system

Abstract

The system according to this embodiment aims to select the optimal pachinko or slot machine and playing method, and to maintain an appropriate balance between investment and return. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a selection unit, a proposal unit, and an advice unit. The collection unit collects data such as performance information, play history, and payout rate for each pachinko or slot machine. The analysis unit analyzes the data collected by the collection unit. The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal playing method based on the machine selected by the selection unit. The advice unit provides advice to maintain the optimal balance between investment and return based on the playing method proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately grasp the performance and payout rate of each pachinko or slot machine and select an optimal playing technique.

[0005] The system according to the embodiment aims to select an optimal machine and playing technique for pachinko or slots and appropriately maintain the balance between the investment amount and the recovery amount.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, a proposal unit, and an advice unit. The collection unit collects data such as performance information, play history, and payout rate for each pachinko or slot machine. The analysis unit analyzes the data collected by the collection unit. The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal playing method based on the machine selected by the selection unit. The advice unit provides advice to maintain the optimal balance between investment and return based on the playing method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can select the optimal pachinko or slot machine and playing method, and maintain an appropriate balance between investment and return. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The pachinko / slot support system according to an embodiment of the present invention is a system in which AI analyzes data such as performance information, play history, and payout rate for each pachinko and slot machine and provides this data to the user, enabling them to select the optimal machine. The pachinko / slot support system uses AI to collect and analyze data such as performance information, play history, and payout rate for each pachinko and slot machine and provide information for selecting the optimal machine. The pachinko / slot support system also learns various pachinko and slot playing techniques and proposes the most suitable playing technique for the given situation. Furthermore, the pachinko / slot support system provides advice to maintain the optimal balance between investment and return. For example, the pachinko / slot support system collects information such as how often a particular machine pays out and which time slots are more likely to pay out based on past play history. Next, the pachinko / slot support system uses AI to analyze the collected data and provide information for selecting the optimal machine. For example, it provides the user with information such as the time slots when a particular machine is more likely to pay out and machines with high payout rates. Furthermore, the pachinko / slot support system learns various pachinko and slot gameplay techniques and suggests the most suitable technique for the given situation. For example, based on data indicating that a particular machine is more likely to pay out during a specific time period, it advises the user to choose that machine. It also suggests a specific gameplay technique if it proves effective. Finally, the pachinko / slot support system provides advice to maintain an optimal balance between investment and return. This allows users to play while properly managing their funds. For example, it supports users' fund management by advising them to stop playing if they exceed a certain amount. In this way, the pachinko / slot support system allows users to obtain information to effectively win at pachinko and slots, select the optimal machine, and execute appropriate gameplay techniques. In addition, by receiving advice on fund management, users can play while maintaining a balance between investment and return.

[0029] The pachinko / slot support system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, a proposal unit, and an advice unit. The collection unit collects data such as performance information, play history, and payout rates for each pachinko or slot machine. For example, the collection unit collects information such as how often a particular machine pays out and which time slots are more likely to pay out based on past play history. The collection unit can automatically collect data such as performance information, play history, and payout rates for each machine using AI. For example, the collection unit acquires data in real time from sensors on each machine and immediately reflects changes in the payout rate. The collection unit can also analyze past play history and optimize the frequency of data collection during specific time periods. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to analyze performance information and payout rates for each machine. The analysis unit can automatically analyze the collected data using AI. For example, the analysis unit cross-analyzes the performance information and payout rate of each machine to extract specific patterns. The analysis unit can also compare past and current data to predict fluctuations in the payout rate. Furthermore, the analysis unit can estimate user emotions and adjust the analysis algorithm based on the estimated emotions. The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. For example, the selection unit selects the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. For example, the selection unit matches the payout rate of each machine with the user's play style to select the optimal machine. The selection unit can also analyze past selection history to optimize the selection algorithm. Furthermore, the selection unit can estimate user emotions and adjust the machine selection criteria based on the estimated emotions. The suggestion unit proposes the optimal playing technique based on the machine selected by the selection unit. For example, the suggestion unit advises the user to choose a particular machine based on data indicating that a particular machine is more likely to pay out during a specific time period. The suggestion unit can use AI to automatically propose the optimal playing technique.For example, the suggestion unit proposes the optimal playing technique, taking into account the payout rate of each machine and the user's playing style. The suggestion unit can also analyze past data and propose the optimal playing technique for a specific time period. Furthermore, the suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. The advice unit provides advice to maintain an optimal balance between investment and return based on the playing technique proposed by the suggestion unit. For example, the advice unit might advise stopping play if a certain amount is exceeded. The advice unit can use AI to automatically provide advice to maintain an optimal balance between investment and return. For example, the advice unit analyzes past data and provides advice to maintain a balance between investment and return. The advice unit can also propose the optimal money management method, taking into account the payout rate of each machine and the user's playing style. Furthermore, the advice unit can estimate the user's emotions and adjust the advice based on those emotions. As a result, the pachinko / slot support system according to this embodiment allows users to obtain information to effectively win at pachinko and slots, select the optimal machine, and execute appropriate playing techniques. Furthermore, receiving advice on fund management makes it possible to play while maintaining a balance between investment and return.

[0030] The data collection unit collects data such as performance information, play history, and payout rates for each pachinko and slot machine. Specifically, it acquires data in real time through sensors installed on each machine and network connections. For example, it collects information such as how often a particular machine pays out and which time periods are more likely to pay out based on past play history. The data collection unit can use AI to automatically collect data such as performance information, play history, and payout rates for each machine. The AI ​​acquires data in real time from the sensors of each machine and immediately reflects changes in the payout rate. The data collection unit can also analyze past play history and optimize the frequency of data collection during specific time periods. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, by adjusting the timing of data collection according to changes in the user's emotions, such as whether the user is excited or calm, more accurate data can be collected. As a result, the data collection unit can efficiently and accurately collect data such as performance information, play history, and payout rates for each machine, improving the overall performance of the system.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes the collected data to determine the performance information and payout rate of each machine. The analysis department can use AI to automatically analyze the collected data. For example, the analysis department can cross-analyze the performance information and payout rate of each machine to extract specific patterns. The analysis department can also compare past data with current data to predict fluctuations in the payout rate. Furthermore, the analysis department can estimate the emotions of users and adjust the analysis algorithm based on the estimated emotions. For example, by adjusting the analysis algorithm according to changes in emotions, such as whether the user is excited or calm, more accurate analysis results can be obtained. This allows the analysis department to quickly and accurately analyze the collected data and grasp the performance information and payout rate of each machine. Furthermore, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past payout data, it can predict fluctuations in risk for specific machines or time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. Specifically, it selects the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. For example, the selection unit matches the payout rate of each machine with the user's play style to select the optimal machine. The selection unit can also analyze past selection history and optimize the selection algorithm. Furthermore, the selection unit can estimate the user's emotions and adjust the machine selection criteria based on the estimated user emotions. For example, by adjusting the machine selection criteria according to changes in the user's emotions, such as whether the user is excited or calm, it can select a more appropriate machine. As a result, the selection unit can efficiently and accurately select the optimal machine based on the analysis results, improving the user's playing experience. Furthermore, the selection unit can make individually optimized machine selections by considering the user's play style and past selection history. As a result, the selection unit can provide customized machine selections for each user, achieving higher satisfaction.

[0033] The suggestion unit proposes the optimal playing strategy based on the machine selected by the selection unit. Specifically, it advises the user to choose a machine based on data indicating that a particular machine is more likely to pay out during a specific time period. The suggestion unit can automatically propose the optimal playing strategy using AI. For example, the suggestion unit proposes the optimal playing strategy considering the payout rate of each machine and the user's playing style. It can also analyze past data and propose the optimal playing strategy for a specific time period. Furthermore, the suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, by adjusting the suggestions according to changes in the user's emotions, such as whether they are excited or calm, it can propose a more appropriate playing strategy. As a result, the suggestion unit can efficiently and accurately propose the optimal playing strategy based on the selected machine, improving the user's playing experience. Moreover, the suggestion unit can propose individually optimized playing strategies considering the user's playing style and past data. As a result, the suggestion unit can provide customized playing strategies for each user, achieving higher satisfaction.

[0034] The advice unit provides advice to maintain the optimal balance between investment and return based on the gameplay techniques proposed by the suggestion unit. Specifically, it advises stopping play if a certain amount is exceeded. The advice unit can use AI to automatically provide advice to maintain the optimal balance between investment and return. For example, the advice unit can analyze past data and provide advice to maintain the balance between investment and return. The advice unit can also propose the optimal money management method by considering the payout rate of each machine and the user's play style. Furthermore, the advice unit can estimate the user's emotions and adjust the advice based on the estimated emotions. For example, by adjusting the advice according to changes in the user's emotions, such as whether the user is excited or calm, it can propose a more appropriate money management method. In this way, the advice unit can provide advice to efficiently and accurately maintain the optimal balance between investment and return, improving the user's playing experience. Furthermore, the advice unit can propose individually optimized money management methods by considering the user's play style and past data. In this way, the advice unit can provide customized money management methods for each user, achieving higher satisfaction.

[0035] The data collection unit can collect information such as how often a particular machine pays out, and which time slots are more likely to pay out based on past play history. For example, the data collection unit can collect information on how often a particular machine pays out. The data collection unit can use AI to automatically collect the payout frequency of each machine. For example, the data collection unit can acquire data in real time from the sensors of each machine and immediately reflect the payout frequency. The data collection unit can also collect information on which time slots are more likely to pay out based on past play history. The data collection unit can use AI to automatically analyze past play history and collect information on which time slots are more likely to pay out. For example, based on past play history, if the payout rate is high during a particular time slot, the data collection unit can increase the frequency of data collection during that time slot. Conversely, based on past play history, if the payout rate is low during a particular time slot, the data collection unit can decrease the frequency of data collection during that time slot. In this way, by collecting information on the payout frequency and time slots of a particular machine, information can be provided to help the user select the optimal machine. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors on each machine into a generating AI, which can then perform data collection related to the frequency of payouts and time periods.

[0036] The analysis unit can analyze the collected data and analyze the performance information and payout rate of each machine. For example, the analysis unit can analyze the collected data and analyze the performance information of each machine. The analysis unit can use AI to automatically analyze the collected data. For example, the analysis unit can cross-analyze the performance information and payout rate of each machine and extract specific patterns. The analysis unit can also compare past data with current data and predict fluctuations in the payout rate. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. As a result, by analyzing the collected data, it is possible to understand the performance information and payout rate of each machine and provide information for selecting the optimal machine. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of the performance information and payout rate of each machine.

[0037] The selection unit can select the optimal machine based on the analysis results. For example, the selection unit can select the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. For example, the selection unit can match the payout rate of each machine with the user's playing style to select the optimal machine. The selection unit can also analyze past selection history and optimize the selection algorithm. Furthermore, the selection unit can estimate the user's emotions and adjust the machine selection criteria based on the estimated user emotions. This allows the user to choose a machine that will help them win effectively by selecting the optimal machine based on the analysis results. Some or all of the above processes in the selection unit may be performed using AI or not. For example, the selection unit can input the analysis results into a generating AI and have the generating AI perform the selection of the optimal machine.

[0038] The suggestion unit can advise users to choose a particular machine based on data indicating that a particular machine is more likely to pay out during a specific time period. For example, the suggestion unit can advise users to choose a particular machine based on data indicating that a particular machine is more likely to pay out during a specific time period. The suggestion unit can use AI to automatically suggest the optimal playing technique. For example, the suggestion unit can suggest the optimal playing technique by considering the payout rate of each machine and the user's playing style. The suggestion unit can also analyze past data and suggest the optimal playing technique for a specific time period. Furthermore, the suggestion unit can estimate the user's emotions and adjust the suggestions based on the estimated emotions. This allows users to execute the optimal playing technique by providing advice based on data indicating that a particular machine is more likely to pay out during a specific time period. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input data indicating that a particular machine is more likely to pay out during a specific time period into a generating AI and have the generating AI suggest the optimal playing technique.

[0039] The advice unit can advise players to stop playing if they exceed a certain amount. For example, the advice unit can advise players to stop playing if they exceed a certain amount. The advice unit can use AI to automatically provide advice to maintain an optimal balance between investment and return. For example, the advice unit can analyze past data and provide advice to maintain a balance between investment and return. The advice unit can also propose the optimal money management method by considering the payout rate of each machine and the user's playing style. Furthermore, the advice unit can estimate the user's emotions and adjust the advice based on the estimated emotions. This allows users to manage their money appropriately by advising them to stop playing if they exceed a certain amount. Some or all of the above processes in the advice unit may be performed using AI or not. For example, the advice unit can have a generating AI execute the process of advising players to stop playing if they exceed a certain amount.

[0040] The data collection unit can analyze past play history and optimize the frequency of data collection during specific time periods. For example, if the payout rate is high during a particular time period based on past play history, the data collection unit will increase the frequency of data collection during that time period. The data collection unit can also use AI to automatically analyze past play history and optimize the frequency of data collection during specific time periods. For example, if the payout rate is high during a particular time period based on past play history, the data collection unit will increase the frequency of data collection during that time period. Furthermore, if the payout rate is low during a particular time period based on past play history, the data collection unit can decrease the frequency of data collection during that time period. In addition, the data collection unit can adjust the frequency of data collection on specific days of the week or time periods based on past play history. This allows for the optimization of data collection frequency during specific time periods by analyzing past play history. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past play history into a generating AI and have the generating AI perform the optimization of data collection frequency.

[0041] The data collection unit can collect performance information from each machine in real time and immediately reflect the fluctuating payout rate. For example, the data collection unit can acquire data from sensors on each machine in real time and immediately reflect the change in the payout rate. The data collection unit can use AI to automatically collect performance information from each machine in real time. For example, the data collection unit can monitor the play status of each machine in real time and immediately reflect the change in the payout rate. The data collection unit can also immediately collect data when the payout rate of each machine changes and provide it to the user. In this way, by collecting performance information from each machine in real time, the fluctuating payout rate can be immediately reflected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors on each machine into a generating AI and have the generating AI perform the action of reflecting the change in the payout rate.

[0042] The data collection unit can prioritize collecting data from machines in specific regions based on the user's geographical location information. For example, if the user is in a specific region, the data collection unit will prioritize collecting data from machines in that region. The data collection unit can use AI to automatically acquire the user's geographical location information and prioritize collecting data from machines in specific regions. For example, if the user is on the move, the data collection unit will prioritize collecting data from machines in regions close to the user's current location. The data collection unit can also prioritize collecting data from machines in a specific region if the user frequently visits that region. In this way, by collecting data based on the user's geographical location information, information about machines in specific regions can be provided preferentially. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of data from machines in specific regions.

[0043] The data collection unit can analyze social media trends and collect data on popular slot machines. For example, the data collection unit can prioritize collecting data on slot machines that are trending on social media. The data collection unit can use AI to automatically analyze social media trends and collect data on popular slot machines. For example, the data collection unit can analyze social media trends and collect data on popular slot machines. The data collection unit can also collect data on popular slot machines by referring to user reviews on social media. In this way, data on popular slot machines can be collected by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media trend data into a generating AI and have the generating AI perform the collection of data on popular slot machines.

[0044] The analysis unit can cross-analyze the performance information and payout rate of each machine to extract specific patterns. For example, the analysis unit can cross-analyze the performance information and payout rate of each machine to extract the characteristics of machines with high payout rates. The analysis unit can use AI to automatically cross-analyze the performance information and payout rate of each machine. For example, the analysis unit can cross-analyze the performance information and payout rate of each machine to extract the characteristics of machines with high payout rates. The analysis unit can also cross-analyze the performance information and payout rate of each machine to extract the characteristics of machines with low payout rates. Furthermore, the analysis unit can cross-analyze the performance information and payout rate of each machine to extract patterns of machines with high payout rates during specific time periods. In this way, specific patterns can be extracted by cross-analyzing the performance information and payout rate of each machine. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the performance information and payout rate data of each machine into a generating AI and have the generating AI perform the extraction of specific patterns.

[0045] The analysis unit can compare past and present data to predict fluctuations in the payout rate. For example, the analysis unit can compare past and present data to predict an upward trend in the payout rate. The analysis unit can use AI to automatically compare past and present data to predict fluctuations in the payout rate. For example, the analysis unit can compare past and present data to predict an upward trend in the payout rate. The analysis unit can also compare past and present data to predict a downward trend in the payout rate. Furthermore, the analysis unit can compare past and present data to predict a stable trend in the payout rate. In this way, fluctuations in the payout rate can be predicted by comparing past and present data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past and present data into a generating AI and have the generating AI perform the prediction of fluctuations in the payout rate.

[0046] The analysis unit can analyze differences in payout rates across regions based on geographical data. For example, the analysis unit can analyze machines with high payout rates in a specific region based on geographical data. The analysis unit can use AI to automatically acquire geographical data and analyze differences in payout rates across regions. For example, the analysis unit can analyze machines with high payout rates in a specific region based on geographical data. The analysis unit can also analyze machines with low payout rates in a specific region based on geographical data. Furthermore, the analysis unit can analyze differences in payout rates across regions based on geographical data. In this way, differences in payout rates across regions can be grasped by analyzing based on geographical data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical data into a generating AI and have the generating AI perform an analysis of differences in payout rates across regions.

[0047] The analysis department can incorporate social media data and reflect user opinions in its analysis. For example, the analysis department can analyze social media reviews and reflect user opinions. The analysis department can use AI to automatically acquire social media data and reflect user opinions in its analysis. For example, the analysis department can analyze social media reviews and reflect user opinions. The analysis department can also analyze social media trends and reflect user opinions. Furthermore, the analysis department can analyze social media comments and reflect user opinions. In this way, by incorporating social media data, user opinions can be reflected in the analysis. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input social media data into a generating AI and have the generating AI perform an analysis of user opinions.

[0048] The selection unit can match the payout rate of each machine with the user's playing style to select the optimal machine. For example, the selection unit can match the payout rate of each machine with the user's playing style and select a machine with a high payout rate. The selection unit can also use AI to automatically match the payout rate of each machine with the user's playing style and select the optimal machine. For example, the selection unit can match the payout rate of each machine with the user's playing style and select a machine with a high payout rate. The selection unit can also match the payout rate of each machine with the user's playing style and select a machine with a stable payout rate. Furthermore, the selection unit can match the payout rate of each machine with the user's playing style and select a machine that the user tends to take risks on. In this way, the optimal machine can be selected by matching the payout rate of each machine with the user's playing style. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input data on the payout rate of each machine and the user's playing style into a generating AI and have the generating AI perform the selection of the optimal machine.

[0049] The selection unit can analyze past selection history and optimize the selection algorithm. For example, the selection unit can analyze past selection history and optimize the algorithm for selecting machines with a high payout rate. The selection unit can use AI to automatically analyze past selection history and optimize the selection algorithm. For example, the selection unit can analyze past selection history and optimize the algorithm for selecting machines with a high payout rate. The selection unit can also analyze past selection history and optimize the algorithm for selecting machines with a stable payout rate. Furthermore, the selection unit can analyze past selection history and optimize the algorithm for selecting machines that tend to take risks. In this way, the selection algorithm can be optimized by analyzing past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0050] The selection unit can select the optimal machine in a specific region based on geographical data. For example, the selection unit can select a machine with a high payout rate in a specific region based on geographical data. The selection unit can use AI to automatically acquire geographical data and select the optimal machine in a specific region. For example, the selection unit can select a machine with a high payout rate in a specific region based on geographical data. The selection unit can also select a machine with a low payout rate in a specific region based on geographical data. Furthermore, the selection unit can select a machine with a stable payout rate in a specific region based on geographical data. In this way, by selecting the optimal machine based on geographical data, the optimal machine in a specific region can be provided. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input geographical data into a generating AI and have the generating AI perform the selection of the optimal machine in a specific region.

[0051] The suggestion unit can analyze past data to propose the optimal gameplay technique for a specific time period. For example, the suggestion unit can analyze past data and propose a gameplay technique with a high payout rate for a specific time period. The suggestion unit can use AI to automatically analyze past data and propose the optimal gameplay technique for a specific time period. For example, the suggestion unit can analyze past data and propose a gameplay technique with a high payout rate for a specific time period. The suggestion unit can also analyze past data and propose a gameplay technique with a low payout rate for a specific time period. Furthermore, the suggestion unit can analyze past data and propose a stable gameplay technique for a specific time period. In this way, by analyzing past data, the optimal gameplay technique for a specific time period can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input past data into a generating AI and have the generating AI execute a proposal for the optimal gameplay technique for a specific time period.

[0052] The suggestion unit can propose the optimal playing technique by considering the payout rate of each machine and the user's playing style. For example, the suggestion unit can propose a playing technique with a high payout rate by considering the payout rate of each machine and the user's playing style. The suggestion unit can use AI to automatically consider the payout rate of each machine and the user's playing style and propose the optimal playing technique. For example, the suggestion unit can propose a playing technique with a high payout rate by considering the payout rate of each machine and the user's playing style. The suggestion unit can also propose a playing technique with a stable payout rate by considering the payout rate of each machine and the user's playing style. Furthermore, the suggestion unit can propose a playing technique that involves taking risks by considering the payout rate of each machine and the user's playing style. In this way, the optimal playing technique can be proposed by considering the payout rate of each machine and the user's playing style. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input data on the payout rate of each machine and the user's playing style into a generating AI and have the generating AI execute a suggestion of the optimal playing technique.

[0053] The proposal unit can propose the optimal gameplay method for a specific region based on geographical data. For example, the proposal unit can propose a gameplay method with a high payout rate in a specific region based on geographical data. The proposal unit can use AI to automatically acquire geographical data and propose the optimal gameplay method for a specific region. For example, the proposal unit can propose a gameplay method with a high payout rate in a specific region based on geographical data. The proposal unit can also propose a gameplay method with a low payout rate in a specific region based on geographical data. Furthermore, the proposal unit can propose a stable gameplay method in a specific region based on geographical data. In this way, by proposing the optimal gameplay method based on geographical data, the optimal gameplay method for a specific region can be provided. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input geographical data into a generating AI and have the generating AI execute a proposal for the optimal gameplay method for a specific region.

[0054] The suggestion unit can incorporate social media data and propose popular gameplay techniques. For example, the suggestion unit can prioritize suggesting gameplay techniques that are trending on social media. The suggestion unit can use AI to automatically acquire social media data and propose popular gameplay techniques. For example, the suggestion unit can analyze social media trends and propose popular gameplay techniques. The suggestion unit can also propose popular gameplay techniques by referring to user reviews on social media. In this way, popular gameplay techniques can be proposed by incorporating social media data. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input social media data into a generating AI and have the generating AI execute suggestions for popular gameplay techniques.

[0055] The advisory unit can analyze historical data to maintain a balance between investment and return. For example, the advisory unit can analyze historical data and provide advice to maintain a balance between investment and return. The advisory unit can use AI to automatically analyze historical data and provide advice to maintain a balance between investment and return. For example, the advisory unit can analyze historical data and provide advice to maintain a balance between investment and return. The advisory unit can also analyze historical data and provide advice to reduce investment. Furthermore, the advisory unit can analyze historical data and provide advice to increase return. In this way, by analyzing historical data, it is possible to provide advice to maintain a balance between investment and return. Some or all of the above processing in the advisory unit may be performed using AI or not. For example, the advisory unit can input historical data into a generating AI and have the generating AI execute advice to maintain a balance between investment and return.

[0056] The advice unit can propose the optimal money management method by considering the payout rate of each machine and the user's playing style. For example, the advice unit can consider the payout rate of each machine and the user's playing style and propose a money management method for machines with a high payout rate. The advice unit can use AI to automatically consider the payout rate of each machine and the user's playing style and propose the optimal money management method. For example, the advice unit can consider the payout rate of each machine and the user's playing style and propose a money management method for machines with a high payout rate. The advice unit can also consider the payout rate of each machine and the user's playing style and propose a money management method for machines with a stable payout rate. Furthermore, the advice unit can consider the payout rate of each machine and the user's playing style and propose a money management method for machines where risk is taken. In this way, by considering the payout rate of each machine and the user's playing style, the optimal money management method can be proposed. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input data on the payout rate of each machine and the user's playing style into a generating AI, which can then execute a proposal for the optimal money management method.

[0057] The advisory unit can propose optimal fund management methods for a specific region based on geographical data. For example, the advisory unit can propose fund management methods for a specific region based on geographical data. The advisory unit can use AI to automatically acquire geographical data and propose optimal fund management methods for a specific region. For example, the advisory unit can propose fund management methods for a specific region based on geographical data. Furthermore, the advisory unit can propose methods to reduce investment amounts in a specific region based on geographical data. In addition, the advisory unit can propose methods to increase returns in a specific region based on geographical data. Thus, by proposing optimal fund management methods based on geographical data, the advisory unit can provide optimal fund management methods for a specific region. Some or all of the above processing in the advisory unit may be performed using AI or not. For example, the advisory unit can input geographical data into a generating AI and have the generating AI execute a proposal for optimal fund management methods for a specific region.

[0058] The advice department can incorporate social media data and propose popular money management methods. For example, the advice department can prioritize suggesting money management methods that are trending on social media. The advice department can use AI to automatically acquire social media data and propose popular money management methods. For example, the advice department can analyze social media trends and propose popular money management methods. The advice department can also suggest popular money management methods by referring to user reviews on social media. In this way, by incorporating social media data, it is possible to propose popular money management methods. Some or all of the above processes in the advice department may be performed using AI or not. For example, the advice department can input social media data into a generating AI and have the generating AI produce suggestions for popular money management methods.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] Pachinko and slot machine support systems can also include a function to predict the payout rate of a specific machine based on the user's playing style. For example, the data collection unit analyzes the user's past playing history and collects trends in payout rates for specific playing styles. The analysis unit predicts the payout rate according to the user's playing style based on the collected data. The selection unit selects the optimal machine for the user based on the predicted payout rate. This allows for the selection of the optimal machine according to the user's playing style, enabling more effective gameplay.

[0061] Pachinko and slot machine support systems can also include a function to select the optimal machine in a specific area based on the user's geographical location. For example, the data collection unit acquires the user's current location and collects data on pachinko and slot machines in that area. The analysis unit analyzes the payout rate and play history in that area based on the collected data. The selection unit selects the optimal machine for the user based on the analysis results. This allows users to select the best machine for each area they visit.

[0062] Pachinko and slot machine support systems can also incorporate social media data and include features to suggest popular machines and gameplay techniques. For example, the data collection unit gathers social media trends and user reviews. The analysis unit analyzes popular machines and gameplay techniques based on the collected data. The suggestion unit then suggests popular machines and gameplay techniques to users based on the analysis results. This allows users to enjoy playing based on the latest trends.

[0063] Pachinko and slot machine support systems can also include a function that suggests the optimal playing technique for a specific time period based on the user's play history. For example, the data collection unit collects the user's past play history and analyzes the payout rate and play style for a specific time period. The analysis unit suggests the optimal playing technique for that time period based on the collected data. The suggestion unit suggests the optimal playing technique to the user based on the analysis results. This allows the user to execute the optimal playing technique for a specific time period.

[0064] Pachinko and slot machine support systems can also include a function that suggests the optimal playing method for a specific region based on the user's geographical location. For example, the data collection unit acquires the user's current location and collects data on pachinko and slot machines in that region. The analysis unit analyzes the payout rate and play history in that region based on the collected data. The suggestion unit then suggests the optimal playing method to the user based on the analysis results. This allows users to execute the most suitable playing method for each region they visit.

[0065] Pachinko and slot machine support systems can also include a function to predict the payout rate of a specific machine based on the user's playing style. For example, the data collection unit analyzes the user's past playing history and collects trends in payout rates for specific playing styles. The analysis unit predicts the payout rate according to the user's playing style based on the collected data. The selection unit selects the optimal machine for the user based on the predicted payout rate. This allows for the selection of the optimal machine according to the user's playing style, enabling more effective gameplay.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data such as performance information, play history, and payout rates for each pachinko and slot machine. For example, it collects information such as how often a particular machine pays out and which time slots are more likely to pay out based on past play history. The data collection unit can automatically collect data such as performance information, play history, and payout rates for each machine using AI. It acquires data in real time from sensors on each machine and immediately reflects changes in the payout rate. It can also analyze past play history and optimize the frequency of data collection during specific time periods. Furthermore, it can estimate user emotions and adjust the timing of data collection based on the estimated user emotions. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data to determine the performance information and payout rate of each machine. The analysis unit can use AI to automatically analyze the collected data. It can cross-analyze the performance information and payout rate of each machine to extract specific patterns. It can also compare past data with current data to predict fluctuations in the payout rate. Furthermore, it can estimate user sentiment and adjust the analysis algorithm based on the estimated user sentiment. Step 3: The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. For example, it selects the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. It matches the payout rate of each machine with the user's playing style to select the optimal machine. It can also analyze past selection history and optimize the selection algorithm. Furthermore, it can estimate the user's emotions and adjust the machine selection criteria based on the estimated user emotions. Step 4: The suggestion unit proposes the optimal playing strategy based on the machine selected by the selection unit. For example, based on data indicating that a particular machine is more likely to pay out during a specific time period, it advises the user to choose that machine. The suggestion unit can automatically propose the optimal playing strategy using AI. It proposes the optimal playing strategy considering the payout rate of each machine and the user's playing style. It can also analyze past data and propose the optimal playing strategy for a specific time period. Furthermore, it can estimate the user's emotions and adjust the suggestions based on those estimated emotions. Step 5: The advice unit provides advice to maintain the optimal balance between investment and return based on the playing techniques proposed by the suggestion unit. For example, it may advise stopping play if a certain amount is exceeded. The advice unit can use AI to automatically provide advice to maintain the optimal balance between investment and return. It analyzes past data to provide advice to maintain the balance between investment and return. It can also propose the optimal money management method by considering the payout rate of each machine and the user's playing style. Furthermore, it can estimate the user's emotions and adjust the advice based on the estimated emotions of the user.

[0068] (Example of form 2) The pachinko / slot support system according to an embodiment of the present invention is a system in which AI analyzes data such as performance information, play history, and payout rate for each pachinko and slot machine and provides this data to the user, enabling them to select the optimal machine. The pachinko / slot support system uses AI to collect and analyze data such as performance information, play history, and payout rate for each pachinko and slot machine and provide information for selecting the optimal machine. The pachinko / slot support system also learns various pachinko and slot playing techniques and proposes the most suitable playing technique for the given situation. Furthermore, the pachinko / slot support system provides advice to maintain the optimal balance between investment and return. For example, the pachinko / slot support system collects information such as how often a particular machine pays out and which time slots are more likely to pay out based on past play history. Next, the pachinko / slot support system uses AI to analyze the collected data and provide information for selecting the optimal machine. For example, it provides the user with information such as the time slots when a particular machine is more likely to pay out and machines with high payout rates. Furthermore, the pachinko / slot support system learns various pachinko and slot gameplay techniques and suggests the most suitable technique for the given situation. For example, based on data indicating that a particular machine is more likely to pay out during a specific time period, it advises the user to choose that machine. It also suggests a specific gameplay technique if it proves effective. Finally, the pachinko / slot support system provides advice to maintain an optimal balance between investment and return. This allows users to play while properly managing their funds. For example, it supports users' fund management by advising them to stop playing if they exceed a certain amount. In this way, the pachinko / slot support system allows users to obtain information to effectively win at pachinko and slots, select the optimal machine, and execute appropriate gameplay techniques. In addition, by receiving advice on fund management, users can play while maintaining a balance between investment and return.

[0069] The pachinko / slot support system according to this embodiment comprises a collection unit, an analysis unit, a selection unit, a proposal unit, and an advice unit. The collection unit collects data such as performance information, play history, and payout rates for each pachinko or slot machine. For example, the collection unit collects information such as how often a particular machine pays out and which time slots are more likely to pay out based on past play history. The collection unit can automatically collect data such as performance information, play history, and payout rates for each machine using AI. For example, the collection unit acquires data in real time from sensors on each machine and immediately reflects changes in the payout rate. The collection unit can also analyze past play history and optimize the frequency of data collection during specific time periods. Furthermore, the collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to analyze performance information and payout rates for each machine. The analysis unit can automatically analyze the collected data using AI. For example, the analysis unit cross-analyzes the performance information and payout rate of each machine to extract specific patterns. The analysis unit can also compare past and current data to predict fluctuations in the payout rate. Furthermore, the analysis unit can estimate user emotions and adjust the analysis algorithm based on the estimated emotions. The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. For example, the selection unit selects the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. For example, the selection unit matches the payout rate of each machine with the user's play style to select the optimal machine. The selection unit can also analyze past selection history to optimize the selection algorithm. Furthermore, the selection unit can estimate user emotions and adjust the machine selection criteria based on the estimated emotions. The suggestion unit proposes the optimal playing technique based on the machine selected by the selection unit. For example, the suggestion unit advises the user to choose a particular machine based on data indicating that a particular machine is more likely to pay out during a specific time period. The suggestion unit can use AI to automatically propose the optimal playing technique.For example, the suggestion unit proposes the optimal playing technique, taking into account the payout rate of each machine and the user's playing style. The suggestion unit can also analyze past data and propose the optimal playing technique for a specific time period. Furthermore, the suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. The advice unit provides advice to maintain an optimal balance between investment and return based on the playing technique proposed by the suggestion unit. For example, the advice unit might advise stopping play if a certain amount is exceeded. The advice unit can use AI to automatically provide advice to maintain an optimal balance between investment and return. For example, the advice unit analyzes past data and provides advice to maintain a balance between investment and return. The advice unit can also propose the optimal money management method, taking into account the payout rate of each machine and the user's playing style. Furthermore, the advice unit can estimate the user's emotions and adjust the advice based on those emotions. As a result, the pachinko / slot support system according to this embodiment allows users to obtain information to effectively win at pachinko and slots, select the optimal machine, and execute appropriate playing techniques. Furthermore, receiving advice on fund management makes it possible to play while maintaining a balance between investment and return.

[0070] The data collection unit collects data such as performance information, play history, and payout rates for each pachinko and slot machine. Specifically, it acquires data in real time through sensors installed on each machine and network connections. For example, it collects information such as how often a particular machine pays out and which time periods are more likely to pay out based on past play history. The data collection unit can use AI to automatically collect data such as performance information, play history, and payout rates for each machine. The AI ​​acquires data in real time from the sensors of each machine and immediately reflects changes in the payout rate. The data collection unit can also analyze past play history and optimize the frequency of data collection during specific time periods. Furthermore, the data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, by adjusting the timing of data collection according to changes in the user's emotions, such as whether the user is excited or calm, more accurate data can be collected. As a result, the data collection unit can efficiently and accurately collect data such as performance information, play history, and payout rates for each machine, improving the overall performance of the system.

[0071] The analysis department analyzes the data collected by the data collection department. Specifically, it analyzes the collected data to determine the performance information and payout rate of each machine. The analysis department can use AI to automatically analyze the collected data. For example, the analysis department can cross-analyze the performance information and payout rate of each machine to extract specific patterns. The analysis department can also compare past data with current data to predict fluctuations in the payout rate. Furthermore, the analysis department can estimate the emotions of users and adjust the analysis algorithm based on the estimated emotions. For example, by adjusting the analysis algorithm according to changes in emotions, such as whether the user is excited or calm, more accurate analysis results can be obtained. This allows the analysis department to quickly and accurately analyze the collected data and grasp the performance information and payout rate of each machine. Furthermore, the analysis department can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past payout data, it can predict fluctuations in risk for specific machines or time periods and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. Specifically, it selects the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. For example, the selection unit matches the payout rate of each machine with the user's play style to select the optimal machine. The selection unit can also analyze past selection history and optimize the selection algorithm. Furthermore, the selection unit can estimate the user's emotions and adjust the machine selection criteria based on the estimated user emotions. For example, by adjusting the machine selection criteria according to changes in the user's emotions, such as whether the user is excited or calm, it can select a more appropriate machine. As a result, the selection unit can efficiently and accurately select the optimal machine based on the analysis results, improving the user's playing experience. Furthermore, the selection unit can make individually optimized machine selections by considering the user's play style and past selection history. As a result, the selection unit can provide customized machine selections for each user, achieving higher satisfaction.

[0073] The suggestion unit proposes the optimal playing strategy based on the machine selected by the selection unit. Specifically, it advises the user to choose a machine based on data indicating that a particular machine is more likely to pay out during a specific time period. The suggestion unit can automatically propose the optimal playing strategy using AI. For example, the suggestion unit proposes the optimal playing strategy considering the payout rate of each machine and the user's playing style. It can also analyze past data and propose the optimal playing strategy for a specific time period. Furthermore, the suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, by adjusting the suggestions according to changes in the user's emotions, such as whether they are excited or calm, it can propose a more appropriate playing strategy. As a result, the suggestion unit can efficiently and accurately propose the optimal playing strategy based on the selected machine, improving the user's playing experience. Moreover, the suggestion unit can propose individually optimized playing strategies considering the user's playing style and past data. As a result, the suggestion unit can provide customized playing strategies for each user, achieving higher satisfaction.

[0074] The advice unit provides advice to maintain the optimal balance between investment and return based on the gameplay techniques proposed by the suggestion unit. Specifically, it advises stopping play if a certain amount is exceeded. The advice unit can use AI to automatically provide advice to maintain the optimal balance between investment and return. For example, the advice unit can analyze past data and provide advice to maintain the balance between investment and return. The advice unit can also propose the optimal money management method by considering the payout rate of each machine and the user's play style. Furthermore, the advice unit can estimate the user's emotions and adjust the advice based on the estimated emotions. For example, by adjusting the advice according to changes in the user's emotions, such as whether the user is excited or calm, it can propose a more appropriate money management method. In this way, the advice unit can provide advice to efficiently and accurately maintain the optimal balance between investment and return, improving the user's playing experience. Furthermore, the advice unit can propose individually optimized money management methods by considering the user's play style and past data. In this way, the advice unit can provide customized money management methods for each user, achieving higher satisfaction.

[0075] The data collection unit can collect information such as how often a particular machine pays out, and which time slots are more likely to pay out based on past play history. For example, the data collection unit can collect information on how often a particular machine pays out. The data collection unit can use AI to automatically collect the payout frequency of each machine. For example, the data collection unit can acquire data in real time from the sensors of each machine and immediately reflect the payout frequency. The data collection unit can also collect information on which time slots are more likely to pay out based on past play history. The data collection unit can use AI to automatically analyze past play history and collect information on which time slots are more likely to pay out. For example, based on past play history, if the payout rate is high during a particular time slot, the data collection unit can increase the frequency of data collection during that time slot. Conversely, based on past play history, if the payout rate is low during a particular time slot, the data collection unit can decrease the frequency of data collection during that time slot. In this way, by collecting information on the payout frequency and time slots of a particular machine, information can be provided to help the user select the optimal machine. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors on each machine into a generating AI, which can then perform data collection related to the frequency of payouts and time periods.

[0076] The analysis unit can analyze the collected data and analyze the performance information and payout rate of each machine. For example, the analysis unit can analyze the collected data and analyze the performance information of each machine. The analysis unit can use AI to automatically analyze the collected data. For example, the analysis unit can cross-analyze the performance information and payout rate of each machine and extract specific patterns. The analysis unit can also compare past data with current data and predict fluctuations in the payout rate. Furthermore, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. As a result, by analyzing the collected data, it is possible to understand the performance information and payout rate of each machine and provide information for selecting the optimal machine. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of the performance information and payout rate of each machine.

[0077] The selection unit can select the optimal machine based on the analysis results. For example, the selection unit can select the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. For example, the selection unit can match the payout rate of each machine with the user's playing style to select the optimal machine. The selection unit can also analyze past selection history and optimize the selection algorithm. Furthermore, the selection unit can estimate the user's emotions and adjust the machine selection criteria based on the estimated user emotions. This allows the user to choose a machine that will help them win effectively by selecting the optimal machine based on the analysis results. Some or all of the above processes in the selection unit may be performed using AI or not. For example, the selection unit can input the analysis results into a generating AI and have the generating AI perform the selection of the optimal machine.

[0078] The suggestion unit can advise users to choose a particular machine based on data indicating that a particular machine is more likely to pay out during a specific time period. For example, the suggestion unit can advise users to choose a particular machine based on data indicating that a particular machine is more likely to pay out during a specific time period. The suggestion unit can use AI to automatically suggest the optimal playing technique. For example, the suggestion unit can suggest the optimal playing technique by considering the payout rate of each machine and the user's playing style. The suggestion unit can also analyze past data and suggest the optimal playing technique for a specific time period. Furthermore, the suggestion unit can estimate the user's emotions and adjust the suggestions based on the estimated emotions. This allows users to execute the optimal playing technique by providing advice based on data indicating that a particular machine is more likely to pay out during a specific time period. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input data indicating that a particular machine is more likely to pay out during a specific time period into a generating AI and have the generating AI suggest the optimal playing technique.

[0079] The advice unit can advise players to stop playing if they exceed a certain amount. For example, the advice unit can advise players to stop playing if they exceed a certain amount. The advice unit can use AI to automatically provide advice to maintain an optimal balance between investment and return. For example, the advice unit can analyze past data and provide advice to maintain a balance between investment and return. The advice unit can also propose the optimal money management method by considering the payout rate of each machine and the user's playing style. Furthermore, the advice unit can estimate the user's emotions and adjust the advice based on the estimated emotions. This allows users to manage their money appropriately by advising them to stop playing if they exceed a certain amount. Some or all of the above processes in the advice unit may be performed using AI or not. For example, the advice unit can have a generating AI execute the process of advising players to stop playing if they exceed a certain amount.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is excited, the data collection unit will collect data frequently in real time to provide the latest information. The data collection unit can use AI to automatically estimate the user's emotions and adjust the timing of data collection. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for data collection at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0081] The data collection unit can analyze past play history and optimize the frequency of data collection during specific time periods. For example, if the payout rate is high during a particular time period based on past play history, the data collection unit will increase the frequency of data collection during that time period. The data collection unit can also use AI to automatically analyze past play history and optimize the frequency of data collection during specific time periods. For example, if the payout rate is high during a particular time period based on past play history, the data collection unit will increase the frequency of data collection during that time period. Furthermore, if the payout rate is low during a particular time period based on past play history, the data collection unit can decrease the frequency of data collection during that time period. In addition, the data collection unit can adjust the frequency of data collection on specific days of the week or time periods based on past play history. This allows for the optimization of data collection frequency during specific time periods by analyzing past play history. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past play history into a generating AI and have the generating AI perform the optimization of data collection frequency.

[0082] The data collection unit can collect performance information from each machine in real time and immediately reflect the fluctuating payout rate. For example, the data collection unit can acquire data from sensors on each machine in real time and immediately reflect the change in the payout rate. The data collection unit can use AI to automatically collect performance information from each machine in real time. For example, the data collection unit can monitor the play status of each machine in real time and immediately reflect the change in the payout rate. The data collection unit can also immediately collect data when the payout rate of each machine changes and provide it to the user. In this way, by collecting performance information from each machine in real time, the fluctuating payout rate can be immediately reflected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data acquired from sensors on each machine into a generating AI and have the generating AI perform the action of reflecting the change in the payout rate.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting data from machines with high payout rates. The data collection unit can use AI to automatically estimate the user's emotions and determine the priority of data to collect. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the collection of more appropriate data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0084] The data collection unit can prioritize collecting data from machines in specific regions based on the user's geographical location information. For example, if the user is in a specific region, the data collection unit will prioritize collecting data from machines in that region. The data collection unit can use AI to automatically acquire the user's geographical location information and prioritize collecting data from machines in specific regions. For example, if the user is on the move, the data collection unit will prioritize collecting data from machines in regions close to the user's current location. The data collection unit can also prioritize collecting data from machines in a specific region if the user frequently visits that region. In this way, by collecting data based on the user's geographical location information, information about machines in specific regions can be provided preferentially. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of data from machines in specific regions.

[0085] The data collection unit can analyze social media trends and collect data on popular slot machines. For example, the data collection unit can prioritize collecting data on slot machines that are trending on social media. The data collection unit can use AI to automatically analyze social media trends and collect data on popular slot machines. For example, the data collection unit can analyze social media trends and collect data on popular slot machines. The data collection unit can also collect data on popular slot machines by referring to user reviews on social media. In this way, data on popular slot machines can be collected by analyzing social media trends. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media trend data into a generating AI and have the generating AI perform the collection of data on popular slot machines.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is excited, the analysis unit will adjust the analysis algorithm considering the user's tendency to take risks. The analysis unit can use AI to automatically estimate the user's emotions and adjust the analysis algorithm. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the analysis algorithm.

[0087] The analysis unit can cross-analyze the performance information and payout rate of each machine to extract specific patterns. For example, the analysis unit can cross-analyze the performance information and payout rate of each machine to extract the characteristics of machines with high payout rates. The analysis unit can use AI to automatically cross-analyze the performance information and payout rate of each machine. For example, the analysis unit can cross-analyze the performance information and payout rate of each machine to extract the characteristics of machines with high payout rates. The analysis unit can also cross-analyze the performance information and payout rate of each machine to extract the characteristics of machines with low payout rates. Furthermore, the analysis unit can cross-analyze the performance information and payout rate of each machine to extract patterns of machines with high payout rates during specific time periods. In this way, specific patterns can be extracted by cross-analyzing the performance information and payout rate of each machine. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the performance information and payout rate data of each machine into a generating AI and have the generating AI perform the extraction of specific patterns.

[0088] The analysis unit can compare past and present data to predict fluctuations in the payout rate. For example, the analysis unit can compare past and present data to predict an upward trend in the payout rate. The analysis unit can use AI to automatically compare past and present data to predict fluctuations in the payout rate. For example, the analysis unit can compare past and present data to predict an upward trend in the payout rate. The analysis unit can also compare past and present data to predict a downward trend in the payout rate. Furthermore, the analysis unit can compare past and present data to predict a stable trend in the payout rate. In this way, fluctuations in the payout rate can be predicted by comparing past and present data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input past and present data into a generating AI and have the generating AI perform the prediction of fluctuations in the payout rate.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. The analysis unit can use AI to automatically estimate the user's emotions and adjust the display method of the analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for a more appropriate display method by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust how the analysis results are displayed.

[0090] The analysis unit can analyze differences in payout rates across regions based on geographical data. For example, the analysis unit can analyze machines with high payout rates in a specific region based on geographical data. The analysis unit can use AI to automatically acquire geographical data and analyze differences in payout rates across regions. For example, the analysis unit can analyze machines with high payout rates in a specific region based on geographical data. The analysis unit can also analyze machines with low payout rates in a specific region based on geographical data. Furthermore, the analysis unit can analyze differences in payout rates across regions based on geographical data. In this way, differences in payout rates across regions can be grasped by analyzing based on geographical data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical data into a generating AI and have the generating AI perform an analysis of differences in payout rates across regions.

[0091] The analysis department can incorporate social media data and reflect user opinions in its analysis. For example, the analysis department can analyze social media reviews and reflect user opinions. The analysis department can use AI to automatically acquire social media data and reflect user opinions in its analysis. For example, the analysis department can analyze social media reviews and reflect user opinions. The analysis department can also analyze social media trends and reflect user opinions. Furthermore, the analysis department can analyze social media comments and reflect user opinions. In this way, by incorporating social media data, user opinions can be reflected in the analysis. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input social media data into a generating AI and have the generating AI perform an analysis of user opinions.

[0092] The selection unit can estimate the user's emotions and adjust the selection criteria for the table based on the estimated emotions. For example, if the user is excited, the selection unit will adjust the selection criteria for the table considering the user's tendency to take risks. The selection unit can use AI to automatically estimate the user's emotions and adjust the selection criteria for the table. For example, the selection unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the selection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for the selection of a more appropriate table by adjusting the selection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generating AI and have the AI ​​adjust the selection criteria for the machines.

[0093] The selection unit can match the payout rate of each machine with the user's playing style to select the optimal machine. For example, the selection unit can match the payout rate of each machine with the user's playing style and select a machine with a high payout rate. The selection unit can also use AI to automatically match the payout rate of each machine with the user's playing style and select the optimal machine. For example, the selection unit can match the payout rate of each machine with the user's playing style and select a machine with a high payout rate. The selection unit can also match the payout rate of each machine with the user's playing style and select a machine with a stable payout rate. Furthermore, the selection unit can match the payout rate of each machine with the user's playing style and select a machine that the user tends to take risks on. In this way, the optimal machine can be selected by matching the payout rate of each machine with the user's playing style. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input data on the payout rate of each machine and the user's playing style into a generating AI and have the generating AI perform the selection of the optimal machine.

[0094] The selection unit can analyze past selection history and optimize the selection algorithm. For example, the selection unit can analyze past selection history and optimize the algorithm for selecting machines with a high payout rate. The selection unit can use AI to automatically analyze past selection history and optimize the selection algorithm. For example, the selection unit can analyze past selection history and optimize the algorithm for selecting machines with a high payout rate. The selection unit can also analyze past selection history and optimize the algorithm for selecting machines with a stable payout rate. Furthermore, the selection unit can analyze past selection history and optimize the algorithm for selecting machines that tend to take risks. In this way, the selection algorithm can be optimized by analyzing past selection history. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input past selection history data into a generating AI and have the generating AI perform the optimization of the selection algorithm.

[0095] The selection unit can estimate the user's emotions and adjust the display order of the selection results based on the estimated emotions. For example, if the user is excited, the selection unit will prioritize displaying machines with a high payout rate. The selection unit can use AI to automatically estimate the user's emotions and adjust the display order of the selection results. For example, the selection unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the selection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for a more appropriate display order by adjusting the display order of the selection results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generating AI and have the generating AI adjust the display order of the selection results.

[0096] The selection unit can select the optimal machine in a specific region based on geographical data. For example, the selection unit can select a machine with a high payout rate in a specific region based on geographical data. The selection unit can use AI to automatically acquire geographical data and select the optimal machine in a specific region. For example, the selection unit can select a machine with a high payout rate in a specific region based on geographical data. The selection unit can also select a machine with a low payout rate in a specific region based on geographical data. Furthermore, the selection unit can select a machine with a stable payout rate in a specific region based on geographical data. In this way, by selecting the optimal machine based on geographical data, the optimal machine in a specific region can be provided. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input geographical data into a generating AI and have the generating AI perform the selection of the optimal machine in a specific region.

[0097] The suggestion unit can estimate the user's emotions and adjust its suggestions based on those emotions. For example, if the user is excited, the suggestion unit might suggest risky gameplay techniques. The suggestion unit can use AI to automatically estimate the user's emotions and adjust its suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate suggestions to be provided by adjusting the suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the proposal content.

[0098] The suggestion unit can analyze past data to propose the optimal gameplay technique for a specific time period. For example, the suggestion unit can analyze past data and propose a gameplay technique with a high payout rate for a specific time period. The suggestion unit can use AI to automatically analyze past data and propose the optimal gameplay technique for a specific time period. For example, the suggestion unit can analyze past data and propose a gameplay technique with a high payout rate for a specific time period. The suggestion unit can also analyze past data and propose a gameplay technique with a low payout rate for a specific time period. Furthermore, the suggestion unit can analyze past data and propose a stable gameplay technique for a specific time period. In this way, by analyzing past data, the optimal gameplay technique for a specific time period can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input past data into a generating AI and have the generating AI execute a proposal for the optimal gameplay technique for a specific time period.

[0099] The suggestion unit can propose the optimal playing technique by considering the payout rate of each machine and the user's playing style. For example, the suggestion unit can propose a playing technique with a high payout rate by considering the payout rate of each machine and the user's playing style. The suggestion unit can use AI to automatically consider the payout rate of each machine and the user's playing style and propose the optimal playing technique. For example, the suggestion unit can propose a playing technique with a high payout rate by considering the payout rate of each machine and the user's playing style. The suggestion unit can also propose a playing technique with a stable payout rate by considering the payout rate of each machine and the user's playing style. Furthermore, the suggestion unit can propose a playing technique that involves taking risks by considering the payout rate of each machine and the user's playing style. In this way, the optimal playing technique can be proposed by considering the payout rate of each machine and the user's playing style. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input data on the payout rate of each machine and the user's playing style into a generating AI and have the generating AI execute a suggestion of the optimal playing technique.

[0100] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is excited, the suggestion unit will prioritize suggesting risky play techniques. The suggestion unit can use AI to automatically estimate the user's emotions and determine the priority of suggestions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to provide more appropriate suggestions by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the proposal department can input user emotion data into a generation AI and have the generation AI determine the priority of proposals.

[0101] The proposal unit can propose the optimal gameplay method for a specific region based on geographical data. For example, the proposal unit can propose a gameplay method with a high payout rate in a specific region based on geographical data. The proposal unit can use AI to automatically acquire geographical data and propose the optimal gameplay method for a specific region. For example, the proposal unit can propose a gameplay method with a high payout rate in a specific region based on geographical data. The proposal unit can also propose a gameplay method with a low payout rate in a specific region based on geographical data. Furthermore, the proposal unit can propose a stable gameplay method in a specific region based on geographical data. In this way, by proposing the optimal gameplay method based on geographical data, the optimal gameplay method for a specific region can be provided. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input geographical data into a generating AI and have the generating AI execute a proposal for the optimal gameplay method for a specific region.

[0102] The suggestion unit can incorporate social media data and propose popular gameplay techniques. For example, the suggestion unit can prioritize suggesting gameplay techniques that are trending on social media. The suggestion unit can use AI to automatically acquire social media data and propose popular gameplay techniques. For example, the suggestion unit can analyze social media trends and propose popular gameplay techniques. The suggestion unit can also propose popular gameplay techniques by referring to user reviews on social media. In this way, popular gameplay techniques can be proposed by incorporating social media data. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input social media data into a generating AI and have the generating AI execute suggestions for popular gameplay techniques.

[0103] The advice unit can estimate the user's emotions and adjust the advice based on those emotions. For example, if the user is excited, the advice unit will provide advice that encourages taking risks. The advice unit can use AI to automatically estimate the user's emotions and adjust the advice accordingly. For example, the advice unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The advice unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the advice unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the advice unit to provide more appropriate advice by adjusting it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into a generating AI and have the AI ​​adjust the advice content.

[0104] The advisory unit can analyze historical data to maintain a balance between investment and return. For example, the advisory unit can analyze historical data and provide advice to maintain a balance between investment and return. The advisory unit can use AI to automatically analyze historical data and provide advice to maintain a balance between investment and return. For example, the advisory unit can analyze historical data and provide advice to maintain a balance between investment and return. The advisory unit can also analyze historical data and provide advice to reduce investment. Furthermore, the advisory unit can analyze historical data and provide advice to increase return. In this way, by analyzing historical data, it is possible to provide advice to maintain a balance between investment and return. Some or all of the above processing in the advisory unit may be performed using AI or not. For example, the advisory unit can input historical data into a generating AI and have the generating AI execute advice to maintain a balance between investment and return.

[0105] The advice unit can propose the optimal money management method by considering the payout rate of each machine and the user's playing style. For example, the advice unit can consider the payout rate of each machine and the user's playing style and propose a money management method for machines with a high payout rate. The advice unit can use AI to automatically consider the payout rate of each machine and the user's playing style and propose the optimal money management method. For example, the advice unit can consider the payout rate of each machine and the user's playing style and propose a money management method for machines with a high payout rate. The advice unit can also consider the payout rate of each machine and the user's playing style and propose a money management method for machines with a stable payout rate. Furthermore, the advice unit can consider the payout rate of each machine and the user's playing style and propose a money management method for machines where risk is taken. In this way, by considering the payout rate of each machine and the user's playing style, the optimal money management method can be proposed. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input data on the payout rate of each machine and the user's playing style into a generating AI, which can then execute a proposal for the optimal money management method.

[0106] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is excited, the advice unit will prioritize advice that encourages taking risks. The advice unit can use AI to automatically estimate the user's emotions and determine the priority of advice. For example, the advice unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The advice unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the advice unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the provision of more appropriate advice by prioritizing advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into a generating AI and have the AI ​​determine the priority of the advice.

[0107] The advisory unit can propose optimal fund management methods for a specific region based on geographical data. For example, the advisory unit can propose fund management methods for a specific region based on geographical data. The advisory unit can use AI to automatically acquire geographical data and propose optimal fund management methods for a specific region. For example, the advisory unit can propose fund management methods for a specific region based on geographical data. Furthermore, the advisory unit can propose methods to reduce investment amounts in a specific region based on geographical data. In addition, the advisory unit can propose methods to increase returns in a specific region based on geographical data. Thus, by proposing optimal fund management methods based on geographical data, the advisory unit can provide optimal fund management methods for a specific region. Some or all of the above processing in the advisory unit may be performed using AI or not. For example, the advisory unit can input geographical data into a generating AI and have the generating AI execute a proposal for optimal fund management methods for a specific region.

[0108] The advice department can incorporate social media data and propose popular money management methods. For example, the advice department can prioritize suggesting money management methods that are trending on social media. The advice department can use AI to automatically acquire social media data and propose popular money management methods. For example, the advice department can analyze social media trends and propose popular money management methods. The advice department can also suggest popular money management methods by referring to user reviews on social media. In this way, by incorporating social media data, it is possible to propose popular money management methods. Some or all of the above processes in the advice department may be performed using AI or not. For example, the advice department can input social media data into a generating AI and have the generating AI produce suggestions for popular money management methods.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] Pachinko and slot machine support systems can also include a function to predict the payout rate of a specific machine based on the user's playing style. For example, the data collection unit analyzes the user's past playing history and collects trends in payout rates for specific playing styles. The analysis unit predicts the payout rate according to the user's playing style based on the collected data. The selection unit selects the optimal machine for the user based on the predicted payout rate. This allows for the selection of the optimal machine according to the user's playing style, enabling more effective gameplay.

[0111] The pachinko / slot machine support system can estimate the user's emotions and provide real-time advice during gameplay based on those estimated emotions. For example, the data collection unit collects the user's facial expressions, voice, and biometric data to estimate their emotions. The analysis unit, based on the estimated emotions, provides advice to take risks if the user is relaxed and conservative advice if they are tense. The advice unit provides appropriate advice to the user in real time, maximizing the effectiveness of their gameplay.

[0112] Pachinko and slot machine support systems can also include a function to select the optimal machine in a specific area based on the user's geographical location. For example, the data collection unit acquires the user's current location and collects data on pachinko and slot machines in that area. The analysis unit analyzes the payout rate and play history in that area based on the collected data. The selection unit selects the optimal machine for the user based on the analysis results. This allows users to select the best machine for each area they visit.

[0113] Pachinko and slot machine support systems can also incorporate social media data and include features to suggest popular machines and gameplay techniques. For example, the data collection unit gathers social media trends and user reviews. The analysis unit analyzes popular machines and gameplay techniques based on the collected data. The suggestion unit then suggests popular machines and gameplay techniques to users based on the analysis results. This allows users to enjoy playing based on the latest trends.

[0114] Pachinko and slot machine support systems can also include a function to estimate the user's emotions and adjust the gameplay based on those emotions. For example, a data collection unit collects the user's facial expressions, voice, and biometric data to estimate their emotions. Based on the estimated emotions, an analysis unit suggests continuing the game if the user is excited, or taking a break if they are tired. An advice unit can provide the user with appropriate advice in real time to maximize the effectiveness of the game.

[0115] Pachinko and slot machine support systems can also include a function that suggests the optimal playing technique for a specific time period based on the user's play history. For example, the data collection unit collects the user's past play history and analyzes the payout rate and play style for a specific time period. The analysis unit suggests the optimal playing technique for that time period based on the collected data. The suggestion unit suggests the optimal playing technique to the user based on the analysis results. This allows the user to execute the optimal playing technique for a specific time period.

[0116] Pachinko and slot machine support systems can also include a function to estimate the user's emotions and adjust the machine selection criteria based on those estimated emotions. For example, the data collection unit collects the user's facial expressions, voice, and biometric data to estimate their emotions. The analysis unit, based on the estimated emotions, selects a machine that encourages risk if the user is relaxed, and a more conservative machine if the user is tense. The selection unit selects the optimal machine for the user in real time. This allows the user to choose the machine that best suits their emotions.

[0117] Pachinko and slot machine support systems can also include a function that suggests the optimal playing method for a specific region based on the user's geographical location. For example, the data collection unit acquires the user's current location and collects data on pachinko and slot machines in that region. The analysis unit analyzes the payout rate and play history in that region based on the collected data. The suggestion unit then suggests the optimal playing method to the user based on the analysis results. This allows users to execute the most suitable playing method for each region they visit.

[0118] Pachinko and slot machine support systems can also include a function to estimate the user's emotions and prioritize advice based on those emotions. For example, the data collection unit collects the user's facial expressions, voice, and biometric data to estimate their emotions. The analysis unit, based on the estimated emotions, prioritizes providing risk-taking advice when the user is excited and conservative advice when the user is relaxed. The advice unit can provide appropriate advice to the user in real time, maximizing the effectiveness of their gameplay.

[0119] Pachinko and slot machine support systems can also include a function to predict the payout rate of a specific machine based on the user's playing style. For example, the data collection unit analyzes the user's past playing history and collects trends in payout rates for specific playing styles. The analysis unit predicts the payout rate according to the user's playing style based on the collected data. The selection unit selects the optimal machine for the user based on the predicted payout rate. This allows for the selection of the optimal machine according to the user's playing style, enabling more effective gameplay.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit collects data such as performance information, play history, and payout rates for each pachinko and slot machine. For example, it collects information such as how often a particular machine pays out and which time slots are more likely to pay out based on past play history. The data collection unit can automatically collect data such as performance information, play history, and payout rates for each machine using AI. It acquires data in real time from sensors on each machine and immediately reflects changes in the payout rate. It can also analyze past play history and optimize the frequency of data collection during specific time periods. Furthermore, it can estimate user emotions and adjust the timing of data collection based on the estimated user emotions. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data to determine the performance information and payout rate of each machine. The analysis unit can use AI to automatically analyze the collected data. It can cross-analyze the performance information and payout rate of each machine to extract specific patterns. It can also compare past data with current data to predict fluctuations in the payout rate. Furthermore, it can estimate user sentiment and adjust the analysis algorithm based on the estimated user sentiment. Step 3: The selection unit selects the optimal machine based on the analysis results obtained by the analysis unit. For example, it selects the optimal machine based on the analysis results. The selection unit can use AI to automatically select the optimal machine based on the analysis results. It matches the payout rate of each machine with the user's playing style to select the optimal machine. It can also analyze past selection history and optimize the selection algorithm. Furthermore, it can estimate the user's emotions and adjust the machine selection criteria based on the estimated user emotions. Step 4: The suggestion unit proposes the optimal playing strategy based on the machine selected by the selection unit. For example, based on data indicating that a particular machine is more likely to pay out during a specific time period, it advises the user to choose that machine. The suggestion unit can automatically propose the optimal playing strategy using AI. It proposes the optimal playing strategy considering the payout rate of each machine and the user's playing style. It can also analyze past data and propose the optimal playing strategy for a specific time period. Furthermore, it can estimate the user's emotions and adjust the suggestions based on those estimated emotions. Step 5: The advice unit provides advice to maintain the optimal balance between investment and return based on the playing techniques proposed by the suggestion unit. For example, it may advise stopping play if a certain amount is exceeded. The advice unit can use AI to automatically provide advice to maintain the optimal balance between investment and return. It analyzes past data to provide advice to maintain the balance between investment and return. It can also propose the optimal money management method by considering the payout rate of each machine and the user's playing style. Furthermore, it can estimate the user's emotions and adjust the advice based on the estimated emotions of the user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the data collection unit, analysis unit, selection unit, proposal unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the smart device 14 to collect data such as performance information, play history, and payout rate for each pachinko or slot machine. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to analyze the performance information and payout rate of each machine. The selection unit selects the optimal machine based on the analysis results using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal playing method based on the selected machine using the control unit 46A of the smart device 14. The advice unit provides advice using the specific processing unit 290 of the data processing unit 12 to maintain an optimal balance between investment and recovery. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, selection unit, proposal unit, and advice unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the smart glasses 214 to collect data such as performance information, play history, and payout rate for each pachinko or slot machine. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12, to analyze the performance information and payout rate of each machine. The selection unit selects the optimal machine based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal playing method based on the selected machine, for example, by the control unit 46A of the smart glasses 214. The advice unit provides advice, for example, by the specific processing unit 290 of the data processing unit 12, to maintain the optimal balance between investment and recovery. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, selection unit, proposal unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and sensors of the headset terminal 314 to collect data such as performance information, play history, and payout rate for each pachinko or slot machine. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to analyze the performance information and payout rate of each machine. The selection unit selects the optimal machine based on the analysis results using the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal playing method based on the selected machine using the control unit 46A of the headset terminal 314. The advice unit provides advice using the specific processing unit 290 of the data processing unit 12 to maintain an optimal balance between investment and recovery. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the collection unit, analysis unit, selection unit, proposal unit, and advice unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and sensors of the robot 414 to collect data such as performance information, play history, and payout rate for each pachinko or slot machine. The analysis unit analyzes the collected data, for example, by the specific processing unit 290 of the data processing unit 12, to analyze the performance information and payout rate of each machine. The selection unit selects the optimal machine based on the analysis results, for example, by the specific processing unit 290 of the data processing unit 12. The proposal unit proposes the optimal playing method based on the selected machine, for example, by the control unit 46A of the robot 414. The advice unit provides advice, for example, by the specific processing unit 290 of the data processing unit 12, to maintain the optimal balance between investment and recovery. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A data collection unit that collects data such as performance information, play history, and payout rates for each pachinko and slot machine, An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects the optimal stand based on the analysis results obtained from the analysis unit, A proposal unit that proposes the optimal playing method based on the machine selected by the selection unit, The system includes an advice unit that provides advice on maintaining an optimal balance between investment and return based on the gameplay techniques proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect information such as how often a particular machine pays out, and which time slots are more likely to pay out based on past play history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to determine the performance information and payout rate of each machine. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Select the optimal machine based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on data indicating that certain machines are more likely to pay out during specific time periods, we advise users to choose those machines. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, Advise players to stop playing if they exceed a certain amount. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past play history and optimize the frequency of data collection during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system collects performance information from each machine in real time and instantly reflects the fluctuating payout rate. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Based on the user's geographical location, data from specific regions will be collected preferentially. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze social media trends and collect data on popular games. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is The performance information and payout rate of each machine are cross-analyzed to extract specific patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is By comparing past and current data, we can predict fluctuations in the payout rate. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Based on geographical data, we analyze the differences in payout rates across regions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We incorporate social media data and reflect user feedback in our analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is The system estimates the user's emotions and adjusts the selection criteria for the machine based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is The system matches the payout rate of each machine with the user's playing style to select the optimal machine. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is Analyze past selection history and optimize the selection algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is It estimates the user's emotions and adjusts the display order of the selection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is Based on geographical data, we select the optimal machine for a specific region. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, To propose the optimal playing technique for a specific time period, we analyze past data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, We propose the optimal playing technique, taking into account the payout rate of each machine and the user's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, Based on geographical data, we propose the optimal play methods for a specific region. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, We incorporate social media data to suggest popular gameplay techniques. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, It estimates the user's emotions and adjusts the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, To maintain a balance between investment and return, we analyze historical data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advice section, We propose the optimal money management method, taking into account the payout rate of each machine and the user's playing style. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned advice section, Based on geographical data, we propose the optimal fund management method for a specific region. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned advice section, We incorporate social media data to propose popular money management methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data such as performance information, play history, and payout rates for each pachinko and slot machine, An analysis unit analyzes the data collected by the aforementioned collection unit, A selection unit that selects the optimal stand based on the analysis results obtained from the analysis unit, A proposal unit that proposes the optimal playing method based on the machine selected by the selection unit, The system includes an advice unit that provides advice on maintaining an optimal balance between investment and return based on the gameplay techniques proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect information such as how often a particular machine pays out, and which time slots are more likely to pay out based on past play history. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to determine the performance information and payout rate of each machine. The system according to feature 1.

4. The aforementioned selection unit is Select the optimal machine based on the analysis results. The system according to feature 1.

5. The aforementioned proposal section is, Based on data indicating that certain machines are more likely to pay out during specific time periods, we advise users to choose those machines. The system according to feature 1.

6. The aforementioned advice section, Advise players to stop playing if they exceed a certain amount. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past play history and optimize the frequency of data collection during specific time periods. The system according to feature 1.

9. The aforementioned collection unit is The system collects performance information from each machine in real time and instantly reflects the fluctuating payout rate. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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