system

Abstract

We provide the system. [Solution] A means of collecting past game data and performing statistical analysis, A means of selecting the optimal game and recommending the best time to play based on the collected data, A means of providing users with a play guide, A means of managing play history and issuing risk alerts for gambling, A system that includes this.

Description

Technical Field

[0004] , ,

[0005] , , ,

[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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance 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 entertainment activities such as pachinko and slots, it is difficult for players to obtain strategic information for enjoying the games efficiently and healthily. In particular, there is a lack of means for appropriately grasping information such as the winning rate and setting tendencies for each game. In addition, since there is a risk of excessive dependence on gambling and financial burden, it is required to enjoy the play effectively while managing the play. However, there is a problem that current technologies do not provide a system that satisfies these requirements.

Means for Solving the Problems

[0005] This invention provides a system that statistically analyzes past game data and, based on the results, offers users the optimal game and play time. Furthermore, it collects and analyzes game setting information from local entertainment facilities to support users in making highly accurate setting selections. It also manages play history and warns users of the risk of excessive gambling, thereby promoting healthy play habits. In this way, it enables players to engage in entertainment activities effectively and safely based on data-backed information.

[0006] "Game data" refers to a collection of data including past play history, win rates, and setting information related to various amusement machines such as pachinko and slot machines.

[0007] "Statistical analysis" is the process of using collected data to calculate numerical values ​​and trends, and to gain insights based on that data.

[0008] "Setting information" refers to information that shows the game conditions and internal settings of each amusement device.

[0009] A "play guide" is a set of strategic information compiled to guide users towards the most suitable games and playtime.

[0010] "Play history" refers to a record of the results and details of games a user has played in the past.

[0011] A "risk alert" is a notification that monitors a user's gameplay and warns them if there is a risk of excessive gambling or financial burden.

[0012] "Entertainment facilities" refer to places where users can play games, such as pachinko parlors and slot machine shops. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system according to the present invention mainly consists of three elements: a server, a terminal, and a user. Through statistical data analysis, information provision, play history management, and risk management, it provides users with effective gameplay guidance and supports healthy gameplay habits.

[0035] The server first collects past and present game data using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. The collected data is stored in a database on the server, and statistical analysis is performed using machine learning algorithms. Specifically, it analyzes the win rate, setting patterns, and ease of play for each game machine, and derives data-driven insights.

[0036] The terminal provides users with the latest strategic and recommended information through a user-friendly interface, based on information received from the server. For example, it can inform users about game machines that are expected to have a high win rate on specific days or times. It also records the user's gameplay results, which are then sent back to the server and used for future analysis.

[0037] Users can develop their own playing strategies based on information provided through their devices. By receiving notifications from the server and selecting the optimal device and time of day, users can achieve efficient gameplay. Furthermore, the device application includes features to manage the user's playing time and budget, thus helping to mitigate the risk of gambling addiction.

[0038] This system enables users to make strategic choices based on rational information, while also providing them with tools to prevent excessive gaming. This makes it possible to enjoy recreational activities in a healthy and sustainable way.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server accesses APIs and public data sources of entertainment venues to collect historical play data and configuration information for each game machine. This includes information from online reviews and feedback.

[0042] Step 2:

[0043] The server stores the collected data in a database. During this process, it checks for duplicate or inaccurate data and cleanses the data as needed.

[0044] Step 3:

[0045] The server uses machine learning algorithms to analyze win rates and setting trends for each machine. Based on this analysis, it determines recommended game machines and time slots.

[0046] Step 4:

[0047] The server uses the analysis results to generate a customized play guide for each user. By taking into account conditions such as working hours and holidays, it provides the user with the most suitable strategy.

[0048] Step 5:

[0049] The terminal displays play guides and recommended information received from the server to the user. This information is provided in a visually easy-to-understand format using a graphical user interface.

[0050] Step 6:

[0051] The user reviews the information provided by their device and plans their gameplay based on it. They select specific game consoles and playtime, and prepare to execute their plan.

[0052] Step 7:

[0053] After finishing a game, users use their device to input their game results. The entered data is sent to the server for use in future analysis.

[0054] Step 8:

[0055] The device manages the user's playtime and financial burden, and issues risk alerts if set thresholds are exceeded. It encourages the user to reconsider their gameplay or take a break.

[0056] Step 9:

[0057] The server continuously collects gameplay results and user feedback to improve the accuracy of the system's analysis. This will lead to more accurate gameplay guides for future sessions.

[0058] (Example 1)

[0059] 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."

[0060] Traditional amusement facilities often relied on intuition for game selection and time management, lacking sufficient information for efficient gameplay. Furthermore, there was a risk of users unconsciously engaging in excessive gameplay. Combined with inadequate information management, this made it difficult to properly optimize gameplay and manage risks.

[0061] 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.

[0062] In this invention, the server includes means for extracting data from amusement facilities and publicly available information as well as information from users; means for analyzing game results using a machine learning algorithm based on the accumulated data; and means for providing the analyzed information to the user's terminal. As a result, users can obtain information regarding predictions of wins and losses in games and the selection of optimal time slots, and receive support in creating effective and low-risk game plans.

[0063] An "amusement facility" refers to a place that provides entertainment and games for users.

[0064] "Data extraction means" refers to a function or technology for collecting necessary information from amusement facilities.

[0065] "Public information" refers to information that is generally accessible on the internet.

[0066] "Information from users" refers to data on feedback and experiences provided by individuals who play the game.

[0067] A "machine learning algorithm" refers to a computational method used to find patterns in large amounts of data and perform predictions and analyses.

[0068] "Game results" refers to data on the outcomes and performance obtained during a game.

[0069] A "risk warning" refers to a warning issued to inform users of the possibility of excessive gambling or incurring financial burdens.

[0070] "Game history" refers to detailed record data about past gameplay.

[0071] "Financial management" refers to functions that support users in managing their budgets and expenses.

[0072] "Management methods for indexing and recommendation" refers to technical methods that utilize data collected from users for analysis and use to optimize and recommend future gameplay.

[0073] The embodiments for carrying out the present invention are described below. The system consists of three elements: a server, a terminal, and a user. The server periodically collects game data via an API using a data extraction means from amusement facilities. It also collects publicly available information on the internet and feedback from users. General database technologies are used for data management, such as MySQL® or PostgreSQL.

[0074] The collected data is stored on a server and statistically analyzed using machine learning algorithms. Python libraries such as Scikit-learn are used for the analysis. This allows for detailed analysis of data such as the win rate of amusement machines, setting patterns, and time of day, and helps to create optimal action plans for amusement facilities.

[0075] The terminal receives analysis results sent from the server and displays them to the user. A smartphone app is commonly used on the terminal. The app provides a user-friendly interface and visually presents the analysis results. For example, it might display information such as, "The winning rate for certain slot machines increases between 10 AM and noon," as a gaming trend.

[0076] Users devise optimal game strategies based on information provided through their devices. Furthermore, users input their gameplay results by operating the device, and this information is sent to the server. This data is then used for analysis in subsequent sessions. Users can also use the device's functions to manage their gameplay history and financial status, preventing excessive spending.

[0077] The system's user interface also includes an alert function to mitigate the risk of gambling addiction. This allows users to enjoy playing with peace of mind.

[0078] An example of a prompt message would be a text-based inquiry from a user requesting information, such as, "Please provide the latest information on the winning rates of casino slot machines this weekend. I would especially like to know the times when high winning rates are expected." This allows the user to instantly obtain the information they need.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server collects data via the amusement facility's API. The inputs are the amusement facility's API endpoint and the necessary call parameters. The server makes an API request and retrieves game history data as a response. The retrieved data includes date and time, game results, and setting patterns. This data is stored in a database for subsequent analysis.

[0082] Step 2:

[0083] The server uses web scraping techniques to obtain publicly available information. The input is the URL of the target website. The server executes a scraping script to obtain data related to events and promotions. This information is stored to help understand the layout of amusement facilities and player trends in analysis.

[0084] Step 3:

[0085] The server performs machine learning analysis using the acquired data. The input is gameplay data stored in a database. The server uses Python libraries such as Scikit-learn to analyze win rates and setting patterns, and generates optimal gameplay strategies for each time period. The output is insights obtained from the analysis, such as "average win rate for a specific machine in the afternoon."

[0086] Step 4:

[0087] The server sends the analyzed insights to the terminal. The input is the analysis result data. The server calls an API endpoint to format this for the user and send it to the terminal. The output is play guide information that arrives on the user's terminal. This provides recommendations for the next game.

[0088] Step 5:

[0089] The terminal displays the received information to the user. The input is play guide data sent from the server. The terminal's app displays this data in a visual interface and places the information in push notifications and on the dashboard. The output is strategic information or alerts that the user can access.

[0090] Step 6:

[0091] The user inputs gameplay results via a terminal. The input consists of manually entered gameplay results and feedback. The terminal formats this data and sends it to the server. The output is new gameplay data used for analysis.

[0092] Step 7:

[0093] The server manages data based on user play data to improve future analysis and predictions. The input is new data obtained from users. The server integrates this into a database to improve the accuracy of the analysis model. The output is the updated database state.

[0094] (Application Example 1)

[0095] 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."

[0096] In recreational activities, users face the challenge of difficulty in quickly and appropriately obtaining information necessary for efficient and healthy gameplay. Furthermore, there is a need to improve the user experience while mitigating the risks associated with over-engagement gameplay. Conventional systems lack the means to provide optimal strategies based on individual user history in real time, making it difficult for users to obtain guidance suited to their own play style.

[0097] 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.

[0098] In this invention, the server includes means for collecting and statistically processing past entertainment data, means for selecting appropriate entertainment devices and recommending time slots based on the collected information, means for providing strategic guidance to the user, means for providing information to a robot through voice interaction, and means for recording the user's activity history via voice or touch input and reflecting it in the next recommendation. As a result, the user can receive individually optimized play strategy information in real time and manage healthy gameplay while having fun.

[0099] "Past entertainment data" refers to information about the user's past entertainment activities, including data such as win rates, play time, and types of games played.

[0100] "Statistical processing" refers to calculation and evaluation methods used to analyze collected data and identify trends and relationships.

[0101] "Entertainment equipment" refers to a wide range of devices, including game consoles and computers, that are machines or electronic devices used for user enjoyment.

[0102] "Recommended time slots" refer to information indicating the optimal time when a user is predicted to start playing.

[0103] A "strategy guide" is information that includes advice and instructions to help users enjoy a particular form of entertainment efficiently and advantageously.

[0104] "Means of providing information to a robot through voice interaction" refers to a method of conveying information to a user using voice via a robotic device.

[0105] "Activity history" refers to historical information about the activities a user has performed so far, including data such as gameplay results and selected options.

[0106] "Voice or touch input" refers to an interface used by a user to provide information or instructions, using either voice commands or a touchscreen.

[0107] "Receiving information in real time" means that users can obtain information instantly without any time lag.

[0108] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server first collects historical entertainment data, performs statistical processing, and generates basic data for selecting the optimal entertainment device and recommending the best time of day for the user. A database server is used as the hardware for this purpose, and Python and TensorFlow (registered trademark) are used as the software. Python is used for data collection and management, and TensorFlow is used for statistical processing and executing machine learning algorithms.

[0109] The terminal provides users with information received from the server in real time and plays a role in appropriately conveying strategic guidance through voice interaction. A home robot fulfills this role, incorporating a voice recognition microphone and a touchscreen display to output information in response to the user's voice and touch operations.

[0110] Through this device, users can record their individual activity history using voice or touch input, which will then be reflected in future recommendations. For example, if a user inputs the results of their gameplay on a given day, they can immediately receive information recommending the optimal time to play the following day. Examples of prompts include, "Please tell me the most suitable play time among the entertainment devices you have specified," and "Based on your recent activity results, please state your next recommended plan."

[0111] This system allows users to receive personalized information and enjoy recreational activities in a healthy and effective manner.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server collects historical entertainment data via an API. This data includes win rates, play time, and game types. The collected data is stored in a database server. The input is an external data source, and the output is a structured set of data.

[0115] Step 2:

[0116] The server performs statistical processing using Python and TensorFlow. It analyzes the collected data to derive win rates and recommended information for each time of day. The input is raw data from the database, and the output is the statistical analysis results and recommended actions. This step performs data analysis and generates the optimal play strategy for a specific time of day.

[0117] Step 3:

[0118] The terminal receives analysis results from the server and provides information to the user through a home robot. Voice interaction with the user is set up, and the latest strategic guide is transmitted. The input is the analysis results from the server, and the output is the information output to the user. Here, information is output using a voice recognition microphone and a touchscreen display.

[0119] Step 4:

[0120] The user inputs their gameplay results via voice or touch, based on information provided by the robot. The input results are then sent back to the server to be used in generating recommendations for the next session. The input is user gameplay data, and the output is updated user activity history data.

[0121] Step 5:

[0122] The server generates a new play strategy for future use based on the updated user activity history. The generated strategy information is stored on the server and used for future analysis. The input is the updated user history, and the output is new data for future recommendations. This process ensures that a plan optimized for each individual user is continuously provided.

[0123] 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.

[0124] This invention is a system that recognizes user emotions and seamlessly adjusts play guides and risk management based on those emotions. It mainly consists of four elements: a server, a terminal, an emotion engine, and the user.

[0125] The server first collects past play data and setting information for each game machine using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. In addition, it also collects user emotion data obtained from the emotion engine. All collected data is aggregated in a database on the server and analyzed in detail using machine learning algorithms.

[0126] The device displays game selections and play guides appropriate to the user's current emotional state, based on real-time user emotion data provided by the emotion engine. It also supports users in maintaining healthy gameplay by issuing appropriate breaks and risk alerts based on their stress levels and emotional changes.

[0127] The emotion engine identifies the user's emotional state in real time by analyzing facial expressions, voice, pulse, etc., via the device or emotion recognition devices worn by the user. For example, if the user is in a calm state, it can recommend playing for a long time, but conversely, if the user is stressed, it will instruct them to take a break.

[0128] Based on the information displayed on the device screen, users can select the optimal play strategy for their current emotions and physical condition. For example, if the emotion engine detects a state of tension, it will recommend a relaxing game, allowing users to improve their performance and enjoy the game more.

[0129] This system allows users to enjoy optimal gameplay even in entertainment activities where emotional state directly impacts the quality of play. This is achieved through a more personalized user experience thanks to flexible, emotion-based guidance.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server collects historical game data and configuration information from entertainment facility APIs and publicly available data sources on the internet. In addition, it collects real-time user sentiment data provided by the sentiment engine.

[0133] Step 2:

[0134] The server stores the collected data in a database. The data is stored as multidimensional data, including emotional information, and the database is updated accordingly.

[0135] Step 3:

[0136] The server analyzes accumulated data, including emotional data, using machine learning algorithms to derive win rates and optimal playing times. It also generates play strategies based on the user's emotional state.

[0137] Step 4:

[0138] The device analyzes the user's facial expressions and voice in real time through an emotion engine to identify their emotional state. This data is sent to a server and used to adjust the play guide.

[0139] Step 5:

[0140] The device displays a play guide and emotion-responsive recommendation data sent from the server to the user. It suggests games and playtime that are tailored to the user's current emotional state.

[0141] Step 6:

[0142] Users select a play strategy that matches their mood based on the guide displayed on their device. For example, if they are relaxed, they will choose a game that allows for long play sessions, while if they are stressed, they will choose a game that yields results in a short amount of time.

[0143] Step 7:

[0144] After finishing a game, users enter their results into their device, which are recorded along with their emotional state. This information is sent to the server and used to guide future play sessions.

[0145] Step 8:

[0146] The device issues risk alerts based on the emotional state monitored by the emotion engine. For example, if the user's stress level increases, it sends a notification encouraging them to stop playing and take a break.

[0147] Step 9:

[0148] The server analyzes new gameplay data and sentiment information submitted by users and adjusts parameters to improve the accuracy of various strategies. This allows for further improvements in feedback for future play.

[0149] (Example 2)

[0150] 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".

[0151] In recent years, there has been a growing demand for improving the quality of individual user experiences in entertainment content. However, conventional systems lack flexible guidance based on user emotions and play patterns. As a result, users may engage in gameplay at inappropriate times or with inappropriate content, leading to decreased satisfaction or risky gameplay behavior. Therefore, a system is needed that recognizes users' emotional states in real time and provides personalized guidance based on that information to realize a high-quality user experience.

[0152] 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.

[0153] In this invention, the server includes means for collecting and processing past gameplay data, means for selecting the optimal game and recommending the best time to play based on the collected data, and means for providing guidance information to the user. This makes it possible to analyze the user's emotional state in real time and provide optimal gameplay guidance based on that analysis.

[0154] "Game data" refers to information about a user's past actions and gameplay, including game and activity results, trends, and settings.

[0155] "Computational processing" refers to the process of analyzing data, and is used to reveal user behavior patterns and trends through statistical analysis and machine learning algorithms.

[0156] "Information" refers to guidelines and recommendations provided to users, including game options, appropriate timing for playing, and advice for improving performance.

[0157] A "risk warning" refers to a notification issued when it is determined that a user's gameplay may have an impact on their health or safety, and it provides information to encourage them to stop playing or correct their behavior.

[0158] "Emotional state" refers to the user's psychological and emotional state, and is identified in real time based on data obtained from facial expressions, voice, and psychological indicators.

[0159] "Real-time analysis" refers to a process that analyzes data as soon as it is generated and outputs results immediately, providing appropriate responses based on the user's current state.

[0160] A "machine learning model" is a program that analyzes data and recognizes specific patterns or trends, and is an algorithm that can make predictions and classifications based on past data.

[0161] The system for implementing this invention mainly consists of four elements: a server, a terminal, an emotion engine, and a user. Each element and its function are described in detail below.

[0162] The server uses a cloud platform to collect and store various types of data. This data includes historical gameplay data obtained from amusement facility APIs, publicly available information on the internet, and user feedback. User sentiment data provided by the sentiment engine is also integrated. This data is aggregated in a database on the server and analyzed in detail using a general analytics platform with machine learning algorithms. This process generates predictive models for appropriate play guidance and risk management based on users' past gameplay history and behavioral patterns, including sentiment information.

[0163] The device uses real-time emotional data received from the emotion engine to suggest games and activities best suited to the user's current emotional state. For example, if the user is feeling stressed, the device will recommend games that promote relaxation and display messages encouraging appropriate breaks. Furthermore, the device incorporates applications that work in conjunction with machine learning models, providing dynamic guidance tailored to the user's emotional state and supporting a healthy gaming experience.

[0164] The emotion engine is a system that analyzes biometric information such as facial expressions, voice, and pulse rate through an emotion recognition device worn by the user. This allows the system to identify the user's emotional state in real time and feed that data back to the terminal and server. For example, if the user is calm, it can provide instructions to encourage longer play sessions, while if they are stressed, it can suggest relaxing options.

[0165] Based on the information displayed on the device, users can select the play strategy best suited to their current emotions and physical condition. For example, when the emotion engine detects a user's anxiety, it uses prompts such as "Please recommend a relaxing game" to suggest appropriate games via a generative AI model, providing users with choices that match their needs.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The server collects past gameplay data using APIs from entertainment facilities, publicly available information on the internet, and user feedback as input. This allows it to store basic play history and settings information in a database and generate datasets for subsequent analysis. Specifically, it automates the acquisition of new data by running a crawler on the server and periodically updating the information.

[0169] Step 2:

[0170] The server uses the collected data to apply machine learning models within the database and perform data analysis. It takes historical play data and sentiment data as input to classify and predict user behavior patterns and stress levels. The output generates guidelines including optimal game selection and recommended play time. Specifically, the system implements a process where the program is launched and the model is automatically trained whenever data is updated.

[0171] Step 3:

[0172] The device displays guidance information appropriate to the user's current emotions based on analysis results sent from the server. Input includes receiving optimal play guides from the server and displaying them on the user's device. Specifically, it recognizes the user's emotions in real time from their current heart rate and facial expressions, and presents games accordingly on the screen. In terms of specific actions, it receives data from the emotion engine via Bluetooth and displays prompt messages to facilitate user selection.

[0173] Step 4:

[0174] Based on the information displayed on the device, the user makes the most suitable choice from the presented games and play methods. By using the prompt displayed on the device, "Please recommend some relaxing games," the user can view a list of games provided by the generated AI model. This selection customizes the user's experience, enabling healthy and enjoyable gameplay. The interface utilizes the touchscreen for intuitive selection.

[0175] (Application Example 2)

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

[0177] In modern homes and public spaces, a lack of AI systems capable of flexibly responding to users' emotions is a significant challenge. In particular, improvements in user experience through emotion recognition technology are insufficient, and there is a need to achieve personalized interactions.

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

[0179] In this invention, the server includes means for collecting historical information data and performing statistical analysis; means for making optimal selection and timing recommendations based on the collected data; means for providing guidance to humans; and means for analyzing the human emotional state in real time using emotion recognition technology and adjusting responses accordingly. This enables personalized interaction based on the user's emotion recognition.

[0180] "Information data" refers to data that includes numerical values, records, and setting information based on past activities, and is used for statistical analysis.

[0181] "Statistical analysis" is a method of analyzing trends and patterns based on collected information data to derive optimized choices.

[0182] "Recommendations for choices and timing" refers to activities that suggest optimal actions and timings to users based on statistical analysis.

[0183] "Means of providing guidance to humans" refers to methods of presenting useful information and instructions to users and providing support that assists them in taking action.

[0184] "Means of managing history" refers to management methods that retain records of past actions and choices and use them to inform future activities.

[0185] "Means of issuing warnings about risks" refer to methods of presenting potential dangers to users in advance and providing notifications to enhance safety.

[0186] "Emotion recognition technology" is a technology that uses sensors and analytical algorithms to identify a person's emotional state and acquire that information in real time.

[0187] "Means of adjusting responses" refers to methods of dynamically changing and optimizing the services and actions provided based on acquired emotional data.

[0188] The system for realizing this invention mainly consists of three main components: a server, a terminal, and a user. The server is responsible for collecting and analyzing information data, while the terminal presents information according to the user's emotional state.

[0189] First, the server collects historical data and analyzes it in detail using statistical analysis techniques. This analysis utilizes Python-based machine learning libraries such as TensorFlow and PyTorch. Based on the results of the data analysis, the server calculates recommendations for the optimal choice and timing, and sends them to the terminal.

[0190] The device uses emotion recognition technology to analyze the user's current emotional state in real time. This utilizes hardware such as a voice input device, camera, and heart rate sensor. Based on the user's emotional data, the device dynamically adjusts the information and guidance it provides and issues risk warnings as needed, thereby improving user safety.

[0191] Next, users can make their desired choices and take actions while referring to the guides presented on the device. This system configuration allows users to enjoy personalized interactions based on emotion recognition.

[0192] For example, when the robot is talking to a child at home, if the system detects that the child's attention is waning, the robot can suggest new games or activities to re-engage the child.

[0193] Examples of prompt statements as a generative AI model to maximize effectiveness:

[0194] "Please explain how a home robot can recognize a child's emotions in real time and entertain them through play suggestions."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The server collects historical data. Inputs include information from sensors and databases. After data collection, the server organizes this information and prepares it for statistical analysis. The output is data converted into an analyzable format.

[0198] Step 2:

[0199] The server uses machine learning algorithms to perform statistical analysis based on the collected data. The main purpose of this analysis is to identify past trends and patterns and create a foundation for recommending optimal choices and timings. The input is organized data, and the output is a list of recommended actions.

[0200] Step 3:

[0201] The server sends the recommended action received to the terminal. Upon receiving this, the terminal prepares to proceed smoothly to the next processing step. The input is the recommended action from the server, and the output is the data supplied to the terminal.

[0202] Step 4:

[0203] The device uses emotion recognition technology to analyze the user's emotional state in real time. Inputs include audio, video, and heart rate data. The processing analyzes this data to identify the user's current emotional state. The output is an index or label indicating the emotional state.

[0204] Step 5:

[0205] The device adjusts recommended actions previously received from the server based on the user's emotional state. The input is the emotional state data obtained in step 4, and the output is the optimized action suggestion for the user.

[0206] Step 6:

[0207] The user makes specific choices and takes actions based on the action suggestions received from the device. The input is the information suggestions from the device, and the output is the user's chosen actions. In this process, the user's experience is accumulated in the system as feedback, which helps to improve the accuracy of suggestions in the future.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] The system according to the present invention mainly consists of three elements: a server, a terminal, and a user. Through statistical data analysis, information provision, play history management, and risk management, it provides users with effective gameplay guidance and supports healthy gameplay habits.

[0225] The server first collects past and present game data using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. The collected data is stored in a database on the server, and statistical analysis is performed using machine learning algorithms. Specifically, it analyzes the win rate, setting patterns, and ease of play for each game machine, and derives data-driven insights.

[0226] The terminal provides users with the latest strategic and recommended information through a user-friendly interface, based on information received from the server. For example, it can inform users about game machines that are expected to have a high win rate on specific days or times. It also records the user's gameplay results, which are then sent back to the server and used for future analysis.

[0227] Users can develop their own playing strategies based on information provided through their devices. By receiving notifications from the server and selecting the optimal device and time of day, users can achieve efficient gameplay. Furthermore, the device application includes features to manage the user's playing time and budget, thus helping to mitigate the risk of gambling addiction.

[0228] This system enables users to make strategic choices based on rational information, while also providing them with tools to prevent excessive gaming. This makes it possible to enjoy recreational activities in a healthy and sustainable way.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server accesses APIs and public data sources of entertainment venues to collect historical play data and configuration information for each game machine. This includes information from online reviews and feedback.

[0232] Step 2:

[0233] The server stores the collected data in a database. During this process, it checks for duplicate or inaccurate data and cleanses the data as needed.

[0234] Step 3:

[0235] The server uses machine learning algorithms to analyze win rates and setting trends for each machine. Based on this analysis, it determines recommended game machines and time slots.

[0236] Step 4:

[0237] The server uses the analysis results to generate a customized play guide for each user. By taking into account conditions such as working hours and holidays, it provides the user with the most suitable strategy.

[0238] Step 5:

[0239] The terminal displays play guides and recommended information received from the server to the user. This information is provided in a visually easy-to-understand format using a graphical user interface.

[0240] Step 6:

[0241] The user reviews the information provided by their device and plans their gameplay based on it. They select specific game consoles and playtime, and prepare to execute their plan.

[0242] Step 7:

[0243] After finishing a game, users use their device to input their game results. The entered data is sent to the server for use in future analysis.

[0244] Step 8:

[0245] The device manages the user's playtime and financial burden, and issues risk alerts if set thresholds are exceeded. It encourages the user to reconsider their gameplay or take a break.

[0246] Step 9:

[0247] The server continuously collects gameplay results and user feedback to improve the accuracy of the system's analysis. This will lead to more accurate gameplay guides for future sessions.

[0248] (Example 1)

[0249] 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."

[0250] Traditional amusement facilities often relied on intuition for game selection and time management, lacking sufficient information for efficient gameplay. Furthermore, there was a risk of users unconsciously engaging in excessive gameplay. Combined with inadequate information management, this made it difficult to properly optimize gameplay and manage risks.

[0251] 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.

[0252] In this invention, the server includes means for extracting data from amusement facilities and publicly available information as well as information from users; means for analyzing game results using a machine learning algorithm based on the accumulated data; and means for providing the analyzed information to the user's terminal. As a result, users can obtain information regarding predictions of wins and losses in games and the selection of optimal time slots, and receive support in creating effective and low-risk game plans.

[0253] An "amusement facility" refers to a place that provides entertainment and games for users.

[0254] "Data extraction means" refers to a function or technology for collecting necessary information from amusement facilities.

[0255] "Public information" refers to information that is generally accessible on the internet.

[0256] "Information from users" refers to data on feedback and experiences provided by individuals who play the game.

[0257] A "machine learning algorithm" refers to a computational method used to find patterns in large amounts of data and perform predictions and analyses.

[0258] "Game results" refers to data on the outcomes and performance obtained during a game.

[0259] A "risk warning" refers to a warning issued to inform users of the possibility of excessive gambling or incurring financial burdens.

[0260] "Game history" refers to detailed record data about past gameplay.

[0261] "Financial management" refers to functions that support users in managing their budgets and expenses.

[0262] "Management methods for indexing and recommendation" refers to technical methods that utilize data collected from users for analysis and use to optimize and recommend future gameplay.

[0263] The embodiments for carrying out the present invention are described below. The system consists of three elements: a server, a terminal, and a user. The server periodically collects game data via an API using a data extraction means from amusement facilities. It also collects publicly available information on the internet and feedback from users. General database technologies are used for data management, such as MySQL or PostgreSQL.

[0264] The collected data is stored on a server and statistically analyzed using machine learning algorithms. Python libraries such as Scikit-learn are used for the analysis. This allows for detailed analysis of data such as the win rate of amusement machines, setting patterns, and time of day, and helps to create optimal action plans for amusement facilities.

[0265] The terminal receives analysis results sent from the server and displays them to the user. A smartphone app is commonly used on the terminal. The app provides a user-friendly interface and visually presents the analysis results. For example, it might display information such as, "The winning rate for certain slot machines increases between 10 AM and noon," as a gaming trend.

[0266] Users devise optimal game strategies based on information provided through their devices. Furthermore, users input their gameplay results by operating the device, and this information is sent to the server. This data is then used for analysis in subsequent sessions. Users can also use the device's functions to manage their gameplay history and financial status, preventing excessive spending.

[0267] The system's user interface also includes an alert function to mitigate the risk of gambling addiction. This allows users to enjoy playing with peace of mind.

[0268] An example of a prompt message would be a text-based inquiry from a user requesting information, such as, "Please provide the latest information on the winning rates of casino slot machines this weekend. I would especially like to know the times when high winning rates are expected." This allows the user to instantly obtain the information they need.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The server collects data via the amusement facility's API. The inputs are the amusement facility's API endpoint and the necessary call parameters. The server makes an API request and retrieves game history data as a response. The retrieved data includes date and time, game results, and setting patterns. This data is stored in a database for subsequent analysis.

[0272] Step 2:

[0273] The server uses web scraping techniques to obtain publicly available information. The input is the URL of the target website. The server executes a scraping script to obtain data related to events and promotions. This information is stored to help understand the layout of amusement facilities and player trends in analysis.

[0274] Step 3:

[0275] The server performs machine learning analysis using the acquired data. The input is gameplay data stored in a database. The server uses Python libraries such as Scikit-learn to analyze win rates and setting patterns, and generates optimal gameplay strategies for each time period. The output is insights obtained from the analysis, such as "average win rate for a specific machine in the afternoon."

[0276] Step 4:

[0277] The server sends the analyzed insights to the terminal. The input is the analysis result data. The server calls an API endpoint to format this for the user and send it to the terminal. The output is play guide information that arrives on the user's terminal. This provides recommendations for the next game.

[0278] Step 5:

[0279] The terminal presents the received information to the user. The input is the play guide data transmitted from the server. The terminal's app displays this data through a visual interface and presents the information on push notifications and dashboards. The output is strategic information or alerts accessible to the user.

[0280] Step 6:

[0281] The user inputs the play result through the terminal. The input is the play result or feedback manually entered by the user. The terminal formats this and sends it to the server. The output is new game data for use in analysis.

[0282] Step 7:

[0283] Based on the play data from the user, the server performs data management to enhance the next analysis and prediction. The input is the new data obtained from the user. The server integrates this into the database to improve the accuracy of the analysis model. The output is the updated database state.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In entertainment activities, there is an issue that it is difficult for users to quickly and appropriately obtain information for efficient and healthy play. Also, there is a need to improve the user's play experience while suppressing the risk of overheated play. In conventional systems, there is a lack of means to provide an optimal strategy based on an individual user's history in real time, so it is difficult for users to obtain a guide suitable for their play style.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for collecting and statistically processing past entertainment data, means for selecting appropriate entertainment devices and recommending time slots based on the collected information, means for providing strategic guidance to the user, means for providing information to a robot through voice interaction, and means for recording the user's activity history via voice or touch input and reflecting it in the next recommendation. As a result, the user can receive individually optimized play strategy information in real time and manage healthy gameplay while having fun.

[0289] "Past entertainment data" refers to information about the user's past entertainment activities, including data such as win rates, play time, and types of games played.

[0290] "Statistical processing" refers to calculation and evaluation methods used to analyze collected data and identify trends and relationships.

[0291] "Entertainment equipment" refers to a wide range of devices, including game consoles and computers, that are machines or electronic devices used for user enjoyment.

[0292] "Recommended time slots" refer to information indicating the optimal time when a user is predicted to start playing.

[0293] A "strategy guide" is information that includes advice and instructions to help users enjoy a particular form of entertainment efficiently and advantageously.

[0294] "Means of providing information to a robot through voice interaction" refers to a method of conveying information to a user using voice via a robotic device.

[0295] "Activity history" refers to historical information about the activities a user has performed so far, including data such as gameplay results and selected options.

[0296] "Voice or touch input" refers to an interface used by a user to provide information or instructions, using either voice commands or a touchscreen.

[0297] "Receiving information in real time" means that users can obtain information instantly without any time lag.

[0298] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server first collects historical entertainment data, performs statistical processing, and generates basic data for selecting the optimal entertainment device and recommending the best time of day for the user. A database server is used as the hardware for this purpose, and Python and TensorFlow are used as the software. Python is used for data collection and management, while TensorFlow is used for statistical processing and executing machine learning algorithms.

[0299] The terminal provides users with information received from the server in real time and plays a role in appropriately conveying strategic guidance through voice interaction. A home robot fulfills this role, incorporating a voice recognition microphone and a touchscreen display to output information in response to the user's voice and touch operations.

[0300] Through this device, users can record their individual activity history using voice or touch input, which will then be reflected in future recommendations. For example, if a user inputs the results of their gameplay on a given day, they can immediately receive information recommending the optimal time to play the following day. Examples of prompts include, "Please tell me the most suitable play time among the entertainment devices you have specified," and "Based on your recent activity results, please state your next recommended plan."

[0301] This system allows users to receive personalized information and enjoy recreational activities in a healthy and effective manner.

[0302] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The server collects past entertainment data through the API. This data includes win rates, play times, game types, etc. The collected data is stored in the database server. The input is an external data source, and the output is a set of structured data.

[0305] Step 2:

[0306] The server performs statistical processing using Python and TensorFlow. It analyzes the collected data and derives recommended information such as win rates and recommended information for each time period. The input is raw data from the database, and the output is the statistical analysis results and recommended actions. Data analysis is performed in this step, and the optimal play strategy for a specific time period is generated.

[0307] Step 3:

[0308] The terminal receives the analysis results from the server and provides information to the user through the home robot. A voice interaction with the user is set up, and the latest strategy guide is transmitted. The input is the analysis results from the server, and the output is the output of information to the user. Here, information is output using the voice recognition microphone and touch screen display.

[0309] Step 4:

[0310] The user inputs the play results based on the information provided by the robot through voice or touch operations. The input results are sent back to the server for use in generating the next recommendation. The input is the play data from the user, and the output is the data of the updated user activity history.

[0311] Step 5:

[0312] The server generates a new play strategy for future use based on the updated user activity history. The generated strategy information is stored on the server and used for future analysis. The input is the updated user history, and the output is new data for future recommendations. This process ensures that a plan optimized for each individual user is continuously provided.

[0313] 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.

[0314] This invention is a system that recognizes user emotions and seamlessly adjusts play guides and risk management based on those emotions. It mainly consists of four elements: a server, a terminal, an emotion engine, and the user.

[0315] The server first collects past play data and setting information for each game machine using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. In addition, it also collects user emotion data obtained from the emotion engine. All collected data is aggregated in a database on the server and analyzed in detail using machine learning algorithms.

[0316] The device displays game selections and play guides appropriate to the user's current emotional state, based on real-time user emotion data provided by the emotion engine. It also supports users in maintaining healthy gameplay by issuing appropriate breaks and risk alerts based on their stress levels and emotional changes.

[0317] The emotion engine identifies the user's emotional state in real time by analyzing facial expressions, voice, pulse, etc., via the device or emotion recognition devices worn by the user. For example, if the user is in a calm state, it can recommend playing for a long time, but conversely, if the user is stressed, it will instruct them to take a break.

[0318] Based on the information displayed on the device screen, users can select the optimal play strategy for their current emotions and physical condition. For example, if the emotion engine detects a state of tension, it will recommend a relaxing game, allowing users to improve their performance and enjoy the game more.

[0319] This system allows users to enjoy optimal gameplay even in entertainment activities where emotional state directly impacts the quality of play. This is achieved through a more personalized user experience thanks to flexible, emotion-based guidance.

[0320] The following describes the processing flow.

[0321] Step 1:

[0322] The server collects historical game data and configuration information from entertainment facility APIs and publicly available data sources on the internet. In addition, it collects real-time user sentiment data provided by the sentiment engine.

[0323] Step 2:

[0324] The server stores the collected data in a database. The data is stored as multidimensional data, including emotional information, and the database is updated accordingly.

[0325] Step 3:

[0326] The server analyzes accumulated data, including emotional data, using machine learning algorithms to derive win rates and optimal playing times. It also generates play strategies based on the user's emotional state.

[0327] Step 4:

[0328] The device analyzes the user's facial expressions and voice in real time through an emotion engine to identify their emotional state. This data is sent to a server and used to adjust the play guide.

[0329] Step 5:

[0330] The device displays a play guide and emotion-responsive recommendation data sent from the server to the user. It suggests games and playtime that are tailored to the user's current emotional state.

[0331] Step 6:

[0332] Users select a play strategy that matches their mood based on the guide displayed on their device. For example, if they are relaxed, they will choose a game that allows for long play sessions, while if they are stressed, they will choose a game that yields results in a short amount of time.

[0333] Step 7:

[0334] After finishing a game, users enter their results into their device, which are recorded along with their emotional state. This information is sent to the server and used to guide future play sessions.

[0335] Step 8:

[0336] The device issues risk alerts based on the emotional state monitored by the emotion engine. For example, if the user's stress level increases, it sends a notification encouraging them to stop playing and take a break.

[0337] Step 9:

[0338] The server analyzes new gameplay data and sentiment information submitted by users and adjusts parameters to improve the accuracy of various strategies. This allows for further improvements in feedback for future play.

[0339] (Example 2)

[0340] 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".

[0341] In recent years, there has been a growing demand for improving the quality of individual user experiences in entertainment content. However, conventional systems lack flexible guidance based on user emotions and play patterns. As a result, users may engage in gameplay at inappropriate times or with inappropriate content, leading to decreased satisfaction or risky gameplay behavior. Therefore, a system is needed that recognizes users' emotional states in real time and provides personalized guidance based on that information to realize a high-quality user experience.

[0342] 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.

[0343] In this invention, the server includes means for collecting and processing past gameplay data, means for selecting the optimal game and recommending the best time to play based on the collected data, and means for providing guidance information to the user. This makes it possible to analyze the user's emotional state in real time and provide optimal gameplay guidance based on that analysis.

[0344] "Game data" refers to information about a user's past actions and gameplay, including game and activity results, trends, and settings.

[0345] "Computational processing" refers to the process of analyzing data, and is used to reveal user behavior patterns and trends through statistical analysis and machine learning algorithms.

[0346] "Information" refers to guidelines and recommendations provided to users, including game options, appropriate timing for playing, and advice for improving performance.

[0347] A "risk warning" refers to a notification issued when it is determined that a user's gameplay may have an impact on their health or safety, and it provides information to encourage them to stop playing or correct their behavior.

[0348] "Emotional state" refers to the user's psychological and emotional state, and is identified in real time based on data obtained from facial expressions, voice, and psychological indicators.

[0349] "Real-time analysis" refers to a process that analyzes data as soon as it is generated and outputs results immediately, providing appropriate responses based on the user's current state.

[0350] A "machine learning model" is a program that analyzes data and recognizes specific patterns or trends, and is an algorithm that can make predictions and classifications based on past data.

[0351] The system for implementing this invention mainly consists of four elements: a server, a terminal, an emotion engine, and a user. Each element and its function are described in detail below.

[0352] The server uses a cloud platform to collect and store various types of data. This data includes historical gameplay data obtained from amusement facility APIs, publicly available information on the internet, and user feedback. User sentiment data provided by the sentiment engine is also integrated. This data is aggregated in a database on the server and analyzed in detail using a general analytics platform with machine learning algorithms. This process generates predictive models for appropriate play guidance and risk management based on users' past gameplay history and behavioral patterns, including sentiment information.

[0353] The device uses real-time emotional data received from the emotion engine to suggest games and activities best suited to the user's current emotional state. For example, if the user is feeling stressed, the device will recommend games that promote relaxation and display messages encouraging appropriate breaks. Furthermore, the device incorporates applications that work in conjunction with machine learning models, providing dynamic guidance tailored to the user's emotional state and supporting a healthy gaming experience.

[0354] The emotion engine is a system that analyzes biometric information such as facial expressions, voice, and pulse rate through an emotion recognition device worn by the user. This allows the system to identify the user's emotional state in real time and feed that data back to the terminal and server. For example, if the user is calm, it can provide instructions to encourage longer play sessions, while if they are stressed, it can suggest relaxing options.

[0355] Based on the information displayed on the device, users can select the play strategy best suited to their current emotions and physical condition. For example, when the emotion engine detects a user's anxiety, it uses prompts such as "Please recommend a relaxing game" to suggest appropriate games via a generative AI model, providing users with choices that match their needs.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The server collects past gameplay data using APIs from entertainment facilities, publicly available information on the internet, and user feedback as input. This allows it to store basic play history and settings information in a database and generate datasets for subsequent analysis. Specifically, it automates the acquisition of new data by running a crawler on the server and periodically updating the information.

[0359] Step 2:

[0360] The server uses the collected data to apply machine learning models within the database and perform data analysis. It takes historical play data and sentiment data as input to classify and predict user behavior patterns and stress levels. The output generates guidelines including optimal game selection and recommended play time. Specifically, the system implements a process where the program is launched and the model is automatically trained whenever data is updated.

[0361] Step 3:

[0362] The device displays guidance information appropriate to the user's current emotions based on analysis results sent from the server. Input includes receiving optimal play guides from the server and displaying them on the user's device. Specifically, it recognizes the user's emotions in real time from their current heart rate and facial expressions, and presents games accordingly on the screen. In terms of specific actions, it receives data from the emotion engine via Bluetooth and displays prompt messages to facilitate user selection.

[0363] Step 4:

[0364] Based on the information displayed on the device, the user makes the most suitable choice from the presented games and play methods. By using the prompt displayed on the device, "Please recommend some relaxing games," the user can view a list of games provided by the generated AI model. This selection customizes the user's experience, enabling healthy and enjoyable gameplay. The interface utilizes the touchscreen for intuitive selection.

[0365] (Application Example 2)

[0366] 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."

[0367] In modern homes and public spaces, a lack of AI systems capable of flexibly responding to users' emotions is a significant challenge. In particular, improvements in user experience through emotion recognition technology are insufficient, and there is a need to achieve personalized interactions.

[0368] 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.

[0369] In this invention, the server includes means for collecting historical information data and performing statistical analysis; means for making optimal selection and timing recommendations based on the collected data; means for providing guidance to humans; and means for analyzing the human emotional state in real time using emotion recognition technology and adjusting responses accordingly. This enables personalized interaction based on the user's emotion recognition.

[0370] "Information data" refers to data that includes numerical values, records, and setting information based on past activities, and is used for statistical analysis.

[0371] "Statistical analysis" is a method of analyzing trends and patterns based on collected information data to derive optimized choices.

[0372] "Recommendations for choices and timing" refers to activities that suggest optimal actions and timings to users based on statistical analysis.

[0373] "Means of providing guidance to humans" refers to methods of presenting useful information and instructions to users and providing support that assists them in taking action.

[0374] "Means of managing history" refers to management methods that retain records of past actions and choices and use them to inform future activities.

[0375] "Means of issuing warnings about risks" refer to methods of presenting potential dangers to users in advance and providing notifications to enhance safety.

[0376] "Emotion recognition technology" is a technology that uses sensors and analytical algorithms to identify a person's emotional state and acquire that information in real time.

[0377] "Means of adjusting responses" refers to methods of dynamically changing and optimizing the services and actions provided based on acquired emotional data.

[0378] The system for realizing this invention mainly consists of three main components: a server, a terminal, and a user. The server is responsible for collecting and analyzing information data, while the terminal presents information according to the user's emotional state.

[0379] First, the server collects historical data and analyzes it in detail using statistical analysis techniques. This analysis utilizes Python-based machine learning libraries such as TensorFlow and PyTorch. Based on the results of the data analysis, the server calculates recommendations for the optimal choice and timing, and sends them to the terminal.

[0380] The device uses emotion recognition technology to analyze the user's current emotional state in real time. This utilizes hardware such as a voice input device, camera, and heart rate sensor. Based on the user's emotional data, the device dynamically adjusts the information and guidance it provides and issues risk warnings as needed, thereby improving user safety.

[0381] Next, users can make their desired choices and take actions while referring to the guides presented on the device. This system configuration allows users to enjoy personalized interactions based on emotion recognition.

[0382] For example, when the robot is talking to a child at home, if the system detects that the child's attention is waning, the robot can suggest new games or activities to re-engage the child.

[0383] Examples of prompt statements as a generative AI model to maximize effectiveness:

[0384] "Please explain how a home robot can recognize a child's emotions in real time and entertain them through play suggestions."

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The server collects historical data. Inputs include information from sensors and databases. After data collection, the server organizes this information and prepares it for statistical analysis. The output is data converted into an analyzable format.

[0388] Step 2:

[0389] The server uses machine learning algorithms to perform statistical analysis based on the collected data. The main purpose of this analysis is to identify past trends and patterns and create a foundation for recommending optimal choices and timings. The input is organized data, and the output is a list of recommended actions.

[0390] Step 3:

[0391] The server sends the recommended action received to the terminal. Upon receiving this, the terminal prepares to proceed smoothly to the next processing step. The input is the recommended action from the server, and the output is the data supplied to the terminal.

[0392] Step 4:

[0393] The device uses emotion recognition technology to analyze the user's emotional state in real time. Inputs include audio, video, and heart rate data. The processing analyzes this data to identify the user's current emotional state. The output is an index or label indicating the emotional state.

[0394] Step 5:

[0395] The device adjusts recommended actions previously received from the server based on the user's emotional state. The input is the emotional state data obtained in step 4, and the output is the optimized action suggestion for the user.

[0396] Step 6:

[0397] The user makes specific choices and takes actions based on the action suggestions received from the device. The input is the information suggestions from the device, and the output is the user's chosen actions. In this process, the user's experience is accumulated in the system as feedback, which helps to improve the accuracy of suggestions in the future.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] The system according to the present invention mainly consists of three elements: a server, a terminal, and a user. Through statistical data analysis, information provision, play history management, and risk management, it provides users with effective gameplay guidance and supports healthy gameplay habits.

[0415] The server first collects past and present game data using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. The collected data is stored in a database on the server, and statistical analysis is performed using machine learning algorithms. Specifically, it analyzes the win rate, setting patterns, and ease of play for each game machine, and derives data-driven insights.

[0416] The terminal provides users with the latest strategic and recommended information through a user-friendly interface, based on information received from the server. For example, it can inform users about game machines that are expected to have a high win rate on specific days or times. It also records the user's gameplay results, which are then sent back to the server and used for future analysis.

[0417] Users can develop their own playing strategies based on information provided through their devices. By receiving notifications from the server and selecting the optimal device and time of day, users can achieve efficient gameplay. Furthermore, the device application includes features to manage the user's playing time and budget, thus helping to mitigate the risk of gambling addiction.

[0418] This system enables users to make strategic choices based on rational information, while also providing them with tools to prevent excessive gaming. This makes it possible to enjoy recreational activities in a healthy and sustainable way.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The server accesses APIs and public data sources of entertainment venues to collect historical play data and configuration information for each game machine. This includes information from online reviews and feedback.

[0422] Step 2:

[0423] The server stores the collected data in a database. During this process, it checks for duplicate or inaccurate data and cleanses the data as needed.

[0424] Step 3:

[0425] The server uses machine learning algorithms to analyze win rates and setting trends for each machine. Based on this analysis, it determines recommended game machines and time slots.

[0426] Step 4:

[0427] The server uses the analysis results to generate a customized play guide for each user. By taking into account conditions such as working hours and holidays, it provides the user with the most suitable strategy.

[0428] Step 5:

[0429] The terminal displays play guides and recommended information received from the server to the user. This information is provided in a visually easy-to-understand format using a graphical user interface.

[0430] Step 6:

[0431] The user reviews the information provided by their device and plans their gameplay based on it. They select specific game consoles and playtime, and prepare to execute their plan.

[0432] Step 7:

[0433] After finishing a game, users use their device to input their game results. The entered data is sent to the server for use in future analysis.

[0434] Step 8:

[0435] The device manages the user's playtime and financial burden, and issues risk alerts if set thresholds are exceeded. It encourages the user to reconsider their gameplay or take a break.

[0436] Step 9:

[0437] The server continuously collects gameplay results and user feedback to improve the accuracy of the system's analysis. This will lead to more accurate gameplay guides for future sessions.

[0438] (Example 1)

[0439] 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."

[0440] Traditional amusement facilities often relied on intuition for game selection and time management, lacking sufficient information for efficient gameplay. Furthermore, there was a risk of users unconsciously engaging in excessive gameplay. Combined with inadequate information management, this made it difficult to properly optimize gameplay and manage risks.

[0441] 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.

[0442] In this invention, the server includes means for extracting data from amusement facilities and publicly available information as well as information from users; means for analyzing game results using a machine learning algorithm based on the accumulated data; and means for providing the analyzed information to the user's terminal. As a result, users can obtain information regarding predictions of wins and losses in games and the selection of optimal time slots, and receive support in creating effective and low-risk game plans.

[0443] An "amusement facility" refers to a place that provides entertainment and games for users.

[0444] "Data extraction means" refers to a function or technology for collecting necessary information from amusement facilities.

[0445] "Public information" refers to information that is generally accessible on the internet.

[0446] "Information from users" refers to data on feedback and experiences provided by individuals who play the game.

[0447] A "machine learning algorithm" refers to a computational method used to find patterns in large amounts of data and perform predictions and analyses.

[0448] "Game results" refers to data on the outcomes and performance obtained during a game.

[0449] A "risk warning" refers to a warning issued to inform users of the possibility of excessive gambling or incurring financial burdens.

[0450] "Game history" refers to detailed record data about past gameplay.

[0451] "Financial management" refers to functions that support users in managing their budgets and expenses.

[0452] "Management methods for indexing and recommendation" refers to technical methods that utilize data collected from users for analysis and use to optimize and recommend future gameplay.

[0453] The embodiments for carrying out the present invention are described below. The system consists of three elements: a server, a terminal, and a user. The server periodically collects game data via an API using a data extraction means from amusement facilities. It also collects publicly available information on the internet and feedback from users. General database technologies are used for data management, such as MySQL or PostgreSQL.

[0454] The collected data is stored on a server and statistically analyzed using machine learning algorithms. Python libraries such as Scikit-learn are used for the analysis. This allows for detailed analysis of data such as the win rate of amusement machines, setting patterns, and time of day, and helps to create optimal action plans for amusement facilities.

[0455] The terminal receives analysis results sent from the server and displays them to the user. A smartphone app is commonly used on the terminal. The app provides a user-friendly interface and visually presents the analysis results. For example, it might display information such as, "The winning rate for certain slot machines increases between 10 AM and noon," as a gaming trend.

[0456] Users devise optimal game strategies based on information provided through their devices. Furthermore, users input their gameplay results by operating the device, and this information is sent to the server. This data is then used for analysis in subsequent sessions. Users can also use the device's functions to manage their gameplay history and financial status, preventing excessive spending.

[0457] The system's user interface also includes an alert function to mitigate the risk of gambling addiction. This allows users to enjoy playing with peace of mind.

[0458] An example of a prompt message would be a text-based inquiry from a user requesting information, such as, "Please provide the latest information on the winning rates of casino slot machines this weekend. I would especially like to know the times when high winning rates are expected." This allows the user to instantly obtain the information they need.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The server collects data via the amusement facility's API. The inputs are the amusement facility's API endpoint and the necessary call parameters. The server makes an API request and retrieves game history data as a response. The retrieved data includes date and time, game results, and setting patterns. This data is stored in a database for subsequent analysis.

[0462] Step 2:

[0463] The server uses web scraping techniques to obtain publicly available information. The input is the URL of the target website. The server executes a scraping script to obtain data related to events and promotions. This information is stored to help understand the layout of amusement facilities and player trends in analysis.

[0464] Step 3:

[0465] The server performs machine learning analysis using the acquired data. The input is gameplay data stored in a database. The server uses Python libraries such as Scikit-learn to analyze win rates and setting patterns, and generates optimal gameplay strategies for each time period. The output is insights obtained from the analysis, such as "average win rate for a specific machine in the afternoon."

[0466] Step 4:

[0467] The server sends the analyzed insights to the terminal. The input is the analysis result data. The server calls an API endpoint to format this for the user and send it to the terminal. The output is play guide information that arrives on the user's terminal. This provides recommendations for the next game.

[0468] Step 5:

[0469] The terminal displays the received information to the user. The input is play guide data sent from the server. The terminal's app displays this data in a visual interface and places the information in push notifications and on the dashboard. The output is strategic information or alerts that the user can access.

[0470] Step 6:

[0471] The user inputs gameplay results via a terminal. The input consists of manually entered gameplay results and feedback. The terminal formats this data and sends it to the server. The output is new gameplay data used for analysis.

[0472] Step 7:

[0473] The server manages data based on user play data to improve future analysis and predictions. The input is new data obtained from users. The server integrates this into a database to improve the accuracy of the analysis model. The output is the updated database state.

[0474] (Application Example 1)

[0475] 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."

[0476] In recreational activities, users face the challenge of difficulty in quickly and appropriately obtaining information necessary for efficient and healthy gameplay. Furthermore, there is a need to improve the user experience while mitigating the risks associated with over-engagement gameplay. Conventional systems lack the means to provide optimal strategies based on individual user history in real time, making it difficult for users to obtain guidance suited to their own play style.

[0477] 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.

[0478] In this invention, the server includes means for collecting and statistically processing past entertainment data, means for selecting appropriate entertainment devices and recommending time slots based on the collected information, means for providing strategic guidance to the user, means for providing information to a robot through voice interaction, and means for recording the user's activity history via voice or touch input and reflecting it in the next recommendation. As a result, the user can receive individually optimized play strategy information in real time and manage healthy gameplay while having fun.

[0479] "Past entertainment data" refers to information about the user's past entertainment activities, including data such as win rates, play time, and types of games played.

[0480] "Statistical processing" refers to calculation and evaluation methods used to analyze collected data and identify trends and relationships.

[0481] "Entertainment equipment" refers to a wide range of devices, including game consoles and computers, that are machines or electronic devices used for user enjoyment.

[0482] "Recommended time slots" refer to information indicating the optimal time when a user is predicted to start playing.

[0483] A "strategy guide" is information that includes advice and instructions to help users enjoy a particular form of entertainment efficiently and advantageously.

[0484] "Means of providing information to a robot through voice interaction" refers to a method of conveying information to a user using voice via a robotic device.

[0485] "Activity history" refers to historical information about the activities a user has performed so far, including data such as gameplay results and selected options.

[0486] "Voice or touch input" refers to an interface used by a user to provide information or instructions, using either voice commands or a touchscreen.

[0487] "Receiving information in real time" means that users can obtain information instantly without any time lag.

[0488] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server first collects historical entertainment data, performs statistical processing, and generates basic data for selecting the optimal entertainment device and recommending the best time of day for the user. A database server is used as the hardware for this purpose, and Python and TensorFlow are used as the software. Python is used for data collection and management, while TensorFlow is used for statistical processing and executing machine learning algorithms.

[0489] The terminal provides users with information received from the server in real time and plays a role in appropriately conveying strategic guidance through voice interaction. A home robot fulfills this role, incorporating a voice recognition microphone and a touchscreen display to output information in response to the user's voice and touch operations.

[0490] Through this device, users can record their individual activity history using voice or touch input, which will then be reflected in future recommendations. For example, if a user inputs the results of their gameplay on a given day, they can immediately receive information recommending the optimal time to play the following day. Examples of prompts include, "Please tell me the most suitable play time among the entertainment devices you have specified," and "Based on your recent activity results, please state your next recommended plan."

[0491] This system allows users to receive personalized information and enjoy recreational activities in a healthy and effective manner.

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The server collects historical entertainment data via an API. This data includes win rates, play time, and game types. The collected data is stored in a database server. The input is an external data source, and the output is a structured set of data.

[0495] Step 2:

[0496] The server performs statistical processing using Python and TensorFlow. It analyzes the collected data to derive win rates and recommended information for each time of day. The input is raw data from the database, and the output is the statistical analysis results and recommended actions. This step performs data analysis and generates the optimal play strategy for a specific time of day.

[0497] Step 3:

[0498] The terminal receives analysis results from the server and provides information to the user through a home robot. Voice interaction with the user is set up, and the latest strategic guide is transmitted. The input is the analysis results from the server, and the output is the information output to the user. Here, information is output using a voice recognition microphone and a touchscreen display.

[0499] Step 4:

[0500] The user inputs their gameplay results via voice or touch, based on information provided by the robot. The input results are then sent back to the server to be used in generating recommendations for the next session. The input is user gameplay data, and the output is updated user activity history data.

[0501] Step 5:

[0502] The server generates a new play strategy for future use based on the updated user activity history. The generated strategy information is stored on the server and used for future analysis. The input is the updated user history, and the output is new data for future recommendations. This process ensures that a plan optimized for each individual user is continuously provided.

[0503] 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.

[0504] This invention is a system that recognizes user emotions and seamlessly adjusts play guides and risk management based on those emotions. It mainly consists of four elements: a server, a terminal, an emotion engine, and the user.

[0505] The server first collects past play data and setting information for each game machine using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. In addition, it also collects user emotion data obtained from the emotion engine. All collected data is aggregated in a database on the server and analyzed in detail using machine learning algorithms.

[0506] The device displays game selections and play guides appropriate to the user's current emotional state, based on real-time user emotion data provided by the emotion engine. It also supports users in maintaining healthy gameplay by issuing appropriate breaks and risk alerts based on their stress levels and emotional changes.

[0507] The emotion engine identifies the user's emotional state in real time by analyzing facial expressions, voice, pulse, etc., via the device or emotion recognition devices worn by the user. For example, if the user is in a calm state, it can recommend playing for a long time, but conversely, if the user is stressed, it will instruct them to take a break.

[0508] Based on the information displayed on the device screen, users can select the optimal play strategy for their current emotions and physical condition. For example, if the emotion engine detects a state of tension, it will recommend a relaxing game, allowing users to improve their performance and enjoy the game more.

[0509] This system allows users to enjoy optimal gameplay even in entertainment activities where emotional state directly impacts the quality of play. This is achieved through a more personalized user experience thanks to flexible, emotion-based guidance.

[0510] The following describes the processing flow.

[0511] Step 1:

[0512] The server collects historical game data and configuration information from entertainment facility APIs and publicly available data sources on the internet. In addition, it collects real-time user sentiment data provided by the sentiment engine.

[0513] Step 2:

[0514] The server stores the collected data in a database. The data is stored as multidimensional data, including emotional information, and the database is updated accordingly.

[0515] Step 3:

[0516] The server analyzes accumulated data, including emotional data, using machine learning algorithms to derive win rates and optimal playing times. It also generates play strategies based on the user's emotional state.

[0517] Step 4:

[0518] The device analyzes the user's facial expressions and voice in real time through an emotion engine to identify their emotional state. This data is sent to a server and used to adjust the play guide.

[0519] Step 5:

[0520] The device displays a play guide and emotion-responsive recommendation data sent from the server to the user. It suggests games and playtime that are tailored to the user's current emotional state.

[0521] Step 6:

[0522] Users select a play strategy that matches their mood based on the guide displayed on their device. For example, if they are relaxed, they will choose a game that allows for long play sessions, while if they are stressed, they will choose a game that yields results in a short amount of time.

[0523] Step 7:

[0524] After finishing a game, users enter their results into their device, which are recorded along with their emotional state. This information is sent to the server and used to guide future play sessions.

[0525] Step 8:

[0526] The device issues risk alerts based on the emotional state monitored by the emotion engine. For example, if the user's stress level increases, it sends a notification encouraging them to stop playing and take a break.

[0527] Step 9:

[0528] The server analyzes new gameplay data and sentiment information submitted by users and adjusts parameters to improve the accuracy of various strategies. This allows for further improvements in feedback for future play.

[0529] (Example 2)

[0530] 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."

[0531] In recent years, there has been a growing demand for improving the quality of individual user experiences in entertainment content. However, conventional systems lack flexible guidance based on user emotions and play patterns. As a result, users may engage in gameplay at inappropriate times or with inappropriate content, leading to decreased satisfaction or risky gameplay behavior. Therefore, a system is needed that recognizes users' emotional states in real time and provides personalized guidance based on that information to realize a high-quality user experience.

[0532] 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.

[0533] In this invention, the server includes means for collecting and processing past gameplay data, means for selecting the optimal game and recommending the best time to play based on the collected data, and means for providing guidance information to the user. This makes it possible to analyze the user's emotional state in real time and provide optimal gameplay guidance based on that analysis.

[0534] "Game data" refers to information about a user's past actions and gameplay, including game and activity results, trends, and settings.

[0535] "Computational processing" refers to the process of analyzing data, and is used to reveal user behavior patterns and trends through statistical analysis and machine learning algorithms.

[0536] "Information" refers to guidelines and recommendations provided to users, including game options, appropriate timing for playing, and advice for improving performance.

[0537] A "risk warning" refers to a notification issued when it is determined that a user's gameplay may have an impact on their health or safety, and it provides information to encourage them to stop playing or correct their behavior.

[0538] "Emotional state" refers to the user's psychological and emotional state, and is identified in real time based on data obtained from facial expressions, voice, and psychological indicators.

[0539] "Real-time analysis" refers to a process that analyzes data as soon as it is generated and outputs results immediately, providing appropriate responses based on the user's current state.

[0540] A "machine learning model" is a program that analyzes data and recognizes specific patterns or trends, and is an algorithm that can make predictions and classifications based on past data.

[0541] The system for implementing this invention mainly consists of four elements: a server, a terminal, an emotion engine, and a user. Each element and its function are described in detail below.

[0542] The server uses a cloud platform to collect and store various types of data. This data includes historical gameplay data obtained from amusement facility APIs, publicly available information on the internet, and user feedback. User sentiment data provided by the sentiment engine is also integrated. This data is aggregated in a database on the server and analyzed in detail using a general analytics platform with machine learning algorithms. This process generates predictive models for appropriate play guidance and risk management based on users' past gameplay history and behavioral patterns, including sentiment information.

[0543] The device uses real-time emotional data received from the emotion engine to suggest games and activities best suited to the user's current emotional state. For example, if the user is feeling stressed, the device will recommend games that promote relaxation and display messages encouraging appropriate breaks. Furthermore, the device incorporates applications that work in conjunction with machine learning models, providing dynamic guidance tailored to the user's emotional state and supporting a healthy gaming experience.

[0544] The emotion engine is a system that analyzes biometric information such as facial expressions, voice, and pulse rate through an emotion recognition device worn by the user. This allows the system to identify the user's emotional state in real time and feed that data back to the terminal and server. For example, if the user is calm, it can provide instructions to encourage longer play sessions, while if they are stressed, it can suggest relaxing options.

[0545] Based on the information displayed on the device, users can select the play strategy best suited to their current emotions and physical condition. For example, when the emotion engine detects a user's anxiety, it uses prompts such as "Please recommend a relaxing game" to suggest appropriate games via a generative AI model, providing users with choices that match their needs.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The server collects past gameplay data using APIs from entertainment facilities, publicly available information on the internet, and user feedback as input. This allows it to store basic play history and settings information in a database and generate datasets for subsequent analysis. Specifically, it automates the acquisition of new data by running a crawler on the server and periodically updating the information.

[0549] Step 2:

[0550] The server uses the collected data to apply machine learning models within the database and perform data analysis. It takes historical play data and sentiment data as input to classify and predict user behavior patterns and stress levels. The output generates guidelines including optimal game selection and recommended play time. Specifically, the system implements a process where the program is launched and the model is automatically trained whenever data is updated.

[0551] Step 3:

[0552] The device displays guidance information appropriate to the user's current emotions based on analysis results sent from the server. Input includes receiving optimal play guides from the server and displaying them on the user's device. Specifically, it recognizes the user's emotions in real time from their current heart rate and facial expressions, and presents games accordingly on the screen. In terms of specific actions, it receives data from the emotion engine via Bluetooth and displays prompt messages to facilitate user selection.

[0553] Step 4:

[0554] Based on the information displayed on the device, the user makes the most suitable choice from the presented games and play methods. By using the prompt displayed on the device, "Please recommend some relaxing games," the user can view a list of games provided by the generated AI model. This selection customizes the user's experience, enabling healthy and enjoyable gameplay. The interface utilizes the touchscreen for intuitive selection.

[0555] (Application Example 2)

[0556] 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."

[0557] In modern homes and public spaces, a lack of AI systems capable of flexibly responding to users' emotions is a significant challenge. In particular, improvements in user experience through emotion recognition technology are insufficient, and there is a need to achieve personalized interactions.

[0558] 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.

[0559] In this invention, the server includes means for collecting historical information data and performing statistical analysis; means for making optimal selection and timing recommendations based on the collected data; means for providing guidance to humans; and means for analyzing the human emotional state in real time using emotion recognition technology and adjusting responses accordingly. This enables personalized interaction based on the user's emotion recognition.

[0560] "Information data" refers to data that includes numerical values, records, and setting information based on past activities, and is used for statistical analysis.

[0561] "Statistical analysis" is a method of analyzing trends and patterns based on collected information data to derive optimized choices.

[0562] "Recommendations for choices and timing" refers to activities that suggest optimal actions and timings to users based on statistical analysis.

[0563] "Means of providing guidance to humans" refers to methods of presenting useful information and instructions to users and providing support that assists them in taking action.

[0564] "Means of managing history" refers to management methods that retain records of past actions and choices and use them to inform future activities.

[0565] "Means of issuing warnings about risks" refer to methods of presenting potential dangers to users in advance and providing notifications to enhance safety.

[0566] "Emotion recognition technology" is a technology that uses sensors and analytical algorithms to identify a person's emotional state and acquire that information in real time.

[0567] "Means of adjusting responses" refers to methods of dynamically changing and optimizing the services and actions provided based on acquired emotional data.

[0568] The system for realizing this invention mainly consists of three main components: a server, a terminal, and a user. The server is responsible for collecting and analyzing information data, while the terminal presents information according to the user's emotional state.

[0569] First, the server collects historical data and analyzes it in detail using statistical analysis techniques. This analysis utilizes Python-based machine learning libraries such as TensorFlow and PyTorch. Based on the results of the data analysis, the server calculates recommendations for the optimal choice and timing, and sends them to the terminal.

[0570] The device uses emotion recognition technology to analyze the user's current emotional state in real time. This utilizes hardware such as a voice input device, camera, and heart rate sensor. Based on the user's emotional data, the device dynamically adjusts the information and guidance it provides and issues risk warnings as needed, thereby improving user safety.

[0571] Next, users can make their desired choices and take actions while referring to the guides presented on the device. This system configuration allows users to enjoy personalized interactions based on emotion recognition.

[0572] For example, when the robot is talking to a child at home, if the system detects that the child's attention is waning, the robot can suggest new games or activities to re-engage the child.

[0573] Examples of prompt statements as a generative AI model to maximize effectiveness:

[0574] "Please explain how a home robot can recognize a child's emotions in real time and entertain them through play suggestions."

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The server collects historical data. Inputs include information from sensors and databases. After data collection, the server organizes this information and prepares it for statistical analysis. The output is data converted into an analyzable format.

[0578] Step 2:

[0579] The server uses machine learning algorithms to perform statistical analysis based on the collected data. The main purpose of this analysis is to identify past trends and patterns and create a foundation for recommending optimal choices and timings. The input is organized data, and the output is a list of recommended actions.

[0580] Step 3:

[0581] The server sends the recommended action received to the terminal. Upon receiving this, the terminal prepares to proceed smoothly to the next processing step. The input is the recommended action from the server, and the output is the data supplied to the terminal.

[0582] Step 4:

[0583] The device uses emotion recognition technology to analyze the user's emotional state in real time. Inputs include audio, video, and heart rate data. The processing analyzes this data to identify the user's current emotional state. The output is an index or label indicating the emotional state.

[0584] Step 5:

[0585] The device adjusts recommended actions previously received from the server based on the user's emotional state. The input is the emotional state data obtained in step 4, and the output is the optimized action suggestion for the user.

[0586] Step 6:

[0587] The user makes specific choices and takes actions based on the action suggestions received from the device. The input is the information suggestions from the device, and the output is the user's chosen actions. In this process, the user's experience is accumulated in the system as feedback, which helps to improve the accuracy of suggestions in the future.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] The system according to the present invention mainly consists of three elements: a server, a terminal, and a user. Through statistical data analysis, information provision, play history management, and risk management, it provides users with effective gameplay guidance and supports healthy gameplay habits.

[0606] The server first collects past and present game data using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. The collected data is stored in a database on the server, and statistical analysis is performed using machine learning algorithms. Specifically, it analyzes the win rate, setting patterns, and ease of play for each game machine, and derives data-driven insights.

[0607] The terminal provides users with the latest strategic and recommended information through a user-friendly interface, based on information received from the server. For example, it can inform users about game machines that are expected to have a high win rate on specific days or times. It also records the user's gameplay results, which are then sent back to the server and used for future analysis.

[0608] Users can develop their own playing strategies based on information provided through their devices. By receiving notifications from the server and selecting the optimal device and time of day, users can achieve efficient gameplay. Furthermore, the device application includes features to manage the user's playing time and budget, thus helping to mitigate the risk of gambling addiction.

[0609] This system enables users to make strategic choices based on rational information, while also providing them with tools to prevent excessive gaming. This makes it possible to enjoy recreational activities in a healthy and sustainable way.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The server accesses APIs and public data sources of entertainment venues to collect historical play data and configuration information for each game machine. This includes information from online reviews and feedback.

[0613] Step 2:

[0614] The server stores the collected data in a database. During this process, it checks for duplicate or inaccurate data and cleanses the data as needed.

[0615] Step 3:

[0616] The server uses machine learning algorithms to analyze win rates and setting trends for each machine. Based on this analysis, it determines recommended game machines and time slots.

[0617] Step 4:

[0618] The server uses the analysis results to generate a customized play guide for each user. By taking into account conditions such as working hours and holidays, it provides the user with the most suitable strategy.

[0619] Step 5:

[0620] The terminal displays play guides and recommended information received from the server to the user. This information is provided in a visually easy-to-understand format using a graphical user interface.

[0621] Step 6:

[0622] The user reviews the information provided by their device and plans their gameplay based on it. They select specific game consoles and playtime, and prepare to execute their plan.

[0623] Step 7:

[0624] After finishing a game, users use their device to input their game results. The entered data is sent to the server for use in future analysis.

[0625] Step 8:

[0626] The device manages the user's playtime and financial burden, and issues risk alerts if set thresholds are exceeded. It encourages the user to reconsider their gameplay or take a break.

[0627] Step 9:

[0628] The server continuously collects gameplay results and user feedback to improve the accuracy of the system's analysis. This will lead to more accurate gameplay guides for future sessions.

[0629] (Example 1)

[0630] 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".

[0631] Traditional amusement facilities often relied on intuition for game selection and time management, lacking sufficient information for efficient gameplay. Furthermore, there was a risk of users unconsciously engaging in excessive gameplay. Combined with inadequate information management, this made it difficult to properly optimize gameplay and manage risks.

[0632] 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.

[0633] In this invention, the server includes means for extracting data from amusement facilities and publicly available information as well as information from users; means for analyzing game results using a machine learning algorithm based on the accumulated data; and means for providing the analyzed information to the user's terminal. As a result, users can obtain information regarding predictions of wins and losses in games and the selection of optimal time slots, and receive support in creating effective and low-risk game plans.

[0634] An "amusement facility" refers to a place that provides entertainment and games for users.

[0635] "Data extraction means" refers to a function or technology for collecting necessary information from amusement facilities.

[0636] "Public information" refers to information that is generally accessible on the internet.

[0637] "Information from users" refers to data on feedback and experiences provided by individuals who play the game.

[0638] A "machine learning algorithm" refers to a computational method used to find patterns in large amounts of data and perform predictions and analyses.

[0639] "Game results" refers to data on the outcomes and performance obtained during a game.

[0640] A "risk warning" refers to a warning issued to inform users of the possibility of excessive gambling or incurring financial burdens.

[0641] "Game history" refers to detailed record data about past gameplay.

[0642] "Financial management" refers to functions that support users in managing their budgets and expenses.

[0643] "Management methods for indexing and recommendation" refers to technical methods that utilize data collected from users for analysis and use to optimize and recommend future gameplay.

[0644] The embodiments for carrying out the present invention are described below. The system consists of three elements: a server, a terminal, and a user. The server periodically collects game data via an API using a data extraction means from amusement facilities. It also collects publicly available information on the internet and feedback from users. General database technologies are used for data management, such as MySQL or PostgreSQL.

[0645] The collected data is stored on a server and statistically analyzed using machine learning algorithms. Python libraries such as Scikit-learn are used for the analysis. This allows for detailed analysis of data such as the win rate of amusement machines, setting patterns, and time of day, and helps to create optimal action plans for amusement facilities.

[0646] The terminal receives analysis results sent from the server and displays them to the user. A smartphone app is commonly used on the terminal. The app provides a user-friendly interface and visually presents the analysis results. For example, it might display information such as, "The winning rate for certain slot machines increases between 10 AM and noon," as a gaming trend.

[0647] Users devise optimal game strategies based on information provided through their devices. Furthermore, users input their gameplay results by operating the device, and this information is sent to the server. This data is then used for analysis in subsequent sessions. Users can also use the device's functions to manage their gameplay history and financial status, preventing excessive spending.

[0648] The system's user interface also includes an alert function to mitigate the risk of gambling addiction. This allows users to enjoy playing with peace of mind.

[0649] An example of a prompt message would be a text-based inquiry from a user requesting information, such as, "Please provide the latest information on the winning rates of casino slot machines this weekend. I would especially like to know the times when high winning rates are expected." This allows the user to instantly obtain the information they need.

[0650] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0651] Step 1:

[0652] The server collects data via the amusement facility's API. The inputs are the amusement facility's API endpoint and the necessary call parameters. The server makes an API request and retrieves game history data as a response. The retrieved data includes date and time, game results, and setting patterns. This data is stored in a database for subsequent analysis.

[0653] Step 2:

[0654] The server uses web scraping techniques to obtain publicly available information. The input is the URL of the target website. The server executes a scraping script to obtain data related to events and promotions. This information is stored to help understand the layout of amusement facilities and player trends in analysis.

[0655] Step 3:

[0656] The server performs machine learning analysis using the acquired data. The input is gameplay data stored in a database. The server uses Python libraries such as Scikit-learn to analyze win rates and setting patterns, and generates optimal gameplay strategies for each time period. The output is insights obtained from the analysis, such as "average win rate for a specific machine in the afternoon."

[0657] Step 4:

[0658] The server sends the analyzed insights to the terminal. The input is the analysis result data. The server calls an API endpoint to format this for the user and send it to the terminal. The output is play guide information that arrives on the user's terminal. This provides recommendations for the next game.

[0659] Step 5:

[0660] The terminal displays the received information to the user. The input is play guide data sent from the server. The terminal's app displays this data in a visual interface and places the information in push notifications and on the dashboard. The output is strategic information or alerts that the user can access.

[0661] Step 6:

[0662] The user inputs gameplay results via a terminal. The input consists of manually entered gameplay results and feedback. The terminal formats this data and sends it to the server. The output is new gameplay data used for analysis.

[0663] Step 7:

[0664] The server manages data based on user play data to improve future analysis and predictions. The input is new data obtained from users. The server integrates this into a database to improve the accuracy of the analysis model. The output is the updated database state.

[0665] (Application Example 1)

[0666] 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".

[0667] In recreational activities, users face the challenge of difficulty in quickly and appropriately obtaining information necessary for efficient and healthy gameplay. Furthermore, there is a need to improve the user experience while mitigating the risks associated with over-engagement gameplay. Conventional systems lack the means to provide optimal strategies based on individual user history in real time, making it difficult for users to obtain guidance suited to their own play style.

[0668] 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.

[0669] In this invention, the server includes means for collecting and statistically processing past entertainment data, means for selecting appropriate entertainment devices and recommending time slots based on the collected information, means for providing strategic guidance to the user, means for providing information to a robot through voice interaction, and means for recording the user's activity history via voice or touch input and reflecting it in the next recommendation. As a result, the user can receive individually optimized play strategy information in real time and manage healthy gameplay while having fun.

[0670] "Past entertainment data" refers to information about the user's past entertainment activities, including data such as win rates, play time, and types of games played.

[0671] "Statistical processing" refers to calculation and evaluation methods used to analyze collected data and identify trends and relationships.

[0672] "Entertainment equipment" refers to a wide range of devices, including game consoles and computers, that are machines or electronic devices used for user enjoyment.

[0673] "Recommended time slots" refer to information indicating the optimal time when a user is predicted to start playing.

[0674] A "strategy guide" is information that includes advice and instructions to help users enjoy a particular form of entertainment efficiently and advantageously.

[0675] "Means of providing information to a robot through voice interaction" refers to a method of conveying information to a user using voice via a robotic device.

[0676] "Activity history" refers to historical information about the activities a user has performed so far, including data such as gameplay results and selected options.

[0677] "Voice or touch input" refers to an interface used by a user to provide information or instructions, using either voice commands or a touchscreen.

[0678] "Receiving information in real time" means that users can obtain information instantly without any time lag.

[0679] The system for implementing this invention mainly consists of three elements: a server, a terminal, and a user. The server first collects historical entertainment data, performs statistical processing, and generates basic data for selecting the optimal entertainment device and recommending the best time of day for the user. A database server is used as the hardware for this purpose, and Python and TensorFlow are used as the software. Python is used for data collection and management, while TensorFlow is used for statistical processing and executing machine learning algorithms.

[0680] The terminal provides users with information received from the server in real time and plays a role in appropriately conveying strategic guidance through voice interaction. A home robot fulfills this role, incorporating a voice recognition microphone and a touchscreen display to output information in response to the user's voice and touch operations.

[0681] Through this device, users can record their individual activity history using voice or touch input, which will then be reflected in future recommendations. For example, if a user inputs the results of their gameplay on a given day, they can immediately receive information recommending the optimal time to play the following day. Examples of prompts include, "Please tell me the most suitable play time among the entertainment devices you have specified," and "Based on your recent activity results, please state your next recommended plan."

[0682] This system allows users to receive personalized information and enjoy recreational activities in a healthy and effective manner.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The server collects historical entertainment data via an API. This data includes win rates, play time, and game types. The collected data is stored in a database server. The input is an external data source, and the output is a structured set of data.

[0686] Step 2:

[0687] The server performs statistical processing using Python and TensorFlow. It analyzes the collected data to derive win rates and recommended information for each time of day. The input is raw data from the database, and the output is the statistical analysis results and recommended actions. This step performs data analysis and generates the optimal play strategy for a specific time of day.

[0688] Step 3:

[0689] The terminal receives analysis results from the server and provides information to the user through a home robot. Voice interaction with the user is set up, and the latest strategic guide is transmitted. The input is the analysis results from the server, and the output is the information output to the user. Here, information is output using a voice recognition microphone and a touchscreen display.

[0690] Step 4:

[0691] The user inputs their gameplay results via voice or touch, based on information provided by the robot. The input results are then sent back to the server to be used in generating recommendations for the next session. The input is user gameplay data, and the output is updated user activity history data.

[0692] Step 5:

[0693] The server generates a new play strategy for future use based on the updated user activity history. The generated strategy information is stored on the server and used for future analysis. The input is the updated user history, and the output is new data for future recommendations. This process ensures that a plan optimized for each individual user is continuously provided.

[0694] 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.

[0695] This invention is a system that recognizes user emotions and seamlessly adjusts play guides and risk management based on those emotions. It mainly consists of four elements: a server, a terminal, an emotion engine, and the user.

[0696] The server first collects past play data and setting information for each game machine using APIs provided by entertainment facilities, publicly available information on the internet, and user feedback. In addition, it also collects user emotion data obtained from the emotion engine. All collected data is aggregated in a database on the server and analyzed in detail using machine learning algorithms.

[0697] The device displays game selections and play guides appropriate to the user's current emotional state, based on real-time user emotion data provided by the emotion engine. It also supports users in maintaining healthy gameplay by issuing appropriate breaks and risk alerts based on their stress levels and emotional changes.

[0698] The emotion engine identifies the user's emotional state in real time by analyzing facial expressions, voice, pulse, etc., via the device or emotion recognition devices worn by the user. For example, if the user is in a calm state, it can recommend playing for a long time, but conversely, if the user is stressed, it will instruct them to take a break.

[0699] Based on the information displayed on the device screen, users can select the optimal play strategy for their current emotions and physical condition. For example, if the emotion engine detects a state of tension, it will recommend a relaxing game, allowing users to improve their performance and enjoy the game more.

[0700] This system allows users to enjoy optimal gameplay even in entertainment activities where emotional state directly impacts the quality of play. This is achieved through a more personalized user experience thanks to flexible, emotion-based guidance.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The server collects historical game data and configuration information from entertainment facility APIs and publicly available data sources on the internet. In addition, it collects real-time user sentiment data provided by the sentiment engine.

[0704] Step 2:

[0705] The server stores the collected data in a database. The data is stored as multidimensional data, including emotional information, and the database is updated accordingly.

[0706] Step 3:

[0707] The server analyzes accumulated data, including emotional data, using machine learning algorithms to derive win rates and optimal playing times. It also generates play strategies based on the user's emotional state.

[0708] Step 4:

[0709] The device analyzes the user's facial expressions and voice in real time through an emotion engine to identify their emotional state. This data is sent to a server and used to adjust the play guide.

[0710] Step 5:

[0711] The device displays a play guide and emotion-responsive recommendation data sent from the server to the user. It suggests games and playtime that are tailored to the user's current emotional state.

[0712] Step 6:

[0713] Users select a play strategy that matches their mood based on the guide displayed on their device. For example, if they are relaxed, they will choose a game that allows for long play sessions, while if they are stressed, they will choose a game that yields results in a short amount of time.

[0714] Step 7:

[0715] After finishing a game, users enter their results into their device, which are recorded along with their emotional state. This information is sent to the server and used to guide future play sessions.

[0716] Step 8:

[0717] The device issues risk alerts based on the emotional state monitored by the emotion engine. For example, if the user's stress level increases, it sends a notification encouraging them to stop playing and take a break.

[0718] Step 9:

[0719] The server analyzes new gameplay data and sentiment information submitted by users and adjusts parameters to improve the accuracy of various strategies. This allows for further improvements in feedback for future play.

[0720] (Example 2)

[0721] 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".

[0722] In recent years, there has been a growing demand for improving the quality of individual user experiences in entertainment content. However, conventional systems lack flexible guidance based on user emotions and play patterns. As a result, users may engage in gameplay at inappropriate times or with inappropriate content, leading to decreased satisfaction or risky gameplay behavior. Therefore, a system is needed that recognizes users' emotional states in real time and provides personalized guidance based on that information to realize a high-quality user experience.

[0723] 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.

[0724] In this invention, the server includes means for collecting and processing past gameplay data, means for selecting the optimal game and recommending the best time to play based on the collected data, and means for providing guidance information to the user. This makes it possible to analyze the user's emotional state in real time and provide optimal gameplay guidance based on that analysis.

[0725] "Game data" refers to information about a user's past actions and gameplay, including game and activity results, trends, and settings.

[0726] "Computational processing" refers to the process of analyzing data, and is used to reveal user behavior patterns and trends through statistical analysis and machine learning algorithms.

[0727] "Information" refers to guidelines and recommendations provided to users, including game options, appropriate timing for playing, and advice for improving performance.

[0728] A "risk warning" refers to a notification issued when it is determined that a user's gameplay may have an impact on their health or safety, and it provides information to encourage them to stop playing or correct their behavior.

[0729] "Emotional state" refers to the user's psychological and emotional state, and is identified in real time based on data obtained from facial expressions, voice, and psychological indicators.

[0730] "Real-time analysis" refers to a process that analyzes data as soon as it is generated and outputs results immediately, providing appropriate responses based on the user's current state.

[0731] A "machine learning model" is a program that analyzes data and recognizes specific patterns or trends, and is an algorithm that can make predictions and classifications based on past data.

[0732] The system for implementing this invention mainly consists of four elements: a server, a terminal, an emotion engine, and a user. Each element and its function are described in detail below.

[0733] The server uses a cloud platform to collect and store various types of data. This data includes historical gameplay data obtained from amusement facility APIs, publicly available information on the internet, and user feedback. User sentiment data provided by the sentiment engine is also integrated. This data is aggregated in a database on the server and analyzed in detail using a general analytics platform with machine learning algorithms. This process generates predictive models for appropriate play guidance and risk management based on users' past gameplay history and behavioral patterns, including sentiment information.

[0734] The device uses real-time emotional data received from the emotion engine to suggest games and activities best suited to the user's current emotional state. For example, if the user is feeling stressed, the device will recommend games that promote relaxation and display messages encouraging appropriate breaks. Furthermore, the device incorporates applications that work in conjunction with machine learning models, providing dynamic guidance tailored to the user's emotional state and supporting a healthy gaming experience.

[0735] The emotion engine is a system that analyzes biometric information such as facial expressions, voice, and pulse rate through an emotion recognition device worn by the user. This allows the system to identify the user's emotional state in real time and feed that data back to the terminal and server. For example, if the user is calm, it can provide instructions to encourage longer play sessions, while if they are stressed, it can suggest relaxing options.

[0736] Based on the information displayed on the device, users can select the play strategy best suited to their current emotions and physical condition. For example, when the emotion engine detects a user's anxiety, it uses prompts such as "Please recommend a relaxing game" to suggest appropriate games via a generative AI model, providing users with choices that match their needs.

[0737] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0738] Step 1:

[0739] The server collects past gameplay data using APIs from entertainment facilities, publicly available information on the internet, and user feedback as input. This allows it to store basic play history and settings information in a database and generate datasets for subsequent analysis. Specifically, it automates the acquisition of new data by running a crawler on the server and periodically updating the information.

[0740] Step 2:

[0741] The server uses the collected data to apply machine learning models within the database and perform data analysis. It takes historical play data and sentiment data as input to classify and predict user behavior patterns and stress levels. The output generates guidelines including optimal game selection and recommended play time. Specifically, the system implements a process where the program is launched and the model is automatically trained whenever data is updated.

[0742] Step 3:

[0743] The device displays guidance information appropriate to the user's current emotions based on analysis results sent from the server. Input includes receiving optimal play guides from the server and displaying them on the user's device. Specifically, it recognizes the user's emotions in real time from their current heart rate and facial expressions, and presents games accordingly on the screen. In terms of specific actions, it receives data from the emotion engine via Bluetooth and displays prompt messages to facilitate user selection.

[0744] Step 4:

[0745] Based on the information displayed on the device, the user makes the most suitable choice from the presented games and play methods. By using the prompt displayed on the device, "Please recommend some relaxing games," the user can view a list of games provided by the generated AI model. This selection customizes the user's experience, enabling healthy and enjoyable gameplay. The interface utilizes the touchscreen for intuitive selection.

[0746] (Application Example 2)

[0747] 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".

[0748] In modern homes and public spaces, a lack of AI systems capable of flexibly responding to users' emotions is a significant challenge. In particular, improvements in user experience through emotion recognition technology are insufficient, and there is a need to achieve personalized interactions.

[0749] 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.

[0750] In this invention, the server includes means for collecting historical information data and performing statistical analysis; means for making optimal selection and timing recommendations based on the collected data; means for providing guidance to humans; and means for analyzing the human emotional state in real time using emotion recognition technology and adjusting responses accordingly. This enables personalized interaction based on the user's emotion recognition.

[0751] "Information data" refers to data that includes numerical values, records, and setting information based on past activities, and is used for statistical analysis.

[0752] "Statistical analysis" is a method of analyzing trends and patterns based on collected information data to derive optimized choices.

[0753] "Recommendations for choices and timing" refers to activities that suggest optimal actions and timings to users based on statistical analysis.

[0754] "Means of providing guidance to humans" refers to methods of presenting useful information and instructions to users and providing support that assists them in taking action.

[0755] "Means of managing history" refers to management methods that retain records of past actions and choices and use them to inform future activities.

[0756] "Means of issuing warnings about risks" refer to methods of presenting potential dangers to users in advance and providing notifications to enhance safety.

[0757] "Emotion recognition technology" is a technology that uses sensors and analytical algorithms to identify a person's emotional state and acquire that information in real time.

[0758] "Means of adjusting responses" refers to methods of dynamically changing and optimizing the services and actions provided based on acquired emotional data.

[0759] The system for realizing this invention mainly consists of three main components: a server, a terminal, and a user. The server is responsible for collecting and analyzing information data, while the terminal presents information according to the user's emotional state.

[0760] First, the server collects historical data and analyzes it in detail using statistical analysis techniques. This analysis utilizes Python-based machine learning libraries such as TensorFlow and PyTorch. Based on the results of the data analysis, the server calculates recommendations for the optimal choice and timing, and sends them to the terminal.

[0761] The device uses emotion recognition technology to analyze the user's current emotional state in real time. This utilizes hardware such as a voice input device, camera, and heart rate sensor. Based on the user's emotional data, the device dynamically adjusts the information and guidance it provides and issues risk warnings as needed, thereby improving user safety.

[0762] Next, users can make their desired choices and take actions while referring to the guides presented on the device. This system configuration allows users to enjoy personalized interactions based on emotion recognition.

[0763] For example, when the robot is talking to a child at home, if the system detects that the child's attention is waning, the robot can suggest new games or activities to re-engage the child.

[0764] Examples of prompt statements as a generative AI model to maximize effectiveness:

[0765] "Please explain how a home robot can recognize a child's emotions in real time and entertain them through play suggestions."

[0766] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0767] Step 1:

[0768] The server collects historical data. Inputs include information from sensors and databases. After data collection, the server organizes this information and prepares it for statistical analysis. The output is data converted into an analyzable format.

[0769] Step 2:

[0770] The server uses machine learning algorithms to perform statistical analysis based on the collected data. The main purpose of this analysis is to identify past trends and patterns and create a foundation for recommending optimal choices and timings. The input is organized data, and the output is a list of recommended actions.

[0771] Step 3:

[0772] The server sends the recommended action received to the terminal. Upon receiving this, the terminal prepares to proceed smoothly to the next processing step. The input is the recommended action from the server, and the output is the data supplied to the terminal.

[0773] Step 4:

[0774] The device uses emotion recognition technology to analyze the user's emotional state in real time. Inputs include audio, video, and heart rate data. The processing analyzes this data to identify the user's current emotional state. The output is an index or label indicating the emotional state.

[0775] Step 5:

[0776] The device adjusts recommended actions previously received from the server based on the user's emotional state. The input is the emotional state data obtained in step 4, and the output is the optimized action suggestion for the user.

[0777] Step 6:

[0778] The user makes specific choices and takes actions based on the action suggestions received from the device. The input is the information suggestions from the device, and the output is the user's chosen actions. In this process, the user's experience is accumulated in the system as feedback, which helps to improve the accuracy of suggestions in the future.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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."

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] A means of collecting past game data and performing statistical analysis,

[0803] A means of selecting the optimal game and recommending the best time to play based on the collected data,

[0804] A means of providing users with a play guide,

[0805] A means of managing play history and issuing risk alerts for gambling,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, characterized by comprising means for collecting setting information related to games at local entertainment facilities and analyzing setting trends from that information.

[0809] (Claim 3)

[0810] The system according to claim 1, characterized in that it includes means for recording play results based on user input and reflecting that information in recommendations for the next play session.

[0811] "Example 1"

[0812] (Claim 1)

[0813] A means for extracting data from amusement facilities and publicly available information, as well as a means for acquiring information from users,

[0814] A means of analyzing game results using a machine learning algorithm based on accumulated data,

[0815] A means of providing the analyzed information to the user's terminal,

[0816] A management means for using play data received from users again for indexing and recommendations,

[0817] Means of warning about risks through gaming history and financial management,

[0818] A system that includes this.

[0819] (Claim 2)

[0820] The system according to claim 1, comprising means for collecting setting information regarding the devices of local amusement facilities and analyzing setting trends from said information.

[0821] (Claim 3)

[0822] The system according to claim 1, which has means for recording game results based on user input and reflecting that data in recommendations for the next game.

[0823] "Application Example 1"

[0824] (Claim 1)

[0825] A means of collecting and statistically processing past entertainment data,

[0826] A means of selecting appropriate entertainment equipment and recommending the appropriate time of day based on the collected information,

[0827] A means of providing strategic guidance to users,

[0828] A means of managing activity history and issuing risk warnings regarding gambling,

[0829] A means of providing information to a robot through voice interaction,

[0830] A means to record the user's activity history via voice or touch input and reflect it in future recommendations,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, characterized in that it enables interaction with the user using a household robot.

[0834] (Claim 3)

[0835] The system according to claim 1, characterized in that it includes means for providing users with strategic information generated based on collected data in real time.

[0836] "Example 2 of combining an emotion engine"

[0837] (Claim 1)

[0838] A means for collecting past game data and performing calculations,

[0839] A means of selecting the optimal game and recommending the best time of day based on the collected data,

[0840] A means of providing guidance information to users,

[0841] A means of managing game history and issuing warnings about risks,

[0842] A means of recognizing the user's emotional state and dynamically adjusting guidance information based on that emotion,

[0843] A means of identifying the user's psychological state in real time using emotion recognition functionality,

[0844] A means of analyzing collected data using machine learning models and generating individual guidelines,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, comprising means for collecting setting information related to games at local amusement facilities and analyzing setting trends from that information.

[0848] (Claim 3)

[0849] The system according to claim 1, comprising means for recording game results based on user input and reflecting that information in recommendations for the next game.

[0850] "Application example 2 when combining with an emotional engine"

[0851] (Claim 1)

[0852] A means of collecting historical information data and performing statistical analysis,

[0853] A means of making optimal selection and timing recommendations based on collected data,

[0854] A means of providing guidance to humans,

[0855] A means of managing history and issuing warnings about risks,

[0856] A means of analyzing a person's emotional state in real time using emotion recognition technology and adjusting responses based on this analysis,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, characterized by having means for collecting setting information of local entertainment venues and analyzing trends from that information.

[0860] (Claim 3)

[0861] The system according to claim 1, characterized in that it includes means for recording results based on human input and reflecting that information in future recommendations. [Explanation of Symbols]

[0862] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting past game data and performing statistical analysis, A means of selecting the optimal game and recommending the best time to play based on the collected data, A means of providing users with a play guide, A means of managing play history and issuing risk alerts for gambling, A system that includes this.

2. The system according to claim 1, characterized by comprising means for collecting setting information related to games at local entertainment facilities and analyzing setting trends from that information.

3. The system according to claim 1, characterized in that it includes means for recording play results based on user input and reflecting that information in recommendations for the next play session.

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