System

Abstract

A system is provided.SOLUTION: A system includes a means for collecting data of at least a game time, a rotation number, and a balance from a game terminal, a means for analyzing the collected data to calculate information on an operation status and a profit of each game terminal, a means having a generation artificial intelligence model for generating setting data for optimizing setting of each game terminal based on the analysis result, a means for changing setting of each game terminal according to the optimized setting data, and a means for generating a report including an operation status and a profit prediction of each game terminal after the setting change and notifying a manager of the report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In pachinko parlors, the adjustment of gaming terminal settings has traditionally depended on the experience and intuition of the parlor manager and staff, making accurate revenue management and efficient business operations difficult, especially during busy hours and at night. Furthermore, with the pachinko market shrinking, it is necessary to attract customers and maximize revenue. To address these challenges, a system that can accurately adjust settings and streamline business operations based on data is required. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for collecting data on at least game time, number of spins, and income and expenditure from gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the results of the analysis; means for changing the settings of each gaming terminal in accordance with the optimized setting data; and means for generating a report including the operating status and revenue forecast of the gaming terminals after the setting change and notifying the manager of the report. This system can automatically set low settings for gaming terminals with high operating rates to increase revenue, and high settings for gaming terminals with low operating rates to attract customers, thereby achieving efficient business operations and optimized revenue.

[0006] "Gaming terminal" refers to entertainment machines such as pachinko and slot machines, and is a device on which users can play games.

[0007] The "data collection means" is a hardware and software system for collecting data such as game time, number of spins, and balance from each gaming terminal.

[0008] "Data analysis means" refers to an algorithm and software system for analyzing collected data and calculating operating status and revenue information for each gaming terminal.

[0009] A "generative artificial intelligence model" is an artificial intelligence system that is trained using machine learning and deep learning based on past data to generate optimal setting data for each gaming terminal.

[0010] The "setting change means" is a hardware and software system for remotely changing the settings of each gaming terminal based on the generated optimal setting data.

[0011] The "report generation means" is a software system that generates a report based on the operating status and revenue forecast of the gaming terminal after the settings have been changed, and notifies the administrator of the report.

[0012] The "operating rate" is an index that indicates the percentage of time that a gaming terminal is in operation within a specific period of time.

[0013] The "profit rate" is an index showing the efficiency of the profits obtained from a gaming terminal, and is calculated by dividing the profit and loss by the number of turns. [Brief explanation of the drawings]

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

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

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

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

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

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

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, 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), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

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

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

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

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

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

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0035] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[0036] This system consists of a server, gaming terminals, and users. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. Users receive reports and take additional actions as necessary.

[0037] The program for this system begins with the server sending data collection requests from each gaming terminal at a specified interval. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The server stores this data in a database and performs analysis.

[0038] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. These calculation results are input into a generative AI model, which generates optimal setting data. Based on past data and current operating status, the AI ​​model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0039] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[0040] The server generates a report based on the data after the configuration change. This report includes the performance status and revenue forecast before and after the configuration change. The report is sent to the user, who can use it to implement additional marketing strategies or adjust the configuration.

[0041] As a concrete example, we will explain the processing for terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a profit of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0042] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server then sends this setting change data to device A and device B, which then change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can then make additional decisions based on this report.

[0043] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of pachinko parlors, and significantly improve business efficiency.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[0047] Step 2:

[0048] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[0049] Step 3:

[0050] The server stores the data received from each device in a database, which also stores past data and is used for analysis.

[0051] Step 4:

[0052] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[0053] Operating rate = Playing time / Maximum operating time

[0054] Profitability rate = (revenue / turnover) 100

[0055] Step 5:

[0056] The server inputs the analysis data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data and current operating conditions.

[0057] Step 6:

[0058] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates.

[0059] Step 7:

[0060] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[0061] Step 8:

[0062] The device notifies the server that the setting change was successful, including the changed setting data.

[0063] Step 9:

[0064] The server compares the updated data with past data and generates a report based on the new operating status and revenue forecast, including the history of settings changes and forecast revenue for each gaming terminal.

[0065] Step 10:

[0066] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[0067] Step 11:

[0068] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[0069] In this way, by repeating the processes from step 1 to step 11, the overall profitability and operational efficiency of the pachinko parlor can be optimized.

[0070] Example 1

[0071] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0072] Currently, many gaming parlors manually monitor and optimize gaming terminal settings and operating status, but this work is time-consuming and labor-intensive, making it extremely inefficient. Manual setting changes also increase the likelihood of human error, making it difficult to manage revenue and improve operating efficiency at pachinko parlors. Furthermore, maintaining optimal gaming terminal settings requires complex decisions that take into account past data and current conditions, which is difficult to achieve with conventional systems.

[0073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0074] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means for having a generative AI model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying an administrator; means for the server to send data collection requests to each gaming terminal at specified intervals; means for the server to store the data in a database and perform analysis; means for the server to input prompts into the generative AI model to obtain optimal setting data and for the server to send setting change commands to the terminals. This enables automatic and efficient optimization of gaming terminal settings, maximizing revenue, and improving business efficiency.

[0075] A "gaming terminal" is a device that is operated by a player to play games, and has the function of recording and transmitting game information.

[0076] A "server" is a central control device that collects data from gaming terminals, performs analytical processing, and transmits necessary instructions and information.

[0077] A "data collection request" is a request message sent by the server to a gaming terminal, and is intended to collect data relating to the operating status and revenue of the gaming terminal.

[0078] A "generative AI model" is an algorithm or trained model that uses artificial intelligence to generate configuration data to optimize the settings and operating conditions of gaming terminals.

[0079] A "prompt" is an instruction or question that is input to a generative AI model to generate optimal configuration data.

[0080] A "setting change command" is an instruction message sent by the server to a gaming terminal to change the settings of the gaming terminal.

[0081] A "database" is a storage device or system that stores and manages collected data in an organized manner and enables analysis and reference.

[0082] "Analysis results" are information calculated or evaluated based on collected data, and relate to the operating status and revenue of gaming terminals.

[0083] The "report" is a report that includes the operating status of the gaming terminal after the setting change and a revenue forecast, and is a summary of information that is notified to the administrator.

[0084] "Administrator" refers to the person in charge or responsible for the operation and management of gaming terminals and the entire system.

[0085] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of gaming parlors. This system consists of a server, gaming terminals, and users. The processing of each entity and its specific operation are explained in detail below.

[0086] Server Processing

[0087] The server first sends a data collection request to each gaming terminal at a specified interval. This request is to collect information necessary to understand the current gaming situation (play time, number of spins, balance, etc.). The server saves the received data in a database and performs analysis. This analysis may use a programming language such as Python or an SQL database.

[0088] As a result of the analysis, the server calculates the operating status (operating rate, profit rate) of each gaming terminal. Based on this analysis data, the server inputs a prompt sentence into the generative AI model to generate optimal setting data. Models such as OpenAI's GPT-3 can be used as the generative AI model. For example, the prompt sentence is as follows:

[0089] Prompt: "Generate optimal settings based on the utilization rate and profitability of gaming terminals. Terminal A: Utilization rate 83.3%, Profitability rate 1.67%. Terminal B: Utilization rate 50%, Profitability rate 1%."

[0090] Based on the generated setting data, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. This can be done using the HTTP protocol or a dedicated communication API.

[0091] Based on the data after the setting change, the server generates a report containing operational data and revenue forecasts and notifies the user. This report details the operational status and revenue forecasts before and after the setting change, and the user can make further decisions based on this report.

[0092] Gaming terminal processing

[0093] The terminal receives a data collection request from the server, and collects and compiles information such as game time, number of spins, and balance from its internal data records. The collected data is immediately sent back to the server.

[0094] The gaming terminal that receives the setting change command from the server changes the setting in accordance with the command, and if the setting change is successful, reports the result to the server.

[0095] User Action

[0096] Users can receive reports from the server and check the settings and operating status of their gaming terminals. Based on these reports, they can take additional actions, such as changing the placement of gaming terminals or implementing new marketing strategies.

[0097] Specific examples

[0098] For example, suppose the server sends a data collection request to terminal A and terminal B. Terminal A reports data showing 10 hours of play time, 3,000 spins, and a profit of 5,000 yen. Terminal B reports data showing 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes it to find that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0099] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can make additional decisions based on this report.

[0100] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of gaming parlors, and significantly improve business efficiency.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1: Submit a data collection request

[0103] Server processing: The server sends a data collection request to each gaming terminal at a specified time interval. The data collection request includes instructions to report operating status such as game time, number of spins, and balance.

[0104] Input: Data collection request

[0105] Output: Each gaming device receives a data collection request

[0106] Step 2: Collect and send data

[0107] Terminal processing: Each gaming terminal receives a data collection request and, according to the instructions, compiles data such as game time, number of spins, and balance from its internal data records. The collected data is then sent back to the server. For example, terminal A reports a game time of 10 hours, 3,000 spins, and a balance of 5,000 yen.

[0108] Input: Data collection request

[0109] Output: Data reports from each device (e.g., game time, number of spins, balance)

[0110] Step 3: Data storage and analysis

[0111] Server processing: The server stores the data received from the terminals in a database. This can be done using a database management system such as SQL. Next, the server calculates the utilization rate and profitability of each gaming terminal based on the stored data. For example, it analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profitability is 1.67% (5,000 yen / 3,000 times).

[0112] Input: Data reports from each device

[0113] Output: Analysis results of utilization rate and profitability

[0114] Step 4: Generate optimal settings

[0115] Server processing: The server inputs a prompt statement into the generative AI model based on the analysis results, generating optimal configuration data. An example of a prompt statement is, "Generate the optimal configuration based on the utilization rate and profit rate of the gaming terminal. Terminal A: utilization rate 83.3%, profit rate 1.67%. Terminal B: utilization rate 50%, profit rate 1%." For example, OpenAI's GPT-3 can be used as the generative AI model.

[0116] Input: Analysis result, prompt statement

[0117] Output: Optimal setting data

[0118] Step 5: Sending configuration change commands

[0119] Server processing: Based on the setting data obtained from the generative AI model, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. In this case, the communication protocol may be HTTP or a dedicated communication API.

[0120] Input: Optimal setting data

[0121] Output: Configuration change command

[0122] Step 6: Implement and report configuration changes

[0123] Terminal processing: Each gaming terminal that receives a setting change command from the server changes the setting as instructed. If the setting change is successful, it reports the result to the server. For example, if terminal A completes the change to setting 1, it reports this to the server.

[0124] Input: Change setting command

[0125] Output: Report of the configuration change result

[0126] Step 7: Reporting and Notifications

[0127] Server processing: The server generates a report based on the new operational data and revenue forecast after the configuration change. This report includes the operational status and revenue forecast before and after the configuration change. This report is notified to the user (administrator).

[0128] Input: Operation data after setting change, revenue forecast

[0129] Output: Report notification

[0130] Through these steps, the system automatically optimizes gaming terminal settings, maximizing profits and improving operational efficiency.

[0131] (Application example 1)

[0132] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0133] Conventional gaming terminal setting optimization systems change settings based only on the operational data of individual terminals, so they are unable to fully consider the operational efficiency and profit maximization of the entire store. Furthermore, there are insufficient means for communicating the effects of setting changes to managers, making it difficult to use the results for marketing strategies or additional setting adjustments. This prevents optimal operation and limits profit and efficiency improvements.

[0134] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0135] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminals after the setting change and notifying the manager; means for optimizing the operational settings of the entire arcade by collecting and analyzing operational data of the arcade from multiple terminals; and means for supporting the manager in implementing additional marketing strategies and setting adjustments based on the generated report. This enables the manager to implement prompt and effective marketing strategies and setting adjustments while maximizing the operational efficiency and revenue of the entire arcade.

[0136] "Gaming terminal" refers to a terminal device used by customers to play games, including pachinko and pachislot machines.

[0137] "Data collection means" refers to a device or software that has the function of collecting data such as game time, number of spins, and income and expenditure from gaming terminals.

[0138] "Analysis means" refers to devices or software that have the function of analyzing collected data and calculating the operating status and revenue information of each gaming terminal.

[0139] "Generative artificial intelligence model" refers to an AI model that generates optimal configuration data based on collected data.

[0140] "Settings optimization means" refers to a device or software that has the function of changing the settings of each gaming terminal based on the setting data generated by the generative artificial intelligence model.

[0141] "Report generation means" refers to a device or software that has the function of generating a report including the operating status and revenue forecast of the gaming terminal after the settings have been changed, and notifying the administrator.

[0142] "Multiple terminal operation data collection means" refers to a device or software that has the function of collecting operation data from multiple gaming terminals in a lump.

[0143] "Store operation optimization function" refers to devices or software that have the ability to analyze collected operation data from multiple terminals and generate settings to optimize the operation of the entire store.

[0144] "Marketing strategy support function" refers to a device or software that has a function to support the administrator in implementing additional marketing strategies or adjusting settings based on the generated reports.

[0145] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the revenue and expenditure management and operational efficiency of gaming facilities. This system is composed of gaming terminals, a server, and an administrator.

[0146] Roles and functions of each component

[0147] 1. Gaming terminals:

[0148] The gaming terminal collects data such as game time, number of spins, and balance, and transmits it to the server.

[0149] 2. Server:

[0150] The server has the following features:

[0151] 1. Data collection method: At least the following data will be collected from each gaming terminal: playing time, number of spins, and income / expenses.

[0152] 2. Analysis method: Analyze the collected data and calculate information regarding the operating status and revenue of each gaming terminal.

[0153] 3. Generative AI model: Based on the results of the analysis, configuration data is generated to optimize the settings of each gaming terminal. A generative AI model trained using past gaming data and operational data from the entire arcade is used.

[0154] 4. Setting optimization means: Changes the settings of each gaming terminal according to the optimized setting data generated by the generating artificial intelligence model.

[0155] 5. Report generation means: Generates a report including the operating status of the gaming terminal and revenue forecast after the setting change, and notifies the administrator.

[0156] 6. Means for collecting operation data from multiple terminals: This has the function of optimizing the operation settings of the entire store by collecting and analyzing store operation data from multiple terminals.

[0157] 7. Marketing strategy support function: Provides support functions that allow administrators to implement additional marketing strategies and adjust settings based on the generated reports.

[0158] Natural language processing explanation

[0159] 1. Data Collection:

[0160] The server collects data on at least the game time, number of spins, and balance from the gaming terminals. To do this, the server sends a data collection request using an HTTP request.

[0161] 2. Data Analysis:

[0162] The collected data is analyzed on a server to calculate the operating status and profitability of each gaming terminal, and basic arithmetic operations are performed using programming languages ​​such as Python.

[0163] 3. Optimize settings:

[0164] The generative AI model generates optimal configuration data based on the analysis results. In particular, it uses a virtual AI model (using TensorFlow or PyTorch, for example) to analyze the data and propose optimal settings.

[0165] 4. Change the settings:

[0166] The server changes the settings of the gaming terminal based on the generated setting data using an HTTP POST request, etc. Once the changes are confirmed, the results are reported to the server.

[0167] 5. Reporting and Notifications:

[0168] Based on the data from the changed settings, the server generates a report containing new performance and revenue forecasts, which is then sent to the administrator, who can use it to implement additional marketing strategies or adjust settings.

[0169] Specific examples

[0170] For example, the server sends data collection requests from terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The server receives this data and analyzes the utilization rate and profitability of each terminal. These analysis results are input into a generative AI model, and the server recommends low settings for terminal A and high settings for terminal B.

[0171] Example prompt sentence:

[0172] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] Submitting a Data Collection Request

[0176] The server sends a data collection request to the gaming terminal. Specifically, it sends a request to the gaming terminal's API endpoint using an HTTP GET request. The server's URL and API endpoint are required as input, and the output includes data collected by the terminal, such as game time, number of spins, and balance.

[0177] Step 2:

[0178] Data collection from gaming terminals

[0179] The terminal receives a data collection request from the server, compiles the specified game data (play time, number of spins, income and expenditure), and sends it to the server. Specifically, it extracts the necessary data from the terminal's database and returns it to the server as structured data in JSON format or similar. The input is the terminal's internal database, and the output is the data sent to the server.

[0180] Step 3:

[0181] Data analysis

[0182] The server analyzes the collected data and calculates information about the operating status and profitability of each gaming terminal. Specifically, it uses programs such as Python to analyze gaming time, rotations, and income and expenditures, and calculates operating rates and profitability. The input is the collected gaming data, and the output is numerical data on operating rates and profitability.

[0183] Step 4:

[0184] Optimizing configuration data with generative AI models

[0185] The server inputs the analysis results into a generative AI model to generate configuration data for optimizing the settings of each gaming terminal. Specifically, it analyzes past and current data using an AI model (using TensorFlow or PyTorch, for example) to determine the optimal settings. The inputs include analysis results on operating status and revenue, and the output is optimized configuration data for each terminal.

[0186] Step 5:

[0187] Changing device settings

[0188] The server sends a setting change command to each gaming terminal based on the generated setting data. Specifically, the internal settings of the terminal are changed by sending the setting data to the terminal's setting API endpoint using an HTTP POST request. The input is the optimized setting data, and the output is a success status of the setting change.

[0189] Step 6:

[0190] Report generation and administrator notification

[0191] The server generates a report based on the new operating status and forecasted revenue after the settings have been changed, and notifies the administrator. Specifically, a report is generated based on the analysis results and optimization settings, and sent to the administrator via email or a notification system. The input is the operating data after the settings have been changed and forecasted revenue data, and the output is a notification report sent to the administrator.

[0192] Step 7:

[0193] Marketing strategy support

[0194] The user receives the generated report and uses it to develop marketing strategies and adjust additional settings. Specifically, the user analyzes the report and takes necessary measures. The user can also further optimize settings by inputting new data as prompts to the generative AI model. For example, the prompts are as follows:

[0195] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[0196] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0197] This invention is a system that optimizes the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[0198] This system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives reports and takes additional action as necessary.

[0199] The system's program begins with the server sending data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as game time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0200] In the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed.

[0201] The analysis results are input into a generative AI model, which generates optimal settings. The generative AI model determines the settings (settings 1 to 6) for each gaming terminal based on past data, current operating status, and user emotional information to maximize profits.

[0202] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[0203] The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust the settings.

[0204] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0205] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0206] In this way, the system of the present invention can automatically optimize gaming terminal settings and utilize user emotional information to more accurately maximize profits and improve operational efficiency.

[0207] The processing flow will be explained below.

[0208] Step 1:

[0209] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[0210] Step 2:

[0211] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[0212] Step 3:

[0213] The emotion engine recognizes the user's facial expressions, voice, and biometric information in real time to generate emotional data about the user, including whether the user is excited, relaxed, stressed, etc.

[0214] Step 4:

[0215] The server stores the data received from each device and the emotion data received from the emotion engine in a database. The database also stores past data and uses it for analysis.

[0216] Step 5:

[0217] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[0218] Operating rate = Playing time / Maximum operating time

[0219] Profitability rate = (revenue / turnover) 100

[0220] Step 6:

[0221] The server inputs the analysis data and the user's emotional data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data, current operating status, and the user's emotional state.

[0222] Step 7:

[0223] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates. It may also consider emotional information and apply a high setting to users who are excited.

[0224] Step 8:

[0225] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[0226] Step 9:

[0227] The device notifies the server that the setting change was successful, including the changed setting data.

[0228] Step 10:

[0229] The server generates a report based on the data after the setting change and the emotion data, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[0230] Step 11:

[0231] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[0232] Step 12:

[0233] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[0234] In this way, by repeating the processes from step 1 to step 12, it is possible to optimize the overall profit and operational efficiency of a pachinko parlor. In addition, by combining it with an emotion engine, it becomes possible to change settings taking into account the user's emotional state, enabling more accurate maximization of profit and improvement of operational efficiency.

[0235] Example 2

[0236] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0237] In conventional pachinko parlors, the settings and operational efficiency of gaming terminals were mainly managed manually, which was inefficient. Furthermore, it was difficult to maximize profits and improve customer satisfaction without taking into account users' emotional information. This resulted in problems such as reduced management efficiency and customer attrition.

[0238] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0239] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying the manager; and means for recognizing emotions based on user facial expressions, voice, and biometric information and using that information for analysis. This enables optimization of gaming terminal settings, maximization of revenue, and more accurate management decisions based on user emotional information.

[0240] A "gaming terminal" is a gaming device used in pachinko parlors, game centers, etc., and is a device on which users can play games.

[0241] "Data collection means" refers to a device or software that has the function of collecting necessary data such as game time, number of spins, and income and expenditure from gaming terminals.

[0242] The "analysis means" refers to a device or software that has the function of analyzing the collected data and calculating information relating to the operating status and revenue of each gaming terminal.

[0243] A "generative artificial intelligence model" is an artificial intelligence model for generating optimal configuration data based on collected and analyzed data.

[0244] The "setting change means" is a device or software that has the function of changing the settings of each gaming terminal based on the generated setting data.

[0245] The "report generating means" is a device or software that has the function of generating a report including the operating status of the gaming terminal and a revenue forecast after the settings have been changed, and notifying the administrator of the report.

[0246] "Emotion recognition means" refers to devices or software that have the function of recognizing emotions based on a user's facial expressions, voice, and biological information, and using that information for analysis.

[0247] A "database" is an electronic data storage system for storing collected and analyzed data.

[0248] A "prompt" is an instruction or question used to input analysis results into a generative AI model.

[0249] This invention is a system that aims to improve the income and expenditure management and operational efficiency of gaming facilities by optimizing the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation, while the gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[0250] Specifically, the server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0251] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0252] The server then sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status and revenue forecast before and after the setting change, as well as the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust settings.

[0253] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0254] These analysis results are input into a generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, suggesting that a high setting may be more likely to be applied to excited users. The server then sends this setting change data to device A and device B, which then change their settings. The server then generates a detailed report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0255] An example of a prompt sentence to input to the generative AI model is as follows:

[0256] 1. "Generate optimal settings for each device based on past operating data, current operating status, and user emotional information."

[0257] 2. "To maximize profits, please recommend the following settings for the gaming device: Device A, Settings 1-6, User Sentiment Information, Historical Data."

[0258] In this way, the system according to the present invention can maximize profits and improve business efficiency by automatically optimizing the settings of gaming terminals and utilizing user emotional information.

[0259] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0260] Step 1:

[0261] The server sends a data collection request to each gaming terminal at a specified interval, which includes the type of data to be collected (playing time, number of spins, balance, etc.).

[0262] Input: Server timer trigger or polling settings

[0263] Output: Data collection request to gaming terminal

[0264] Step 2:

[0265] Based on the received data collection request, the terminal compiles data such as game time, number of spins, and income and expenditure.

[0266] Input: Data Collection Request

[0267] Output: Aggregated data such as game time, number of spins, income and expenditure

[0268] Step 3:

[0269] The terminal transmits the collected data to the server.

[0270] Input: Aggregate data

[0271] Output: Send data to the server

[0272] Step 4:

[0273] The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information.

[0274] Input: User's facial expression, voice, biometric information

[0275] Output: Recognized emotion data

[0276] Step 5:

[0277] The emotion engine transmits the recognized emotion data to the server.

[0278] Input: Emotion data

[0279] Output: Send data to the server

[0280] Step 6:

[0281] The server stores the game data from the terminal and the emotion data from the emotion engine in a database.

[0282] Input: Game data, emotion data

[0283] Output: Save to database

[0284] Step 7:

[0285] The server calculates the utilization rate and profitability rate of each terminal based on the stored data.

[0286] Input: Saved game data, emotional data

[0287] Output: Calculation results of utilization rate and profitability rate

[0288] Step 8:

[0289] The server inputs the analysis results into the generative AI model. Specifically, it sends prompts to the generative AI model to generate optimal settings based on past operation data, current operation data, and user emotion information.

[0290] Input: Analysis results, emotion data

[0291] Output: Prompt generation and input to the AI ​​model

[0292] Step 9:

[0293] The generative AI model generates optimal setting data (settings 1 to 6) based on the prompts received.

[0294] Input: prompt

[0295] Output: Optimal setting data

[0296] Step 10:

[0297] The server creates a setting change command based on the generated setting data and transmits it to each gaming terminal.

[0298] Input: Optimal setting data

[0299] Output: Creating a setting change command and sending it to the gaming terminal

[0300] Step 11:

[0301] The terminal receives this setting change command and automatically changes the setting.

[0302] Input: Change setting command

[0303] Output: Configuration changes made

[0304] Step 12:

[0305] If the setting change is successful, the terminal reports the result to the server.

[0306] Input: Result of setting change

[0307] Output: Report to server

[0308] Step 13:

[0309] The server generates a detailed report based on the data after the setting change and the user's emotional state, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[0310] Input: Data after setting changes, user emotion data

[0311] Output: Detailed report

[0312] Step 14:

[0313] The server notifies the user of the generated report.

[0314] Input: Detailed report

[0315] Output: User notification

[0316] In this way, the systems work together to optimize gaming terminal settings and improve management efficiency.

[0317] (Application example 2)

[0318] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0319] The objective of this invention is to provide a means to maximize revenue while improving the customer experience in brick-and-mortar stores by optimizing the settings and operational efficiency of gaming terminals using generative AI models and emotion engines. Furthermore, by taking into account customer emotion data, it aims to increase customer satisfaction and encourage repeat customers.

[0320] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying a manager; means for collecting and analyzing customer emotions; means for generating setting data for adjusting the store environment based on the analyzed emotion data; and means for notifying a manager of the setting data for optimizing on-site engagement in the store. This makes it possible to dynamically adjust the store environment and gaming terminal settings using customer emotion information to improve the customer experience.

[0321] "Gaming terminal" refers to a machine installed for entertainment purposes, such as a pachinko machine or slot machine.

[0322] "Game time" is data indicating the total time that a gaming terminal is actually operating.

[0323] The "number of spins" is data indicating the frequency with which a specific action (for example, the number of times a ball on a pachinko machine makes a specific spin) is executed on a gaming terminal.

[0324] "Balance" is data indicating the total profits and losses generated by a gaming terminal.

[0325] "Operating status" is information that comprehensively indicates the usage status of the gaming terminal and usage information such as operating time, rotation speed, income and expenditure.

[0326] "Revenue" refers to monetary profits obtained through gaming terminals.

[0327] A "generative artificial intelligence model" is an algorithm or model that uses artificial intelligence to perform data analysis and predictions for specific purposes.

[0328] "Setting data" refers to data that includes specific numerical values ​​and commands for adjusting the operating conditions and specifications of the gaming terminal based on the analysis results.

[0329] "Analysis results" are conclusions or judgments reached by applying specific calculations or algorithms to collected data.

[0330] A "report" is a report generated to inform the manager of the results of data analysis and the status of the gaming terminal after the settings have been changed.

[0331] "Emotion" is information that indicates the user's psychological state, and represents specific emotions such as excitement, relaxation, and happiness.

[0332] An "emotion engine" is a software algorithm and hardware configuration that recognizes and analyzes user emotions in real time based on sensor data and user input.

[0333] "Store environment" is a general term for elements that affect the customer experience within a store, such as music, lighting, and product placement.

[0334] "On-site engagement" refers to the interactions that occur when a customer visits a store and interacts with a product or service face-to-face.

[0335] This invention is a system that utilizes a generative AI model and an emotion engine to optimize gaming terminal settings and operating efficiency, improving the customer experience at brick-and-mortar stores. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[0336] The server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0337] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0338] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the generative AI model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the administrator, who can use it to implement additional marketing strategies and adjust the settings.

[0339] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0340] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0341] To optimize the store environment, the server collects customer emotion data and dynamically adjusts the music, lighting, and display content in the store accordingly. For example, if a customer expresses "happy," the server can respond by changing the music in the store to an upbeat tone. The hardware used in this process includes a Raspberry Pi and a camera module, and the software includes Keras and OpenCV.

[0342] Example prompts to input to a generative AI model:

[0343] How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?

[0344] In this way, the system of the present invention optimizes not only the gaming terminals but also the environment of the entire store based on customer emotion data, thereby improving customer experience and maximizing profits.

[0345] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0346] Step 1:

[0347] The server transmits a data collection request to each gaming terminal.

[0348] Specifically, it sends data collection requests at specified time intervals and waits for responses from gaming terminals. The input is the data collection request. The output is data on game time, number of spins, and balance collected from each gaming terminal.

[0349] Step 2:

[0350] The terminal receives the data collection request and compiles data on playing time, number of spins, and income and expenditure.

[0351] Specifically, it collects the latest gaming data from an internal database, aggregates it, and returns it to the server. The input is a data collection request from the server. The output is data on gaming time, number of spins, and balance.

[0352] Step 3:

[0353] The emotion engine collects and recognizes the user's emotion information and sends it to the server.

[0354] Specifically, it uses cameras, microphones, sensors, etc. to collect and analyze the user's facial expressions, voice, and biometric information in real time. The inputs include the user's facial expressions, voice, and biometric information. The output is the analyzed emotional data sent to a server.

[0355] Step 4:

[0356] The server stores the received data in a database and performs analysis.

[0357] Specifically, data sent from gaming terminals and emotion data sent from the emotion engine are stored in a database and analyzed to calculate utilization rates and profit rates. The inputs are game data and emotion data. The output is the calculated utilization rate and profit rate for each gaming terminal.

[0358] Step 5:

[0359] The server inputs the analysis results into a generative AI model to generate optimal configuration data.

[0360] Specifically, past data, current operating conditions, and emotional information are provided as inputs to the generative AI model, which then generates configuration data that maximizes profits. The inputs include analysis results, and the output is optimized configuration data.

[0361] Step 6:

[0362] The server transmits setting change commands to each gaming terminal based on the setting data obtained from the generated AI model.

[0363] Specifically, it generates and transmits commands to each gaming terminal based on the setting data, instructing them to make specific setting changes. The input is the generated setting data. The output is a setting change command sent to the gaming terminal.

[0364] Step 7:

[0365] The terminal receives the setting change command and changes the setting.

[0366] Specifically, the internal settings of the gaming terminal are changed based on the received setting change command. The input is a setting change command from the server. The output is the execution of the setting change.

[0367] Step 8:

[0368] The server generates a report based on the data after the setting change and the emotion data, and notifies the administrator.

[0369] Specifically, the gameplay data and emotion data collected again after the settings are changed are analyzed, and a report is generated summarizing the operating status, revenue forecast, and emotional state before and after the settings are changed. The inputs are the gameplay data and emotion data after the settings are changed. The output is to notify the administrator of the generated report.

[0370] Step 9:

[0371] The user makes further decisions based on the report.

[0372] Specifically, administrators review the report content and implement additional marketing strategies or configuration adjustments as necessary. The input is the generated report. The output is additional actions by the user.

[0373] Step 10:

[0374] The server collects customer emotion data and generates setting data to adjust the store environment.

[0375] Specifically, it analyzes user emotional data, generates setting data for dynamically changing music, lighting, and display settings in the store, and sends this data to the store's management system. The input is customer emotional data, and the output is setting data for adjusting the store environment.

[0376] Example prompt sentence:

[0377] "How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?"

[0378] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0379] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0380] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0381] [Second embodiment]

[0382] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0383] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0384] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0385] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0386] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0387] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0388] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0389] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0390] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0391] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0392] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0393] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0394] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[0395] This system consists of a server, gaming terminals, and users. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. Users receive reports and take additional actions as necessary.

[0396] The program for this system begins with the server sending data collection requests from each gaming terminal at a specified interval. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The server stores this data in a database and performs analysis.

[0397] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. These calculation results are input into a generative AI model, which generates optimal setting data. Based on past data and current operating status, the AI ​​model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0398] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[0399] The server generates a report based on the data after the configuration change. This report includes the performance status and revenue forecast before and after the configuration change. The report is sent to the user, who can use it to implement additional marketing strategies or adjust the configuration.

[0400] As a concrete example, we will explain the processing for terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a profit of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0401] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server then sends this setting change data to device A and device B, which then change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can then make additional decisions based on this report.

[0402] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of pachinko parlors, and significantly improve business efficiency.

[0403] The processing flow will be explained below.

[0404] Step 1:

[0405] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[0406] Step 2:

[0407] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[0408] Step 3:

[0409] The server stores the data received from each device in a database, which also stores past data and is used for analysis.

[0410] Step 4:

[0411] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[0412] Operating rate = Playing time / Maximum operating time

[0413] Profitability rate = (revenue / turnover) 100

[0414] Step 5:

[0415] The server inputs the analysis data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data and current operating conditions.

[0416] Step 6:

[0417] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates.

[0418] Step 7:

[0419] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[0420] Step 8:

[0421] The device notifies the server that the setting change was successful, including the changed setting data.

[0422] Step 9:

[0423] The server compares the updated data with past data and generates a report based on the new operating status and revenue forecast, including the history of settings changes and forecast revenue for each gaming terminal.

[0424] Step 10:

[0425] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[0426] Step 11:

[0427] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[0428] In this way, by repeating the processes from step 1 to step 11, the overall profitability and operational efficiency of the pachinko parlor can be optimized.

[0429] Example 1

[0430] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0431] Currently, many gaming parlors manually monitor and optimize gaming terminal settings and operating status, but this work is time-consuming and labor-intensive, making it extremely inefficient. Manual setting changes also increase the likelihood of human error, making it difficult to manage revenue and improve operating efficiency at pachinko parlors. Furthermore, maintaining optimal gaming terminal settings requires complex decisions that take into account past data and current conditions, which is difficult to achieve with conventional systems.

[0432] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0433] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means for having a generative AI model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying an administrator; means for the server to send data collection requests to each gaming terminal at specified intervals; means for the server to store the data in a database and perform analysis; means for the server to input prompts into the generative AI model to obtain optimal setting data and for the server to send setting change commands to the terminals. This enables automatic and efficient optimization of gaming terminal settings, maximizing revenue, and improving business efficiency.

[0434] A "gaming terminal" is a device that is operated by a player to play games, and has the function of recording and transmitting game information.

[0435] A "server" is a central control device that collects data from gaming terminals, performs analytical processing, and transmits necessary instructions and information.

[0436] A "data collection request" is a request message sent by the server to a gaming terminal, and is intended to collect data relating to the operating status and revenue of the gaming terminal.

[0437] A "generative AI model" is an algorithm or trained model that uses artificial intelligence to generate configuration data to optimize the settings and operating conditions of gaming terminals.

[0438] A "prompt" is an instruction or question that is input to a generative AI model to generate optimal configuration data.

[0439] A "setting change command" is an instruction message sent by the server to a gaming terminal to change the settings of the gaming terminal.

[0440] A "database" is a storage device or system that stores and manages collected data in an organized manner and enables analysis and reference.

[0441] "Analysis results" are information calculated or evaluated based on collected data, and relate to the operating status and revenue of gaming terminals.

[0442] The "report" is a report that includes the operating status of the gaming terminal after the setting change and a revenue forecast, and is a summary of information that is notified to the administrator.

[0443] "Administrator" refers to the person in charge or responsible for the operation and management of gaming terminals and the entire system.

[0444] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of gaming parlors. This system consists of a server, gaming terminals, and users. The processing of each entity and its specific operation are explained in detail below.

[0445] Server Processing

[0446] The server first sends a data collection request to each gaming terminal at a specified interval. This request is to collect information necessary to understand the current gaming situation (play time, number of spins, balance, etc.). The server saves the received data in a database and performs analysis. This analysis may use a programming language such as Python or an SQL database.

[0447] As a result of the analysis, the server calculates the operating status (operating rate, profit rate) of each gaming terminal. Based on this analysis data, the server inputs a prompt sentence into the generative AI model to generate optimal setting data. Models such as OpenAI's GPT-3 can be used as the generative AI model. For example, the prompt sentence is as follows:

[0448] Prompt: "Generate optimal settings based on the utilization rate and profitability of gaming terminals. Terminal A: Utilization rate 83.3%, Profitability rate 1.67%. Terminal B: Utilization rate 50%, Profitability rate 1%."

[0449] Based on the generated setting data, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. This can be done using the HTTP protocol or a dedicated communication API.

[0450] Based on the data after the setting change, the server generates a report containing operational data and revenue forecasts and notifies the user. This report details the operational status and revenue forecasts before and after the setting change, and the user can make further decisions based on this report.

[0451] Gaming terminal processing

[0452] The terminal receives a data collection request from the server, and collects and compiles information such as game time, number of spins, and balance from its internal data records. The collected data is immediately sent back to the server.

[0453] The gaming terminal that receives the setting change command from the server changes the setting in accordance with the command, and if the setting change is successful, reports the result to the server.

[0454] User Action

[0455] Users can receive reports from the server and check the settings and operating status of their gaming terminals. Based on these reports, they can take additional actions, such as changing the placement of gaming terminals or implementing new marketing strategies.

[0456] Specific examples

[0457] For example, suppose the server sends a data collection request to terminal A and terminal B. Terminal A reports data showing 10 hours of play time, 3,000 spins, and a profit of 5,000 yen. Terminal B reports data showing 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes it to find that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0458] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can make additional decisions based on this report.

[0459] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of gaming parlors, and significantly improve business efficiency.

[0460] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0461] Step 1: Submit a data collection request

[0462] Server processing: The server sends a data collection request to each gaming terminal at a specified time interval. The data collection request includes instructions to report operating status such as game time, number of spins, and balance.

[0463] Input: Data collection request

[0464] Output: Each gaming device receives a data collection request

[0465] Step 2: Collect and send data

[0466] Terminal processing: Each gaming terminal receives a data collection request and, according to the instructions, compiles data such as game time, number of spins, and balance from its internal data records. The collected data is then sent back to the server. For example, terminal A reports a game time of 10 hours, 3,000 spins, and a balance of 5,000 yen.

[0467] Input: Data collection request

[0468] Output: Data reports from each device (e.g., game time, number of spins, balance)

[0469] Step 3: Data storage and analysis

[0470] Server processing: The server stores the data received from the terminals in a database. This can be done using a database management system such as SQL. Next, the server calculates the utilization rate and profitability of each gaming terminal based on the stored data. For example, it analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profitability is 1.67% (5,000 yen / 3,000 times).

[0471] Input: Data reports from each device

[0472] Output: Analysis results of utilization rate and profitability

[0473] Step 4: Generate optimal settings

[0474] Server processing: The server inputs a prompt statement into the generative AI model based on the analysis results, generating optimal configuration data. An example of a prompt statement is, "Generate the optimal configuration based on the utilization rate and profit rate of the gaming terminal. Terminal A: utilization rate 83.3%, profit rate 1.67%. Terminal B: utilization rate 50%, profit rate 1%." For example, OpenAI's GPT-3 can be used as the generative AI model.

[0475] Input: Analysis result, prompt statement

[0476] Output: Optimal setting data

[0477] Step 5: Sending configuration change commands

[0478] Server processing: Based on the setting data obtained from the generative AI model, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. In this case, the communication protocol may be HTTP or a dedicated communication API.

[0479] Input: Optimal setting data

[0480] Output: Configuration change command

[0481] Step 6: Implement and report configuration changes

[0482] Terminal processing: Each gaming terminal that receives a setting change command from the server changes the setting as instructed. If the setting change is successful, it reports the result to the server. For example, if terminal A completes the change to setting 1, it reports this to the server.

[0483] Input: Change setting command

[0484] Output: Report of the configuration change result

[0485] Step 7: Reporting and Notifications

[0486] Server processing: The server generates a report based on the new operational data and revenue forecast after the configuration change. This report includes the operational status and revenue forecast before and after the configuration change. This report is notified to the user (administrator).

[0487] Input: Operation data after setting change, revenue forecast

[0488] Output: Report notification

[0489] Through these steps, the system automatically optimizes gaming terminal settings, maximizing profits and improving operational efficiency.

[0490] (Application example 1)

[0491] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0492] Conventional gaming terminal setting optimization systems change settings based only on the operational data of individual terminals, so they are unable to fully consider the operational efficiency and profit maximization of the entire store. Furthermore, there are insufficient means for communicating the effects of setting changes to managers, making it difficult to use the results for marketing strategies or additional setting adjustments. This prevents optimal operation and limits profit and efficiency improvements.

[0493] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0494] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminals after the setting change and notifying the manager; means for optimizing the operational settings of the entire arcade by collecting and analyzing operational data of the arcade from multiple terminals; and means for supporting the manager in implementing additional marketing strategies and setting adjustments based on the generated report. This enables the manager to implement prompt and effective marketing strategies and setting adjustments while maximizing the operational efficiency and revenue of the entire arcade.

[0495] "Gaming terminal" refers to a terminal device used by customers to play games, including pachinko and pachislot machines.

[0496] "Data collection means" refers to a device or software that has the function of collecting data such as game time, number of spins, and income and expenditure from gaming terminals.

[0497] "Analysis means" refers to devices or software that have the function of analyzing collected data and calculating the operating status and revenue information of each gaming terminal.

[0498] "Generative artificial intelligence model" refers to an AI model that generates optimal configuration data based on collected data.

[0499] "Settings optimization means" refers to a device or software that has the function of changing the settings of each gaming terminal based on the setting data generated by the generative artificial intelligence model.

[0500] "Report generation means" refers to a device or software that has the function of generating a report including the operating status and revenue forecast of the gaming terminal after the settings have been changed, and notifying the administrator.

[0501] "Multiple terminal operation data collection means" refers to a device or software that has the function of collecting operation data from multiple gaming terminals in a lump.

[0502] "Store operation optimization function" refers to devices or software that have the ability to analyze collected operation data from multiple terminals and generate settings to optimize the operation of the entire store.

[0503] "Marketing strategy support function" refers to a device or software that has a function to support the administrator in implementing additional marketing strategies or adjusting settings based on the generated reports.

[0504] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the revenue and expenditure management and operational efficiency of gaming facilities. This system is composed of gaming terminals, a server, and an administrator.

[0505] Roles and functions of each component

[0506] 1. Gaming terminals:

[0507] The gaming terminal collects data such as game time, number of spins, and balance, and transmits it to the server.

[0508] 2. Server:

[0509] The server has the following features:

[0510] 1. Data collection method: At least the following data will be collected from each gaming terminal: playing time, number of spins, and income / expenses.

[0511] 2. Analysis method: Analyze the collected data and calculate information regarding the operating status and revenue of each gaming terminal.

[0512] 3. Generative AI model: Based on the results of the analysis, configuration data is generated to optimize the settings of each gaming terminal. A generative AI model trained using past gaming data and operational data from the entire arcade is used.

[0513] 4. Setting optimization means: Changes the settings of each gaming terminal according to the optimized setting data generated by the generating artificial intelligence model.

[0514] 5. Report generation means: Generates a report including the operating status of the gaming terminal and revenue forecast after the setting change, and notifies the administrator.

[0515] 6. Means for collecting operation data from multiple terminals: This has the function of optimizing the operation settings of the entire store by collecting and analyzing store operation data from multiple terminals.

[0516] 7. Marketing strategy support function: Provides support functions that allow administrators to implement additional marketing strategies and adjust settings based on the generated reports.

[0517] Natural language processing explanation

[0518] 1. Data Collection:

[0519] The server collects data on at least the game time, number of spins, and balance from the gaming terminals. To do this, the server sends a data collection request using an HTTP request.

[0520] 2. Data Analysis:

[0521] The collected data is analyzed on a server to calculate the operating status and profitability of each gaming terminal, and basic arithmetic operations are performed using programming languages ​​such as Python.

[0522] 3. Optimize settings:

[0523] The generative AI model generates optimal configuration data based on the analysis results. In particular, it uses a virtual AI model (using TensorFlow or PyTorch, for example) to analyze the data and propose optimal settings.

[0524] 4. Change the settings:

[0525] The server changes the settings of the gaming terminal based on the generated setting data using an HTTP POST request, etc. Once the changes are confirmed, the results are reported to the server.

[0526] 5. Reporting and Notifications:

[0527] Based on the data from the changed settings, the server generates a report containing new performance and revenue forecasts, which is then sent to the administrator, who can use it to implement additional marketing strategies or adjust settings.

[0528] Specific examples

[0529] For example, the server sends data collection requests from terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The server receives this data and analyzes the utilization rate and profitability of each terminal. These analysis results are input into a generative AI model, and the server recommends low settings for terminal A and high settings for terminal B.

[0530] Example prompt sentence:

[0531] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[0532] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0533] Step 1:

[0534] Submitting a Data Collection Request

[0535] The server sends a data collection request to the gaming terminal. Specifically, it sends a request to the gaming terminal's API endpoint using an HTTP GET request. The server's URL and API endpoint are required as input, and the output includes data collected by the terminal, such as game time, number of spins, and balance.

[0536] Step 2:

[0537] Data collection from gaming terminals

[0538] The terminal receives a data collection request from the server, compiles the specified game data (play time, number of spins, income and expenditure), and sends it to the server. Specifically, it extracts the necessary data from the terminal's database and returns it to the server as structured data in JSON format or similar. The input is the terminal's internal database, and the output is the data sent to the server.

[0539] Step 3:

[0540] Data analysis

[0541] The server analyzes the collected data and calculates information about the operating status and profitability of each gaming terminal. Specifically, it uses programs such as Python to analyze gaming time, rotations, and income and expenditures, and calculates operating rates and profitability. The input is the collected gaming data, and the output is numerical data on operating rates and profitability.

[0542] Step 4:

[0543] Optimizing configuration data with generative AI models

[0544] The server inputs the analysis results into a generative AI model to generate configuration data for optimizing the settings of each gaming terminal. Specifically, it analyzes past and current data using an AI model (using TensorFlow or PyTorch, for example) to determine the optimal settings. The inputs include analysis results on operating status and revenue, and the output is optimized configuration data for each terminal.

[0545] Step 5:

[0546] Changing device settings

[0547] The server sends a setting change command to each gaming terminal based on the generated setting data. Specifically, the internal settings of the terminal are changed by sending the setting data to the terminal's setting API endpoint using an HTTP POST request. The input is the optimized setting data, and the output is a success status of the setting change.

[0548] Step 6:

[0549] Report generation and administrator notification

[0550] The server generates a report based on the new operating status and forecasted revenue after the settings have been changed, and notifies the administrator. Specifically, a report is generated based on the analysis results and optimization settings, and sent to the administrator via email or a notification system. The input is the operating data after the settings have been changed and forecasted revenue data, and the output is a notification report sent to the administrator.

[0551] Step 7:

[0552] Marketing strategy support

[0553] The user receives the generated report and uses it to develop marketing strategies and adjust additional settings. Specifically, the user analyzes the report and takes necessary measures. The user can also further optimize settings by inputting new data as prompts to the generative AI model. For example, the prompts are as follows:

[0554] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[0555] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0556] This invention is a system that optimizes the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[0557] This system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives reports and takes additional action as necessary.

[0558] The system's program begins with the server sending data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as game time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0559] In the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed.

[0560] The analysis results are input into a generative AI model, which generates optimal settings. The generative AI model determines the settings (settings 1 to 6) for each gaming terminal based on past data, current operating status, and user emotional information to maximize profits.

[0561] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[0562] The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust the settings.

[0563] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0564] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0565] In this way, the system of the present invention can automatically optimize gaming terminal settings and utilize user emotional information to more accurately maximize profits and improve operational efficiency.

[0566] The processing flow will be explained below.

[0567] Step 1:

[0568] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[0569] Step 2:

[0570] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[0571] Step 3:

[0572] The emotion engine recognizes the user's facial expressions, voice, and biometric information in real time to generate emotional data about the user, including whether the user is excited, relaxed, stressed, etc.

[0573] Step 4:

[0574] The server stores the data received from each device and the emotion data received from the emotion engine in a database. The database also stores past data and uses it for analysis.

[0575] Step 5:

[0576] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[0577] Operating rate = Playing time / Maximum operating time

[0578] Profitability rate = (revenue / turnover) 100

[0579] Step 6:

[0580] The server inputs the analysis data and the user's emotional data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data, current operating status, and the user's emotional state.

[0581] Step 7:

[0582] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates. It may also consider emotional information and apply a high setting to users who are excited.

[0583] Step 8:

[0584] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[0585] Step 9:

[0586] The device notifies the server that the setting change was successful, including the changed setting data.

[0587] Step 10:

[0588] The server generates a report based on the data after the setting change and the emotion data, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[0589] Step 11:

[0590] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[0591] Step 12:

[0592] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[0593] In this way, by repeating the processes from step 1 to step 12, it is possible to optimize the overall profit and operational efficiency of a pachinko parlor. In addition, by combining it with an emotion engine, it becomes possible to change settings taking into account the user's emotional state, enabling more accurate maximization of profit and improvement of operational efficiency.

[0594] Example 2

[0595] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0596] In conventional pachinko parlors, the settings and operational efficiency of gaming terminals were mainly managed manually, which was inefficient. Furthermore, it was difficult to maximize profits and improve customer satisfaction without taking into account users' emotional information. This resulted in problems such as reduced management efficiency and customer attrition.

[0597] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0598] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying the manager; and means for recognizing emotions based on user facial expressions, voice, and biometric information and using that information for analysis. This enables optimization of gaming terminal settings, maximization of revenue, and more accurate management decisions based on user emotional information.

[0599] A "gaming terminal" is a gaming device used in pachinko parlors, game centers, etc., and is a device on which users can play games.

[0600] "Data collection means" refers to a device or software that has the function of collecting necessary data such as game time, number of spins, and income and expenditure from gaming terminals.

[0601] The "analysis means" refers to a device or software that has the function of analyzing the collected data and calculating information relating to the operating status and revenue of each gaming terminal.

[0602] A "generative artificial intelligence model" is an artificial intelligence model for generating optimal configuration data based on collected and analyzed data.

[0603] The "setting change means" is a device or software that has the function of changing the settings of each gaming terminal based on the generated setting data.

[0604] The "report generating means" is a device or software that has the function of generating a report including the operating status of the gaming terminal and a revenue forecast after the settings have been changed, and notifying the administrator of the report.

[0605] "Emotion recognition means" refers to devices or software that have the function of recognizing emotions based on a user's facial expressions, voice, and biological information, and using that information for analysis.

[0606] A "database" is an electronic data storage system for storing collected and analyzed data.

[0607] A "prompt" is an instruction or question used to input analysis results into a generative AI model.

[0608] This invention is a system that aims to improve the income and expenditure management and operational efficiency of gaming facilities by optimizing the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation, while the gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[0609] Specifically, the server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0610] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0611] The server then sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status and revenue forecast before and after the setting change, as well as the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust settings.

[0612] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0613] These analysis results are input into a generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, suggesting that a high setting may be more likely to be applied to excited users. The server then sends this setting change data to device A and device B, which then change their settings. The server then generates a detailed report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0614] An example of a prompt sentence to input to the generative AI model is as follows:

[0615] 1. "Generate optimal settings for each device based on past operating data, current operating status, and user emotional information."

[0616] 2. "To maximize profits, please recommend the following settings for the gaming device: Device A, Settings 1-6, User Sentiment Information, Historical Data."

[0617] In this way, the system according to the present invention can maximize profits and improve business efficiency by automatically optimizing the settings of gaming terminals and utilizing user emotional information.

[0618] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0619] Step 1:

[0620] The server sends a data collection request to each gaming terminal at a specified interval, which includes the type of data to be collected (playing time, number of spins, balance, etc.).

[0621] Input: Server timer trigger or polling settings

[0622] Output: Data collection request to gaming terminal

[0623] Step 2:

[0624] Based on the received data collection request, the terminal compiles data such as game time, number of spins, and income and expenditure.

[0625] Input: Data Collection Request

[0626] Output: Aggregated data such as game time, number of spins, income and expenditure

[0627] Step 3:

[0628] The terminal transmits the collected data to the server.

[0629] Input: Aggregate data

[0630] Output: Send data to the server

[0631] Step 4:

[0632] The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information.

[0633] Input: User's facial expression, voice, biometric information

[0634] Output: Recognized emotion data

[0635] Step 5:

[0636] The emotion engine transmits the recognized emotion data to the server.

[0637] Input: Emotion data

[0638] Output: Send data to the server

[0639] Step 6:

[0640] The server stores the game data from the terminal and the emotion data from the emotion engine in a database.

[0641] Input: Game data, emotion data

[0642] Output: Save to database

[0643] Step 7:

[0644] The server calculates the utilization rate and profitability rate of each terminal based on the stored data.

[0645] Input: Saved game data, emotional data

[0646] Output: Calculation results of utilization rate and profitability rate

[0647] Step 8:

[0648] The server inputs the analysis results into the generative AI model. Specifically, it sends prompts to the generative AI model to generate optimal settings based on past operation data, current operation data, and user emotion information.

[0649] Input: Analysis results, emotion data

[0650] Output: Prompt generation and input to the AI ​​model

[0651] Step 9:

[0652] The generative AI model generates optimal setting data (settings 1 to 6) based on the prompts received.

[0653] Input: prompt

[0654] Output: Optimal setting data

[0655] Step 10:

[0656] The server creates a setting change command based on the generated setting data and transmits it to each gaming terminal.

[0657] Input: Optimal setting data

[0658] Output: Creating a setting change command and sending it to the gaming terminal

[0659] Step 11:

[0660] The terminal receives this setting change command and automatically changes the setting.

[0661] Input: Change setting command

[0662] Output: Configuration changes made

[0663] Step 12:

[0664] If the setting change is successful, the terminal reports the result to the server.

[0665] Input: Result of setting change

[0666] Output: Report to server

[0667] Step 13:

[0668] The server generates a detailed report based on the data after the setting change and the user's emotional state, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[0669] Input: Data after setting changes, user emotion data

[0670] Output: Detailed report

[0671] Step 14:

[0672] The server notifies the user of the generated report.

[0673] Input: Detailed report

[0674] Output: User notification

[0675] In this way, the systems work together to optimize gaming terminal settings and improve management efficiency.

[0676] (Application example 2)

[0677] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0678] The objective of this invention is to provide a means to maximize revenue while improving the customer experience in brick-and-mortar stores by optimizing the settings and operational efficiency of gaming terminals using generative AI models and emotion engines. Furthermore, by taking into account customer emotion data, it aims to increase customer satisfaction and encourage repeat customers.

[0679] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying a manager; means for collecting and analyzing customer emotions; means for generating setting data for adjusting the store environment based on the analyzed emotion data; and means for notifying a manager of the setting data for optimizing on-site engagement in the store. This makes it possible to dynamically adjust the store environment and gaming terminal settings using customer emotion information to improve the customer experience.

[0680] "Gaming terminal" refers to a machine installed for entertainment purposes, such as a pachinko machine or slot machine.

[0681] "Game time" is data indicating the total time that a gaming terminal is actually operating.

[0682] The "number of spins" is data indicating the frequency with which a specific action (for example, the number of times a ball on a pachinko machine makes a specific spin) is executed on a gaming terminal.

[0683] "Balance" is data indicating the total profits and losses generated by a gaming terminal.

[0684] "Operating status" is information that comprehensively indicates the usage status of the gaming terminal and usage information such as operating time, rotation speed, income and expenditure.

[0685] "Revenue" refers to monetary profits obtained through gaming terminals.

[0686] A "generative artificial intelligence model" is an algorithm or model that uses artificial intelligence to perform data analysis and predictions for specific purposes.

[0687] "Setting data" refers to data that includes specific numerical values ​​and commands for adjusting the operating conditions and specifications of the gaming terminal based on the analysis results.

[0688] "Analysis results" are conclusions or judgments reached by applying specific calculations or algorithms to collected data.

[0689] A "report" is a report generated to inform the manager of the results of data analysis and the status of the gaming terminal after the settings have been changed.

[0690] "Emotion" is information that indicates the user's psychological state, and represents specific emotions such as excitement, relaxation, and happiness.

[0691] An "emotion engine" is a software algorithm and hardware configuration that recognizes and analyzes user emotions in real time based on sensor data and user input.

[0692] "Store environment" is a general term for elements that affect the customer experience within a store, such as music, lighting, and product placement.

[0693] "On-site engagement" refers to the interactions that occur when a customer visits a store and interacts with a product or service face-to-face.

[0694] This invention is a system that utilizes a generative AI model and an emotion engine to optimize gaming terminal settings and operating efficiency, improving the customer experience at brick-and-mortar stores. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[0695] The server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0696] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0697] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the generative AI model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the administrator, who can use it to implement additional marketing strategies and adjust the settings.

[0698] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0699] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0700] To optimize the store environment, the server collects customer emotion data and dynamically adjusts the music, lighting, and display content in the store accordingly. For example, if a customer expresses "happy," the server can respond by changing the music in the store to an upbeat tone. The hardware used in this process includes a Raspberry Pi and a camera module, and the software includes Keras and OpenCV.

[0701] Example prompts to input to a generative AI model:

[0702] How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?

[0703] In this way, the system of the present invention optimizes not only the gaming terminals but also the environment of the entire store based on customer emotion data, thereby improving customer experience and maximizing profits.

[0704] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0705] Step 1:

[0706] The server transmits a data collection request to each gaming terminal.

[0707] Specifically, it sends data collection requests at specified time intervals and waits for responses from gaming terminals. The input is the data collection request. The output is data on game time, number of spins, and balance collected from each gaming terminal.

[0708] Step 2:

[0709] The terminal receives the data collection request and compiles data on playing time, number of spins, and income and expenditure.

[0710] Specifically, it collects the latest gaming data from an internal database, aggregates it, and returns it to the server. The input is a data collection request from the server. The output is data on gaming time, number of spins, and balance.

[0711] Step 3:

[0712] The emotion engine collects and recognizes the user's emotion information and sends it to the server.

[0713] Specifically, it uses cameras, microphones, sensors, etc. to collect and analyze the user's facial expressions, voice, and biometric information in real time. The inputs include the user's facial expressions, voice, and biometric information. The output is the analyzed emotional data sent to a server.

[0714] Step 4:

[0715] The server stores the received data in a database and performs analysis.

[0716] Specifically, data sent from gaming terminals and emotion data sent from the emotion engine are stored in a database and analyzed to calculate utilization rates and profit rates. The inputs are game data and emotion data. The output is the calculated utilization rate and profit rate for each gaming terminal.

[0717] Step 5:

[0718] The server inputs the analysis results into a generative AI model to generate optimal configuration data.

[0719] Specifically, past data, current operating conditions, and emotional information are provided as inputs to the generative AI model, which then generates configuration data that maximizes profits. The inputs include analysis results, and the output is optimized configuration data.

[0720] Step 6:

[0721] The server transmits setting change commands to each gaming terminal based on the setting data obtained from the generated AI model.

[0722] Specifically, it generates and transmits commands to each gaming terminal based on the setting data, instructing them to make specific setting changes. The input is the generated setting data. The output is a setting change command sent to the gaming terminal.

[0723] Step 7:

[0724] The terminal receives the setting change command and changes the setting.

[0725] Specifically, the internal settings of the gaming terminal are changed based on the received setting change command. The input is a setting change command from the server. The output is the execution of the setting change.

[0726] Step 8:

[0727] The server generates a report based on the data after the setting change and the emotion data, and notifies the administrator.

[0728] Specifically, the gameplay data and emotion data collected again after the settings are changed are analyzed, and a report is generated summarizing the operating status, revenue forecast, and emotional state before and after the settings are changed. The inputs are the gameplay data and emotion data after the settings are changed. The output is to notify the administrator of the generated report.

[0729] Step 9:

[0730] The user makes further decisions based on the report.

[0731] Specifically, administrators review the report content and implement additional marketing strategies or configuration adjustments as necessary. The input is the generated report. The output is additional actions by the user.

[0732] Step 10:

[0733] The server collects customer emotion data and generates setting data to adjust the store environment.

[0734] Specifically, it analyzes user emotional data, generates setting data for dynamically changing music, lighting, and display settings in the store, and sends this data to the store's management system. The input is customer emotional data, and the output is setting data for adjusting the store environment.

[0735] Example prompt sentence:

[0736] "How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?"

[0737] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0738] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0739] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0740] [Third embodiment]

[0741] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0742] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0743] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0744] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0745] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0746] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0747] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0748] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0749] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0750] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0751] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0752] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0753] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[0754] This system consists of a server, gaming terminals, and users. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. Users receive reports and take additional actions as necessary.

[0755] The program for this system begins with the server sending data collection requests from each gaming terminal at a specified interval. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The server stores this data in a database and performs analysis.

[0756] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. These calculation results are input into a generative AI model, which generates optimal setting data. Based on past data and current operating status, the AI ​​model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0757] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[0758] The server generates a report based on the data after the configuration change. This report includes the performance status and revenue forecast before and after the configuration change. The report is sent to the user, who can use it to implement additional marketing strategies or adjust the configuration.

[0759] As a concrete example, we will explain the processing for terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a profit of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0760] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server then sends this setting change data to device A and device B, which then change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can then make additional decisions based on this report.

[0761] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of pachinko parlors, and significantly improve business efficiency.

[0762] The processing flow will be explained below.

[0763] Step 1:

[0764] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[0765] Step 2:

[0766] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[0767] Step 3:

[0768] The server stores the data received from each device in a database, which also stores past data and is used for analysis.

[0769] Step 4:

[0770] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[0771] Operating rate = Playing time / Maximum operating time

[0772] Profitability rate = (revenue / turnover) 100

[0773] Step 5:

[0774] The server inputs the analysis data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data and current operating conditions.

[0775] Step 6:

[0776] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates.

[0777] Step 7:

[0778] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[0779] Step 8:

[0780] The device notifies the server that the setting change was successful, including the changed setting data.

[0781] Step 9:

[0782] The server compares the updated data with past data and generates a report based on the new operating status and revenue forecast, including the history of settings changes and forecast revenue for each gaming terminal.

[0783] Step 10:

[0784] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[0785] Step 11:

[0786] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[0787] In this way, by repeating the processes from step 1 to step 11, the overall profitability and operational efficiency of the pachinko parlor can be optimized.

[0788] Example 1

[0789] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0790] Currently, many gaming parlors manually monitor and optimize gaming terminal settings and operating status, but this work is time-consuming and labor-intensive, making it extremely inefficient. Manual setting changes also increase the likelihood of human error, making it difficult to manage revenue and improve operating efficiency at pachinko parlors. Furthermore, maintaining optimal gaming terminal settings requires complex decisions that take into account past data and current conditions, which is difficult to achieve with conventional systems.

[0791] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0792] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means for having a generative AI model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying an administrator; means for the server to send data collection requests to each gaming terminal at specified intervals; means for the server to store the data in a database and perform analysis; means for the server to input prompts into the generative AI model to obtain optimal setting data and for the server to send setting change commands to the terminals. This enables automatic and efficient optimization of gaming terminal settings, maximizing revenue, and improving business efficiency.

[0793] A "gaming terminal" is a device that is operated by a player to play games, and has the function of recording and transmitting game information.

[0794] A "server" is a central control device that collects data from gaming terminals, performs analytical processing, and transmits necessary instructions and information.

[0795] A "data collection request" is a request message sent by the server to a gaming terminal, and is intended to collect data relating to the operating status and revenue of the gaming terminal.

[0796] A "generative AI model" is an algorithm or trained model that uses artificial intelligence to generate configuration data to optimize the settings and operating conditions of gaming terminals.

[0797] A "prompt" is an instruction or question that is input to a generative AI model to generate optimal configuration data.

[0798] A "setting change command" is an instruction message sent by the server to a gaming terminal to change the settings of the gaming terminal.

[0799] A "database" is a storage device or system that stores and manages collected data in an organized manner and enables analysis and reference.

[0800] "Analysis results" are information calculated or evaluated based on collected data, and relate to the operating status and revenue of gaming terminals.

[0801] The "report" is a report that includes the operating status of the gaming terminal after the setting change and a revenue forecast, and is a summary of information that is notified to the administrator.

[0802] "Administrator" refers to the person in charge or responsible for the operation and management of gaming terminals and the entire system.

[0803] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of gaming parlors. This system consists of a server, gaming terminals, and users. The processing of each entity and its specific operation are explained in detail below.

[0804] Server Processing

[0805] The server first sends a data collection request to each gaming terminal at a specified interval. This request is to collect information necessary to understand the current gaming situation (play time, number of spins, balance, etc.). The server saves the received data in a database and performs analysis. This analysis may use a programming language such as Python or an SQL database.

[0806] As a result of the analysis, the server calculates the operating status (operating rate, profit rate) of each gaming terminal. Based on this analysis data, the server inputs a prompt sentence into the generative AI model to generate optimal setting data. Models such as OpenAI's GPT-3 can be used as the generative AI model. For example, the prompt sentence is as follows:

[0807] Prompt: "Generate optimal settings based on the utilization rate and profitability of gaming terminals. Terminal A: Utilization rate 83.3%, Profitability rate 1.67%. Terminal B: Utilization rate 50%, Profitability rate 1%."

[0808] Based on the generated setting data, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. This can be done using the HTTP protocol or a dedicated communication API.

[0809] Based on the data after the setting change, the server generates a report containing operational data and revenue forecasts and notifies the user. This report details the operational status and revenue forecasts before and after the setting change, and the user can make further decisions based on this report.

[0810] Gaming terminal processing

[0811] The terminal receives a data collection request from the server, and collects and compiles information such as game time, number of spins, and balance from its internal data records. The collected data is immediately sent back to the server.

[0812] The gaming terminal that receives the setting change command from the server changes the setting in accordance with the command, and if the setting change is successful, reports the result to the server.

[0813] User Action

[0814] Users can receive reports from the server and check the settings and operating status of their gaming terminals. Based on these reports, they can take additional actions, such as changing the placement of gaming terminals or implementing new marketing strategies.

[0815] Specific examples

[0816] For example, suppose the server sends a data collection request to terminal A and terminal B. Terminal A reports data showing 10 hours of play time, 3,000 spins, and a profit of 5,000 yen. Terminal B reports data showing 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes it to find that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0817] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can make additional decisions based on this report.

[0818] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of gaming parlors, and significantly improve business efficiency.

[0819] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0820] Step 1: Submit a data collection request

[0821] Server processing: The server sends a data collection request to each gaming terminal at a specified time interval. The data collection request includes instructions to report operating status such as game time, number of spins, and balance.

[0822] Input: Data collection request

[0823] Output: Each gaming device receives a data collection request

[0824] Step 2: Collect and send data

[0825] Terminal processing: Each gaming terminal receives a data collection request and, according to the instructions, compiles data such as game time, number of spins, and balance from its internal data records. The collected data is then sent back to the server. For example, terminal A reports a game time of 10 hours, 3,000 spins, and a balance of 5,000 yen.

[0826] Input: Data collection request

[0827] Output: Data reports from each device (e.g., game time, number of spins, balance)

[0828] Step 3: Data storage and analysis

[0829] Server processing: The server stores the data received from the terminals in a database. This can be done using a database management system such as SQL. Next, the server calculates the utilization rate and profitability of each gaming terminal based on the stored data. For example, it analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profitability is 1.67% (5,000 yen / 3,000 times).

[0830] Input: Data reports from each device

[0831] Output: Analysis results of utilization rate and profitability

[0832] Step 4: Generate optimal settings

[0833] Server processing: The server inputs a prompt statement into the generative AI model based on the analysis results, generating optimal configuration data. An example of a prompt statement is, "Generate the optimal configuration based on the utilization rate and profit rate of the gaming terminal. Terminal A: utilization rate 83.3%, profit rate 1.67%. Terminal B: utilization rate 50%, profit rate 1%." For example, OpenAI's GPT-3 can be used as the generative AI model.

[0834] Input: Analysis result, prompt statement

[0835] Output: Optimal setting data

[0836] Step 5: Sending configuration change commands

[0837] Server processing: Based on the setting data obtained from the generative AI model, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. In this case, the communication protocol may be HTTP or a dedicated communication API.

[0838] Input: Optimal setting data

[0839] Output: Configuration change command

[0840] Step 6: Implement and report configuration changes

[0841] Terminal processing: Each gaming terminal that receives a setting change command from the server changes the setting as instructed. If the setting change is successful, it reports the result to the server. For example, if terminal A completes the change to setting 1, it reports this to the server.

[0842] Input: Change setting command

[0843] Output: Report of the configuration change result

[0844] Step 7: Reporting and Notifications

[0845] Server processing: The server generates a report based on the new operational data and revenue forecast after the configuration change. This report includes the operational status and revenue forecast before and after the configuration change. This report is notified to the user (administrator).

[0846] Input: Operation data after setting change, revenue forecast

[0847] Output: Report notification

[0848] Through these steps, the system automatically optimizes gaming terminal settings, maximizing profits and improving operational efficiency.

[0849] (Application example 1)

[0850] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0851] Conventional gaming terminal setting optimization systems change settings based only on the operational data of individual terminals, so they are unable to fully consider the operational efficiency and profit maximization of the entire store. Furthermore, there are insufficient means for communicating the effects of setting changes to managers, making it difficult to use the results for marketing strategies or additional setting adjustments. This prevents optimal operation and limits profit and efficiency improvements.

[0852] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0853] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminals after the setting change and notifying the manager; means for optimizing the operational settings of the entire arcade by collecting and analyzing operational data of the arcade from multiple terminals; and means for supporting the manager in implementing additional marketing strategies and setting adjustments based on the generated report. This enables the manager to implement prompt and effective marketing strategies and setting adjustments while maximizing the operational efficiency and revenue of the entire arcade.

[0854] "Gaming terminal" refers to a terminal device used by customers to play games, including pachinko and pachislot machines.

[0855] "Data collection means" refers to a device or software that has the function of collecting data such as game time, number of spins, and income and expenditure from gaming terminals.

[0856] "Analysis means" refers to devices or software that have the function of analyzing collected data and calculating the operating status and revenue information of each gaming terminal.

[0857] "Generative artificial intelligence model" refers to an AI model that generates optimal configuration data based on collected data.

[0858] "Settings optimization means" refers to a device or software that has the function of changing the settings of each gaming terminal based on the setting data generated by the generative artificial intelligence model.

[0859] "Report generation means" refers to a device or software that has the function of generating a report including the operating status and revenue forecast of the gaming terminal after the settings have been changed, and notifying the administrator.

[0860] "Multiple terminal operation data collection means" refers to a device or software that has the function of collecting operation data from multiple gaming terminals in a lump.

[0861] "Store operation optimization function" refers to devices or software that have the ability to analyze collected operation data from multiple terminals and generate settings to optimize the operation of the entire store.

[0862] "Marketing strategy support function" refers to a device or software that has a function to support the administrator in implementing additional marketing strategies or adjusting settings based on the generated reports.

[0863] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the revenue and expenditure management and operational efficiency of gaming facilities. This system is composed of gaming terminals, a server, and an administrator.

[0864] Roles and functions of each component

[0865] 1. Gaming terminals:

[0866] The gaming terminal collects data such as game time, number of spins, and balance, and transmits it to the server.

[0867] 2. Server:

[0868] The server has the following features:

[0869] 1. Data collection method: At least the following data will be collected from each gaming terminal: playing time, number of spins, and income / expenses.

[0870] 2. Analysis method: Analyze the collected data and calculate information regarding the operating status and revenue of each gaming terminal.

[0871] 3. Generative AI model: Based on the results of the analysis, configuration data is generated to optimize the settings of each gaming terminal. A generative AI model trained using past gaming data and operational data from the entire arcade is used.

[0872] 4. Setting optimization means: Changes the settings of each gaming terminal according to the optimized setting data generated by the generating artificial intelligence model.

[0873] 5. Report generation means: Generates a report including the operating status of the gaming terminal and revenue forecast after the setting change, and notifies the administrator.

[0874] 6. Means for collecting operation data from multiple terminals: This has the function of optimizing the operation settings of the entire store by collecting and analyzing store operation data from multiple terminals.

[0875] 7. Marketing strategy support function: Provides support functions that allow administrators to implement additional marketing strategies and adjust settings based on the generated reports.

[0876] Natural language processing explanation

[0877] 1. Data Collection:

[0878] The server collects data on at least the game time, number of spins, and balance from the gaming terminals. To do this, the server sends a data collection request using an HTTP request.

[0879] 2. Data Analysis:

[0880] The collected data is analyzed on a server to calculate the operating status and profitability of each gaming terminal, and basic arithmetic operations are performed using programming languages ​​such as Python.

[0881] 3. Optimize settings:

[0882] The generative AI model generates optimal configuration data based on the analysis results. In particular, it uses a virtual AI model (using TensorFlow or PyTorch, for example) to analyze the data and propose optimal settings.

[0883] 4. Change the settings:

[0884] The server changes the settings of the gaming terminal based on the generated setting data using an HTTP POST request, etc. Once the changes are confirmed, the results are reported to the server.

[0885] 5. Reporting and Notifications:

[0886] Based on the data from the changed settings, the server generates a report containing new performance and revenue forecasts, which is then sent to the administrator, who can use it to implement additional marketing strategies or adjust settings.

[0887] Specific examples

[0888] For example, the server sends data collection requests from terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The server receives this data and analyzes the utilization rate and profitability of each terminal. These analysis results are input into a generative AI model, and the server recommends low settings for terminal A and high settings for terminal B.

[0889] Example prompt sentence:

[0890] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[0891] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0892] Step 1:

[0893] Submitting a Data Collection Request

[0894] The server sends a data collection request to the gaming terminal. Specifically, it sends a request to the gaming terminal's API endpoint using an HTTP GET request. The server's URL and API endpoint are required as input, and the output includes data collected by the terminal, such as game time, number of spins, and balance.

[0895] Step 2:

[0896] Data collection from gaming terminals

[0897] The terminal receives a data collection request from the server, compiles the specified game data (play time, number of spins, income and expenditure), and sends it to the server. Specifically, it extracts the necessary data from the terminal's database and returns it to the server as structured data in JSON format or similar. The input is the terminal's internal database, and the output is the data sent to the server.

[0898] Step 3:

[0899] Data analysis

[0900] The server analyzes the collected data and calculates information about the operating status and profitability of each gaming terminal. Specifically, it uses programs such as Python to analyze gaming time, rotations, and income and expenditures, and calculates operating rates and profitability. The input is the collected gaming data, and the output is numerical data on operating rates and profitability.

[0901] Step 4:

[0902] Optimizing configuration data with generative AI models

[0903] The server inputs the analysis results into a generative AI model to generate configuration data for optimizing the settings of each gaming terminal. Specifically, it analyzes past and current data using an AI model (using TensorFlow or PyTorch, for example) to determine the optimal settings. The inputs include analysis results on operating status and revenue, and the output is optimized configuration data for each terminal.

[0904] Step 5:

[0905] Changing device settings

[0906] The server sends a setting change command to each gaming terminal based on the generated setting data. Specifically, the internal settings of the terminal are changed by sending the setting data to the terminal's setting API endpoint using an HTTP POST request. The input is the optimized setting data, and the output is a success status of the setting change.

[0907] Step 6:

[0908] Report generation and administrator notification

[0909] The server generates a report based on the new operating status and forecasted revenue after the settings have been changed, and notifies the administrator. Specifically, a report is generated based on the analysis results and optimization settings, and sent to the administrator via email or a notification system. The input is the operating data after the settings have been changed and forecasted revenue data, and the output is a notification report sent to the administrator.

[0910] Step 7:

[0911] Marketing strategy support

[0912] The user receives the generated report and uses it to develop marketing strategies and adjust additional settings. Specifically, the user analyzes the report and takes necessary measures. The user can also further optimize settings by inputting new data as prompts to the generative AI model. For example, the prompts are as follows:

[0913] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[0914] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0915] This invention is a system that optimizes the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[0916] This system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives reports and takes additional action as necessary.

[0917] The system's program begins with the server sending data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as game time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0918] In the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed.

[0919] The analysis results are input into a generative AI model, which generates optimal settings. The generative AI model determines the settings (settings 1 to 6) for each gaming terminal based on past data, current operating status, and user emotional information to maximize profits.

[0920] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[0921] The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust the settings.

[0922] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0923] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0924] In this way, the system of the present invention can automatically optimize gaming terminal settings and utilize user emotional information to more accurately maximize profits and improve operational efficiency.

[0925] The processing flow will be explained below.

[0926] Step 1:

[0927] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[0928] Step 2:

[0929] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[0930] Step 3:

[0931] The emotion engine recognizes the user's facial expressions, voice, and biometric information in real time to generate emotional data about the user, including whether the user is excited, relaxed, stressed, etc.

[0932] Step 4:

[0933] The server stores the data received from each device and the emotion data received from the emotion engine in a database. The database also stores past data and uses it for analysis.

[0934] Step 5:

[0935] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[0936] Operating rate = Playing time / Maximum operating time

[0937] Profitability rate = (revenue / turnover) 100

[0938] Step 6:

[0939] The server inputs the analysis data and the user's emotional data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data, current operating status, and the user's emotional state.

[0940] Step 7:

[0941] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates. It may also consider emotional information and apply a high setting to users who are excited.

[0942] Step 8:

[0943] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[0944] Step 9:

[0945] The device notifies the server that the setting change was successful, including the changed setting data.

[0946] Step 10:

[0947] The server generates a report based on the data after the setting change and the emotion data, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[0948] Step 11:

[0949] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[0950] Step 12:

[0951] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[0952] In this way, by repeating the processes from step 1 to step 12, it is possible to optimize the overall profit and operational efficiency of a pachinko parlor. In addition, by combining it with an emotion engine, it becomes possible to change settings taking into account the user's emotional state, enabling more accurate maximization of profit and improvement of operational efficiency.

[0953] Example 2

[0954] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0955] In conventional pachinko parlors, the settings and operational efficiency of gaming terminals were mainly managed manually, which was inefficient. Furthermore, it was difficult to maximize profits and improve customer satisfaction without taking into account users' emotional information. This resulted in problems such as reduced management efficiency and customer attrition.

[0956] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0957] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying the manager; and means for recognizing emotions based on user facial expressions, voice, and biometric information and using that information for analysis. This enables optimization of gaming terminal settings, maximization of revenue, and more accurate management decisions based on user emotional information.

[0958] A "gaming terminal" is a gaming device used in pachinko parlors, game centers, etc., and is a device on which users can play games.

[0959] "Data collection means" refers to a device or software that has the function of collecting necessary data such as game time, number of spins, and income and expenditure from gaming terminals.

[0960] The "analysis means" refers to a device or software that has the function of analyzing the collected data and calculating information relating to the operating status and revenue of each gaming terminal.

[0961] A "generative artificial intelligence model" is an artificial intelligence model for generating optimal configuration data based on collected and analyzed data.

[0962] The "setting change means" is a device or software that has the function of changing the settings of each gaming terminal based on the generated setting data.

[0963] The "report generating means" is a device or software that has the function of generating a report including the operating status of the gaming terminal and a revenue forecast after the settings have been changed, and notifying the administrator of the report.

[0964] "Emotion recognition means" refers to devices or software that have the function of recognizing emotions based on a user's facial expressions, voice, and biological information, and using that information for analysis.

[0965] A "database" is an electronic data storage system for storing collected and analyzed data.

[0966] A "prompt" is an instruction or question used to input analysis results into a generative AI model.

[0967] This invention is a system that aims to improve the income and expenditure management and operational efficiency of gaming facilities by optimizing the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation, while the gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[0968] Specifically, the server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[0969] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[0970] The server then sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status and revenue forecast before and after the setting change, as well as the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust settings.

[0971] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[0972] These analysis results are input into a generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, suggesting that a high setting may be more likely to be applied to excited users. The server then sends this setting change data to device A and device B, which then change their settings. The server then generates a detailed report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[0973] An example of a prompt sentence to input to the generative AI model is as follows:

[0974] 1. "Generate optimal settings for each device based on past operating data, current operating status, and user emotional information."

[0975] 2. "To maximize profits, please recommend the following settings for the gaming device: Device A, Settings 1-6, User Sentiment Information, Historical Data."

[0976] In this way, the system according to the present invention can maximize profits and improve business efficiency by automatically optimizing the settings of gaming terminals and utilizing user emotional information.

[0977] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0978] Step 1:

[0979] The server sends a data collection request to each gaming terminal at a specified interval, which includes the type of data to be collected (playing time, number of spins, balance, etc.).

[0980] Input: Server timer trigger or polling settings

[0981] Output: Data collection request to gaming terminal

[0982] Step 2:

[0983] Based on the received data collection request, the terminal compiles data such as game time, number of spins, and income and expenditure.

[0984] Input: Data Collection Request

[0985] Output: Aggregated data such as game time, number of spins, income and expenditure

[0986] Step 3:

[0987] The terminal transmits the collected data to the server.

[0988] Input: Aggregate data

[0989] Output: Send data to the server

[0990] Step 4:

[0991] The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information.

[0992] Input: User's facial expression, voice, biometric information

[0993] Output: Recognized emotion data

[0994] Step 5:

[0995] The emotion engine transmits the recognized emotion data to the server.

[0996] Input: Emotion data

[0997] Output: Send data to the server

[0998] Step 6:

[0999] The server stores the game data from the terminal and the emotion data from the emotion engine in a database.

[1000] Input: Game data, emotion data

[1001] Output: Save to database

[1002] Step 7:

[1003] The server calculates the utilization rate and profitability rate of each terminal based on the stored data.

[1004] Input: Saved game data, emotional data

[1005] Output: Calculation results of utilization rate and profitability rate

[1006] Step 8:

[1007] The server inputs the analysis results into the generative AI model. Specifically, it sends prompts to the generative AI model to generate optimal settings based on past operation data, current operation data, and user emotion information.

[1008] Input: Analysis results, emotion data

[1009] Output: Prompt generation and input to the AI ​​model

[1010] Step 9:

[1011] The generative AI model generates optimal setting data (settings 1 to 6) based on the prompts received.

[1012] Input: prompt

[1013] Output: Optimal setting data

[1014] Step 10:

[1015] The server creates a setting change command based on the generated setting data and transmits it to each gaming terminal.

[1016] Input: Optimal setting data

[1017] Output: Creating a setting change command and sending it to the gaming terminal

[1018] Step 11:

[1019] The terminal receives this setting change command and automatically changes the setting.

[1020] Input: Change setting command

[1021] Output: Configuration changes made

[1022] Step 12:

[1023] If the setting change is successful, the terminal reports the result to the server.

[1024] Input: Result of setting change

[1025] Output: Report to server

[1026] Step 13:

[1027] The server generates a detailed report based on the data after the setting change and the user's emotional state, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[1028] Input: Data after setting changes, user emotion data

[1029] Output: Detailed report

[1030] Step 14:

[1031] The server notifies the user of the generated report.

[1032] Input: Detailed report

[1033] Output: User notification

[1034] In this way, the systems work together to optimize gaming terminal settings and improve management efficiency.

[1035] (Application example 2)

[1036] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1037] The objective of this invention is to provide a means to maximize revenue while improving the customer experience in brick-and-mortar stores by optimizing the settings and operational efficiency of gaming terminals using generative AI models and emotion engines. Furthermore, by taking into account customer emotion data, it aims to increase customer satisfaction and encourage repeat customers.

[1038] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying a manager; means for collecting and analyzing customer emotions; means for generating setting data for adjusting the store environment based on the analyzed emotion data; and means for notifying a manager of the setting data for optimizing on-site engagement in the store. This makes it possible to dynamically adjust the store environment and gaming terminal settings using customer emotion information to improve the customer experience.

[1039] "Gaming terminal" refers to a machine installed for entertainment purposes, such as a pachinko machine or slot machine.

[1040] "Game time" is data indicating the total time that a gaming terminal is actually operating.

[1041] The "number of spins" is data indicating the frequency with which a specific action (for example, the number of times a ball on a pachinko machine makes a specific spin) is executed on a gaming terminal.

[1042] "Balance" is data indicating the total profits and losses generated by a gaming terminal.

[1043] "Operating status" is information that comprehensively indicates the usage status of the gaming terminal and usage information such as operating time, rotation speed, income and expenditure.

[1044] "Revenue" refers to monetary profits obtained through gaming terminals.

[1045] A "generative artificial intelligence model" is an algorithm or model that uses artificial intelligence to perform data analysis and predictions for specific purposes.

[1046] "Setting data" refers to data that includes specific numerical values ​​and commands for adjusting the operating conditions and specifications of the gaming terminal based on the analysis results.

[1047] "Analysis results" are conclusions or judgments reached by applying specific calculations or algorithms to collected data.

[1048] A "report" is a report generated to inform the manager of the results of data analysis and the status of the gaming terminal after the settings have been changed.

[1049] "Emotion" is information that indicates the user's psychological state, and represents specific emotions such as excitement, relaxation, and happiness.

[1050] An "emotion engine" is a software algorithm and hardware configuration that recognizes and analyzes user emotions in real time based on sensor data and user input.

[1051] "Store environment" is a general term for elements that affect the customer experience within a store, such as music, lighting, and product placement.

[1052] "On-site engagement" refers to the interactions that occur when a customer visits a store and interacts with a product or service face-to-face.

[1053] This invention is a system that utilizes a generative AI model and an emotion engine to optimize gaming terminal settings and operating efficiency, improving the customer experience at brick-and-mortar stores. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[1054] The server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[1055] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[1056] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the generative AI model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the administrator, who can use it to implement additional marketing strategies and adjust the settings.

[1057] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[1058] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[1059] To optimize the store environment, the server collects customer emotion data and dynamically adjusts the music, lighting, and display content in the store accordingly. For example, if a customer expresses "happy," the server can respond by changing the music in the store to an upbeat tone. The hardware used in this process includes a Raspberry Pi and a camera module, and the software includes Keras and OpenCV.

[1060] Example prompts to input to a generative AI model:

[1061] How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?

[1062] In this way, the system of the present invention optimizes not only the gaming terminals but also the environment of the entire store based on customer emotion data, thereby improving customer experience and maximizing profits.

[1063] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1064] Step 1:

[1065] The server transmits a data collection request to each gaming terminal.

[1066] Specifically, it sends data collection requests at specified time intervals and waits for responses from gaming terminals. The input is the data collection request. The output is data on game time, number of spins, and balance collected from each gaming terminal.

[1067] Step 2:

[1068] The terminal receives the data collection request and compiles data on playing time, number of spins, and income and expenditure.

[1069] Specifically, it collects the latest gaming data from an internal database, aggregates it, and returns it to the server. The input is a data collection request from the server. The output is data on gaming time, number of spins, and balance.

[1070] Step 3:

[1071] The emotion engine collects and recognizes the user's emotion information and sends it to the server.

[1072] Specifically, it uses cameras, microphones, sensors, etc. to collect and analyze the user's facial expressions, voice, and biometric information in real time. The inputs include the user's facial expressions, voice, and biometric information. The output is the analyzed emotional data sent to a server.

[1073] Step 4:

[1074] The server stores the received data in a database and performs analysis.

[1075] Specifically, data sent from gaming terminals and emotion data sent from the emotion engine are stored in a database and analyzed to calculate utilization rates and profit rates. The inputs are game data and emotion data. The output is the calculated utilization rate and profit rate for each gaming terminal.

[1076] Step 5:

[1077] The server inputs the analysis results into a generative AI model to generate optimal configuration data.

[1078] Specifically, past data, current operating conditions, and emotional information are provided as inputs to the generative AI model, which then generates configuration data that maximizes profits. The inputs include analysis results, and the output is optimized configuration data.

[1079] Step 6:

[1080] The server transmits setting change commands to each gaming terminal based on the setting data obtained from the generated AI model.

[1081] Specifically, it generates and transmits commands to each gaming terminal based on the setting data, instructing them to make specific setting changes. The input is the generated setting data. The output is a setting change command sent to the gaming terminal.

[1082] Step 7:

[1083] The terminal receives the setting change command and changes the setting.

[1084] Specifically, the internal settings of the gaming terminal are changed based on the received setting change command. The input is a setting change command from the server. The output is the execution of the setting change.

[1085] Step 8:

[1086] The server generates a report based on the data after the setting change and the emotion data, and notifies the administrator.

[1087] Specifically, the gameplay data and emotion data collected again after the settings are changed are analyzed, and a report is generated summarizing the operating status, revenue forecast, and emotional state before and after the settings are changed. The inputs are the gameplay data and emotion data after the settings are changed. The output is to notify the administrator of the generated report.

[1088] Step 9:

[1089] The user makes further decisions based on the report.

[1090] Specifically, administrators review the report content and implement additional marketing strategies or configuration adjustments as necessary. The input is the generated report. The output is additional actions by the user.

[1091] Step 10:

[1092] The server collects customer emotion data and generates setting data to adjust the store environment.

[1093] Specifically, it analyzes user emotional data, generates setting data for dynamically changing music, lighting, and display settings in the store, and sends this data to the store's management system. The input is customer emotional data, and the output is setting data for adjusting the store environment.

[1094] Example prompt sentence:

[1095] "How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?"

[1096] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1098] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1099] [Fourth embodiment]

[1100] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1101] 7, a 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.

[1102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1103] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1104] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1107] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1108] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1109] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1111] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1112] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1113] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[1114] This system consists of a server, gaming terminals, and users. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. Users receive reports and take additional actions as necessary.

[1115] The program for this system begins with the server sending data collection requests from each gaming terminal at a specified interval. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The server stores this data in a database and performs analysis.

[1116] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. These calculation results are input into a generative AI model, which generates optimal setting data. Based on past data and current operating status, the AI ​​model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[1117] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[1118] The server generates a report based on the data after the configuration change. This report includes the performance status and revenue forecast before and after the configuration change. The report is sent to the user, who can use it to implement additional marketing strategies or adjust the configuration.

[1119] As a concrete example, we will explain the processing for terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a profit of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[1120] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server then sends this setting change data to device A and device B, which then change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can then make additional decisions based on this report.

[1121] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of pachinko parlors, and significantly improve business efficiency.

[1122] The processing flow will be explained below.

[1123] Step 1:

[1124] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[1125] Step 2:

[1126] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[1127] Step 3:

[1128] The server stores the data received from each device in a database, which also stores past data and is used for analysis.

[1129] Step 4:

[1130] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[1131] Operating rate = Playing time / Maximum operating time

[1132] Profitability rate = (revenue / turnover) 100

[1133] Step 5:

[1134] The server inputs the analysis data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data and current operating conditions.

[1135] Step 6:

[1136] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates.

[1137] Step 7:

[1138] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[1139] Step 8:

[1140] The device notifies the server that the setting change was successful, including the changed setting data.

[1141] Step 9:

[1142] The server compares the updated data with past data and generates a report based on the new operating status and revenue forecast, including the history of settings changes and forecast revenue for each gaming terminal.

[1143] Step 10:

[1144] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[1145] Step 11:

[1146] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[1147] In this way, by repeating the processes from step 1 to step 11, the overall profitability and operational efficiency of the pachinko parlor can be optimized.

[1148] Example 1

[1149] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1150] Currently, many gaming parlors manually monitor and optimize gaming terminal settings and operating status, but this work is time-consuming and labor-intensive, making it extremely inefficient. Manual setting changes also increase the likelihood of human error, making it difficult to manage revenue and improve operating efficiency at pachinko parlors. Furthermore, maintaining optimal gaming terminal settings requires complex decisions that take into account past data and current conditions, which is difficult to achieve with conventional systems.

[1151] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1152] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means for having a generative AI model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying an administrator; means for the server to send data collection requests to each gaming terminal at specified intervals; means for the server to store the data in a database and perform analysis; means for the server to input prompts into the generative AI model to obtain optimal setting data and for the server to send setting change commands to the terminals. This enables automatic and efficient optimization of gaming terminal settings, maximizing revenue, and improving business efficiency.

[1153] A "gaming terminal" is a device that is operated by a player to play games, and has the function of recording and transmitting game information.

[1154] A "server" is a central control device that collects data from gaming terminals, performs analytical processing, and transmits necessary instructions and information.

[1155] A "data collection request" is a request message sent by the server to a gaming terminal, and is intended to collect data relating to the operating status and revenue of the gaming terminal.

[1156] A "generative AI model" is an algorithm or trained model that uses artificial intelligence to generate configuration data to optimize the settings and operating conditions of gaming terminals.

[1157] A "prompt" is an instruction or question that is input to a generative AI model to generate optimal configuration data.

[1158] A "setting change command" is an instruction message sent by the server to a gaming terminal to change the settings of the gaming terminal.

[1159] A "database" is a storage device or system that stores and manages collected data in an organized manner and enables analysis and reference.

[1160] "Analysis results" are information calculated or evaluated based on collected data, and relate to the operating status and revenue of gaming terminals.

[1161] The "report" is a report that includes the operating status of the gaming terminal after the setting change and a revenue forecast, and is a summary of information that is notified to the administrator.

[1162] "Administrator" refers to the person in charge or responsible for the operation and management of gaming terminals and the entire system.

[1163] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the income and expenditure management and operational efficiency of gaming parlors. This system consists of a server, gaming terminals, and users. The processing of each entity and its specific operation are explained in detail below.

[1164] Server Processing

[1165] The server first sends a data collection request to each gaming terminal at a specified interval. This request is to collect information necessary to understand the current gaming situation (play time, number of spins, balance, etc.). The server saves the received data in a database and performs analysis. This analysis may use a programming language such as Python or an SQL database.

[1166] As a result of the analysis, the server calculates the operating status (operating rate, profit rate) of each gaming terminal. Based on this analysis data, the server inputs a prompt sentence into the generative AI model to generate optimal setting data. Models such as OpenAI's GPT-3 can be used as the generative AI model. For example, the prompt sentence is as follows:

[1167] Prompt: "Generate optimal settings based on the utilization rate and profitability of gaming terminals. Terminal A: Utilization rate 83.3%, Profitability rate 1.67%. Terminal B: Utilization rate 50%, Profitability rate 1%."

[1168] Based on the generated setting data, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. This can be done using the HTTP protocol or a dedicated communication API.

[1169] Based on the data after the setting change, the server generates a report containing operational data and revenue forecasts and notifies the user. This report details the operational status and revenue forecasts before and after the setting change, and the user can make further decisions based on this report.

[1170] Gaming terminal processing

[1171] The terminal receives a data collection request from the server, and collects and compiles information such as game time, number of spins, and balance from its internal data records. The collected data is immediately sent back to the server.

[1172] The gaming terminal that receives the setting change command from the server changes the setting in accordance with the command, and if the setting change is successful, reports the result to the server.

[1173] User Action

[1174] Users can receive reports from the server and check the settings and operating status of their gaming terminals. Based on these reports, they can take additional actions, such as changing the placement of gaming terminals or implementing new marketing strategies.

[1175] Specific examples

[1176] For example, suppose the server sends a data collection request to terminal A and terminal B. Terminal A reports data showing 10 hours of play time, 3,000 spins, and a profit of 5,000 yen. Terminal B reports data showing 6 hours of play time, 2,000 spins, and a profit of 2,000 yen. The server receives this data and analyzes it to find that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[1177] These analysis results are input into the generative AI model, and the server recommends low settings (setting 1) for device A and high settings (setting 6) for device B. The server sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status and predicted revenue and notifies the user. The user can make additional decisions based on this report.

[1178] In this way, the system according to the present invention can automatically optimize the settings of gaming terminals, stabilize the profits of gaming parlors, and significantly improve business efficiency.

[1179] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1180] Step 1: Submit a data collection request

[1181] Server processing: The server sends a data collection request to each gaming terminal at a specified time interval. The data collection request includes instructions to report operating status such as game time, number of spins, and balance.

[1182] Input: Data collection request

[1183] Output: Each gaming device receives a data collection request

[1184] Step 2: Collect and send data

[1185] Terminal processing: Each gaming terminal receives a data collection request and, according to the instructions, compiles data such as game time, number of spins, and balance from its internal data records. The collected data is then sent back to the server. For example, terminal A reports a game time of 10 hours, 3,000 spins, and a balance of 5,000 yen.

[1186] Input: Data collection request

[1187] Output: Data reports from each device (e.g., game time, number of spins, balance)

[1188] Step 3: Data storage and analysis

[1189] Server processing: The server stores the data received from the terminals in a database. This can be done using a database management system such as SQL. Next, the server calculates the utilization rate and profitability of each gaming terminal based on the stored data. For example, it analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profitability is 1.67% (5,000 yen / 3,000 times).

[1190] Input: Data reports from each device

[1191] Output: Analysis results of utilization rate and profitability

[1192] Step 4: Generate optimal settings

[1193] Server processing: The server inputs a prompt statement into the generative AI model based on the analysis results, generating optimal configuration data. An example of a prompt statement is, "Generate the optimal configuration based on the utilization rate and profit rate of the gaming terminal. Terminal A: utilization rate 83.3%, profit rate 1.67%. Terminal B: utilization rate 50%, profit rate 1%." For example, OpenAI's GPT-3 can be used as the generative AI model.

[1194] Input: Analysis result, prompt statement

[1195] Output: Optimal setting data

[1196] Step 5: Sending configuration change commands

[1197] Server processing: Based on the setting data obtained from the generative AI model, the server sends setting change commands to each gaming terminal. For example, it sends a low setting (setting 1) to terminal A and a high setting (setting 6) to terminal B. In this case, the communication protocol may be HTTP or a dedicated communication API.

[1198] Input: Optimal setting data

[1199] Output: Configuration change command

[1200] Step 6: Implement and report configuration changes

[1201] Terminal processing: Each gaming terminal that receives a setting change command from the server changes the setting as instructed. If the setting change is successful, it reports the result to the server. For example, if terminal A completes the change to setting 1, it reports this to the server.

[1202] Input: Change setting command

[1203] Output: Report of the configuration change result

[1204] Step 7: Reporting and Notifications

[1205] Server processing: The server generates a report based on the new operational data and revenue forecast after the configuration change. This report includes the operational status and revenue forecast before and after the configuration change. This report is notified to the user (administrator).

[1206] Input: Operation data after setting change, revenue forecast

[1207] Output: Report notification

[1208] Through these steps, the system automatically optimizes gaming terminal settings, maximizing profits and improving operational efficiency.

[1209] (Application example 1)

[1210] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1211] Conventional gaming terminal setting optimization systems change settings based only on the operational data of individual terminals, so they are unable to fully consider the operational efficiency and profit maximization of the entire store. Furthermore, there are insufficient means for communicating the effects of setting changes to managers, making it difficult to use the results for marketing strategies or additional setting adjustments. This prevents optimal operation and limits profit and efficiency improvements.

[1212] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1213] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminals after the setting change and notifying the manager; means for optimizing the operational settings of the entire arcade by collecting and analyzing operational data of the arcade from multiple terminals; and means for supporting the manager in implementing additional marketing strategies and setting adjustments based on the generated report. This enables the manager to implement prompt and effective marketing strategies and setting adjustments while maximizing the operational efficiency and revenue of the entire arcade.

[1214] "Gaming terminal" refers to a terminal device used by customers to play games, including pachinko and pachislot machines.

[1215] "Data collection means" refers to a device or software that has the function of collecting data such as game time, number of spins, and income and expenditure from gaming terminals.

[1216] "Analysis means" refers to devices or software that have the function of analyzing collected data and calculating the operating status and revenue information of each gaming terminal.

[1217] "Generative artificial intelligence model" refers to an AI model that generates optimal configuration data based on collected data.

[1218] "Settings optimization means" refers to a device or software that has the function of changing the settings of each gaming terminal based on the setting data generated by the generative artificial intelligence model.

[1219] "Report generation means" refers to a device or software that has the function of generating a report including the operating status and revenue forecast of the gaming terminal after the settings have been changed, and notifying the administrator.

[1220] "Multiple terminal operation data collection means" refers to a device or software that has the function of collecting operation data from multiple gaming terminals in a lump.

[1221] "Store operation optimization function" refers to devices or software that have the ability to analyze collected operation data from multiple terminals and generate settings to optimize the operation of the entire store.

[1222] "Marketing strategy support function" refers to a device or software that has a function to support the administrator in implementing additional marketing strategies or adjusting settings based on the generated reports.

[1223] This invention is a system that utilizes a generative AI model to optimize the settings and operational efficiency of gaming terminals, thereby improving the revenue and expenditure management and operational efficiency of gaming facilities. This system is composed of gaming terminals, a server, and an administrator.

[1224] Roles and functions of each component

[1225] 1. Gaming terminals:

[1226] The gaming terminal collects data such as game time, number of spins, and balance, and transmits it to the server.

[1227] 2. Server:

[1228] The server has the following features:

[1229] 1. Data collection method: At least the following data will be collected from each gaming terminal: playing time, number of spins, and income / expenses.

[1230] 2. Analysis method: Analyze the collected data and calculate information regarding the operating status and revenue of each gaming terminal.

[1231] 3. Generative AI model: Based on the results of the analysis, configuration data is generated to optimize the settings of each gaming terminal. A generative AI model trained using past gaming data and operational data from the entire arcade is used.

[1232] 4. Setting optimization means: Changes the settings of each gaming terminal according to the optimized setting data generated by the generating artificial intelligence model.

[1233] 5. Report generation means: Generates a report including the operating status of the gaming terminal and revenue forecast after the setting change, and notifies the administrator.

[1234] 6. Means for collecting operation data from multiple terminals: This has the function of optimizing the operation settings of the entire store by collecting and analyzing store operation data from multiple terminals.

[1235] 7. Marketing strategy support function: Provides support functions that allow administrators to implement additional marketing strategies and adjust settings based on the generated reports.

[1236] Natural language processing explanation

[1237] 1. Data Collection:

[1238] The server collects data on at least the game time, number of spins, and balance from the gaming terminals. To do this, the server sends a data collection request using an HTTP request.

[1239] 2. Data Analysis:

[1240] The collected data is analyzed on a server to calculate the operating status and profitability of each gaming terminal, and basic arithmetic operations are performed using programming languages ​​such as Python.

[1241] 3. Optimize settings:

[1242] The generative AI model generates optimal configuration data based on the analysis results. In particular, it uses a virtual AI model (using TensorFlow or PyTorch, for example) to analyze the data and propose optimal settings.

[1243] 4. Change the settings:

[1244] The server changes the settings of the gaming terminal based on the generated setting data using an HTTP POST request, etc. Once the changes are confirmed, the results are reported to the server.

[1245] 5. Reporting and Notifications:

[1246] Based on the data from the changed settings, the server generates a report containing new performance and revenue forecasts, which is then sent to the administrator, who can use it to implement additional marketing strategies or adjust settings.

[1247] Specific examples

[1248] For example, the server sends data collection requests from terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The server receives this data and analyzes the utilization rate and profitability of each terminal. These analysis results are input into a generative AI model, and the server recommends low settings for terminal A and high settings for terminal B.

[1249] Example prompt sentence:

[1250] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[1251] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1252] Step 1:

[1253] Submitting a Data Collection Request

[1254] The server sends a data collection request to the gaming terminal. Specifically, it sends a request to the gaming terminal's API endpoint using an HTTP GET request. The server's URL and API endpoint are required as input, and the output includes data collected by the terminal, such as game time, number of spins, and balance.

[1255] Step 2:

[1256] Data collection from gaming terminals

[1257] The terminal receives a data collection request from the server, compiles the specified game data (play time, number of spins, income and expenditure), and sends it to the server. Specifically, it extracts the necessary data from the terminal's database and returns it to the server as structured data in JSON format or similar. The input is the terminal's internal database, and the output is the data sent to the server.

[1258] Step 3:

[1259] Data analysis

[1260] The server analyzes the collected data and calculates information about the operating status and profitability of each gaming terminal. Specifically, it uses programs such as Python to analyze gaming time, rotations, and income and expenditures, and calculates operating rates and profitability. The input is the collected gaming data, and the output is numerical data on operating rates and profitability.

[1261] Step 4:

[1262] Optimizing configuration data with generative AI models

[1263] The server inputs the analysis results into a generative AI model to generate configuration data for optimizing the settings of each gaming terminal. Specifically, it analyzes past and current data using an AI model (using TensorFlow or PyTorch, for example) to determine the optimal settings. The inputs include analysis results on operating status and revenue, and the output is optimized configuration data for each terminal.

[1264] Step 5:

[1265] Changing device settings

[1266] The server sends a setting change command to each gaming terminal based on the generated setting data. Specifically, the internal settings of the terminal are changed by sending the setting data to the terminal's setting API endpoint using an HTTP POST request. The input is the optimized setting data, and the output is a success status of the setting change.

[1267] Step 6:

[1268] Report generation and administrator notification

[1269] The server generates a report based on the new operating status and forecasted revenue after the settings have been changed, and notifies the administrator. Specifically, a report is generated based on the analysis results and optimization settings, and sent to the administrator via email or a notification system. The input is the operating data after the settings have been changed and forecasted revenue data, and the output is a notification report sent to the administrator.

[1270] Step 7:

[1271] Marketing strategy support

[1272] The user receives the generated report and uses it to develop marketing strategies and adjust additional settings. Specifically, the user analyzes the report and takes necessary measures. The user can also further optimize settings by inputting new data as prompts to the generative AI model. For example, the prompts are as follows:

[1273] "Optimize the settings of each gaming device based on past data and current operating conditions. As a concrete example, please use the data for a device at an amusement facility (10 hours of play time, 3,000 spins, and a balance of 5,000 yen)."

[1274] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1275] This invention is a system that optimizes the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine, thereby improving the income and expenditure management and operational efficiency of pachinko parlors. Below, we will explain the program processing and specific examples of this system in natural language.

[1276] This system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for collecting data, analyzing it, changing settings, and generating reports. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives reports and takes additional action as necessary.

[1277] The system's program begins with the server sending data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as game time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[1278] In the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed.

[1279] The analysis results are input into a generative AI model, which generates optimal settings. The generative AI model determines the settings (settings 1 to 6) for each gaming terminal based on past data, current operating status, and user emotional information to maximize profits.

[1280] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server.

[1281] The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust the settings.

[1282] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[1283] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[1284] In this way, the system of the present invention can automatically optimize gaming terminal settings and utilize user emotional information to more accurately maximize profits and improve operational efficiency.

[1285] The processing flow will be explained below.

[1286] Step 1:

[1287] The server sends a data collection request to each gaming terminal at a set interval (for example, every hour), which includes instructions to provide data such as game time, number of spins, and balance.

[1288] Step 2:

[1289] When a terminal receives a request from the server, it collects the latest data, such as game time, number of spins, and balance, and sends it back to the server. The data collected by each terminal also includes detailed information such as peak times.

[1290] Step 3:

[1291] The emotion engine recognizes the user's facial expressions, voice, and biometric information in real time to generate emotional data about the user, including whether the user is excited, relaxed, stressed, etc.

[1292] Step 4:

[1293] The server stores the data received from each device and the emotion data received from the emotion engine in a database. The database also stores past data and uses it for analysis.

[1294] Step 5:

[1295] The server analyzes the operating status and revenue status of each gaming terminal based on the new and past data stored in the database. Specifically, it calculates the following indicators:

[1296] Operating rate = Playing time / Maximum operating time

[1297] Profitability rate = (revenue / turnover) 100

[1298] Step 6:

[1299] The server inputs the analysis data and the user's emotional data into the generative AI model, which calculates the optimal settings (settings 1 to 6) for each device based on past data, current operating status, and the user's emotional state.

[1300] Step 7:

[1301] The server generates appropriate setting change commands for each device based on the optimal setting data obtained from the generative AI model. For example, it recommends a low setting (setting 1) for devices with high utilization rates and a high setting (setting 6) for devices with low utilization rates. It may also consider emotional information and apply a high setting to users who are excited.

[1302] Step 8:

[1303] The server transmits the generated setting change command to each gaming terminal, which receives the command and makes the specified changes to the settings.

[1304] Step 9:

[1305] The device notifies the server that the setting change was successful, including the changed setting data.

[1306] Step 10:

[1307] The server generates a report based on the data after the setting change and the emotion data, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[1308] Step 11:

[1309] The server will notify the user (administrator) of the generated report via email or dashboard, and the user can take any further action required based on the report.

[1310] Step 12:

[1311] After checking the report, the user can consider further setting adjustments and marketing strategies, and if necessary, issue new instructions to the server, which will then collect and analyze data again.

[1312] In this way, by repeating the processes from step 1 to step 12, it is possible to optimize the overall profit and operational efficiency of a pachinko parlor. In addition, by combining it with an emotion engine, it becomes possible to change settings taking into account the user's emotional state, enabling more accurate maximization of profit and improvement of operational efficiency.

[1313] Example 2

[1314] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1315] In conventional pachinko parlors, the settings and operational efficiency of gaming terminals were mainly managed manually, which was inefficient. Furthermore, it was difficult to maximize profits and improve customer satisfaction without taking into account users' emotional information. This resulted in problems such as reduced management efficiency and customer attrition.

[1316] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1317] In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from the gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying the manager; and means for recognizing emotions based on user facial expressions, voice, and biometric information and using that information for analysis. This enables optimization of gaming terminal settings, maximization of revenue, and more accurate management decisions based on user emotional information.

[1318] A "gaming terminal" is a gaming device used in pachinko parlors, game centers, etc., and is a device on which users can play games.

[1319] "Data collection means" refers to a device or software that has the function of collecting necessary data such as game time, number of spins, and income and expenditure from gaming terminals.

[1320] The "analysis means" refers to a device or software that has the function of analyzing the collected data and calculating information relating to the operating status and revenue of each gaming terminal.

[1321] A "generative artificial intelligence model" is an artificial intelligence model for generating optimal configuration data based on collected and analyzed data.

[1322] The "setting change means" is a device or software that has the function of changing the settings of each gaming terminal based on the generated setting data.

[1323] The "report generating means" is a device or software that has the function of generating a report including the operating status of the gaming terminal and a revenue forecast after the settings have been changed, and notifying the administrator of the report.

[1324] "Emotion recognition means" refers to devices or software that have the function of recognizing emotions based on a user's facial expressions, voice, and biological information, and using that information for analysis.

[1325] A "database" is an electronic data storage system for storing collected and analyzed data.

[1326] A "prompt" is an instruction or question used to input analysis results into a generative AI model.

[1327] This invention is a system that aims to improve the income and expenditure management and operational efficiency of gaming facilities by optimizing the settings and operational efficiency of gaming terminals using a generative AI model and an emotion engine. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation, while the gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[1328] Specifically, the server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[1329] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[1330] The server then sends a setting change command to each gaming terminal based on the setting data obtained from the AI ​​model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status and revenue forecast before and after the setting change, as well as the user's emotional state. The report is notified to the user, who can use it to implement additional marketing strategies or adjust settings.

[1331] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[1332] These analysis results are input into a generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, suggesting that a high setting may be more likely to be applied to excited users. The server then sends this setting change data to device A and device B, which then change their settings. The server then generates a detailed report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[1333] An example of a prompt sentence to input to the generative AI model is as follows:

[1334] 1. "Generate optimal settings for each device based on past operating data, current operating status, and user emotional information."

[1335] 2. "To maximize profits, please recommend the following settings for the gaming device: Device A, Settings 1-6, User Sentiment Information, Historical Data."

[1336] In this way, the system according to the present invention can maximize profits and improve business efficiency by automatically optimizing the settings of gaming terminals and utilizing user emotional information.

[1337] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1338] Step 1:

[1339] The server sends a data collection request to each gaming terminal at a specified interval, which includes the type of data to be collected (playing time, number of spins, balance, etc.).

[1340] Input: Server timer trigger or polling settings

[1341] Output: Data collection request to gaming terminal

[1342] Step 2:

[1343] Based on the received data collection request, the terminal compiles data such as game time, number of spins, and income and expenditure.

[1344] Input: Data Collection Request

[1345] Output: Aggregated data such as game time, number of spins, income and expenditure

[1346] Step 3:

[1347] The terminal transmits the collected data to the server.

[1348] Input: Aggregate data

[1349] Output: Send data to the server

[1350] Step 4:

[1351] The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information.

[1352] Input: User's facial expression, voice, biometric information

[1353] Output: Recognized emotion data

[1354] Step 5:

[1355] The emotion engine transmits the recognized emotion data to the server.

[1356] Input: Emotion data

[1357] Output: Send data to the server

[1358] Step 6:

[1359] The server stores the game data from the terminal and the emotion data from the emotion engine in a database.

[1360] Input: Game data, emotion data

[1361] Output: Save to database

[1362] Step 7:

[1363] The server calculates the utilization rate and profitability rate of each terminal based on the stored data.

[1364] Input: Saved game data, emotional data

[1365] Output: Calculation results of utilization rate and profitability rate

[1366] Step 8:

[1367] The server inputs the analysis results into the generative AI model. Specifically, it sends prompts to the generative AI model to generate optimal settings based on past operation data, current operation data, and user emotion information.

[1368] Input: Analysis results, emotion data

[1369] Output: Prompt generation and input to the AI ​​model

[1370] Step 9:

[1371] The generative AI model generates optimal setting data (settings 1 to 6) based on the prompts received.

[1372] Input: prompt

[1373] Output: Optimal setting data

[1374] Step 10:

[1375] The server creates a setting change command based on the generated setting data and transmits it to each gaming terminal.

[1376] Input: Optimal setting data

[1377] Output: Creating a setting change command and sending it to the gaming terminal

[1378] Step 11:

[1379] The terminal receives this setting change command and automatically changes the setting.

[1380] Input: Change setting command

[1381] Output: Configuration changes made

[1382] Step 12:

[1383] If the setting change is successful, the terminal reports the result to the server.

[1384] Input: Result of setting change

[1385] Output: Report to server

[1386] Step 13:

[1387] The server generates a detailed report based on the data after the setting change and the user's emotional state, including the operating status, revenue forecast, and the user's emotional state before and after the setting change.

[1388] Input: Data after setting changes, user emotion data

[1389] Output: Detailed report

[1390] Step 14:

[1391] The server notifies the user of the generated report.

[1392] Input: Detailed report

[1393] Output: User notification

[1394] In this way, the systems work together to optimize gaming terminal settings and improve management efficiency.

[1395] (Application example 2)

[1396] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1397] The objective of this invention is to provide a means to maximize revenue while improving the customer experience in brick-and-mortar stores by optimizing the settings and operational efficiency of gaming terminals using generative AI models and emotion engines. Furthermore, by taking into account customer emotion data, it aims to increase customer satisfaction and encourage repeat customers.

[1398] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting data on at least game time, number of spins, and income and expenditure from gaming terminals; means for analyzing the collected data to calculate information on the operating status and revenue of each gaming terminal; means having a generative artificial intelligence model for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying a manager; means for collecting and analyzing customer emotions; means for generating setting data for adjusting the store environment based on the analyzed emotion data; and means for notifying a manager of the setting data for optimizing on-site engagement in the store. This makes it possible to dynamically adjust the store environment and gaming terminal settings using customer emotion information to improve the customer experience.

[1399] "Gaming terminal" refers to a machine installed for entertainment purposes, such as a pachinko machine or slot machine.

[1400] "Game time" is data indicating the total time that a gaming terminal is actually operating.

[1401] The "number of spins" is data indicating the frequency with which a specific action (for example, the number of times a ball on a pachinko machine makes a specific spin) is executed on a gaming terminal.

[1402] "Balance" is data indicating the total profits and losses generated by a gaming terminal.

[1403] "Operating status" is information that comprehensively indicates the usage status of the gaming terminal and usage information such as operating time, rotation speed, income and expenditure.

[1404] "Revenue" refers to monetary profits obtained through gaming terminals.

[1405] A "generative artificial intelligence model" is an algorithm or model that uses artificial intelligence to perform data analysis and predictions for specific purposes.

[1406] "Setting data" refers to data that includes specific numerical values ​​and commands for adjusting the operating conditions and specifications of the gaming terminal based on the analysis results.

[1407] "Analysis results" are conclusions or judgments reached by applying specific calculations or algorithms to collected data.

[1408] A "report" is a report generated to inform the manager of the results of data analysis and the status of the gaming terminal after the settings have been changed.

[1409] "Emotion" is information that indicates the user's psychological state, and represents specific emotions such as excitement, relaxation, and happiness.

[1410] An "emotion engine" is a software algorithm and hardware configuration that recognizes and analyzes user emotions in real time based on sensor data and user input.

[1411] "Store environment" is a general term for elements that affect the customer experience within a store, such as music, lighting, and product placement.

[1412] "On-site engagement" refers to the interactions that occur when a customer visits a store and interacts with a product or service face-to-face.

[1413] This invention is a system that utilizes a generative AI model and an emotion engine to optimize gaming terminal settings and operating efficiency, improving the customer experience at brick-and-mortar stores. The system consists of a server, gaming terminals, users, and an emotion engine. The server is responsible for data collection, analysis, setting changes, and report generation. The gaming terminals play games and send data to the server. The emotion engine recognizes the user's emotions and provides that information to the server. The user receives the reports and takes additional action as needed.

[1414] The server first sends data collection requests from each gaming terminal at specified intervals. The gaming terminal that receives the data collection request compiles data such as playing time, number of spins, and balance, and sends it back to the server. The emotion engine recognizes emotions based on the user's facial expressions, voice, and biometric information, and sends the data to the server. The server stores this data in a database and performs analysis.

[1415] During the analysis, the server calculates the utilization rate and profitability of each gaming terminal. In addition, emotional data obtained from the emotion engine is also used in the analysis. This takes into account emotional information such as whether the user is excited or relaxed. The analysis results are input into the generative AI model, which generates optimal setting data. Based on past data, current operating status, and user emotional information, the generative AI model determines the settings (settings 1 to 6) for each gaming terminal to maximize profits.

[1416] Next, the server sends a setting change command to each gaming terminal based on the setting data obtained from the generative AI model. The gaming terminal receives this setting change command and changes its settings. If the setting change is successful, the gaming terminal reports the result to the server. The server generates a report based on the data after the setting change and the user's emotional data. This report includes the operating status before and after the setting change, revenue forecast, and the user's emotional state. The report is notified to the administrator, who can use it to implement additional marketing strategies and adjust the settings.

[1417] As a specific example, we will explain the processing of terminal A and terminal B. The server first sends a data collection request to terminal A and terminal B. Terminal A reports 10 hours of play time, 3,000 spins, and a balance of 5,000 yen, while terminal B reports 6 hours of play time, 2,000 spins, and a balance of 2,000 yen. The emotion engine reports that user A is in an excited state and user B is in a relaxed state. The server receives this data and analyzes that terminal A's utilization rate is 83.3% (10 hours / 12 hours) and its profit rate is 1.67% (5,000 yen / 3,000 spins). Similarly, it calculates terminal B's utilization rate and profit rate.

[1418] These analysis results are input into the generative AI model, and the server recommends a low setting (setting 1) for device A and a high setting (setting 6) for device B. Furthermore, emotional information is taken into account, so that a high setting is more likely to be applied to users who are excited. The server then sends this setting change data to device A and device B, and device A and device B change their settings. After the change, the server generates a report based on the new operating status, predicted revenue, and emotional information, and notifies the user. The user can then make further decisions based on this report.

[1419] To optimize the store environment, the server collects customer emotion data and dynamically adjusts the music, lighting, and display content in the store accordingly. For example, if a customer expresses "happy," the server can respond by changing the music in the store to an upbeat tone. The hardware used in this process includes a Raspberry Pi and a camera module, and the software includes Keras and OpenCV.

[1420] Example prompts to input to a generative AI model:

[1421] How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?

[1422] In this way, the system of the present invention optimizes not only the gaming terminals but also the environment of the entire store based on customer emotion data, thereby improving customer experience and maximizing profits.

[1423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1424] Step 1:

[1425] The server transmits a data collection request to each gaming terminal.

[1426] Specifically, it sends data collection requests at specified time intervals and waits for responses from gaming terminals. The input is the data collection request. The output is data on game time, number of spins, and balance collected from each gaming terminal.

[1427] Step 2:

[1428] The terminal receives the data collection request and compiles data on playing time, number of spins, and income and expenditure.

[1429] Specifically, it collects the latest gaming data from an internal database, aggregates it, and returns it to the server. The input is a data collection request from the server. The output is data on gaming time, number of spins, and balance.

[1430] Step 3:

[1431] The emotion engine collects and recognizes the user's emotion information and sends it to the server.

[1432] Specifically, it uses cameras, microphones, sensors, etc. to collect and analyze the user's facial expressions, voice, and biometric information in real time. The inputs include the user's facial expressions, voice, and biometric information. The output is the analyzed emotional data sent to a server.

[1433] Step 4:

[1434] The server stores the received data in a database and performs analysis.

[1435] Specifically, data sent from gaming terminals and emotion data sent from the emotion engine are stored in a database and analyzed to calculate utilization rates and profit rates. The inputs are game data and emotion data. The output is the calculated utilization rate and profit rate for each gaming terminal.

[1436] Step 5:

[1437] The server inputs the analysis results into a generative AI model to generate optimal configuration data.

[1438] Specifically, past data, current operating conditions, and emotional information are provided as inputs to the generative AI model, which then generates configuration data that maximizes profits. The inputs include analysis results, and the output is optimized configuration data.

[1439] Step 6:

[1440] The server transmits setting change commands to each gaming terminal based on the setting data obtained from the generated AI model.

[1441] Specifically, it generates and transmits commands to each gaming terminal based on the setting data, instructing them to make specific setting changes. The input is the generated setting data. The output is a setting change command sent to the gaming terminal.

[1442] Step 7:

[1443] The terminal receives the setting change command and changes the setting.

[1444] Specifically, the internal settings of the gaming terminal are changed based on the received setting change command. The input is a setting change command from the server. The output is the execution of the setting change.

[1445] Step 8:

[1446] The server generates a report based on the data after the setting change and the emotion data, and notifies the administrator.

[1447] Specifically, the gameplay data and emotion data collected again after the settings are changed are analyzed, and a report is generated summarizing the operating status, revenue forecast, and emotional state before and after the settings are changed. The inputs are the gameplay data and emotion data after the settings are changed. The output is to notify the administrator of the generated report.

[1448] Step 9:

[1449] The user makes further decisions based on the report.

[1450] Specifically, administrators review the report content and implement additional marketing strategies or configuration adjustments as necessary. The input is the generated report. The output is additional actions by the user.

[1451] Step 10:

[1452] The server collects customer emotion data and generates setting data to adjust the store environment.

[1453] Specifically, it analyzes user emotional data, generates setting data for dynamically changing music, lighting, and display settings in the store, and sends this data to the store's management system. The input is customer emotional data, and the output is setting data for adjusting the store environment.

[1454] Example prompt sentence:

[1455] "How can we optimize the store environment (music, lighting, displays) in real time based on user sentiment data?"

[1456] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1457] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1458] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1459] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1460] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1461] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1462] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1463] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1464] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1465] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1466] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1467] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1468] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1469] 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.

[1470] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1471] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1472] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1473] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1474] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1475] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1476] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1477] The following is further disclosed regarding the above embodiment.

[1478] (Claim 1)

[1479] means for collecting data on at least game time, number of spins, and balance from the gaming terminal;

[1480] means for analyzing the collected data and calculating information relating to the operating status and revenue of each gaming terminal;

[1481] means for generating artificial intelligence models for generating setting data for optimizing the settings of each gaming terminal based on the analysis results;

[1482] means for changing the settings of each gaming terminal in accordance with the optimized setting data;

[1483] means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying the manager of the report;

[1484] A system including:

[1485] (Claim 2)

[1486] 2. The system according to claim 1, wherein, when generating the setting data, a generating artificial intelligence model is trained using past gaming data.

[1487] (Claim 3)

[1488] The system according to claim 1, further comprising a means for setting a low value for gaming terminals with high utilization rates to increase profits and a high value for gaming terminals with low utilization rates to attract customers based on the analysis results.

[1489] "Example 1"

[1490] (Claim 1)

[1491] means for collecting data on at least game time, number of spins, and balance from the gaming terminal;

[1492] means for analyzing the collected data and calculating information relating to the operating status and revenue of each gaming terminal;

[1493] means for generating artificial intelligence models for generating setting data for optimizing the settings of each gaming terminal based on the analysis results;

[1494] means for changing the settings of each gaming terminal in accordance with the optimized setting data;

[1495] means for generating a report including the operating status and revenue forecast of the gamin...

Claims

1. means for collecting data on at least game time, number of spins, and balance from the gaming terminal; means for analyzing the collected data and calculating information relating to the operating status and revenue of each gaming terminal; means for generating artificial intelligence models for generating setting data for optimizing the settings of each gaming terminal based on the analysis results; means for changing the settings of each gaming terminal in accordance with the optimized setting data; means for generating a report including the operating status and revenue forecast of the gaming terminal after the setting change and notifying the manager of the report; A system including:

2. 2. The system according to claim 1, wherein, when generating the setting data, the generating artificial intelligence model is trained using past game data.

3. The system according to claim 1, further comprising means for setting a low value for gaming terminals with high utilization rates to increase profits and a high value for gaming terminals with low utilization rates to attract customers based on the analysis results.

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