system

Abstract

The system according to the embodiment aims to optimally adjust the settings of gaming machines, stabilize income and expenditure, and improve business efficiency. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a setting adjustment unit, and a suggestion providing unit. The collection unit collects data for each gaming machine. The analysis unit analyzes the data collected by the collection unit. The setting adjustment unit appropriately adjusts the settings of each gaming machine based on the analysis results obtained by the analysis unit. The suggestion providing unit provides the setting adjustments suggested by the setting adjustment unit to an employee.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the settings of gaming machines had to be adjusted manually, which created challenges in stabilizing income and expenses and improving operational efficiency.

[0005] The system according to the embodiment aims to optimally adjust the settings of gaming machines, stabilize income and expenditure, and improve business efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a setting adjustment unit, and a suggestion providing unit. The collection unit collects data for each gaming machine. The analysis unit analyzes the data collected by the collection unit. The setting adjustment unit appropriately adjusts the settings of each gaming machine based on the analysis results obtained by the analysis unit. The suggestion providing unit provides the setting adjustments suggested by the setting adjustment unit to employees. [Effects of the Invention]

[0007] The system according to the embodiment can optimally adjust the settings of gaming machines, stabilizing income and expenditure and improving business efficiency. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A pachinko parlor income / expense adjustment system according to an embodiment of the present invention uses a generation AI to analyze data for each gaming machine, stabilizing income / expenses and improving employee work efficiency. This system collects data for each gaming machine, which the generation AI analyzes. Based on the analysis results, the generation AI then optimally adjusts the settings of each gaming machine. This stabilizes income / expenses. Furthermore, since employees only need to follow the setting adjustment suggestions provided by the generation AI, work efficiency is improved. For example, when collecting data for each gaming machine, detailed data such as the machine's operating status and player behavior data is collected. For example, data such as the operating time of each gaming machine, player bet amounts, and win / loss results are collected. This allows for detailed data on each gaming machine to be understood. The generation AI then analyzes the collected data. The generation AI analyzes the collected data and determines the income / expense status of each gaming machine. For example, if a specific gaming machine is out of balance, the system can identify the cause and suggest optimal setting adjustments. This stabilizes income / expenses. Furthermore, the generating AI optimally adjusts the settings of each gaming machine based on the analysis results. For example, by changing the settings of a specific gaming machine, it is possible to balance income and expenses. This leads to stabilization of income and expenses. In addition, since employees only need to follow the setting adjustment suggestions provided by the generating AI, work efficiency is improved. For example, employees can work efficiently by implementing the setting adjustments suggested by the generating AI. This reduces the workload of employees and improves work efficiency. This system stabilizes the income and expenses of pachislot parlors and improves the work efficiency of employees. As a result, the income and expenses adjustment system for pachislot parlors can stabilize income and expenses and improve the work efficiency of employees.

[0029] A pachinko parlor income / expense adjustment system according to an embodiment includes a collection unit, an analysis unit, a setting adjustment unit, and a proposal provision unit. The collection unit collects data for each gaming machine. For example, the collection unit collects data on the operation status of each gaming machine and player behavior data. For example, the collection unit collects data such as the operation time of each gaming machine, player bet amounts, and win / loss results. The collection unit can also monitor the operation status of each gaming machine in real time and immediately notify the user if an abnormality occurs. For example, the collection unit monitors the operation status of each gaming machine in real time and issues an alert if an abnormality occurs. The collection unit can also collect biometric information of players (e.g., heart rate, body temperature, etc.). For example, the collection unit monitors the player's heart rate in real time and notifies the user if an abnormality occurs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and determines the income / expense status of each gaming machine. For example, if a specific gaming machine is out of balance, the analysis unit identifies the cause and proposes optimal setting adjustments. The analysis unit can also analyze player behavior patterns in addition to the income / expense status of each gaming machine to identify factors that affect income / expense. For example, the analysis unit analyzes the player's bet amount and win / loss results to identify factors that affect income / expense. Furthermore, the analysis unit can predict the long-term income / expense status of a gaming machine and predict future fluctuations in income / expense. For example, the analysis unit predicts future fluctuations in income / expense based on past income / expense data. The setting adjustment unit optimally adjusts the settings of each gaming machine based on the analysis results obtained by the analysis unit. The setting adjustment unit can balance income / expenses, for example, by changing the settings of a specific gaming machine. For example, the setting adjustment unit changes the settings of each gaming machine to stabilize income / expense. The setting adjustment unit can also monitor the effects of the setting adjustment in real time and immediately make readjustments as necessary. For example, the setting adjustment unit monitors the income / expense status after the setting adjustment in real time and makes readjustments as necessary. The suggestion providing unit provides the setting adjustments proposed by the setting adjustment unit to employees. For example, the suggestion providing unit provides the setting adjustments proposed by the generation AI to employees. For example, the suggestion providing unit visually displays the suggested settings adjustments in the form of graphs or charts.The suggestion providing unit can also provide audio guidance on the suggestion content. For example, the suggestion providing unit provides audio guidance on the suggestion content, allowing employees to execute the suggestion without relying on visual information. This allows the income and expenditure adjustment system for a pachinko parlor according to the embodiment to stabilize income and expenditure and improve business efficiency.

[0030] The collection unit can collect data on the operation status of gaming machines or player behavior. The collection unit, for example, collects data on the operation status of gaming machines. For example, the collection unit collects data such as the operation time and number of plays of each gaming machine. The collection unit can also collect data on player behavior. For example, the collection unit collects data such as player bet amounts and win / loss results. By collecting data on the operation status of gaming machines and player behavior, analysis based on detailed data becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input data on the operation status of gaming machines and player behavior into the generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze the collected data and determine the income / expense status of each gaming machine. For example, the analysis unit can analyze the collected data and determine the income / expense status of each gaming machine. For example, if a specific gaming machine is out of balance, the analysis unit can identify the cause and suggest optimal setting adjustments. In addition to the income / expense status of each gaming machine, the analysis unit can also analyze player behavior patterns to identify factors affecting the income / expense status. For example, the analysis unit can analyze player bet amounts and win / loss results to identify factors affecting the income / expense status. Furthermore, the analysis unit can forecast the income / expense status of each gaming machine over the long term and predict future fluctuations in income / expense status. For example, the analysis unit can predict future fluctuations in income / expense status based on past income / expense data. This allows the analysis of the collected data and the determination of the income / expense status of each gaming machine to grasp the income / expense balance. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI analyze the data.

[0032] The setting adjustment unit can change the settings of each gaming machine to stabilize income and expenditure. The setting adjustment unit can, for example, balance income and expenditure by changing the settings of a specific gaming machine. For example, the setting adjustment unit changes the settings of each gaming machine to stabilize income and expenditure. The setting adjustment unit can also monitor the effects of the setting adjustment in real time and immediately readjust as necessary. For example, the setting adjustment unit can monitor the income and expenditure status after the setting adjustment in real time and readjust as necessary. In this way, by changing the settings of each gaming machine to balance income and expenditure, income and expenditure are stabilized. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using a generation AI or may be performed without using a generation AI. For example, the setting adjustment unit can input the settings of each gaming machine into the generation AI and have the generation AI execute the setting changes.

[0033] The suggestion providing unit can provide the setting adjustments suggested by the generation AI to the employee. The suggestion providing unit, for example, provides the setting adjustments suggested by the generation AI to the employee. For example, the suggestion providing unit visually displays the proposed setting adjustments using graphs or charts. The suggestion providing unit can also provide audio guidance on the proposed content. For example, the suggestion providing unit provides audio guidance on the proposed content, allowing the employee to execute the adjustments without relying on visual information. This improves work efficiency by providing the setting adjustments suggested by the generation AI to the employee. Some or all of the above-described processing in the suggestion providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion providing unit can input the setting adjustments suggested by the generation AI to the generation AI and have the generation AI execute the provision of the proposed content.

[0034] The suggestion providing unit can provide support to help employees perform their work efficiently based on the suggestions. The suggestion providing unit, for example, provides support to help employees perform their work efficiently based on the suggestions. For example, the suggestion providing unit provides support to help employees perform their work efficiently based on the suggestions. This provides support to help employees perform their work efficiently based on the suggestions, thereby further improving work efficiency. Some or all of the above-described processing in the suggestion providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion providing unit can input support to help employees perform their work efficiently based on the suggestions into the generation AI, and have the generation AI provide the support.

[0035] Furthermore, the pachislot parlor's income / expense adjustment system includes a collection unit that collects players' biometric information (heart rate, body temperature, etc.) in addition to the operating status of gaming machines. The collection unit, for example, monitors the player's heart rate in real time and notifies the player if any abnormalities are detected. The collection unit can also periodically measure the player's body temperature to monitor their health condition. Furthermore, the collection unit can collect players' biometric information and analyze it in association with the operating status of gaming machines. This allows for more detailed data analysis by collecting players' biometric information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input players' biometric information into the generation AI and have the generation AI collect and analyze the data.

[0036] Furthermore, the pachislot parlor income / expense adjustment system includes a collection unit that monitors the operation status of gaming machines in real time and immediately notifies the user if an abnormality occurs. The collection unit, for example, monitors the operation status of gaming machines in real time and issues an alert if an abnormality occurs. The collection unit can also immediately notify employees if an abnormality occurs and encourage them to take prompt action. Furthermore, the collection unit can also suggest appropriate response methods depending on the type of abnormality. This enables rapid response by monitoring the operation status of gaming machines in real time and immediately notifying the user if an abnormality occurs. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the operation status of gaming machines into the generation AI and have the generation AI detect and notify abnormalities.

[0037] Furthermore, the income / expense adjustment system for a pachislot parlor includes a collection unit that collects environmental data within the parlor (such as temperature, humidity, and noise level) in addition to the operating status of gaming machines. The collection unit, for example, periodically measures the temperature within the parlor to maintain a comfortable environment. The collection unit can also monitor the humidity within the parlor and maintain an appropriate humidity level. Furthermore, the collection unit can measure the noise level within the parlor and issue a notification when the noise exceeds a certain level. In this way, by collecting environmental data within the parlor, data for maintaining a comfortable environment can be obtained. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input environmental data within the parlor into a generation AI and have the generation AI collect and analyze the data.

[0038] Furthermore, the pachislot parlor income / expense adjustment system includes a collection unit that analyzes players' social media activities and collects related behavioral data. The collection unit, for example, analyzes players' social media posts to understand trends in gaming machine selection. The collection unit can also predict the frequency of parlor visits based on players' social media activities. Furthermore, the collection unit can collect players' social media feedback and use it to improve services. In this way, more detailed behavioral data can be obtained by analyzing players' social media activities. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit may input players' social media data into the generation AI and have the generation AI collect and analyze the data.

[0039] Furthermore, the pachislot parlor's income / expense adjustment system includes an analysis unit that analyzes player behavior patterns and identifies factors affecting income / expenses, in addition to the income / expense status of each gaming machine. The analysis unit, for example, analyzes a player's bet amount and win / loss results to identify factors affecting income / expenses. The analysis unit can also analyze the relationship between a player's playing time and income / expenses and suggest optimal playing times. The analysis unit can also analyze a player's behavior patterns and identify factors affecting income / expenses. In this way, by analyzing a player's behavior patterns, factors affecting income / expenses can be identified. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input player behavior data into the generation AI and have the generation AI analyze the data and identify factors.

[0040] Furthermore, the pachislot parlor income / expense adjustment system includes an analysis unit that predicts the long-term income / expense status of gaming machines and predicts future income / expense fluctuations. The analysis unit predicts future income / expense fluctuations based on, for example, past income / expense data. The analysis unit can also predict income / expense fluctuations taking into account the effects of seasons and events. The analysis unit can also predict future income / expense fluctuations based on player behavior patterns. This predicts future income / expense fluctuations, thereby stabilizing long-term income / expenses. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past income / expense data into the generation AI and have the generation AI predict future income / expenses.

[0041] Furthermore, the pachislot parlor income / expense adjustment system includes an analysis unit that analyzes the income / expense status of the entire parlor in addition to the income / expense status of each gaming machine and evaluates the overall income / expense balance. The analysis unit, for example, aggregates the income / expense status of each gaming machine and evaluates the income / expense balance of the entire parlor. The analysis unit can also analyze the income / expense data of the entire parlor and identify imbalances in income / expenses. Furthermore, the analysis unit can evaluate the income / expense balance of the entire parlor and propose an optimal income / expense balance. In this way, the overall income / expense balance can be evaluated by analyzing the income / expense status of the entire parlor. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the income / expense data of the entire parlor into the generation AI and have the generation AI analyze the data and evaluate the balance.

[0042] Furthermore, the pachislot parlor's income and expenditure adjustment system includes an analysis unit that compares the income and expenditure status of gaming machines with that of other parlors and performs benchmark analysis. The analysis unit, for example, collects income and expenditure data from other parlors and compares it with the income and expenditure status of the parlor itself. The analysis unit can also evaluate the income and expenditure balance of the parlor itself by referring to the income and expenditure balances of other parlors. Furthermore, the analysis unit can perform benchmark analysis based on the income and expenditure data of other parlors and propose an optimal income and expenditure balance. This allows the income and expenditure status of the parlor itself to be evaluated by comparing it with other parlors. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input income and expenditure data from other parlors into the generation AI and have the generation AI perform a benchmark analysis.

[0043] When changing the settings of each gaming machine, the setting adjustment unit can select optimal settings by taking into account the player's past behavioral data. The setting adjustment unit selects optimal settings based on, for example, the player's past bet amount and win / loss results. The setting adjustment unit can also select optimal settings by taking into account the player's past playing time. Furthermore, the setting adjustment unit can analyze the player's past behavioral patterns and select optimal settings. This allows for more optimal settings to be selected by taking into account the player's past behavioral data. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input the player's past behavioral data into the generation AI and have the generation AI select optimal settings.

[0044] Furthermore, the income / expense adjustment system for a pachislot parlor includes a setting adjustment unit that monitors the effects of the setting adjustment in real time and immediately readjusts as necessary. The setting adjustment unit, for example, monitors the income / expense status after the setting adjustment in real time and readjusts as necessary. The setting adjustment unit can also monitor the player's behavior patterns after the setting adjustment and readjust as necessary. The setting adjustment unit can also monitor the operating status of the gaming machine after the setting adjustment and readjust as necessary. In this way, by monitoring the effects of the setting adjustment in real time and readjusting as necessary, income / expenses can be stabilized. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input data after the setting adjustment into the generation AI, determine the need for readjustment, and have the generation AI execute the readjustment.

[0045] When changing the settings of each gaming machine, the setting adjustment unit can make adjustments based on interactions with other gaming machines. The setting adjustment unit, for example, selects optimal settings taking into account the operating status of other gaming machines. The setting adjustment unit can also select optimal settings taking into account the income and expenditure status of other gaming machines. Furthermore, the setting adjustment unit can also select optimal settings taking into account the behavior patterns of players at other gaming machines. This allows the overall income and expenditure balance to be optimized by taking into account interactions with other gaming machines. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input data of other gaming machines into the generation AI and cause the generation AI to adjust the settings taking into account the interactions.

[0046] Furthermore, the income / expense adjustment system for a pachinko parlor includes a setting adjustment unit with an interface that visually displays the content of the setting adjustment proposal when providing it to employees. The setting adjustment unit visually displays the content of the setting adjustment proposal using graphs or charts, for example. The setting adjustment unit can also display the content of the setting adjustment proposal in a dashboard format to make it easy for employees to understand. The setting adjustment unit can also display the content of the setting adjustment proposal using animation to make it visually easy to understand. In this way, by visually displaying the content of the proposal, employees can easily understand and implement it. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the setting adjustment unit can input the content of the proposal into the generation AI and have the generation AI execute the visual display.

[0047] When providing a proposal, the proposal providing unit can select the optimal proposal by referring to the employee's past execution history. For example, the proposal providing unit analyzes the employee's past execution history and selects the most effective proposal. The proposal providing unit can also select the optimal proposal based on setting adjustments that the employee has made successfully in the past. Furthermore, the proposal providing unit can select the most efficient proposal from the employee's past execution history. This makes it possible to provide more effective proposals by referring to the employee's past execution history. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input the employee's execution history data into the generation AI and have the generation AI select the optimal proposal.

[0048] Furthermore, the pachislot parlor income / expense adjustment system includes a proposal providing unit that collects the effectiveness of proposals as feedback and reflects it in the next proposal. The proposal providing unit, for example, collects the effects of the proposals after they are implemented as feedback and reflects them in the next proposal. The proposal providing unit can also quantitatively evaluate the effectiveness of the proposals and reflect it in the next proposal. Furthermore, the proposal providing unit can collect the effectiveness of the proposals as feedback from employees and reflect it in the next proposal. In this way, collecting the effectiveness of the proposals as feedback improves the accuracy of the next proposal. Some or all of the above-described processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input feedback data into the generation AI and have the generation AI execute a process to reflect the feedback data in the next proposal.

[0049] When providing a proposal, the proposal providing unit can select the optimal proposal by taking into account the employee's skill level. For example, the proposal providing unit evaluates the employee's skill level and provides the optimal proposal. The proposal providing unit can also provide proposals according to the employee's skill level based on the employee's past performance. Furthermore, the proposal providing unit can adjust the level of detail of the proposal according to the employee's skill level. This enables more appropriate proposals by taking the employee's skill level into consideration. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input employee skill level data into the generation AI and have the generation AI select the optimal proposal.

[0050] Furthermore, the pachislot parlor income / expense adjustment system includes a proposal providing unit that has a function of providing audio guidance on the proposal content when providing the proposal content. The proposal providing unit, for example, provides audio guidance on the proposal content, allowing employees to execute the proposal content without relying on visual information. The proposal providing unit can also provide audio guidance on the proposal content, allowing employees to execute the proposal content without using their hands. The proposal providing unit can also provide audio guidance on the proposal content, allowing employees to perform their work efficiently. Thus, by providing audio guidance on the proposal content, employees can execute the proposal content without relying on visual information. Some or all of the above-described processing in the proposal providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the proposal providing unit can input the proposal content into the generation AI and have the generation AI execute the audio guidance.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] In addition to the operating status of gaming machines and player behavior data, the collection unit can also collect environmental data within the store (such as temperature, humidity, and noise levels). For example, the collection unit can periodically measure the temperature within the store to maintain a comfortable environment. The collection unit can also monitor the humidity within the store and maintain appropriate humidity. Furthermore, the collection unit can measure the noise level within the store and issue a notification when the noise exceeds a certain level. In this way, by collecting environmental data within the store, data for maintaining a comfortable environment can be obtained. Some or all of the above-mentioned processing by the collection unit may be performed using or without the generation AI. For example, the collection unit can input environmental data within the store into the generation AI and have the generation AI collect and analyze the data.

[0053] When analyzing the collected data, the analysis unit can also refer to the income and expenditure data of other stores and perform benchmark analysis. For example, the analysis unit collects income and expenditure data of other stores and compares it with the income and expenditure situation of the store itself. The analysis unit can also evaluate the income and expenditure balance of the store itself by referring to the income and expenditure balance of other stores. Furthermore, the analysis unit can perform benchmark analysis based on the income and expenditure data of other stores and propose an optimal income and expenditure balance. This allows the income and expenditure situation of the store itself to be evaluated by comparing it with other stores. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the income and expenditure data of other stores into the generation AI and have the generation AI perform a benchmark analysis.

[0054] When changing the settings of each gaming machine, the setting adjustment unit can select optimal settings by taking into account the player's past behavioral data. For example, the setting adjustment unit selects optimal settings based on the player's past bet amount and win / loss results. The setting adjustment unit can also select optimal settings by taking into account the player's past playing time. Furthermore, the setting adjustment unit can analyze the player's past behavioral patterns and select optimal settings. In this way, by taking into account the player's past behavioral data, more optimal settings can be selected. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input the player's past behavioral data into the generation AI and have the generation AI select optimal settings.

[0055] When providing a proposal, the proposal providing unit can select the optimal proposal by taking into account the employee's skill level. For example, the proposal providing unit evaluates the employee's skill level and provides the optimal proposal. The proposal providing unit can also provide proposals according to the employee's skill level based on the employee's past performance. Furthermore, the proposal providing unit can adjust the level of detail of the proposal according to the employee's skill level. This enables more appropriate proposals by taking the employee's skill level into consideration. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input employee skill level data into the generation AI and have the generation AI select the optimal proposal.

[0056] The proposal providing unit can collect the effectiveness of the proposal as feedback and reflect it in the next proposal. For example, the proposal providing unit can collect the effectiveness of the proposal after it is implemented as feedback and reflect it in the next proposal. The proposal providing unit can also quantitatively evaluate the effectiveness of the proposal and reflect it in the next proposal. Furthermore, the proposal providing unit can collect the effectiveness of the proposal as feedback from employees and reflect it in the next proposal. In this way, by collecting the effectiveness of the proposal as feedback, the accuracy of the next proposal is improved. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input feedback data into the generation AI and cause the generation AI to execute processing to reflect the feedback data in the next proposal.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects data for each gaming machine. For example, the collection unit collects data on the operating status of gaming machines and player behavior. Specifically, the collection unit collects data such as the operating time of each gaming machine, the amount of player bets, and win / loss results. The collection unit can also monitor the operating status of gaming machines in real time and immediately notify if an abnormality occurs. Furthermore, the collection unit can also collect biometric information of players (heart rate, body temperature, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data and determines the income and expenditure status of each gaming machine. If a specific gaming machine is out of balance, it identifies the cause and suggests optimal setting adjustments. It can also analyze player behavior patterns and identify factors that affect income and expenditure. It can also forecast the income and expenditure status of gaming machines over the long term and predict future fluctuations in income and expenditure. Step 3: The settings adjustment unit optimally adjusts the settings of each gaming machine based on the analysis results obtained by the analysis unit. For example, changing the settings of a specific gaming machine can balance income and expenses. The effects of the setting adjustments can be monitored in real time, and immediate readjustments can be made if necessary. Step 4: The proposal providing unit provides the employee with the setting adjustments suggested by the setting adjustment unit. For example, the proposal providing unit provides the employee with the setting adjustments suggested by the generation AI and visually displays the proposed content in graphs and charts. The proposed content can also be provided via voice guidance.

[0059] (Example 2) A pachinko parlor income / expense adjustment system according to an embodiment of the present invention uses a generation AI to analyze data for each gaming machine, stabilizing income / expenses and improving employee work efficiency. This system collects data for each gaming machine, which the generation AI analyzes. Based on the analysis results, the generation AI then optimally adjusts the settings of each gaming machine. This stabilizes income / expenses. Furthermore, since employees only need to follow the setting adjustment suggestions provided by the generation AI, work efficiency is improved. For example, when collecting data for each gaming machine, detailed data such as the machine's operating status and player behavior data is collected. For example, data such as the operating time of each gaming machine, player bet amounts, and win / loss results are collected. This allows for detailed data on each gaming machine to be understood. The generation AI then analyzes the collected data. The generation AI analyzes the collected data and determines the income / expense status of each gaming machine. For example, if a specific gaming machine is out of balance, the system can identify the cause and suggest optimal setting adjustments. This stabilizes income / expenses. Furthermore, the generating AI optimally adjusts the settings of each gaming machine based on the analysis results. For example, by changing the settings of a specific gaming machine, it is possible to balance income and expenses. This leads to stabilization of income and expenses. In addition, since employees only need to follow the setting adjustment suggestions provided by the generating AI, work efficiency is improved. For example, employees can work efficiently by implementing the setting adjustments suggested by the generating AI. This reduces the workload of employees and improves work efficiency. This system stabilizes the income and expenses of pachislot parlors and improves the work efficiency of employees. As a result, the income and expenses adjustment system for pachislot parlors can stabilize income and expenses and improve the work efficiency of employees.

[0060] A pachinko parlor income / expense adjustment system according to an embodiment includes a collection unit, an analysis unit, a setting adjustment unit, and a proposal provision unit. The collection unit collects data for each gaming machine. For example, the collection unit collects data on the operation status of each gaming machine and player behavior data. For example, the collection unit collects data such as the operation time of each gaming machine, player bet amounts, and win / loss results. The collection unit can also monitor the operation status of each gaming machine in real time and immediately notify the user if an abnormality occurs. For example, the collection unit monitors the operation status of each gaming machine in real time and issues an alert if an abnormality occurs. The collection unit can also collect biometric information of players (e.g., heart rate, body temperature, etc.). For example, the collection unit monitors the player's heart rate in real time and notifies the user if an abnormality occurs. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and determines the income / expense status of each gaming machine. For example, if a specific gaming machine is out of balance, the analysis unit identifies the cause and proposes optimal setting adjustments. The analysis unit can also analyze player behavior patterns in addition to the income / expense status of each gaming machine to identify factors that affect income / expense. For example, the analysis unit analyzes the player's bet amount and win / loss results to identify factors that affect income / expense. Furthermore, the analysis unit can predict the long-term income / expense status of a gaming machine and predict future fluctuations in income / expense. For example, the analysis unit predicts future fluctuations in income / expense based on past income / expense data. The setting adjustment unit optimally adjusts the settings of each gaming machine based on the analysis results obtained by the analysis unit. The setting adjustment unit can balance income / expenses, for example, by changing the settings of a specific gaming machine. For example, the setting adjustment unit changes the settings of each gaming machine to stabilize income / expense. The setting adjustment unit can also monitor the effects of the setting adjustment in real time and immediately make readjustments as necessary. For example, the setting adjustment unit monitors the income / expense status after the setting adjustment in real time and makes readjustments as necessary. The suggestion providing unit provides the setting adjustments proposed by the setting adjustment unit to employees. For example, the suggestion providing unit provides the setting adjustments proposed by the generation AI to employees. For example, the suggestion providing unit visually displays the suggested settings adjustments in the form of graphs or charts.The suggestion providing unit can also provide audio guidance on the suggestion content. For example, the suggestion providing unit provides audio guidance on the suggestion content, allowing employees to execute the suggestion without relying on visual information. This allows the income and expenditure adjustment system for a pachinko parlor according to the embodiment to stabilize income and expenditure and improve business efficiency.

[0061] The collection unit can collect data on the operation status of gaming machines or player behavior. The collection unit, for example, collects data on the operation status of gaming machines. For example, the collection unit collects data such as the operation time and number of plays of each gaming machine. The collection unit can also collect data on player behavior. For example, the collection unit collects data such as player bet amounts and win / loss results. By collecting data on the operation status of gaming machines and player behavior, analysis based on detailed data becomes possible. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input data on the operation status of gaming machines and player behavior into the generation AI and have the generation AI collect the data.

[0062] The analysis unit can analyze the collected data and determine the income / expense status of each gaming machine. For example, the analysis unit can analyze the collected data and determine the income / expense status of each gaming machine. For example, if a specific gaming machine is out of balance, the analysis unit can identify the cause and suggest optimal setting adjustments. In addition to the income / expense status of each gaming machine, the analysis unit can also analyze player behavior patterns to identify factors affecting the income / expense status. For example, the analysis unit can analyze player bet amounts and win / loss results to identify factors affecting the income / expense status. Furthermore, the analysis unit can forecast the income / expense status of each gaming machine over the long term and predict future fluctuations in income / expense status. For example, the analysis unit can predict future fluctuations in income / expense status based on past income / expense data. This allows the analysis of the collected data and the determination of the income / expense status of each gaming machine to grasp the income / expense balance. Some or all of the above-described processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI analyze the data.

[0063] The setting adjustment unit can change the settings of each gaming machine to stabilize income and expenditure. The setting adjustment unit can, for example, balance income and expenditure by changing the settings of a specific gaming machine. For example, the setting adjustment unit changes the settings of each gaming machine to stabilize income and expenditure. The setting adjustment unit can also monitor the effects of the setting adjustment in real time and immediately readjust as necessary. For example, the setting adjustment unit can monitor the income and expenditure status after the setting adjustment in real time and readjust as necessary. In this way, by changing the settings of each gaming machine to balance income and expenditure, income and expenditure are stabilized. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using a generation AI or may be performed without using a generation AI. For example, the setting adjustment unit can input the settings of each gaming machine into the generation AI and have the generation AI execute the setting changes.

[0064] The suggestion providing unit can provide the setting adjustments suggested by the generation AI to the employee. The suggestion providing unit, for example, provides the setting adjustments suggested by the generation AI to the employee. For example, the suggestion providing unit visually displays the proposed setting adjustments using graphs or charts. The suggestion providing unit can also provide audio guidance on the proposed content. For example, the suggestion providing unit provides audio guidance on the proposed content, allowing the employee to execute the adjustments without relying on visual information. This improves work efficiency by providing the setting adjustments suggested by the generation AI to the employee. Some or all of the above-described processing in the suggestion providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion providing unit can input the setting adjustments suggested by the generation AI to the generation AI and have the generation AI execute the provision of the proposed content.

[0065] The suggestion providing unit can provide support to help employees perform their work efficiently based on the suggestions. The suggestion providing unit, for example, provides support to help employees perform their work efficiently based on the suggestions. For example, the suggestion providing unit provides support to help employees perform their work efficiently based on the suggestions. This provides support to help employees perform their work efficiently based on the suggestions, thereby further improving work efficiency. Some or all of the above-described processing in the suggestion providing unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion providing unit can input support to help employees perform their work efficiently based on the suggestions into the generation AI, and have the generation AI provide the support.

[0066] The pachinko parlor income / expense adjustment system further includes a collection unit that estimates a user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, when the user is excited, the collection unit increases the frequency of data collection and updates the data in real time. Furthermore, when the user is relaxed, the collection unit can also reduce the frequency of data collection and update the data periodically. Furthermore, when the user is stressed, the collection unit can adjust the timing of data collection to reduce the burden on the user. This enables more appropriate data collection by adjusting the timing of data collection based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without the generation AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0067] Furthermore, the pachislot parlor's income / expense adjustment system includes a collection unit that collects players' biometric information (heart rate, body temperature, etc.) in addition to the operating status of gaming machines. The collection unit, for example, monitors the player's heart rate in real time and notifies the player if any abnormalities are detected. The collection unit can also periodically measure the player's body temperature to monitor their health condition. Furthermore, the collection unit can collect players' biometric information and analyze it in association with the operating status of gaming machines. This allows for more detailed data analysis by collecting players' biometric information. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input players' biometric information into the generation AI and have the generation AI collect and analyze the data.

[0068] Furthermore, the pachislot parlor income / expense adjustment system includes a collection unit that monitors the operation status of gaming machines in real time and immediately notifies the user if an abnormality occurs. The collection unit, for example, monitors the operation status of gaming machines in real time and issues an alert if an abnormality occurs. The collection unit can also immediately notify employees if an abnormality occurs and encourage them to take prompt action. Furthermore, the collection unit can also suggest appropriate response methods depending on the type of abnormality. This enables rapid response by monitoring the operation status of gaming machines in real time and immediately notifying the user if an abnormality occurs. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the operation status of gaming machines into the generation AI and have the generation AI detect and notify abnormalities.

[0069] The pachinko parlor income / expense adjustment system further includes a collection unit that estimates a user's emotions and prioritizes data collection based on the estimated user emotions. For example, if the user is excited, the collection unit prioritizes collecting player behavior data. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting gaming machine operation status data. Furthermore, if the user is stressed, the collection unit can limit the types of data to collect to reduce the burden. By prioritizing data collection based on the user's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the data priorities.

[0070] Furthermore, the income / expense adjustment system for a pachislot parlor includes a collection unit that collects environmental data within the parlor (such as temperature, humidity, and noise level) in addition to the operating status of gaming machines. The collection unit, for example, periodically measures the temperature within the parlor to maintain a comfortable environment. The collection unit can also monitor the humidity within the parlor and maintain an appropriate humidity level. Furthermore, the collection unit can measure the noise level within the parlor and issue a notification when the noise exceeds a certain level. In this way, by collecting environmental data within the parlor, data for maintaining a comfortable environment can be obtained. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input environmental data within the parlor into a generation AI and have the generation AI collect and analyze the data.

[0071] Furthermore, the pachislot parlor income / expense adjustment system includes a collection unit that analyzes players' social media activities and collects related behavioral data. The collection unit, for example, analyzes players' social media posts to understand trends in gaming machine selection. The collection unit can also predict the frequency of parlor visits based on players' social media activities. Furthermore, the collection unit can collect players' social media feedback and use it to improve services. In this way, more detailed behavioral data can be obtained by analyzing players' social media activities. Some or all of the above-described processing in the collection unit may be performed using or without the generation AI. For example, the collection unit may input players' social media data into the generation AI and have the generation AI collect and analyze the data.

[0072] The pachinko parlor income / expense adjustment system further includes an analysis unit that estimates a user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, the analysis unit uses an algorithm that performs a quick analysis when the user is excited. The analysis unit can also use an algorithm that performs a detailed analysis when the user is relaxed. Furthermore, the analysis unit can also use an algorithm that performs a simplified analysis when the user is stressed. This allows for more appropriate analysis by adjusting the analysis algorithm based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0073] Furthermore, the pachislot parlor's income / expense adjustment system includes an analysis unit that analyzes player behavior patterns and identifies factors affecting income / expenses, in addition to the income / expense status of each gaming machine. The analysis unit, for example, analyzes a player's bet amount and win / loss results to identify factors affecting income / expenses. The analysis unit can also analyze the relationship between a player's playing time and income / expenses and suggest optimal playing times. The analysis unit can also analyze a player's behavior patterns and identify factors affecting income / expenses. In this way, by analyzing a player's behavior patterns, factors affecting income / expenses can be identified. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input player behavior data into the generation AI and have the generation AI analyze the data and identify factors.

[0074] Furthermore, the pachislot parlor income / expense adjustment system includes an analysis unit that predicts the long-term income / expense status of gaming machines and predicts future income / expense fluctuations. The analysis unit predicts future income / expense fluctuations based on, for example, past income / expense data. The analysis unit can also predict income / expense fluctuations taking into account the effects of seasons and events. The analysis unit can also predict future income / expense fluctuations based on player behavior patterns. This predicts future income / expense fluctuations, thereby stabilizing long-term income / expenses. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past income / expense data into the generation AI and have the generation AI predict future income / expenses.

[0075] The pachinko parlor income / expense adjustment system further includes an analysis unit that estimates a user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit provides a visually stimulating display method. Furthermore, if the user is relaxed, the analysis unit can also provide a calming display method. Furthermore, if the user is stressed, the analysis unit can also provide a simple, highly visible display method. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without the generation AI. For example, the analysis unit may input the user's emotion data into the generation AI and have the generation AI adjust the display method.

[0076] Furthermore, the pachislot parlor income / expense adjustment system includes an analysis unit that analyzes the income / expense status of the entire parlor in addition to the income / expense status of each gaming machine and evaluates the overall income / expense balance. The analysis unit, for example, aggregates the income / expense status of each gaming machine and evaluates the income / expense balance of the entire parlor. The analysis unit can also analyze the income / expense data of the entire parlor and identify imbalances in income / expenses. Furthermore, the analysis unit can evaluate the income / expense balance of the entire parlor and propose an optimal income / expense balance. In this way, the overall income / expense balance can be evaluated by analyzing the income / expense status of the entire parlor. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the income / expense data of the entire parlor into the generation AI and have the generation AI analyze the data and evaluate the balance.

[0077] Furthermore, the pachislot parlor's income and expenditure adjustment system includes an analysis unit that compares the income and expenditure status of gaming machines with that of other parlors and performs benchmark analysis. The analysis unit, for example, collects income and expenditure data from other parlors and compares it with the income and expenditure status of the parlor itself. The analysis unit can also evaluate the income and expenditure balance of the parlor itself by referring to the income and expenditure balances of other parlors. Furthermore, the analysis unit can perform benchmark analysis based on the income and expenditure data of other parlors and propose an optimal income and expenditure balance. This allows the income and expenditure status of the parlor itself to be evaluated by comparing it with other parlors. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input income and expenditure data from other parlors into the generation AI and have the generation AI perform a benchmark analysis.

[0078] Furthermore, the pachislot parlor income / expense adjustment system includes a setting adjustment unit that estimates a user's emotions and adjusts the setting adjustment method based on the estimated user emotions. For example, the setting adjustment unit suggests a method for performing quick setting adjustment when the user is excited. Furthermore, the setting adjustment unit can also suggest a method for performing detailed setting adjustment when the user is relaxed. Furthermore, the setting adjustment unit can also suggest a method for performing simplified setting adjustment when the user is stressed. This enables more appropriate setting adjustment by adjusting the setting adjustment method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the setting adjustment unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the setting adjustment unit may input user emotion data into the generation AI and have the generation AI adjust the setting adjustment method.

[0079] When changing the settings of each gaming machine, the setting adjustment unit can select optimal settings by taking into account the player's past behavioral data. The setting adjustment unit selects optimal settings based on, for example, the player's past bet amount and win / loss results. The setting adjustment unit can also select optimal settings by taking into account the player's past playing time. Furthermore, the setting adjustment unit can analyze the player's past behavioral patterns and select optimal settings. This allows for more optimal settings to be selected by taking into account the player's past behavioral data. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input the player's past behavioral data into the generation AI and have the generation AI select optimal settings.

[0080] Furthermore, the income / expense adjustment system for a pachislot parlor includes a setting adjustment unit that monitors the effects of the setting adjustment in real time and immediately readjusts as necessary. The setting adjustment unit, for example, monitors the income / expense status after the setting adjustment in real time and readjusts as necessary. The setting adjustment unit can also monitor the player's behavior patterns after the setting adjustment and readjust as necessary. The setting adjustment unit can also monitor the operating status of the gaming machine after the setting adjustment and readjust as necessary. In this way, by monitoring the effects of the setting adjustment in real time and readjusting as necessary, income / expenses can be stabilized. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input data after the setting adjustment into the generation AI, determine the need for readjustment, and have the generation AI execute the readjustment.

[0081] The pachinko parlor income / expense adjustment system further includes a setting adjustment unit that estimates a user's emotions and prioritizes setting adjustments based on the estimated user emotions. For example, when the user is excited, the setting adjustment unit prioritizes important setting adjustments. Furthermore, when the user is relaxed, the setting adjustment unit can also prioritize detailed setting adjustments. Furthermore, when the user is stressed, the setting adjustment unit can also prioritize simplified setting adjustments. Thus, by prioritizing setting adjustments based on the user's emotions, more important setting adjustments can be prioritized. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the setting adjustment unit may be performed using the generation AI, or may be performed without the generation AI. For example, the setting adjustment unit may input user emotion data into the generation AI and have the generation AI determine the priority of setting adjustments.

[0082] When changing the settings of each gaming machine, the setting adjustment unit can make adjustments based on interactions with other gaming machines. The setting adjustment unit, for example, selects optimal settings taking into account the operating status of other gaming machines. The setting adjustment unit can also select optimal settings taking into account the income and expenditure status of other gaming machines. Furthermore, the setting adjustment unit can also select optimal settings taking into account the behavior patterns of players at other gaming machines. This allows the overall income and expenditure balance to be optimized by taking into account interactions with other gaming machines. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input data of other gaming machines into the generation AI and cause the generation AI to adjust the settings taking into account the interactions.

[0083] Furthermore, the income / expense adjustment system for a pachinko parlor includes a setting adjustment unit with an interface that visually displays the content of the setting adjustment proposal when providing it to employees. The setting adjustment unit visually displays the content of the setting adjustment proposal using graphs or charts, for example. The setting adjustment unit can also display the content of the setting adjustment proposal in a dashboard format to make it easy for employees to understand. The setting adjustment unit can also display the content of the setting adjustment proposal using animation to make it visually easy to understand. In this way, by visually displaying the content of the proposal, employees can easily understand and implement it. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the setting adjustment unit can input the content of the proposal into the generation AI and have the generation AI execute the visual display.

[0084] The pachinko parlor income / expense adjustment system further includes a proposal providing unit that estimates a user's emotions and adjusts the way the proposal content is expressed based on the estimated user emotions. For example, if the user is excited, the proposal providing unit provides visually stimulating proposal content. Furthermore, if the user is relaxed, the proposal providing unit can also provide calming proposal content. Furthermore, if the user is stressed, the proposal providing unit can also provide simple, highly visible proposal content. This enables more appropriate proposals by adjusting the way the proposal content is expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the way the proposal content is expressed.

[0085] When providing a proposal, the proposal providing unit can select the optimal proposal by referring to the employee's past execution history. For example, the proposal providing unit analyzes the employee's past execution history and selects the most effective proposal. The proposal providing unit can also select the optimal proposal based on setting adjustments that the employee has made successfully in the past. Furthermore, the proposal providing unit can select the most efficient proposal from the employee's past execution history. This makes it possible to provide more effective proposals by referring to the employee's past execution history. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input the employee's execution history data into the generation AI and have the generation AI select the optimal proposal.

[0086] Furthermore, the pachislot parlor income / expense adjustment system includes a proposal providing unit that collects the effectiveness of proposals as feedback and reflects it in the next proposal. The proposal providing unit, for example, collects the effects of the proposals after they are implemented as feedback and reflects them in the next proposal. The proposal providing unit can also quantitatively evaluate the effectiveness of the proposals and reflect it in the next proposal. Furthermore, the proposal providing unit can collect the effectiveness of the proposals as feedback from employees and reflect it in the next proposal. In this way, collecting the effectiveness of the proposals as feedback improves the accuracy of the next proposal. Some or all of the above-described processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input feedback data into the generation AI and have the generation AI execute a process to reflect the feedback data in the next proposal.

[0087] The pachinko parlor income / expense adjustment system further includes a suggestion providing unit that estimates a user's emotions and prioritizes suggestions based on the estimated user emotions. For example, when the user is excited, the suggestion providing unit prioritizes providing important suggestions. Furthermore, when the user is relaxed, the suggestion providing unit can also prioritize providing detailed suggestions. Furthermore, when the user is stressed, the suggestion providing unit can prioritize providing simplified suggestions. This allows for prioritizing suggestions based on the user's emotions, thereby prioritizing more important suggestions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the suggestion providing unit may be performed using the generation AI, or may be performed without the generation AI. For example, the suggestion providing unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the suggestions.

[0088] When providing a proposal, the proposal providing unit can select the optimal proposal by taking into account the employee's skill level. For example, the proposal providing unit evaluates the employee's skill level and provides the optimal proposal. The proposal providing unit can also provide proposals according to the employee's skill level based on the employee's past performance. Furthermore, the proposal providing unit can adjust the level of detail of the proposal according to the employee's skill level. This enables more appropriate proposals by taking the employee's skill level into consideration. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input employee skill level data into the generation AI and have the generation AI select the optimal proposal.

[0089] Furthermore, the pachislot parlor income / expense adjustment system includes a proposal providing unit that has a function of providing audio guidance on the proposal content when providing the proposal content. The proposal providing unit, for example, provides audio guidance on the proposal content, allowing employees to execute the proposal content without relying on visual information. The proposal providing unit can also provide audio guidance on the proposal content, allowing employees to execute the proposal content without using their hands. The proposal providing unit can also provide audio guidance on the proposal content, allowing employees to perform their work efficiently. Thus, by providing audio guidance on the proposal content, employees can execute the proposal content without relying on visual information. Some or all of the above-described processing in the proposal providing unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the proposal providing unit can input the proposal content into the generation AI and have the generation AI execute the audio guidance. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, setting adjustment unit, and suggestion providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data for each gaming machine using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the income / expense status of each gaming machine. The setting adjustment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, optimally adjusts the settings of each gaming machine based on the analysis results. The suggestion providing unit, realized, for example, by the control unit 46A of the smart device 14, provides suggestions for setting adjustment to employees. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, setting adjustment unit, and suggestion providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data for each gaming machine using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to determine the income and expenditure status of each gaming machine. The setting adjustment unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and optimally adjusts the settings of each gaming machine based on the analysis results. The suggestion providing unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides suggested settings adjustments to employees. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, setting adjustment unit, and suggestion providing unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data for each gaming machine using the camera 42 and microphone 238 of the headset terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the income / expense status of each gaming machine. The setting adjustment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, optimally adjusts the settings of each gaming machine based on the analysis results. The suggestion providing unit, realized, for example, by the control unit 46A of the headset terminal 314, provides suggestions for setting adjustment to employees. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, setting adjustment unit, and suggestion providing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data for each gaming machine using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and determines the income / expense status of each gaming machine. The setting adjustment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, optimally adjusts the settings of each gaming machine based on the analysis results. The suggestion providing unit, realized, for example, by the control unit 46A of the robot 414, provides suggestions for setting adjustment to employees.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] In addition to the operating status of gaming machines and player behavior data, the collection unit can also collect environmental data within the store (such as temperature, humidity, and noise levels). For example, the collection unit can periodically measure the temperature within the store to maintain a comfortable environment. The collection unit can also monitor the humidity within the store and maintain appropriate humidity. Furthermore, the collection unit can measure the noise level within the store and issue a notification when the noise exceeds a certain level. In this way, by collecting environmental data within the store, data for maintaining a comfortable environment can be obtained. Some or all of the above-mentioned processing by the collection unit may be performed using or without the generation AI. For example, the collection unit can input environmental data within the store into the generation AI and have the generation AI collect and analyze the data.

[0092] When analyzing the collected data, the analysis unit can also refer to the income and expenditure data of other stores and perform benchmark analysis. For example, the analysis unit collects income and expenditure data of other stores and compares it with the income and expenditure situation of the store itself. The analysis unit can also evaluate the income and expenditure balance of the store itself by referring to the income and expenditure balance of other stores. Furthermore, the analysis unit can perform benchmark analysis based on the income and expenditure data of other stores and propose an optimal income and expenditure balance. This allows the income and expenditure situation of the store itself to be evaluated by comparing it with other stores. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the income and expenditure data of other stores into the generation AI and have the generation AI perform a benchmark analysis.

[0093] When changing the settings of each gaming machine, the setting adjustment unit can select optimal settings by taking into account the player's past behavioral data. For example, the setting adjustment unit selects optimal settings based on the player's past bet amount and win / loss results. The setting adjustment unit can also select optimal settings by taking into account the player's past playing time. Furthermore, the setting adjustment unit can analyze the player's past behavioral patterns and select optimal settings. In this way, by taking into account the player's past behavioral data, more optimal settings can be selected. Some or all of the above-mentioned processing in the setting adjustment unit may be performed using or without the generation AI. For example, the setting adjustment unit can input the player's past behavioral data into the generation AI and have the generation AI select optimal settings.

[0094] When providing a proposal, the proposal providing unit can select the optimal proposal by taking into account the employee's skill level. For example, the proposal providing unit evaluates the employee's skill level and provides the optimal proposal. The proposal providing unit can also provide proposals according to the employee's skill level based on the employee's past performance. Furthermore, the proposal providing unit can adjust the level of detail of the proposal according to the employee's skill level. This enables more appropriate proposals by taking the employee's skill level into consideration. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input employee skill level data into the generation AI and have the generation AI select the optimal proposal.

[0095] The proposal providing unit can collect the effectiveness of the proposal as feedback and reflect it in the next proposal. For example, the proposal providing unit can collect the effectiveness of the proposal after it is implemented as feedback and reflect it in the next proposal. The proposal providing unit can also quantitatively evaluate the effectiveness of the proposal and reflect it in the next proposal. Furthermore, the proposal providing unit can collect the effectiveness of the proposal as feedback from employees and reflect it in the next proposal. In this way, by collecting the effectiveness of the proposal as feedback, the accuracy of the next proposal is improved. Some or all of the above-mentioned processing in the proposal providing unit may be performed using or without the generation AI. For example, the proposal providing unit can input feedback data into the generation AI and cause the generation AI to execute processing to reflect the feedback data in the next proposal.

[0096] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is excited, the collection unit can increase the frequency of data collection and update the data in real time. Furthermore, when the user is relaxed, the collection unit can reduce the frequency of data collection and update the data periodically. Furthermore, when the user is stressed, the collection unit can adjust the timing of data collection to reduce the burden on the user. This enables more appropriate data collection by adjusting the timing of data collection based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0097] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is excited, the analysis unit can use an algorithm that performs a quick analysis. If the user is relaxed, the analysis unit can also use an algorithm that performs a detailed analysis. Furthermore, if the user is stressed, the analysis unit can use an algorithm that performs a simplified analysis. This allows for more appropriate analysis by adjusting the analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0098] The setting adjustment unit can estimate the user's emotions and adjust the setting adjustment method based on the estimated user's emotions. For example, the setting adjustment unit can suggest a method for rapid setting adjustment when the user is excited. The setting adjustment unit can also suggest a method for detailed setting adjustment when the user is relaxed. Furthermore, the setting adjustment unit can also suggest a method for simplified setting adjustment when the user is stressed. This enables more appropriate setting adjustment by adjusting the setting adjustment method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the setting adjustment unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the setting adjustment unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the setting adjustment method.

[0099] The suggestion providing unit can estimate the user's emotions and adjust the way the suggestion content is expressed based on the estimated user's emotions. For example, if the user is excited, the suggestion providing unit can provide visually stimulating suggestion content. Furthermore, if the user is relaxed, the suggestion providing unit can provide calming suggestion content. Furthermore, if the user is stressed, the suggestion providing unit can provide simple, highly visible suggestion content. This enables more appropriate suggestions by adjusting the way the suggestion content is expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion providing unit may be performed using or without the generation AI. For example, the suggestion providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestion content is expressed.

[0100] The suggestion providing unit can estimate the user's emotions and prioritize the suggestions based on the estimated user emotions. For example, if the user is excited, the suggestion providing unit can prioritize important suggestions. Furthermore, if the user is relaxed, the suggestion providing unit can prioritize detailed suggestions. Furthermore, if the user is stressed, the suggestion providing unit can prioritize simplified suggestions. Thus, by prioritizing the suggestions based on the user's emotions, more important suggestions can be prioritized. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion providing unit can be performed using the generation AI, or can be performed without the generation AI. For example, the suggestion providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the suggestions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects data for each gaming machine. For example, the collection unit collects data on the operating status of gaming machines and player behavior. Specifically, the collection unit collects data such as the operating time of each gaming machine, the amount of player bets, and win / loss results. The collection unit can also monitor the operating status of gaming machines in real time and immediately notify if an abnormality occurs. Furthermore, the collection unit can also collect biometric information of players (heart rate, body temperature, etc.). Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the collected data and determines the income and expenditure status of each gaming machine. If a specific gaming machine is out of balance, it identifies the cause and suggests optimal setting adjustments. It can also analyze player behavior patterns and identify factors that affect income and expenditure. It can also forecast the income and expenditure status of gaming machines over the long term and predict future fluctuations in income and expenditure. Step 3: The settings adjustment unit optimally adjusts the settings of each gaming machine based on the analysis results obtained by the analysis unit. For example, changing the settings of a specific gaming machine can balance income and expenses. The effects of the setting adjustments can be monitored in real time, and immediate readjustments can be made if necessary. Step 4: The proposal providing unit provides the employee with the setting adjustments suggested by the setting adjustment unit. For example, the proposal providing unit provides the employee with the setting adjustments suggested by the generation AI and visually displays the proposed content in graphs and charts. The proposed content can also be provided via voice guidance.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data for each gaming machine; an analysis unit that analyzes the data collected by the collection unit; A setting adjustment unit that appropriately adjusts the settings of each gaming machine based on the analysis results obtained by the analysis unit; a suggestion providing unit that provides employees with the setting adjustments suggested by the setting adjustment unit. A system characterized by:

2. The collecting unit Collecting data on machine operation status or player behavior The system of claim 1 .

3. The analysis unit Analyze the collected data and determine the profit and loss status of each gaming machine The system of claim 1 .

4. The setting adjustment unit Change the settings of each gaming machine to stabilize income and expenditures The system of claim 1 .

5. The proposal providing unit Generative AI provides suggested settings adjustments to employees The system of claim 1 .

6. The proposal providing unit Support employees to work efficiently based on suggestions The system of claim 1 .

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

8. The collecting unit Collecting the operating status of gaming machines and biometric information of players The system of claim 1 .

9. The collecting unit Equipped with a function to monitor the operation status of gaming machines in real time and immediately notify if an abnormality occurs The system of claim 1 .

10. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions The system of claim 1 .

Images