refers to a mechanism for providing users with notifications or information regarding prediction results.

[0098] A "monitoring mechanism" is a function that monitors the user's activity history and issues a warning when excessive habits are detected.

[0099] A "guidance provision method" is a system that suggests the optimal visiting time and facilities to use in order to recommend the optimization of facility utilization.

[0100] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server assists the user in optimizing facility usage by operating data acquisition means, analysis means, notification means, monitoring means, and guidance provision means.

[0101] The server collects various information from local entertainment and tourist facilities. Data acquisition methods utilize technologies such as API access and web scraping to obtain this information and store it in a database. Subsequently, analysis tools analyze this data using machine learning algorithms to predict optimal visit times for specific facilities. This analysis uses programming languages ​​such as Python and machine learning libraries.

[0102] The notification system plays the role of providing users with prediction results. Information is sent to the user's smartphone or tablet device in the form of push notifications, etc. Based on this, the user can create an appropriate visit plan.

[0103] The monitoring system tracks the user's usage history and automatically sends notifications if excessive usage is detected. This helps manage the user's daily routine and promotes a healthy lifestyle.

[0104] The guidance system has the function of suggesting optimal visit times and stay plans to users. Users can obtain information by inputting prompts into the AI ​​model, such as, "Please tell me the best time to watch a movie in Tokyo and a shopping mall visit schedule. Please also consider current crowd forecasts and user reviews to suggest the most relaxing plan."

[0105] This allows users to plan their activities in smart cities in an efficient and enjoyable way, enabling them to enjoy the facilities while preventing excessive behavior.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server collects information on local entertainment and tourist facilities. Using API access and web scraping techniques, it retrieves facility operating status and event information, and stores it in a database. This information is received through data acquisition methods. Inputs include facility URLs and API endpoints, and output is detailed facility information added to the database in JSON format.

[0109] Step 2:

[0110] The server uses analytical tools based on the collected information to predict optimal visit times and facility usage. By applying machine learning algorithms and analyzing past data and trends, it creates an optimal visit schedule for the user. Inputs include detailed facility information and past visit data, and the output is the generation of an optimal visit plan.

[0111] Step 3:

[0112] The server uses notification methods to provide analysis results to the user's device. Information is delivered via push notifications and in-app notifications to help users plan more easily. The input is the visit plan obtained through analysis, and the output is notification information displayed on the user's device.

[0113] Step 4:

[0114] Users create a visit plan via their smartphone or tablet based on the provided information. By inputting specified prompts into an AI model, users can obtain a detailed visit schedule and a list of recommended facilities. Inputs include user requests or search queries, and output displays detailed schedule information and a list of recommended facilities.

[0115] Step 5:

[0116] The server tracks user usage history through monitoring mechanisms and monitors for excessive facility use. If the set limits are exceeded, it sends a notification to the user's terminal to encourage healthy facility use. Inputs include user activity logs and set limits, while outputs include necessary notifications and warning messages.

[0117] Step 6:

[0118] Users can use the guidance system as needed to adjust the optimal visiting route and travel plan between multiple facilities. Input is a request to visit multiple facilities, and output is detailed route guidance and transportation options.

[0119] 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.

[0120] This invention is a system that incorporates an emotion engine to detect the user's emotional state and personalize the gaming experience. The system consists of a server, a terminal (the user's device), and the user.

[0121] The server retrieves machine information, past payout data, and user reviews from local amusement facilities. This data is collected using API access and web scraping techniques and stored in a database. The information in the database is analyzed using machine learning algorithms to predict the likelihood of a particular gaming machine being set to a high payout rate and the optimal time to play. Based on this analysis, the server generates personalized recommendations for gaming time and machines for each user.

[0122] Furthermore, the emotion engine operates on the user's device, analyzing emotions from the camera, microphone, or user interactions. This analysis is sent to a server and used to further personalize recommendations. For example, if a user is feeling stressed, the server suggests a simple game; if a relaxed state is detected, it offers a new challenge.

[0123] Based on notifications received from their devices, users select the optimal gaming machine and time to play at the facility. During gameplay, the server monitors the user's emotional state in real time and issues warnings if the user exceeds time or budget limits set by the monitoring system, or if an abnormal emotional state is detected. For example, if a user becomes excessively excited or disappointed, a pre-set alert is sent to prompt them to temporarily stop playing.

[0124] As a concrete example, when a user visits a gaming facility, the emotion engine detects the user's tension. Based on this information, the server notifies the user's device of a simple gaming approach to help them relax. The user follows the instructions and begins playing, and throughout the game, optimal instructions continue to be sent in accordance with changes in their emotions. As a result, the user can enjoy a gaming experience that is emotionally appropriate for them.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server collects data from local amusement facilities. Using API access and web scraping, it retrieves items such as machine settings, payout data, and user reviews, and stores them in a database.

[0128] Step 2:

[0129] The server applies machine learning algorithms based on the collected data to predict the settings of specific gaming machines. This prediction helps determine which machines are likely to have high settings or to determine the optimal time to play.

[0130] Step 3:

[0131] The emotion engine operates on the device and uses the user's facial recognition technology and voice analysis to determine their emotional state in real time. Relevant information is sent to a server and integrated with the analysis results.

[0132] Step 4:

[0133] The server recommends the optimal gaming time and machine for the user based on analysis results, including data on the user's emotional state. The recommendation results are sent to the user's device as push notifications or in-app notifications.

[0134] Step 5:

[0135] Users check notifications from their devices and act based on recommended gaming machines and times. Users begin playing at the gaming facility and follow the suggested strategies.

[0136] Step 6:

[0137] The server continuously receives emotional data from the emotion engine while the user is playing. This allows the server to monitor the user's emotional state in real time, and if emotions such as stress or excessive excitement are detected, it provides appropriate recommendations.

[0138] Step 7:

[0139] The server will send a warning to the user's device if an abnormal emotional state occurs or if the set limits are exceeded. The warning may include temporarily suspending gameplay or suggesting an alternative approach.

[0140] Step 8:

[0141] Users can check warnings from their devices, adjust their gameplay as needed, and strive for a healthy gaming experience. They can utilize the feedback from the emotion engine to take appropriate action until their emotional state stabilizes.

[0142] (Example 2)

[0143] 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".

[0144] The goal is to address the challenges users face in managing their emotional state while playing games and enjoying an optimized entertainment experience. Furthermore, it is necessary to mitigate potential frustrations arising from the lack of location-based setting predictions and personalized recommendations.

[0145] 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.

[0146] In this invention, the server includes data collection means for collecting items from an information aggregation area, analysis means using a machine learning algorithm for predicting setting values ​​based on the items, and emotion analysis means for analyzing the emotional state on the user's terminal. This enables the user to obtain an appropriate game experience in real time.

[0147] An "information aggregation area" is a resource for accumulating and managing data related to a region or specific location.

[0148] A "data collection method" is a system for acquiring data from various sources and storing it in a format that can be used for subsequent processing.

[0149] A "machine learning algorithm" is a computational model used to analyze large amounts of data and find patterns and regularities.

[0150] "Analysis methods" refer to the process of extracting useful information and results by analyzing collected data.

[0151] "Emotion analysis means" refers to a technology that estimates a user's emotional state based on the user's biometric information and behavioral data.

[0152] "Information provision means" refers to methods and interfaces for communicating analysis results and recommendations to users.

[0153] "Operation monitoring means" refers to a function that checks the user's actions and status in real time and provides warnings and advice as needed.

[0154] "Personalized recommendation methods" are mechanisms for presenting the optimal options based on the user's characteristics and circumstances.

[0155] A "control means" is a method of managing user behavior and system operation, and making adjustments according to pre-set conditions.

[0156] This system is designed to provide users with an optimized gaming experience. The server, terminal, and user work together to collect, analyze, and recommend data, enabling gameplay that matches the user's emotional state and environment.

[0157] The server collects data from the information aggregation area using API access and web scraping techniques, and stores it in a database. This collected data includes gaming machine settings, past play data, and user reviews. Systems such as MongoDB and PostgreSQL are used for the database. The server utilizes machine learning algorithms (e.g., Random Forest and GradientBoost) to predict the gaming machine settings and optimal play times. These analysis results are provided as personalized recommendations for each user.

[0158] The terminal functions via dedicated software installed on the user's device. This software analyzes the user's emotional state in real time through emotion analysis technology using the camera and microphone. Libraries such as OpenCV and TENSORFLOW® are utilized for emotion analysis. The results are sent to a server and used to further personalize recommendations.

[0159] Based on notifications received on their device, users select a game machine and play time recommended by the server. For example, if the system analyzes the user's emotions and detects that they are nervous, a simple, relaxing game will be recommended on their device. An example of a prompt given to the generating AI model might be, "Consider the user's current emotional state and recommend the most suitable game machine and play time. The user is nervous."

[0160] In this way, the system constantly collects and analyzes data to provide users with the optimal gaming experience, offering recommendations based on their emotions and environment. This allows users to enjoy entertainment tailored to their own emotional state.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The server collects data from the data aggregation area using API access and web scraping techniques. This data includes gaming machine settings, past play data, and user reviews. This data is stored in databases such as MongoDB and PostgreSQL. The input is raw data from the data aggregation area, and the output is organized database entries. Specifically, a scraping script periodically collects data and stores it in the database according to the data format.

[0164] Step 2:

[0165] The server uses information stored in the database and applies machine learning algorithms to predict the settings and play times of gaming machines. Algorithms used include Random Forest and GradientBoost. The input is organized database information, and the output is the analysis results from the predictive model. Specifically, the process involves preprocessing the data, performing pattern recognition using the machine learning model, and then aggregating and saving the results.

[0166] Step 3:

[0167] The terminal runs emotion analysis technology on the user's device. It utilizes the camera and microphone to analyze the user's emotional state in real time, employing libraries such as OpenCV and TensorFlow. The input is real-time video and audio data from the user, and the output is the analyzed emotional state. Specifically, the system acquires data from device sensors, inputs it into an emotion recognition model, and calculates the result.

[0168] Step 4:

[0169] The server integrates analyzed sentiment data with the results of predictive models to recommend the optimal gaming console and playtime for the user. The input is the analyzed sentiment data and predictive model results, while the output is personalized recommendations. Specifically, the server processes the integrated data in real time, automatically generates recommendations based on the user profile, and notifies the device.

[0170] Step 5:

[0171] The user receives notifications provided by the device and acts according to the recommended game machine and play time. The input is recommendations from the server, and the output is the user's chosen action plan. For example, when the user's stress level is detected, the device will send a notification such as "We recommend slot machine 123 on the second floor." The user then acts based on that recommendation.

[0172] (Application Example 2)

[0173] 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".

[0174] Modern entertainment facilities face the challenge of failing to provide maximum satisfaction to users because they do not adequately personalize the experience by considering the user's emotional state. Furthermore, mechanisms to prevent excessive behavior or inappropriate choices are insufficient. These challenges need to be addressed to provide users with a more personalized, safe, and enjoyable entertainment experience.

[0175] 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.

[0176] In this invention, the server includes data acquisition means for collecting information items from amusement facilities; information analysis means for predicting the conditions of a specific amusement device based on the information items; information transmission means for communicating the prediction results to the user; behavior monitoring means for observing the user's entertainment history and issuing a warning when excessive behavior is identified; emotion analysis means for analyzing the user's facial expressions and voice to detect their emotional state; and behavior selection means for selecting and implementing actions that are highly compatible with the user based on their emotional state. This enables the user to receive a personalized entertainment experience that is in line with their emotions, ensuring safe and satisfying use.

[0177] A "data acquisition means" is a processing device for collecting various information items from amusement facilities.

[0178] An "information analysis device" is a computing device used to estimate the conditions and characteristics of entertainment equipment based on collected information items.

[0179] "Information transmission means" refers to a communication device used to provide the user with the results of the analysis.

[0180] "Behavioral monitoring means" refers to a monitoring device that observes the user's behavioral history and identifies specific behaviors.

[0181] An "emotional analysis device" is an analytical device that analyzes the user's facial expressions and voice to evaluate their emotional state.

[0182] A "behavioral selection mechanism" is a function that, based on the results of emotion analysis, selects and executes the most appropriate behavior or response for the user.

[0183] This invention is a system for personalizing the gaming experience according to the user's emotional state. The server acquires various types of information using data acquisition means that collect information items from gaming facilities. Specifically, the server collects facility equipment information and historical data via the internet using API access or web scraping technology. This allows the latest information to be stored in a database.

[0184] Subsequently, the server uses information analysis tools to predict the conditions for specific entertainment devices based on the collected data. This process utilizes machine learning algorithms, and through statistical learning from past data, it can predict why a particular device is preferred and how long it will be played. The prediction results are then transmitted to the user's terminal via information transmission tools to support the selection of games.

[0185] Meanwhile, the user's device is equipped with emotion analysis capabilities, using a camera and microphone to analyze the user's facial expressions and voice. Technologies such as OpenCV and TensorFlow are utilized to accurately determine the user's emotional state. Information corresponding to the user's stress and relaxation levels is fed back to the server, which then provides further personalized recommendations.

[0186] As an example of its use, when a user uses a robot assistant after returning home, the system checks for stress levels from the user's face detected by a camera. Using a generative AI model, it generates prompts such as, "What kind of help do you need right now?" If high stress levels are detected, it suggests playing relaxing music tailored to the user's preferences.

[0187] An example of a prompt to input into a generative AI model is, "When the user's smile is captured on camera, generate a cheerful voice message accordingly. Please think of a message for when the user is smiling." This ensures that the user is always provided with a pleasant experience, and the system can respond flexibly to the user's emotions.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server activates data acquisition mechanisms to collect information items from amusement facilities. It receives information from APIs and websites as input, performs web scraping and API access, and saves historical data, reviews, and installation locations of amusement machines to a database. This allows for the construction of a detailed amusement machine information database.

[0191] Step 2:

[0192] The server analyzes the information stored in the database using information analysis tools. It receives collected gaming machine information as input and uses machine learning algorithms to predict how popular a particular gaming machine is, its optimal usage time, and other factors. These prediction results are obtained as output and used in subsequent processing.

[0193] Step 3:

[0194] The server uses an information transmission method to send the analysis results to the user's terminal. Specifically, it notifies the user of the optimal gaming machine and recommended time, assisting in game selection. This information is displayed on the terminal, preparing it for the user to use.

[0195] Step 4:

[0196] The user's device uses emotion analysis to capture facial expressions and voice to detect the user's emotional state. It receives video and audio of the user's face as input and analyzes emotions in real time using OpenCV and TensorFlow. As output, it sends the emotional state, such as stress levels and joy, as the result of the analysis to the server.

[0197] Step 5:

[0198] The server receives the sentiment analysis results and makes action choices based on them. It takes the sentiment analysis results as input and uses an AI model to determine the optimal entertainment options and suggestions. As output, it generates customized recommendations for the user, further enhancing personalization.

[0199] Step 6:

[0200] The user's device receives customized recommendation information from the server and displays or voices appropriate content according to the user's action selection method. Specific actions include playing music that matches the user's mood or suggesting entertainment. At this point, information aimed at improving satisfaction is provided to the user.

[0201] Through these steps, users can enjoy an emotionally engaging entertainment experience in real time.

[0202] 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.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] 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.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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".

[0218] This invention is a system for analyzing data from each facility that provides amusement machines and providing information based on that analysis to users. This system consists of a server, a terminal (the user's device), and users.

[0219] The server runs a program to collect machine information, payout data, and user reviews from local amusement facilities. This data is collected using techniques such as API access and web scraping and stored in a database. The server then performs analysis using the collected data. By applying machine learning algorithms, the server predicts how likely a particular gaming machine is to have high settings, or at what times of day the winning rate is higher.

[0220] Based on the analysis results, the server generates information recommending suitable gaming times and machines for the user. This information is configured to be sent to the user's device as a push notification or in-app notification as needed.

[0221] Users receive notifications from the server via their devices and make optimal gaming decisions based on that information. Specifically, they can choose to play during recommended times and on recommended game machines.

[0222] The server also monitors users' gameplay in real time and analyzes their game history. If the set time or budget limits are exceeded, the server sends an alert to the terminal, prompting the user to pause play or reset their budget.

[0223] As a concrete example, suppose the server notifies the user's smartphone of the optimal timing for setting up a gaming machine. The user then goes to the arcade at the designated time and starts playing on the recommended machine. If the play time is extended and exceeds the set limit, the server automatically sends a notification to the user's device to encourage them to maintain a healthy play time.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The server retrieves data from local amusement facilities. Using API access and web scraping techniques, it collects machine information, past payout data, and user reviews. The collected data is stored in a database.

[0227] Step 2:

[0228] The server analyzes the data in the database. It applies machine learning algorithms to predict whether a particular gaming machine is likely to have high settings, or during which time periods the winning rate is higher. The analysis results are stored for use in subsequent processes.

[0229] Step 3:

[0230] The server generates recommendations to send to the user based on the analysis results. Specifically, it compiles information such as recommended play time and game models. This information is personalized according to the user's past play history and preferences.

[0231] Step 4:

[0232] The server sends the generated recommendations to the user's device. The information is configured to be sent as a push notification or in-app notification, ensuring that the user receives the information in a timely manner.

[0233] Step 5:

[0234] The user receives a notification on their device and reviews the recommendations. Based on the recommended time slot and device, the user visits the facility and begins playing.

[0235] Step 6:

[0236] The server monitors users' gaming history and play time in real time. If an anomaly is detected through analysis, an alert is generated triggered when the set limits (time or budget) are exceeded.

[0237] Step 7:

[0238] The server sends the generated alerts to the user's device. The user can check the alerts on their device and receive instructions to pause gameplay or review their budget as needed.

[0239] (Example 1)

[0240] 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."

[0241] One challenge is that users find it difficult to select the optimal game based on the different settings and conditions at each facility providing the gaming machines. Furthermore, managing the time and budget constraints imposed by excessive gaming is also difficult. This can lead to users wasting time and resources.

[0242] 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.

[0243] In this invention, the server includes: information gathering means for collecting information from local amusement facilities; means for storing the collected information in a data storage device; data analysis means for applying machine learning techniques using the information to predict the likelihood that a particular amusement machine is set to a high setting; information generation means for recommending the optimal playing time and amusement machine to the user based on the prediction results; information notification means for transmitting the generated recommendation information to the user's device; and game monitoring means for monitoring the user's playing status and issuing a warning if the set time or budget limit is exceeded. This enables the user to effectively and efficiently select and manage their games.

[0244] "Information gathering means" refers to technology or mechanisms for automatically collecting necessary information from local amusement facilities.

[0245] A "data storage device" is a storage medium or database used to store and manage collected information.

[0246] "Data analysis means" refers to algorithms or software used to analyze collected information and estimate the likelihood that a particular gaming machine is set to a high payout setting.

[0247] "Information generation means" refers to a mechanism or program for determining the recommended gaming time and machine type for the user based on the analysis results.

[0248] "Information notification means" refers to a system or technology for sending generated recommendation information to the user's device.

[0249] "Game monitoring means" refers to a device or function that continuously checks the user's gameplay and issues a warning if the set conditions are exceeded.

[0250] A description of an embodiment for carrying out this invention will be provided. This system consists of a server, a terminal (user's device), and a user.

[0251] The server collects data from local amusement facilities. The information collected includes machine model information, payout data, and user reviews. This data is automatically retrieved using API access and web scraping techniques. For example, this could involve web scraping using Python's BeautifulSoup or API access using HTTP requests. This data is stored in relational databases such as MySQL and PostgreSQL.

[0252] After storing the data, the server uses machine learning techniques to analyze it. Specifically, it uses the Python scikit-learn library to create a machine learning model based on the collected data. This model is used to predict the likelihood that a particular gaming machine is set to a high payout rate, or the time of day when the winning rate is higher. For example, random forests or linear regression models can be applied to analyze important features.

[0253] Based on the analysis results, the server generates recommended gaming times and machines for the user using an information generation system. This recommendation information is sent to the user's device in the form of push notifications or in-app notifications. Firebase Cloud Messaging can be used to efficiently deliver these notifications.

[0254] Users can receive notifications from the server via their devices and plan their gaming activities based on the recommendations. For example, they can go to the arcade at a specified time and play the recommended machines.

[0255] The server monitors users' gaming activity in real time and issues warnings if they exceed their set time or budget limits. This helps users avoid excessive gaming and enjoy a safe and healthy gaming experience.

[0256] As a concrete example, the server sends a notification to the user's smartphone stating that "a specific machine may have high settings at 3 PM." The user then acts on this notification, visits the arcade at 3 PM, and starts playing on the recommended machine. If the play time exceeds the set limit, the server immediately issues a warning, encouraging the user to maintain a healthy playing time.

[0257] An example of a prompt for the generating AI model would be, "Based on payout data from local amusement facilities, predict the time when high-setting machines are most likely to be available." This would enable the provision of more accurate recommendations.

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

[0259] Step 1:

[0260] The server uses API access and web scraping techniques to collect data from local amusement facilities. Inputs include URLs and API endpoints for each amusement facility, to which the server sends requests. Outputs include information on the amusement machine models, payout data, and user reviews. Specifically, the server uses the Python library BeautifulSoup to analyze web pages and extract the necessary information.

[0261] Step 2:

[0262] The server stores the collected data in a database. Inputs include gaming machine information and payout data collected by the server. Storing the data in a database enables efficient data retrieval and management. Outputs include organized and saved data recorded in the database. Specific operations include the process of inserting data into the database using SQL.

[0263] Step 3:

[0264] The server executes machine learning algorithms to analyze data in the database. The input includes gaming machine information and payout data retrieved from the database. The server analyzes this data to predict the likelihood of high settings and the time periods when the winning probability increases. The output is the analysis results. Specifically, it uses Python's scikit-learn to build a random forest model and select gaming machines with specific characteristics.

[0265] Step 4:

[0266] The server generates information on recommended gaming time and machine types for the user based on the analysis results. The input is the results of machine learning analysis. From this data, the server creates a message recommending a specific gaming machine and gaming time. The output is a notification message for the user. Specifically, a generation AI model is used to generate a notification message tailored to the recommendations.

[0267] Step 5:

[0268] The server sends the generated recommendation information to the user's device. The input is the generated notification message. The output is information sent to the user's device in the form of push notifications or in-app notifications. Specifically, this involves a process that utilizes the Firebase Cloud Messaging service to send notifications to the user's smartphone.

[0269] Step 6:

[0270] The user receives notifications from the server via their device and creates a gaming plan based on the recommendations. The input is the recommendations displayed on the device. The user then plays at the arcade based on this information. The output is the user's action plan, which is determined by the notification. Specifically, the user visits the arcade at the designated time and starts playing on a specific gaming machine.

[0271] Step 7:

[0272] The server monitors the user's gameplay in real time and issues a warning if the set time or budget limits are exceeded. Inputs include the user's play time and spending history. Based on this, the server determines whether the conditions have been exceeded. The output is a warning message sent to the user's device. Specifically, this involves a process of monitoring real-time data and issuing alerts based on the conditions.

[0273] (Application Example 1)

[0274] 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."

[0275] In smart cities, there is a need to provide optimal visiting schedules that allow users to efficiently utilize entertainment and tourist facilities. Furthermore, systems are needed to curb excessive user behavior and support healthy lifestyles.

[0276] 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.

[0277] In this invention, the server includes data acquisition means for collecting information from local facilities, analysis means for predicting the optimization of operation of specific equipment based on the information, and notification means for providing the prediction results to the user. This enables users to understand the optimal visiting time for each facility, allowing for comfortable and efficient use of the facilities.

[0278] A "data acquisition method" is a system for collecting various types of information from local facilities.

[0279] "Analysis tools" refer to functions that predict the optimal operation of specific equipment based on collected information.

[0280] A "notification method" refers to a mechanism for providing users with notifications or information regarding prediction results.

[0281] A "monitoring mechanism" is a function that monitors the user's activity history and issues a warning when excessive habits are detected.

[0282] A "guidance provision method" is a system that suggests the optimal visiting time and facilities to use in order to recommend the optimization of facility utilization.

[0283] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server utilizes data acquisition means, analysis means, notification means, monitoring means, and guidance provision means to assist the user in optimizing the use of facilities.

[0284] The server collects various information from local entertainment facilities and tourist facilities. The data acquisition means utilizes technologies such as API access and web scraping to obtain this information and accumulate it in a database. Subsequently, the analysis means analyzes these data using machine learning algorithms to predict the optimal visit timing of specific facilities, etc. For this analysis, programming languages such as Python and machine learning libraries are used.

[0285] The notification means is responsible for providing the prediction results to the user. Information is sent to the user's smartphone or tablet terminal in the form of push notifications, etc. Based on this, the user can formulate an appropriate visit plan.

[0286] The monitoring means tracks the user's usage history and automatically sends a notification when excessive usage is detected. This helps manage the user's life rhythm and promotes a healthy lifestyle.

[0287] The guidance provision means has the function of proposing the optimal visit time and stay plan to the user. The user can, for example, input a prompt sentence such as "Please tell me the optimal movie viewing time and shopping mall visit schedule in Tokyo. Please propose the most relaxing plan considering the current congestion forecast and user reviews." into the generative AI model to obtain information.

[0288] This enables the user to plan their activities in the smart city in an efficient and enjoyable manner, and to enjoy the facilities while preventing excessive behavior.

[0289] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0290] Step 1:

[0291] The server collects information on local entertainment and tourist facilities. Using API access and web scraping techniques, it retrieves facility operating status and event information, and stores it in a database. This information is received through data acquisition methods. Inputs include facility URLs and API endpoints, and output is detailed facility information added to the database in JSON format.

[0292] Step 2:

[0293] The server uses analytical tools based on the collected information to predict optimal visit times and facility usage. By applying machine learning algorithms and analyzing past data and trends, it creates an optimal visit schedule for the user. Inputs include detailed facility information and past visit data, and the output is the generation of an optimal visit plan.

[0294] Step 3:

[0295] The server uses notification methods to provide analysis results to the user's device. Information is delivered via push notifications and in-app notifications to help users plan more easily. The input is the visit plan obtained through analysis, and the output is notification information displayed on the user's device.

[0296] Step 4:

[0297] Users create a visit plan via their smartphone or tablet based on the provided information. By inputting specified prompts into an AI model, users can obtain a detailed visit schedule and a list of recommended facilities. Inputs include user requests or search queries, and output displays detailed schedule information and a list of recommended facilities.

[0298] Step 5:

[0299] The server tracks user usage history through monitoring mechanisms and monitors for excessive facility use. If the set limits are exceeded, it sends a notification to the user's terminal to encourage healthy facility use. Inputs include user activity logs and set limits, while outputs include necessary notifications and warning messages.

[0300] Step 6:

[0301] Users can use the guidance system as needed to adjust the optimal visiting route and travel plan between multiple facilities. Input is a request to visit multiple facilities, and output is detailed route guidance and transportation options.

[0302] 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.

[0303] This invention is a system that incorporates an emotion engine to detect the user's emotional state and personalize the gaming experience. The system consists of a server, a terminal (the user's device), and the user.

[0304] The server retrieves machine information, past payout data, and user reviews from local amusement facilities. This data is collected using API access and web scraping techniques and stored in a database. The information in the database is analyzed using machine learning algorithms to predict the likelihood of a particular gaming machine being set to a high payout rate and the optimal time to play. Based on this analysis, the server generates personalized recommendations for gaming time and machines for each user.

[0305] Furthermore, the emotion engine operates on the user's terminal and analyzes emotions from the camera, microphone, or the user's interactions. The analysis results are sent to the server and used to further personalize the recommended content. For example, when the user is feeling stressed, the server proposes games with simple settings, and when a relaxed state is detected, it provides new challenges.

[0306] Based on the notifications received from the terminal, the user selects the optimal gaming machine and time and plays at the facility. During play, the server monitors the emotional state in real time and issues a warning when the set time or budget limit is exceeded or when an abnormal emotional state is detected. For example, when the user becomes overly excited or dejected, a pre-set alert is sent to prompt a temporary interruption of the game.

[0307] As a specific example, when a user visits a gaming facility, the emotion engine detects the user's nervousness. Based on this information, the server notifies the user's terminal of a simple gaming approach for relaxation. The user starts playing according to the instructions, and during the game progress, optimal instructions continue to be sent according to the emotional changes. As a result, the user can enjoy a gaming experience suitable for their emotions.

[0308] The following describes the processing flow.

[0309] Step 1:

[0310] The server collects data from regional gaming facilities. Using API access or web scraping, it obtains items such as gaming machine setting information, payout data, and user reviews, and saves them in the database.

[0311] Step 2:

[0312] The server applies machine learning algorithms based on the collected data to predict the settings of specific gaming machines. This prediction helps determine which machines are likely to have high settings or to determine the optimal time to play.

[0313] Step 3:

[0314] The emotion engine operates on the device and uses the user's facial recognition technology and voice analysis to determine their emotional state in real time. Relevant information is sent to a server and integrated with the analysis results.

[0315] Step 4:

[0316] The server recommends the optimal gaming time and machine for the user based on analysis results, including data on the user's emotional state. The recommendation results are sent to the user's device as push notifications or in-app notifications.

[0317] Step 5:

[0318] Users check notifications from their devices and act based on recommended gaming machines and times. Users begin playing at the gaming facility and follow the suggested strategies.

[0319] Step 6:

[0320] The server continuously receives emotional data from the emotion engine while the user is playing. This allows the server to monitor the user's emotional state in real time, and if emotions such as stress or excessive excitement are detected, it provides appropriate recommendations.

[0321] Step 7:

[0322] The server will send a warning to the user's device if an abnormal emotional state occurs or if the set limits are exceeded. The warning may include temporarily suspending gameplay or suggesting an alternative approach.

[0323] Step 8:

[0324] Users can check warnings from their devices, adjust their gameplay as needed, and strive for a healthy gaming experience. They can utilize the feedback from the emotion engine to take appropriate action until their emotional state stabilizes.

[0325] (Example 2)

[0326] 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".

[0327] The goal is to address the challenges users face in managing their emotional state while playing games and enjoying an optimized entertainment experience. Furthermore, it is necessary to mitigate potential frustrations arising from the lack of location-based setting predictions and personalized recommendations.

[0328] 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.

[0329] In this invention, the server includes data collection means for collecting items from an information aggregation area, analysis means using a machine learning algorithm for predicting setting values ​​based on the items, and emotion analysis means for analyzing the emotional state on the user's terminal. This enables the user to obtain an appropriate game experience in real time.

[0330] An "information aggregation area" is a resource for accumulating and managing data related to a region or specific location.

[0331] A "data collection method" is a system for acquiring data from various sources and storing it in a format that can be used for subsequent processing.

[0332] A "machine learning algorithm" is a computational model used to analyze large amounts of data and find patterns and regularities.

[0333] "Analysis methods" refer to the process of extracting useful information and results by analyzing collected data.

[0334] "Emotion analysis means" refers to a technology that estimates a user's emotional state based on the user's biometric information and behavioral data.

[0335] "Information provision means" refers to methods and interfaces for communicating analysis results and recommendations to users.

[0336] "Operation monitoring means" refers to a function that checks the user's actions and status in real time and provides warnings and advice as needed.

[0337] "Personalized recommendation methods" are mechanisms for presenting the optimal options based on the user's characteristics and circumstances.

[0338] A "control means" is a method of managing user behavior and system operation, and making adjustments according to pre-set conditions.

[0339] This system is designed to provide users with an optimized gaming experience. The server, terminal, and user work together to collect, analyze, and recommend data, enabling gameplay that matches the user's emotional state and environment.

[0340] The server collects data from the information aggregation area using API access and web scraping techniques, and stores it in a database. This collected data includes gaming machine settings, past play data, and user reviews. Systems such as MongoDB and PostgreSQL are used for the database. The server utilizes machine learning algorithms (e.g., Random Forest and GradientBoost) to predict the gaming machine settings and optimal play times. These analysis results are provided as personalized recommendations for each user.

[0341] The terminal functions via dedicated software installed on the user's device. This software analyzes the user's emotional state in real time through sentiment analysis technology using the camera and microphone. Libraries such as OpenCV and TensorFlow are utilized for sentiment analysis. The results are sent to a server and used to further personalize recommendations.

[0342] Based on notifications received on their device, users select a game machine and play time recommended by the server. For example, if the system analyzes the user's emotions and detects that they are nervous, a simple, relaxing game will be recommended on their device. An example of a prompt given to the generating AI model might be, "Consider the user's current emotional state and recommend the most suitable game machine and play time. The user is nervous."

[0343] In this way, the system constantly collects and analyzes data to provide users with the optimal gaming experience, offering recommendations based on their emotions and environment. This allows users to enjoy entertainment tailored to their own emotional state.

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

[0345] Step 1:

[0346] The server collects data from the data aggregation area using API access and web scraping techniques. This data includes gaming machine settings, past play data, and user reviews. This data is stored in databases such as MongoDB and PostgreSQL. The input is raw data from the data aggregation area, and the output is organized database entries. Specifically, a scraping script periodically collects data and stores it in the database according to the data format.

[0347] Step 2:

[0348] The server uses information stored in the database and applies machine learning algorithms to predict the settings and play times of gaming machines. Algorithms used include Random Forest and GradientBoost. The input is organized database information, and the output is the analysis results from the predictive model. Specifically, the process involves preprocessing the data, performing pattern recognition using the machine learning model, and then aggregating and saving the results.

[0349] Step 3:

[0350] The terminal runs emotion analysis technology on the user's device. It utilizes the camera and microphone to analyze the user's emotional state in real time, employing libraries such as OpenCV and TensorFlow. The input is real-time video and audio data from the user, and the output is the analyzed emotional state. Specifically, the system acquires data from device sensors, inputs it into an emotion recognition model, and calculates the result.

[0351] Step 4:

[0352] The server integrates analyzed sentiment data with the results of predictive models to recommend the optimal gaming console and playtime for the user. The input is the analyzed sentiment data and predictive model results, while the output is personalized recommendations. Specifically, the server processes the integrated data in real time, automatically generates recommendations based on the user profile, and notifies the device.

[0353] Step 5:

[0354] The user receives notifications provided by the device and acts according to the recommended game machine and play time. The input is recommendations from the server, and the output is the user's chosen action plan. For example, when the user's stress level is detected, the device will send a notification such as "We recommend slot machine 123 on the second floor." The user then acts based on that recommendation.

[0355] (Application Example 2)

[0356] 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."

[0357] Modern entertainment facilities face the challenge of failing to provide maximum satisfaction to users because they do not adequately personalize the experience by considering the user's emotional state. Furthermore, mechanisms to prevent excessive behavior or inappropriate choices are insufficient. These challenges need to be addressed to provide users with a more personalized, safe, and enjoyable entertainment experience.

[0358] 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.

[0359] In this invention, the server includes data acquisition means for collecting information items from amusement facilities; information analysis means for predicting the conditions of a specific amusement device based on the information items; information transmission means for communicating the prediction results to the user; behavior monitoring means for observing the user's entertainment history and issuing a warning when excessive behavior is identified; emotion analysis means for analyzing the user's facial expressions and voice to detect their emotional state; and behavior selection means for selecting and implementing actions that are highly compatible with the user based on their emotional state. This enables the user to receive a personalized entertainment experience that is in line with their emotions, ensuring safe and satisfying use.

[0360] A "data acquisition means" is a processing device for collecting various information items from amusement facilities.

[0361] An "information analysis device" is a computing device used to estimate the conditions and characteristics of entertainment equipment based on collected information items.

[0362] "Information transmission means" refers to a communication device used to provide the user with the results of the analysis.

[0363] "Behavioral monitoring means" refers to a monitoring device that observes the user's behavioral history and identifies specific behaviors.

[0364] An "emotional analysis device" is an analytical device that analyzes the user's facial expressions and voice to evaluate their emotional state.

[0365] A "behavioral selection mechanism" is a function that, based on the results of emotion analysis, selects and executes the most appropriate behavior or response for the user.

[0366] This invention is a system for personalizing the gaming experience according to the user's emotional state. The server acquires various types of information using data acquisition means that collect information items from gaming facilities. Specifically, the server collects facility equipment information and historical data via the internet using API access or web scraping technology. This allows the latest information to be stored in a database.

[0367] Subsequently, the server uses information analysis tools to predict the conditions for specific entertainment devices based on the collected data. This process utilizes machine learning algorithms, and through statistical learning from past data, it can predict why a particular device is preferred and how long it will be played. The prediction results are then transmitted to the user's terminal via information transmission tools to support the selection of games.

[0368] Meanwhile, the user's device is equipped with emotion analysis capabilities, using a camera and microphone to analyze the user's facial expressions and voice. Technologies such as OpenCV and TensorFlow are utilized to accurately determine the user's emotional state. Information corresponding to the user's stress and relaxation levels is fed back to the server, which then provides further personalized recommendations.

[0369] As an example of its use, when a user uses a robot assistant after returning home, the system checks for stress levels from the user's face detected by a camera. Using a generative AI model, it generates prompts such as, "What kind of help do you need right now?" If high stress levels are detected, it suggests playing relaxing music tailored to the user's preferences.

[0370] An example of a prompt to input into a generative AI model is, "When the user's smile is captured on camera, generate a cheerful voice message accordingly. Please think of a message for when the user is smiling." This ensures that the user is always provided with a pleasant experience, and the system can respond flexibly to the user's emotions.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server activates data acquisition mechanisms to collect information items from amusement facilities. It receives information from APIs and websites as input, performs web scraping and API access, and saves historical data, reviews, and installation locations of amusement machines to a database. This allows for the construction of a detailed amusement machine information database.

[0374] Step 2:

[0375] The server analyzes the information stored in the database using information analysis tools. It receives collected gaming machine information as input and uses machine learning algorithms to predict how popular a particular gaming machine is, its optimal usage time, and other factors. These prediction results are obtained as output and used in subsequent processing.

[0376] Step 3:

[0377] The server uses an information transmission method to send the analysis results to the user's terminal. Specifically, it notifies the user of the optimal gaming machine and recommended time, assisting in game selection. This information is displayed on the terminal, preparing it for the user to use.

[0378] Step 4:

[0379] The user's device uses emotion analysis to capture facial expressions and voice to detect the user's emotional state. It receives video and audio of the user's face as input and analyzes emotions in real time using OpenCV and TensorFlow. As output, it sends the emotional state, such as stress levels and joy, as the result of the analysis to the server.

[0380] Step 5:

[0381] The server receives the sentiment analysis results and makes action choices based on them. It takes the sentiment analysis results as input and uses an AI model to determine the optimal entertainment options and suggestions. As output, it generates customized recommendations for the user, further enhancing personalization.

[0382] Step 6:

[0383] The user's device receives customized recommendation information from the server and displays or voices appropriate content according to the user's action selection method. Specific actions include playing music that matches the user's mood or suggesting entertainment. At this point, information aimed at improving satisfaction is provided to the user.

[0384] Through these steps, users can enjoy an emotionally engaging entertainment experience in real time.

[0385] 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.

[0386] 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.

[0387] 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.

[0388] [Third Embodiment]

[0389] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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".

[0401] This invention is a system for analyzing data from each facility that provides amusement machines and providing information based on that analysis to users. This system consists of a server, a terminal (the user's device), and users.

[0402] The server runs a program to collect machine information, payout data, and user reviews from local amusement facilities. This data is collected using techniques such as API access and web scraping and stored in a database. The server then performs analysis using the collected data. By applying machine learning algorithms, the server predicts how likely a particular gaming machine is to have high settings, or at what times of day the winning rate is higher.

[0403] Based on the analysis results, the server generates information recommending suitable gaming times and machines for the user. This information is configured to be sent to the user's device as a push notification or in-app notification as needed.

[0404] Users receive notifications from the server via their devices and make optimal gaming decisions based on that information. Specifically, they can choose to play during recommended times and on recommended game machines.

[0405] The server also monitors users' gameplay in real time and analyzes their game history. If the set time or budget limits are exceeded, the server sends an alert to the terminal, prompting the user to pause play or reset their budget.

[0406] As a concrete example, suppose the server notifies the user's smartphone of the optimal timing for setting up a gaming machine. The user then goes to the arcade at the designated time and starts playing on the recommended machine. If the play time is extended and exceeds the set limit, the server automatically sends a notification to the user's device to encourage them to maintain a healthy play time.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The server retrieves data from local amusement facilities. Using API access and web scraping techniques, it collects machine information, past payout data, and user reviews. The collected data is stored in a database.

[0410] Step 2:

[0411] The server analyzes the data in the database. It applies machine learning algorithms to predict whether a particular gaming machine is likely to have high settings, or during which time periods the winning rate is higher. The analysis results are stored for use in subsequent processes.

[0412] Step 3:

[0413] The server generates recommendations to send to the user based on the analysis results. Specifically, it compiles information such as recommended play time and game models. This information is personalized according to the user's past play history and preferences.

[0414] Step 4:

[0415] The server sends the generated recommendations to the user's device. The information is configured to be sent as a push notification or in-app notification, ensuring that the user receives the information in a timely manner.

[0416] Step 5:

[0417] The user receives a notification on their device and reviews the recommendations. Based on the recommended time slot and device, the user visits the facility and begins playing.

[0418] Step 6:

[0419] The server monitors users' gaming history and play time in real time. If an anomaly is detected through analysis, an alert is generated triggered when the set limits (time or budget) are exceeded.

[0420] Step 7:

[0421] The server sends the generated alerts to the user's device. The user can check the alerts on their device and receive instructions to pause gameplay or review their budget as needed.

[0422] (Example 1)

[0423] 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."

[0424] One challenge is that users find it difficult to select the optimal game based on the different settings and conditions at each facility providing the gaming machines. Furthermore, managing the time and budget constraints imposed by excessive gaming is also difficult. This can lead to users wasting time and resources.

[0425] 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.

[0426] In this invention, the server includes: information gathering means for collecting information from local amusement facilities; means for storing the collected information in a data storage device; data analysis means for applying machine learning techniques using the information to predict the likelihood that a particular amusement machine is set to a high setting; information generation means for recommending the optimal playing time and amusement machine to the user based on the prediction results; information notification means for transmitting the generated recommendation information to the user's device; and game monitoring means for monitoring the user's playing status and issuing a warning if the set time or budget limit is exceeded. This enables the user to effectively and efficiently select and manage their games.

[0427] "Information gathering means" refers to technology or mechanisms for automatically collecting necessary information from local amusement facilities.

[0428] A "data storage device" is a storage medium or database used to store and manage collected information.

[0429] "Data analysis means" refers to algorithms or software used to analyze collected information and estimate the likelihood that a particular gaming machine is set to a high payout setting.

[0430] "Information generation means" refers to a mechanism or program for determining the recommended gaming time and machine type for the user based on the analysis results.

[0431] "Information notification means" refers to a system or technology for sending generated recommendation information to the user's device.

[0432] "Game monitoring means" refers to a device or function that continuously checks the user's gameplay and issues a warning if the set conditions are exceeded.

[0433] A description of an embodiment for carrying out this invention will be provided. This system consists of a server, a terminal (user's device), and a user.

[0434] The server collects data from local amusement facilities. The information collected includes machine model information, payout data, and user reviews. This data is automatically retrieved using API access and web scraping techniques. For example, this could involve web scraping using Python's BeautifulSoup or API access using HTTP requests. This data is stored in relational databases such as MySQL and PostgreSQL.

[0435] After storing the data, the server uses machine learning techniques to analyze it. Specifically, it uses the Python scikit-learn library to create a machine learning model based on the collected data. This model is used to predict the likelihood that a particular gaming machine is set to a high payout rate, or the time of day when the winning rate is higher. For example, random forests or linear regression models can be applied to analyze important features.

[0436] Based on the analysis results, the server generates recommended gaming times and machines for the user using an information generation system. This recommendation information is sent to the user's device in the form of push notifications or in-app notifications. Firebase Cloud Messaging can be used to efficiently deliver these notifications.

[0437] Users can receive notifications from the server via their devices and plan their gaming activities based on the recommendations. For example, they can go to the arcade at a specified time and play the recommended machines.

[0438] The server monitors users' gaming activity in real time and issues warnings if they exceed their set time or budget limits. This helps users avoid excessive gaming and enjoy a safe and healthy gaming experience.

[0439] As a concrete example, the server sends a notification to the user's smartphone stating that "a specific machine may have high settings at 3 PM." The user then acts on this notification, visits the arcade at 3 PM, and starts playing on the recommended machine. If the play time exceeds the set limit, the server immediately issues a warning, encouraging the user to maintain a healthy playing time.

[0440] An example of a prompt for the generating AI model would be, "Based on payout data from local amusement facilities, predict the time when high-setting machines are most likely to be available." This would enable the provision of more accurate recommendations.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server uses API access and web scraping techniques to collect data from local amusement facilities. Inputs include URLs and API endpoints for each amusement facility, to which the server sends requests. Outputs include information on the amusement machine models, payout data, and user reviews. Specifically, the server uses the Python library BeautifulSoup to analyze web pages and extract the necessary information.

[0444] Step 2:

[0445] The server stores the collected data in a database. Inputs include gaming machine information and payout data collected by the server. Storing the data in a database enables efficient data retrieval and management. Outputs include organized and saved data recorded in the database. Specific operations include the process of inserting data into the database using SQL.

[0446] Step 3:

[0447] The server executes machine learning algorithms to analyze data in the database. The input includes gaming machine information and payout data retrieved from the database. The server analyzes this data to predict the likelihood of high settings and the time periods when the winning probability increases. The output is the analysis results. Specifically, it uses Python's scikit-learn to build a random forest model and select gaming machines with specific characteristics.

[0448] Step 4:

[0449] The server generates information on recommended gaming time and machine types for the user based on the analysis results. The input is the results of machine learning analysis. From this data, the server creates a message recommending a specific gaming machine and gaming time. The output is a notification message for the user. Specifically, a generation AI model is used to generate a notification message tailored to the recommendations.

[0450] Step 5:

[0451] The server sends the generated recommendation information to the user's device. The input is the generated notification message. The output is information sent to the user's device in the form of push notifications or in-app notifications. Specifically, this involves a process that utilizes the Firebase Cloud Messaging service to send notifications to the user's smartphone.

[0452] Step 6:

[0453] The user receives notifications from the server via their device and creates a gaming plan based on the recommendations. The input is the recommendations displayed on the device. The user then plays at the arcade based on this information. The output is the user's action plan, which is determined by the notification. Specifically, the user visits the arcade at the designated time and starts playing on a specific gaming machine.

[0454] Step 7:

[0455] The server monitors the user's gameplay in real time and issues a warning if the set time or budget limits are exceeded. Inputs include the user's play time and spending history. Based on this, the server determines whether the conditions have been exceeded. The output is a warning message sent to the user's device. Specifically, this involves a process of monitoring real-time data and issuing alerts based on the conditions.

[0456] (Application Example 1)

[0457] 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."

[0458] In smart cities, there is a need to provide optimal visiting schedules that allow users to efficiently utilize entertainment and tourist facilities. Furthermore, systems are needed to curb excessive user behavior and support healthy lifestyles.

[0459] 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.

[0460] In this invention, the server includes data acquisition means for collecting information from local facilities, analysis means for predicting the optimization of operation of specific equipment based on the information, and notification means for providing the prediction results to the user. This enables users to understand the optimal visiting time for each facility, allowing for comfortable and efficient use of the facilities.

[0461] A "data acquisition method" is a system for collecting various types of information from local facilities.

[0462] "Analysis tools" refer to functions that predict the optimal operation of specific equipment based on collected information.

[0463] A "notification method" refers to a mechanism for providing users with notifications or information regarding prediction results.

[0464] A "monitoring mechanism" is a function that monitors the user's activity history and issues a warning when excessive habits are detected.

[0465] A "guidance provision method" is a system that suggests the optimal visiting time and facilities to use in order to recommend the optimization of facility utilization.

[0466] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server assists the user in optimizing facility usage by operating data acquisition means, analysis means, notification means, monitoring means, and guidance provision means.

[0467] The server collects various information from local entertainment and tourist facilities. Data acquisition methods utilize technologies such as API access and web scraping to obtain this information and store it in a database. Subsequently, analysis tools analyze this data using machine learning algorithms to predict optimal visit times for specific facilities. This analysis uses programming languages ​​such as Python and machine learning libraries.

[0468] The notification system plays the role of providing users with prediction results. Information is sent to the user's smartphone or tablet device in the form of push notifications, etc. Based on this, the user can create an appropriate visit plan.

[0469] The monitoring system tracks the user's usage history and automatically sends notifications if excessive usage is detected. This helps manage the user's daily routine and promotes a healthy lifestyle.

[0470] The guidance system has the function of suggesting optimal visit times and stay plans to users. Users can obtain information by inputting prompts into the AI ​​model, such as, "Please tell me the best time to watch a movie in Tokyo and a shopping mall visit schedule. Please also consider current crowd forecasts and user reviews to suggest the most relaxing plan."

[0471] This allows users to plan their activities in smart cities in an efficient and enjoyable way, enabling them to enjoy the facilities while preventing excessive behavior.

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The server collects information on local entertainment and tourist facilities. Using API access and web scraping techniques, it retrieves facility operating status and event information, and stores it in a database. This information is received through data acquisition methods. Inputs include facility URLs and API endpoints, and output is detailed facility information added to the database in JSON format.

[0475] Step 2:

[0476] The server uses analytical tools based on the collected information to predict optimal visit times and facility usage. By applying machine learning algorithms and analyzing past data and trends, it creates an optimal visit schedule for the user. Inputs include detailed facility information and past visit data, and the output is the generation of an optimal visit plan.

[0477] Step 3:

[0478] The server uses notification methods to provide analysis results to the user's device. Information is delivered via push notifications and in-app notifications to help users plan more easily. The input is the visit plan obtained through analysis, and the output is notification information displayed on the user's device.

[0479] Step 4:

[0480] Users create a visit plan via their smartphone or tablet based on the provided information. By inputting specified prompts into an AI model, users can obtain a detailed visit schedule and a list of recommended facilities. Inputs include user requests or search queries, and output displays detailed schedule information and a list of recommended facilities.

[0481] Step 5:

[0482] The server tracks user usage history through monitoring mechanisms and monitors for excessive facility use. If the set limits are exceeded, it sends a notification to the user's terminal to encourage healthy facility use. Inputs include user activity logs and set limits, while outputs include necessary notifications and warning messages.

[0483] Step 6:

[0484] Users can use the guidance system as needed to adjust the optimal visiting route and travel plan between multiple facilities. Input is a request to visit multiple facilities, and output is detailed route guidance and transportation options.

[0485] 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.

[0486] This invention is a system that incorporates an emotion engine to detect the user's emotional state and personalize the gaming experience. The system consists of a server, a terminal (the user's device), and the user.

[0487] The server retrieves machine information, past payout data, and user reviews from local amusement facilities. This data is collected using API access and web scraping techniques and stored in a database. The information in the database is analyzed using machine learning algorithms to predict the likelihood of a particular gaming machine being set to a high payout rate and the optimal time to play. Based on this analysis, the server generates personalized recommendations for gaming time and machines for each user.

[0488] Furthermore, the emotion engine operates on the user's device, analyzing emotions from the camera, microphone, or user interactions. This analysis is sent to a server and used to further personalize recommendations. For example, if a user is feeling stressed, the server suggests a simple game; if a relaxed state is detected, it offers a new challenge.

[0489] Based on notifications received from their devices, users select the optimal gaming machine and time to play at the facility. During gameplay, the server monitors the user's emotional state in real time and issues warnings if the user exceeds time or budget limits set by the monitoring system, or if an abnormal emotional state is detected. For example, if a user becomes excessively excited or disappointed, a pre-set alert is sent to prompt them to temporarily stop playing.

[0490] As a concrete example, when a user visits a gaming facility, the emotion engine detects the user's tension. Based on this information, the server notifies the user's device of a simple gaming approach to help them relax. The user follows the instructions and begins playing, and throughout the game, optimal instructions continue to be sent in accordance with changes in their emotions. As a result, the user can enjoy a gaming experience that is emotionally appropriate for them.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] The server collects data from local amusement facilities. Using API access and web scraping, it retrieves items such as machine settings, payout data, and user reviews, and stores them in a database.

[0494] Step 2:

[0495] The server applies machine learning algorithms based on the collected data to predict the settings of specific gaming machines. This prediction helps determine which machines are likely to have high settings or to determine the optimal time to play.

[0496] Step 3:

[0497] The emotion engine operates on the device and uses the user's facial recognition technology and voice analysis to determine their emotional state in real time. Relevant information is sent to a server and integrated with the analysis results.

[0498] Step 4:

[0499] The server recommends the optimal gaming time and machine for the user based on analysis results, including data on the user's emotional state. The recommendation results are sent to the user's device as push notifications or in-app notifications.

[0500] Step 5:

[0501] Users check notifications from their devices and act based on recommended gaming machines and times. Users begin playing at the gaming facility and follow the suggested strategies.

[0502] Step 6:

[0503] The server continuously receives emotional data from the emotion engine while the user is playing. This allows the server to monitor the user's emotional state in real time, and if emotions such as stress or excessive excitement are detected, it provides appropriate recommendations.

[0504] Step 7:

[0505] The server will send a warning to the user's device if an abnormal emotional state occurs or if the set limits are exceeded. The warning may include temporarily suspending gameplay or suggesting an alternative approach.

[0506] Step 8:

[0507] Users can check warnings from their devices, adjust their gameplay as needed, and strive for a healthy gaming experience. They can utilize the feedback from the emotion engine to take appropriate action until their emotional state stabilizes.

[0508] (Example 2)

[0509] 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."

[0510] The goal is to address the challenges users face in managing their emotional state while playing games and enjoying an optimized entertainment experience. Furthermore, it is necessary to mitigate potential frustrations arising from the lack of location-based setting predictions and personalized recommendations.

[0511] 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.

[0512] In this invention, the server includes data collection means for collecting items from an information aggregation area, analysis means using a machine learning algorithm for predicting setting values ​​based on the items, and emotion analysis means for analyzing the emotional state on the user's terminal. This enables the user to obtain an appropriate game experience in real time.

[0513] An "information aggregation area" is a resource for accumulating and managing data related to a region or specific location.

[0514] A "data collection method" is a system for acquiring data from various sources and storing it in a format that can be used for subsequent processing.

[0515] A "machine learning algorithm" is a computational model used to analyze large amounts of data and find patterns and regularities.

[0516] "Analysis methods" refer to the process of extracting useful information and results by analyzing collected data.

[0517] "Emotion analysis means" refers to a technology that estimates a user's emotional state based on the user's biometric information and behavioral data.

[0518] "Information provision means" refers to methods and interfaces for communicating analysis results and recommendations to users.

[0519] "Operation monitoring means" refers to a function that checks the user's actions and status in real time and provides warnings and advice as needed.

[0520] "Personalized recommendation methods" are mechanisms for presenting the optimal options based on the user's characteristics and circumstances.

[0521] A "control means" is a method of managing user behavior and system operation, and making adjustments according to pre-set conditions.

[0522] This system is designed to provide users with an optimized gaming experience. The server, terminal, and user work together to collect, analyze, and recommend data, enabling gameplay that matches the user's emotional state and environment.

[0523] The server collects data from the information aggregation area using API access and web scraping techniques, and stores it in a database. This collected data includes gaming machine settings, past play data, and user reviews. Systems such as MongoDB and PostgreSQL are used for the database. The server utilizes machine learning algorithms (e.g., Random Forest and GradientBoost) to predict the gaming machine settings and optimal play times. These analysis results are provided as personalized recommendations for each user.

[0524] The terminal functions via dedicated software installed on the user's device. This software analyzes the user's emotional state in real time through sentiment analysis technology using the camera and microphone. Libraries such as OpenCV and TensorFlow are utilized for sentiment analysis. The results are sent to a server and used to further personalize recommendations.

[0525] Based on notifications received on their device, users select a game machine and play time recommended by the server. For example, if the system analyzes the user's emotions and detects that they are nervous, a simple, relaxing game will be recommended on their device. An example of a prompt given to the generating AI model might be, "Consider the user's current emotional state and recommend the most suitable game machine and play time. The user is nervous."

[0526] In this way, the system constantly collects and analyzes data to provide users with the optimal gaming experience, offering recommendations based on their emotions and environment. This allows users to enjoy entertainment tailored to their own emotional state.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] The server collects data from the data aggregation area using API access and web scraping techniques. This data includes gaming machine settings, past play data, and user reviews. This data is stored in databases such as MongoDB and PostgreSQL. The input is raw data from the data aggregation area, and the output is organized database entries. Specifically, a scraping script periodically collects data and stores it in the database according to the data format.

[0530] Step 2:

[0531] The server uses information stored in the database and applies machine learning algorithms to predict the settings and play times of gaming machines. Algorithms used include Random Forest and GradientBoost. The input is organized database information, and the output is the analysis results from the predictive model. Specifically, the process involves preprocessing the data, performing pattern recognition using the machine learning model, and then aggregating and saving the results.

[0532] Step 3:

[0533] The terminal runs emotion analysis technology on the user's device. It utilizes the camera and microphone to analyze the user's emotional state in real time, employing libraries such as OpenCV and TensorFlow. The input is real-time video and audio data from the user, and the output is the analyzed emotional state. Specifically, the system acquires data from device sensors, inputs it into an emotion recognition model, and calculates the result.

[0534] Step 4:

[0535] The server integrates analyzed sentiment data with the results of predictive models to recommend the optimal gaming console and playtime for the user. The input is the analyzed sentiment data and predictive model results, while the output is personalized recommendations. Specifically, the server processes the integrated data in real time, automatically generates recommendations based on the user profile, and notifies the device.

[0536] Step 5:

[0537] The user receives notifications provided by the device and acts according to the recommended game machine and play time. The input is recommendations from the server, and the output is the user's chosen action plan. For example, when the user's stress level is detected, the device will send a notification such as "We recommend slot machine 123 on the second floor." The user then acts based on that recommendation.

[0538] (Application Example 2)

[0539] 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."

[0540] Modern entertainment facilities face the challenge of failing to provide maximum satisfaction to users because they do not adequately personalize the experience by considering the user's emotional state. Furthermore, mechanisms to prevent excessive behavior or inappropriate choices are insufficient. These challenges need to be addressed to provide users with a more personalized, safe, and enjoyable entertainment experience.

[0541] 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.

[0542] In this invention, the server includes data acquisition means for collecting information items from amusement facilities; information analysis means for predicting the conditions of a specific amusement device based on the information items; information transmission means for communicating the prediction results to the user; behavior monitoring means for observing the user's entertainment history and issuing a warning when excessive behavior is identified; emotion analysis means for analyzing the user's facial expressions and voice to detect their emotional state; and behavior selection means for selecting and implementing actions that are highly compatible with the user based on their emotional state. This enables the user to receive a personalized entertainment experience that is in line with their emotions, ensuring safe and satisfying use.

[0543] A "data acquisition means" is a processing device for collecting various information items from amusement facilities.

[0544] An "information analysis device" is a computing device used to estimate the conditions and characteristics of entertainment equipment based on collected information items.

[0545] "Information transmission means" refers to a communication device used to provide the user with the results of the analysis.

[0546] "Behavioral monitoring means" refers to a monitoring device that observes the user's behavioral history and identifies specific behaviors.

[0547] An "emotional analysis device" is an analytical device that analyzes the user's facial expressions and voice to evaluate their emotional state.

[0548] A "behavioral selection mechanism" is a function that, based on the results of emotion analysis, selects and executes the most appropriate behavior or response for the user.

[0549] This invention is a system for personalizing the gaming experience according to the user's emotional state. The server acquires various types of information using data acquisition means that collect information items from gaming facilities. Specifically, the server collects facility equipment information and historical data via the internet using API access or web scraping technology. This allows the latest information to be stored in a database.

[0550] Subsequently, the server uses information analysis tools to predict the conditions for specific entertainment devices based on the collected data. This process utilizes machine learning algorithms, and through statistical learning from past data, it can predict why a particular device is preferred and how long it will be played. The prediction results are then transmitted to the user's terminal via information transmission tools to support the selection of games.

[0551] Meanwhile, the user's device is equipped with emotion analysis capabilities, using a camera and microphone to analyze the user's facial expressions and voice. Technologies such as OpenCV and TensorFlow are utilized to accurately determine the user's emotional state. Information corresponding to the user's stress and relaxation levels is fed back to the server, which then provides further personalized recommendations.

[0552] As an example of its use, when a user uses a robot assistant after returning home, the system checks for stress levels from the user's face detected by a camera. Using a generative AI model, it generates prompts such as, "What kind of help do you need right now?" If high stress levels are detected, it suggests playing relaxing music tailored to the user's preferences.

[0553] An example of a prompt to input into a generative AI model is, "When the user's smile is captured on camera, generate a cheerful voice message accordingly. Please think of a message for when the user is smiling." This ensures that the user is always provided with a pleasant experience, and the system can respond flexibly to the user's emotions.

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The server activates data acquisition mechanisms to collect information items from amusement facilities. It receives information from APIs and websites as input, performs web scraping and API access, and saves historical data, reviews, and installation locations of amusement machines to a database. This allows for the construction of a detailed amusement machine information database.

[0557] Step 2:

[0558] The server analyzes the information stored in the database using information analysis tools. It receives collected gaming machine information as input and uses machine learning algorithms to predict how popular a particular gaming machine is, its optimal usage time, and other factors. These prediction results are obtained as output and used in subsequent processing.

[0559] Step 3:

[0560] The server uses an information transmission method to send the analysis results to the user's terminal. Specifically, it notifies the user of the optimal gaming machine and recommended time, assisting in game selection. This information is displayed on the terminal, preparing it for the user to use.

[0561] Step 4:

[0562] The user's device uses emotion analysis to capture facial expressions and voice to detect the user's emotional state. It receives video and audio of the user's face as input and analyzes emotions in real time using OpenCV and TensorFlow. As output, it sends the emotional state, such as stress levels and joy, as the result of the analysis to the server.

[0563] Step 5:

[0564] The server receives the sentiment analysis results and makes action choices based on them. It takes the sentiment analysis results as input and uses an AI model to determine the optimal entertainment options and suggestions. As output, it generates customized recommendations for the user, further enhancing personalization.

[0565] Step 6:

[0566] The user's device receives customized recommendation information from the server and displays or voices appropriate content according to the user's action selection method. Specific actions include playing music that matches the user's mood or suggesting entertainment. At this point, information aimed at improving satisfaction is provided to the user.

[0567] Through these steps, users can enjoy an emotionally engaging entertainment experience in real time.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] [Fourth Embodiment]

[0572] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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".

[0585] This invention is a system for analyzing data from each facility that provides amusement machines and providing information based on that analysis to users. This system consists of a server, a terminal (the user's device), and users.

[0586] The server runs a program to collect machine information, payout data, and user reviews from local amusement facilities. This data is collected using techniques such as API access and web scraping and stored in a database. The server then performs analysis using the collected data. By applying machine learning algorithms, the server predicts how likely a particular gaming machine is to have high settings, or at what times of day the winning rate is higher.

[0587] Based on the analysis results, the server generates information recommending suitable gaming times and machines for the user. This information is configured to be sent to the user's device as a push notification or in-app notification as needed.

[0588] Users receive notifications from the server via their devices and make optimal gaming decisions based on that information. Specifically, they can choose to play during recommended times and on recommended game machines.

[0589] The server also monitors users' gameplay in real time and analyzes their game history. If the set time or budget limits are exceeded, the server sends an alert to the terminal, prompting the user to pause play or reset their budget.

[0590] As a concrete example, suppose the server notifies the user's smartphone of the optimal timing for setting up a gaming machine. The user then goes to the arcade at the designated time and starts playing on the recommended machine. If the play time is extended and exceeds the set limit, the server automatically sends a notification to the user's device to encourage them to maintain a healthy play time.

[0591] The following describes the processing flow.

[0592] Step 1:

[0593] The server retrieves data from local amusement facilities. Using API access and web scraping techniques, it collects machine information, past payout data, and user reviews. The collected data is stored in a database.

[0594] Step 2:

[0595] The server analyzes the data in the database. It applies machine learning algorithms to predict whether a particular gaming machine is likely to have high settings, or during which time periods the winning rate is higher. The analysis results are stored for use in subsequent processes.

[0596] Step 3:

[0597] The server generates recommendations to send to the user based on the analysis results. Specifically, it compiles information such as recommended play time and game models. This information is personalized according to the user's past play history and preferences.

[0598] Step 4:

[0599] The server sends the generated recommendations to the user's device. The information is configured to be sent as a push notification or in-app notification, ensuring that the user receives the information in a timely manner.

[0600] Step 5:

[0601] The user receives a notification on their device and reviews the recommendations. Based on the recommended time slot and device, the user visits the facility and begins playing.

[0602] Step 6:

[0603] The server monitors users' gaming history and play time in real time. If an anomaly is detected through analysis, an alert is generated triggered when the set limits (time or budget) are exceeded.

[0604] Step 7:

[0605] The server sends the generated alerts to the user's device. The user can check the alerts on their device and receive instructions to pause gameplay or review their budget as needed.

[0606] (Example 1)

[0607] 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".

[0608] One challenge is that users find it difficult to select the optimal game based on the different settings and conditions at each facility providing the gaming machines. Furthermore, managing the time and budget constraints imposed by excessive gaming is also difficult. This can lead to users wasting time and resources.

[0609] 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.

[0610] In this invention, the server includes: information gathering means for collecting information from local amusement facilities; means for storing the collected information in a data storage device; data analysis means for applying machine learning techniques using the information to predict the likelihood that a particular amusement machine is set to a high setting; information generation means for recommending the optimal playing time and amusement machine to the user based on the prediction results; information notification means for transmitting the generated recommendation information to the user's device; and game monitoring means for monitoring the user's playing status and issuing a warning if the set time or budget limit is exceeded. This enables the user to effectively and efficiently select and manage their games.

[0611] "Information gathering means" refers to technology or mechanisms for automatically collecting necessary information from local amusement facilities.

[0612] A "data storage device" is a storage medium or database used to store and manage collected information.

[0613] "Data analysis means" refers to algorithms or software used to analyze collected information and estimate the likelihood that a particular gaming machine is set to a high payout setting.

[0614] "Information generation means" refers to a mechanism or program for determining the recommended gaming time and machine type for the user based on the analysis results.

[0615] "Information notification means" refers to a system or technology for sending generated recommendation information to the user's device.

[0616] "Game monitoring means" refers to a device or function that continuously checks the user's gameplay and issues a warning if the set conditions are exceeded.

[0617] A description of an embodiment for carrying out this invention will be provided. This system consists of a server, a terminal (user's device), and a user.

[0618] The server collects data from local amusement facilities. The information collected includes machine model information, payout data, and user reviews. This data is automatically retrieved using API access and web scraping techniques. For example, this could involve web scraping using Python's BeautifulSoup or API access using HTTP requests. This data is stored in relational databases such as MySQL and PostgreSQL.

[0619] After storing the data, the server uses machine learning techniques to analyze it. Specifically, it uses the Python scikit-learn library to create a machine learning model based on the collected data. This model is used to predict the likelihood that a particular gaming machine is set to a high payout rate, or the time of day when the winning rate is higher. For example, random forests or linear regression models can be applied to analyze important features.

[0620] Based on the analysis results, the server generates recommended gaming times and machines for the user using an information generation system. This recommendation information is sent to the user's device in the form of push notifications or in-app notifications. Firebase Cloud Messaging can be used to efficiently deliver these notifications.

[0621] Users can receive notifications from the server via their devices and plan their gaming activities based on the recommendations. For example, they can go to the arcade at a specified time and play the recommended machines.

[0622] The server monitors users' gaming activity in real time and issues warnings if they exceed their set time or budget limits. This helps users avoid excessive gaming and enjoy a safe and healthy gaming experience.

[0623] As a concrete example, the server sends a notification to the user's smartphone stating that "a specific machine may have high settings at 3 PM." The user then acts on this notification, visits the arcade at 3 PM, and starts playing on the recommended machine. If the play time exceeds the set limit, the server immediately issues a warning, encouraging the user to maintain a healthy playing time.

[0624] An example of a prompt for the generating AI model would be, "Based on payout data from local amusement facilities, predict the time when high-setting machines are most likely to be available." This would enable the provision of more accurate recommendations.

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The server uses API access and web scraping techniques to collect data from local amusement facilities. Inputs include URLs and API endpoints for each amusement facility, to which the server sends requests. Outputs include information on the amusement machine models, payout data, and user reviews. Specifically, the server uses the Python library BeautifulSoup to analyze web pages and extract the necessary information.

[0628] Step 2:

[0629] The server stores the collected data in a database. Inputs include gaming machine information and payout data collected by the server. Storing the data in a database enables efficient data retrieval and management. Outputs include organized and saved data recorded in the database. Specific operations include the process of inserting data into the database using SQL.

[0630] Step 3:

[0631] The server executes machine learning algorithms to analyze data in the database. The input includes gaming machine information and payout data retrieved from the database. The server analyzes this data to predict the likelihood of high settings and the time periods when the winning probability increases. The output is the analysis results. Specifically, it uses Python's scikit-learn to build a random forest model and select gaming machines with specific characteristics.

[0632] Step 4:

[0633] The server generates information on recommended gaming time and machine types for the user based on the analysis results. The input is the results of machine learning analysis. From this data, the server creates a message recommending a specific gaming machine and gaming time. The output is a notification message for the user. Specifically, a generation AI model is used to generate a notification message tailored to the recommendations.

[0634] Step 5:

[0635] The server sends the generated recommendation information to the user's device. The input is the generated notification message. The output is information sent to the user's device in the form of push notifications or in-app notifications. Specifically, this involves a process that utilizes the Firebase Cloud Messaging service to send notifications to the user's smartphone.

[0636] Step 6:

[0637] The user receives notifications from the server via their device and creates a gaming plan based on the recommendations. The input is the recommendations displayed on the device. The user then plays at the arcade based on this information. The output is the user's action plan, which is determined by the notification. Specifically, the user visits the arcade at the designated time and starts playing on a specific gaming machine.

[0638] Step 7:

[0639] The server monitors the user's gameplay in real time and issues a warning if the set time or budget limits are exceeded. Inputs include the user's play time and spending history. Based on this, the server determines whether the conditions have been exceeded. The output is a warning message sent to the user's device. Specifically, this involves a process of monitoring real-time data and issuing alerts based on the conditions.

[0640] (Application Example 1)

[0641] 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".

[0642] In smart cities, there is a need to provide optimal visiting schedules that allow users to efficiently utilize entertainment and tourist facilities. Furthermore, systems are needed to curb excessive user behavior and support healthy lifestyles.

[0643] 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.

[0644] In this invention, the server includes data acquisition means for collecting information from local facilities, analysis means for predicting the optimization of operation of specific equipment based on the information, and notification means for providing the prediction results to the user. This enables users to understand the optimal visiting time for each facility, allowing for comfortable and efficient use of the facilities.

[0645] A "data acquisition method" is a system for collecting various types of information from local facilities.

[0646] "Analysis tools" refer to functions that predict the optimal operation of specific equipment based on collected information.

[0647] A "notification method" refers to a mechanism for providing users with notifications or information regarding prediction results.

[0648] A "monitoring mechanism" is a function that monitors the user's activity history and issues a warning when excessive habits are detected.

[0649] A "guidance provision method" is a system that suggests the optimal visiting time and facilities to use in order to recommend the optimization of facility utilization.

[0650] The system for implementing this invention mainly consists of a server, a terminal, and a user. The server assists the user in optimizing facility usage by operating data acquisition means, analysis means, notification means, monitoring means, and guidance provision means.

[0651] The server collects various information from local entertainment and tourist facilities. Data acquisition methods utilize technologies such as API access and web scraping to obtain this information and store it in a database. Subsequently, analysis tools analyze this data using machine learning algorithms to predict optimal visit times for specific facilities. This analysis uses programming languages ​​such as Python and machine learning libraries.

[0652] The notification system plays the role of providing users with prediction results. Information is sent to the user's smartphone or tablet device in the form of push notifications, etc. Based on this, the user can create an appropriate visit plan.

[0653] The monitoring system tracks the user's usage history and automatically sends notifications if excessive usage is detected. This helps manage the user's daily routine and promotes a healthy lifestyle.

[0654] The guidance system has the function of suggesting optimal visit times and stay plans to users. Users can obtain information by inputting prompts into the AI ​​model, such as, "Please tell me the best time to watch a movie in Tokyo and a shopping mall visit schedule. Please also consider current crowd forecasts and user reviews to suggest the most relaxing plan."

[0655] This allows users to plan their activities in smart cities in an efficient and enjoyable way, enabling them to enjoy the facilities while preventing excessive behavior.

[0656] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0657] Step 1:

[0658] The server collects information on local entertainment and tourist facilities. Using API access and web scraping techniques, it retrieves facility operating status and event information, and stores it in a database. This information is received through data acquisition methods. Inputs include facility URLs and API endpoints, and output is detailed facility information added to the database in JSON format.

[0659] Step 2:

[0660] The server uses analytical tools based on the collected information to predict optimal visit times and facility usage. By applying machine learning algorithms and analyzing past data and trends, it creates an optimal visit schedule for the user. Inputs include detailed facility information and past visit data, and the output is the generation of an optimal visit plan.

[0661] Step 3:

[0662] The server uses notification methods to provide analysis results to the user's device. Information is delivered via push notifications and in-app notifications to help users plan more easily. The input is the visit plan obtained through analysis, and the output is notification information displayed on the user's device.

[0663] Step 4:

[0664] Users create a visit plan via their smartphone or tablet based on the provided information. By inputting specified prompts into an AI model, users can obtain a detailed visit schedule and a list of recommended facilities. Inputs include user requests or search queries, and output displays detailed schedule information and a list of recommended facilities.

[0665] Step 5:

[0666] The server tracks user usage history through monitoring mechanisms and monitors for excessive facility use. If the set limits are exceeded, it sends a notification to the user's terminal to encourage healthy facility use. Inputs include user activity logs and set limits, while outputs include necessary notifications and warning messages.

[0667] Step 6:

[0668] Users can use the guidance system as needed to adjust the optimal visiting route and travel plan between multiple facilities. Input is a request to visit multiple facilities, and output is detailed route guidance and transportation options.

[0669] 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.

[0670] This invention is a system that incorporates an emotion engine to detect the user's emotional state and personalize the gaming experience. The system consists of a server, a terminal (the user's device), and the user.

[0671] The server retrieves machine information, past payout data, and user reviews from local amusement facilities. This data is collected using API access and web scraping techniques and stored in a database. The information in the database is analyzed using machine learning algorithms to predict the likelihood of a particular gaming machine being set to a high payout rate and the optimal time to play. Based on this analysis, the server generates personalized recommendations for gaming time and machines for each user.

[0672] Furthermore, the emotion engine operates on the user's device, analyzing emotions from the camera, microphone, or user interactions. This analysis is sent to a server and used to further personalize recommendations. For example, if a user is feeling stressed, the server suggests a simple game; if a relaxed state is detected, it offers a new challenge.

[0673] Based on notifications received from their devices, users select the optimal gaming machine and time to play at the facility. During gameplay, the server monitors the user's emotional state in real time and issues warnings if the user exceeds time or budget limits set by the monitoring system, or if an abnormal emotional state is detected. For example, if a user becomes excessively excited or disappointed, a pre-set alert is sent to prompt them to temporarily stop playing.

[0674] As a concrete example, when a user visits a gaming facility, the emotion engine detects the user's tension. Based on this information, the server notifies the user's device of a simple gaming approach to help them relax. The user follows the instructions and begins playing, and throughout the game, optimal instructions continue to be sent in accordance with changes in their emotions. As a result, the user can enjoy a gaming experience that is emotionally appropriate for them.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] The server collects data from local amusement facilities. Using API access and web scraping, it retrieves items such as machine settings, payout data, and user reviews, and stores them in a database.

[0678] Step 2:

[0679] The server applies machine learning algorithms based on the collected data to predict the settings of specific gaming machines. This prediction helps determine which machines are likely to have high settings or to determine the optimal time to play.

[0680] Step 3:

[0681] The emotion engine operates on the device and uses the user's facial recognition technology and voice analysis to determine their emotional state in real time. Relevant information is sent to a server and integrated with the analysis results.

[0682] Step 4:

[0683] The server recommends the optimal gaming time and machine for the user based on analysis results, including data on the user's emotional state. The recommendation results are sent to the user's device as push notifications or in-app notifications.

[0684] Step 5:

[0685] Users check notifications from their devices and act based on recommended gaming machines and times. Users begin playing at the gaming facility and follow the suggested strategies.

[0686] Step 6:

[0687] The server continuously receives emotional data from the emotion engine while the user is playing. This allows the server to monitor the user's emotional state in real time, and if emotions such as stress or excessive excitement are detected, it provides appropriate recommendations.

[0688] Step 7:

[0689] The server will send a warning to the user's device if an abnormal emotional state occurs or if the set limits are exceeded. The warning may include temporarily suspending gameplay or suggesting an alternative approach.

[0690] Step 8:

[0691] Users can check warnings from their devices, adjust their gameplay as needed, and strive for a healthy gaming experience. They can utilize the feedback from the emotion engine to take appropriate action until their emotional state stabilizes.

[0692] (Example 2)

[0693] 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".

[0694] The goal is to address the challenges users face in managing their emotional state while playing games and enjoying an optimized entertainment experience. Furthermore, it is necessary to mitigate potential frustrations arising from the lack of location-based setting predictions and personalized recommendations.

[0695] 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.

[0696] In this invention, the server includes data collection means for collecting items from an information aggregation area, analysis means using a machine learning algorithm for predicting setting values ​​based on the items, and emotion analysis means for analyzing the emotional state on the user's terminal. This enables the user to obtain an appropriate game experience in real time.

[0697] An "information aggregation area" is a resource for accumulating and managing data related to a region or specific location.

[0698] A "data collection method" is a system for acquiring data from various sources and storing it in a format that can be used for subsequent processing.

[0699] A "machine learning algorithm" is a computational model used to analyze large amounts of data and find patterns and regularities.

[0700] "Analysis methods" refer to the process of extracting useful information and results by analyzing collected data.

[0701] "Emotion analysis means" refers to a technology that estimates a user's emotional state based on the user's biometric information and behavioral data.

[0702] "Information provision means" refers to methods and interfaces for communicating analysis results and recommendations to users.

[0703] "Operation monitoring means" refers to a function that checks the user's actions and status in real time and provides warnings and advice as needed.

[0704] "Personalized recommendation methods" are mechanisms for presenting the optimal options based on the user's characteristics and circumstances.

[0705] A "control means" is a method of managing user behavior and system operation, and making adjustments according to pre-set conditions.

[0706] This system is designed to provide users with an optimized gaming experience. The server, terminal, and user work together to collect, analyze, and recommend data, enabling gameplay that matches the user's emotional state and environment.

[0707] The server collects data from the information aggregation area using API access and web scraping techniques, and stores it in a database. This collected data includes gaming machine settings, past play data, and user reviews. Systems such as MongoDB and PostgreSQL are used for the database. The server utilizes machine learning algorithms (e.g., Random Forest and GradientBoost) to predict the gaming machine settings and optimal play times. These analysis results are provided as personalized recommendations for each user.

[0708] The terminal functions via dedicated software installed on the user's device. This software analyzes the user's emotional state in real time through sentiment analysis technology using the camera and microphone. Libraries such as OpenCV and TensorFlow are utilized for sentiment analysis. The results are sent to a server and used to further personalize recommendations.

[0709] Based on notifications received on their device, users select a game machine and play time recommended by the server. For example, if the system analyzes the user's emotions and detects that they are nervous, a simple, relaxing game will be recommended on their device. An example of a prompt given to the generating AI model might be, "Consider the user's current emotional state and recommend the most suitable game machine and play time. The user is nervous."

[0710] In this way, the system constantly collects and analyzes data to provide users with the optimal gaming experience, offering recommendations based on their emotions and environment. This allows users to enjoy entertainment tailored to their own emotional state.

[0711] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0712] Step 1:

[0713] The server collects data from the data aggregation area using API access and web scraping techniques. This data includes gaming machine settings, past play data, and user reviews. This data is stored in databases such as MongoDB and PostgreSQL. The input is raw data from the data aggregation area, and the output is organized database entries. Specifically, a scraping script periodically collects data and stores it in the database according to the data format.

[0714] Step 2:

[0715] The server uses information stored in the database and applies machine learning algorithms to predict the settings and play times of gaming machines. Algorithms used include Random Forest and GradientBoost. The input is organized database information, and the output is the analysis results from the predictive model. Specifically, the process involves preprocessing the data, performing pattern recognition using the machine learning model, and then aggregating and saving the results.

[0716] Step 3:

[0717] The terminal runs emotion analysis technology on the user's device. It utilizes the camera and microphone to analyze the user's emotional state in real time, employing libraries such as OpenCV and TensorFlow. The input is real-time video and audio data from the user, and the output is the analyzed emotional state. Specifically, the system acquires data from device sensors, inputs it into an emotion recognition model, and calculates the result.

[0718] Step 4:

[0719] The server integrates analyzed sentiment data with the results of predictive models to recommend the optimal gaming console and playtime for the user. The input is the analyzed sentiment data and predictive model results, while the output is personalized recommendations. Specifically, the server processes the integrated data in real time, automatically generates recommendations based on the user profile, and notifies the device.

[0720] Step 5:

[0721] The user receives notifications provided by the device and acts according to the recommended game machine and play time. The input is recommendations from the server, and the output is the user's chosen action plan. For example, when the user's stress level is detected, the device will send a notification such as "We recommend slot machine 123 on the second floor." The user then acts based on that recommendation.

[0722] (Application Example 2)

[0723] 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".

[0724] Modern entertainment facilities face the challenge of failing to provide maximum satisfaction to users because they do not adequately personalize the experience by considering the user's emotional state. Furthermore, mechanisms to prevent excessive behavior or inappropriate choices are insufficient. These challenges need to be addressed to provide users with a more personalized, safe, and enjoyable entertainment experience.

[0725] 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.

[0726] In this invention, the server includes data acquisition means for collecting information items from amusement facilities; information analysis means for predicting the conditions of a specific amusement device based on the information items; information transmission means for communicating the prediction results to the user; behavior monitoring means for observing the user's entertainment history and issuing a warning when excessive behavior is identified; emotion analysis means for analyzing the user's facial expressions and voice to detect their emotional state; and behavior selection means for selecting and implementing actions that are highly compatible with the user based on their emotional state. This enables the user to receive a personalized entertainment experience that is in line with their emotions, ensuring safe and satisfying use.

[0727] A "data acquisition means" is a processing device for collecting various information items from amusement facilities.

[0728] An "information analysis device" is a computing device used to estimate the conditions and characteristics of entertainment equipment based on collected information items.

[0729] "Information transmission means" refers to a communication device used to provide the user with the results of the analysis.

[0730] "Behavioral monitoring means" refers to a monitoring device that observes the user's behavioral history and identifies specific behaviors.

[0731] An "emotional analysis device" is an analytical device that analyzes the user's facial expressions and voice to evaluate their emotional state.

[0732] A "behavioral selection mechanism" is a function that, based on the results of emotion analysis, selects and executes the most appropriate behavior or response for the user.

[0733] This invention is a system for personalizing the gaming experience according to the user's emotional state. The server acquires various types of information using data acquisition means that collect information items from gaming facilities. Specifically, the server collects facility equipment information and historical data via the internet using API access or web scraping technology. This allows the latest information to be stored in a database.

[0734] Subsequently, the server uses information analysis tools to predict the conditions for specific entertainment devices based on the collected data. This process utilizes machine learning algorithms, and through statistical learning from past data, it can predict why a particular device is preferred and how long it will be played. The prediction results are then transmitted to the user's terminal via information transmission tools to support the selection of games.

[0735] Meanwhile, the user's device is equipped with emotion analysis capabilities, using a camera and microphone to analyze the user's facial expressions and voice. Technologies such as OpenCV and TensorFlow are utilized to accurately determine the user's emotional state. Information corresponding to the user's stress and relaxation levels is fed back to the server, which then provides further personalized recommendations.

[0736] As an example of its use, when a user uses a robot assistant after returning home, the system checks for stress levels from the user's face detected by a camera. Using a generative AI model, it generates prompts such as, "What kind of help do you need right now?" If high stress levels are detected, it suggests playing relaxing music tailored to the user's preferences.

[0737] An example of a prompt to input into a generative AI model is, "When the user's smile is captured on camera, generate a cheerful voice message accordingly. Please think of a message for when the user is smiling." This ensures that the user is always provided with a pleasant experience, and the system can respond flexibly to the user's emotions.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server activates data acquisition mechanisms to collect information items from amusement facilities. It receives information from APIs and websites as input, performs web scraping and API access, and saves historical data, reviews, and installation locations of amusement machines to a database. This allows for the construction of a detailed amusement machine information database.

[0741] Step 2:

[0742] The server analyzes the information stored in the database using information analysis tools. It receives collected gaming machine information as input and uses machine learning algorithms to predict how popular a particular gaming machine is, its optimal usage time, and other factors. These prediction results are obtained as output and used in subsequent processing.

[0743] Step 3:

[0744] The server uses an information transmission method to send the analysis results to the user's terminal. Specifically, it notifies the user of the optimal gaming machine and recommended time, assisting in game selection. This information is displayed on the terminal, preparing it for the user to use.

[0745] Step 4:

[0746] The user's device uses emotion analysis to capture facial expressions and voice to detect the user's emotional state. It receives video and audio of the user's face as input and analyzes emotions in real time using OpenCV and TensorFlow. As output, it sends the emotional state, such as stress levels and joy, as the result of the analysis to the server.

[0747] Step 5:

[0748] The server receives the sentiment analysis results and makes action choices based on them. It takes the sentiment analysis results as input and uses an AI model to determine the optimal entertainment options and suggestions. As output, it generates customized recommendations for the user, further enhancing personalization.

[0749] Step 6:

[0750] The user's device receives customized recommendation information from the server and displays or voices appropriate content according to the user's action selection method. Specific actions include playing music that matches the user's mood or suggesting entertainment. At this point, information aimed at improving satisfaction is provided to the user.

[0751] Through these steps, users can enjoy an emotionally engaging entertainment experience in real time.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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."

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] The following is further disclosed regarding the embodiments described above.

[0774] (Claim 1)

[0775] A means of acquiring information by collecting items from local amusement facilities,

[0776] An analysis means for predicting the setting value of a specific gaming machine based on the aforementioned items,

[0777] A notification means for notifying the user of the prediction results,

[0778] A monitoring system that monitors the user's gaming history and issues warnings when excessive behavior is detected,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising a recommendation means for recommending the optimal game time and game machine based on the analysis results.

[0782] (Claim 3)

[0783] The system according to claim 1, further comprising management means for setting budget and time limits related to gaming according to user settings.

[0784] "Example 1"

[0785] (Claim 1)

[0786] Information gathering methods for collecting information from local amusement facilities,

[0787] A means for storing the collected information in a data storage device,

[0788] A data analysis means for predicting the likelihood that a particular gaming machine is set to a high setting by applying machine learning techniques using the aforementioned information,

[0789] Information generation means for recommending the optimal gaming time and gaming machine to the user based on the aforementioned prediction results,

[0790] Information notification means for transmitting generated recommendation information to the user's device,

[0791] A game monitoring system for monitoring the user's gameplay and issuing a warning if the set time or budget limit is exceeded,

[0792] A system that includes this.

[0793] (Claim 2)

[0794] The system according to claim 1, comprising information generation means for generating notification content using a generative AI model.

[0795] (Claim 3)

[0796] The system according to claim 1, comprising control means for managing budget and time limits according to user settings.

[0797] "Application Example 1"

[0798] (Claim 1)

[0799] A data acquisition method for collecting information from local facilities,

[0800] An analytical means for predicting the operational optimization of a specific piece of equipment based on the aforementioned information,

[0801] A notification means for providing the user with the aforementioned prediction results,

[0802] A monitoring system that monitors the user's activity history and alerts them when excessive habits are detected,

[0803] A means of providing guidance to recommend the optimization of facility utilization,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, further comprising means for analyzing usage information at multiple facilities and recommending the optimal visit time and facility to use.

[0807] (Claim 3)

[0808] The system according to claim 1, further comprising adjustment means for managing the budget and time related to activities according to user settings.

[0809] "Example 2 of combining an emotion engine"

[0810] (Claim 1)

[0811] A data collection means for collecting items from an information aggregation area,

[0812] An analysis means using a machine learning algorithm to predict the set value based on the above items,

[0813] A means for analyzing the emotional state on a user's device,

[0814] Information provision means for notifying the user of the prediction results and sentiment analysis results,

[0815] A behavioral monitoring system that monitors the user's activity history and issues a warning when excessive emotional fluctuations are detected,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, further comprising a personalized recommendation means for recommending the optimal activity time and equipment based on the analysis results and the emotion analysis results.

[0819] (Claim 3)

[0820] The system according to claim 1, further comprising control means for imposing restrictions on activities according to user settings.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A data acquisition method for collecting information items from amusement facilities,

[0824] Information analysis means for predicting the conditions of a specific entertainment device based on the aforementioned information items,

[0825] Information transmission means for communicating the prediction results to the user,

[0826] A behavioral monitoring system that observes the user's entertainment history and issues warnings when excessive behavior is identified,

[0827] An emotion analysis means that detects the emotional state by analyzing the user's facial expressions and voice,

[0828] A means for selecting and implementing actions that are highly compatible with the user based on the aforementioned emotional state,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, further comprising a recommendation device that suggests the optimal entertainment time and entertainment equipment based on the analysis results.

[0832] (Claim 3)

[0833] The system according to claim 1, further comprising an adjustment function for limiting expenses and time related to entertainment, according to user settings. [Explanation of symbols]

[0834] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data acquisition method for collecting information from local facilities, An analytical means for predicting the operational optimization of a specific piece of equipment based on the aforementioned information, A notification means for providing the user with the aforementioned prediction results, A monitoring system that monitors the user's activity history and issues warnings when excessive habits are detected, A means of providing guidance to recommend the optimization of facility utilization, A system that includes this.

2. The system according to claim 1, further comprising means for analyzing usage information at multiple facilities and recommending the optimal visit time and facility to use.

3. The system according to claim 1, further comprising adjustment means for managing the budget and time related to activities according to user settings.

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