system

Abstract

The system according to the embodiment aims to learn user behavior patterns and efficiently monitor the performance of apps and web services. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a simulation unit, and a monitoring unit. The collection unit collects user operation logs and behavioral data. The analysis unit analyzes the data collected by the collection unit and learns the user's behavioral patterns. The simulation unit simulates the behavioral patterns learned by the analysis unit. The monitoring unit monitors the performance of an app or web service based on the behavioral patterns simulated by the simulation unit.

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] Conventional technologies have faced challenges in accurately understanding user behavior patterns and efficiently monitoring the performance of apps and web services.

[0005] The system according to the embodiment aims to learn user behavior patterns and efficiently monitor the performance of apps and web services. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a simulation unit, and a monitoring unit. The collection unit collects user operation logs and behavioral data. The analysis unit analyzes the data collected by the collection unit and learns the user's behavioral patterns. The simulation unit simulates the behavioral patterns learned by the analysis unit. The monitoring unit monitors the performance of the app or web service based on the behavioral patterns simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can learn user behavior patterns and efficiently monitor the performance of apps and web services. [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 system according to an embodiment of the present invention uses a generative AI to reproduce user behavior and improve the quality of applications and services. In this system, the generative AI learns how actual users use apps and web services, simulates the learned user behavior patterns, and identifies areas for improvement in the service. Furthermore, the generative AI automatically tests and monitors performance to identify and correct potential user issues. This mechanism enables the simulation of user behavior patterns across various age groups and genders, reducing testing efforts, identifying daily service improvements, and providing better service to users. For example, the generative AI learns how actual users use apps and web services. During this process, the generative AI collects and analyzes user operation logs and behavioral data. For example, detailed data such as which buttons users clicked, which pages they viewed, and what operations they performed is collected. This allows the generative AI to understand user behavior patterns. Next, the generative AI simulates the learned user behavior patterns. Based on the learned data, the generative AI reproduces user behavior patterns across various age groups and genders. For example, it simulates how young users interact with apps and how older people use web services. This makes it possible to identify areas for improvement in the service. Furthermore, the generative AI automatically performs tests and monitors performance. The generative AI monitors the performance of apps and web services based on simulated user behavior patterns. For example, it measures page loading speeds and operation response times to identify potential problems users may be experiencing. This makes it possible to correct problems and improve service quality. This system reduces verification efforts and makes it possible to identify areas for improvement in the service on a daily basis. Because the generative AI automatically performs tests, manual testing is no longer necessary, making it possible to efficiently improve service quality. Furthermore, simulating user behavior patterns makes it possible to provide services that are suited to a variety of users.For example, it can improve the interface for younger users or add operation guides for older users. This will improve the user experience and provide better services. By collecting, analyzing, simulating, and monitoring user operation logs and behavioral data, the system can improve the quality of applications and services.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a simulation unit, and a monitoring unit. The collection unit collects user operation logs and behavioral data. The user operation logs and behavioral data include, but are not limited to, clicks, page views, and input operations. The collection unit can, for example, collect data in real time. The collection unit can also collect data using batch processing. The collection unit can also collect data when a specific event occurs. For example, the collection unit can collect data when a user clicks a specific button. The analysis unit analyzes the data collected by the collection unit to learn user behavioral patterns. The analysis unit can, for example, analyze data using data mining techniques. The analysis unit can also analyze data using machine learning algorithms. The analysis unit can also analyze data using techniques such as supervised learning, unsupervised learning, and reinforcement learning. For example, the analysis unit can learn user behavioral patterns using supervised learning. The simulation unit simulates the behavioral patterns learned by the analysis unit. The simulation unit can, for example, simulate the behavioral patterns using Monte Carlo simulation. The simulator can also simulate behavioral patterns using agent-based simulation. Furthermore, the simulator can reproduce user behavioral patterns of various ages and genders based on the learned data. For example, the simulator can simulate how young users operate an app. The monitor can monitor the performance of the app or web service based on the behavioral patterns simulated by the simulator. The monitor can measure, for example, page loading speed and operation response time. The monitor can also detect potential problems users may be experiencing. Furthermore, the monitor can correct the problems and improve the quality of the service. For example, if a page loading speed is slow, the monitor can identify and correct the cause.As a result, the system according to the embodiment can improve the quality of applications and services by collecting, analyzing, simulating, and monitoring user operation logs and behavioral data.

[0030] The collection unit can collect detailed data such as which buttons the user clicked, which pages the user viewed, and what operations the user performed. The detailed data includes, but is not limited to, the click location, the time spent on the page, and the operation sequence. For example, the collection unit can record the location of the button the user clicked. The collection unit can also record the time the user spent on the page. Furthermore, the collection unit can record the order of the operations performed by the user. For example, the collection unit can record the order of the first button the user clicked, the next button the user clicked, and the last button the user clicked. This allows for more accurate analysis of behavioral patterns by collecting detailed user operation data. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's operation log into the generation AI and have the generation AI collect detailed data.

[0031] The analysis unit can analyze the collected data and learn the user's behavioral patterns. Examples of behavioral patterns include, but are not limited to, frequency analysis and sequence pattern analysis. The analysis unit can analyze the user's behavioral patterns using, for example, data mining technology. The analysis unit can also learn the user's behavioral patterns using a machine learning algorithm. Furthermore, the analysis unit can learn the user's behavioral patterns using techniques such as supervised learning, unsupervised learning, and reinforcement learning. For example, the analysis unit can learn the user's behavioral patterns using supervised learning. In this way, by analyzing the collected data, the user's behavioral patterns can be learned and the accuracy of the simulation can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the behavioral patterns.

[0032] The simulator can reproduce user behavior patterns for multiple age groups and genders based on the learned data. Examples of multiple age groups and genders include, but are not limited to, teenagers, people in their twenties, men, and women. The simulator can, for example, simulate how a young user operates an app. The simulator can also simulate how an elderly user uses a web service. The simulator can also simulate how a male user operates an app. For example, the simulator can simulate how a female user uses a web service. This allows for the reproduction of user behavior patterns for various age groups and genders, thereby improving services to accommodate a wider variety of users. Some or all of the above-described processing in the simulator can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the simulator can input learned data into the generation AI and cause the generation AI to simulate behavior patterns.

[0033] The monitoring unit can measure page load speeds, operation response times, and the like based on simulated user behavior patterns to discover potential problems the user may be experiencing. Examples of page load speeds and operation response times include, but are not limited to, measurements in milliseconds and average response times. For example, the monitoring unit can measure page load speeds in milliseconds. The monitoring unit can also measure operation response times as average response times. Furthermore, the monitoring unit can discover potential problems the user may be experiencing. For example, if the page load speed is slow, the monitoring unit can identify and correct the cause. This can discover potential problems the user may be experiencing and improve service quality. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input simulated user behavior patterns into the generation AI and have the generation AI measure page load speeds and operation response times.

[0034] The monitoring unit can correct problems and improve the quality of the service. Problems include, but are not limited to, bugs, performance degradation, and usability issues. The monitoring unit can, for example, correct bugs. The monitoring unit can also identify and correct causes of performance degradation. The monitoring unit can also discover and improve usability issues. For example, the monitoring unit can improve an interface that is difficult for users to operate. This can improve the quality of the service by correcting the problems. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input discovered problems into a generation AI and have the generation AI execute a suggested correction method.

[0035] The collection unit can analyze the user's past operation history and select the optimal data collection method. For example, the collection unit prioritizes collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and collect data during that time period. The collection unit can also analyze the user's past operation history and select the most efficient data collection method. For example, the collection unit can prioritize collection of data on buttons that the user frequently clicked in the past. The collection unit can also predict operations that the user performed during a specific time period in the past and collect data during that time period. In this way, by analyzing the user's past operation history, the optimal data collection method can be selected and data can be collected efficiently. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's past operation history into the generation AI and cause the generation AI to select the optimal data collection method.

[0036] When collecting data, the collection unit can perform filtering based on the user's current usage status and areas of interest. For example, the collection unit collects only relevant data based on the content of the page the user is currently viewing. The collection unit can also prioritize collecting specific operation logs based on the user's areas of interest. Furthermore, the collection unit can analyze the user's current usage status in real time and collect only necessary data. For example, the collection unit can analyze the content of the page the user is currently viewing and collect only relevant data. The collection unit can also prioritize collecting specific operation logs based on the user's areas of interest. This allows filtering based on the user's current usage status and areas of interest to efficiently collect only necessary data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's current usage status and areas of interest into the generation AI and have the generation AI perform filtering.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also collect area-specific operation logs based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the area to which the user is traveling. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also collect area-specific operation logs based on the user's geographical location information. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0038] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related operation logs based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. The collection unit can also analyze operations performed by the user on social media and collect related data. For example, the collection unit can collect related operation logs based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with a high business impact. The analysis unit can also perform a detailed analysis on data with a high user impact. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply a natural language processing algorithm to text data to analyze the content of the text. The analysis unit can also apply an image recognition algorithm to image data to analyze the content of the image. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI select the analysis algorithm to apply.

[0041] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the data was collected. For example, the analysis unit can prioritize analysis of real-time data. The analysis unit can also analyze the most recent data while referring to past data. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. 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 time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. Furthermore, the analysis unit can prioritize analysis of important data while leaving less relevant data for later analysis. For example, the analysis unit can evaluate the relevance of data using correlation analysis and prioritize analysis of highly relevant data. The analysis unit can also evaluate the relevance of data using causal analysis and prioritize analysis of highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The simulator can improve the accuracy of the simulation by taking into account user interactions during the simulation. For example, the simulator performs the simulation by taking into account how a user interacts with other users. The simulator can also improve the accuracy of the simulation based on the user interactions. Furthermore, the simulator can analyze the user interactions in real time and reflect them in the simulation. For example, the simulator can evaluate the user interactions using social network analysis and reflect them in the simulation. The simulator can also evaluate the user interactions using co-occurrence analysis and reflect them in the simulation. In this way, the accuracy of the simulation can be improved by taking the user interactions into account. Some or all of the above-described processing in the simulator can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the simulator can input user interaction data into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0044] The simulator unit can perform the simulation while taking into account the user's attribute information. The simulator unit can adjust the content of the simulation based on, for example, the user's age and gender. The simulator unit can also customize the content of the simulation based on the user's interests and concerns. The simulator unit can also analyze the user's attribute information in real time and reflect the analysis results in the simulation. For example, the simulator unit can adjust the content of the simulation based on the user's age and gender. The simulator unit can also customize the content of the simulation based on the user's interests and concerns. This allows for a more accurate simulation by taking the user's attribute information into consideration. Some or all of the above-described processing in the simulator unit can be performed using, or without, a generation AI. For example, the simulator unit can input the user's attribute information into the generation AI and cause the generation AI to adjust the content of the simulation.

[0045] The simulation unit can perform a simulation taking into account the geographical distribution of users. For example, if a user is in a specific area, the simulation unit performs a simulation related to that area. The simulation unit can also adjust the content of the simulation based on the geographical distribution of users. Furthermore, if a user is traveling, the simulation unit can perform a simulation related to the area to which the user is traveling. For example, if a user is in a specific area, the simulation unit can perform a simulation related to that area. The simulation unit can also adjust the content of the simulation based on the geographical distribution of users. This allows for a more accurate simulation by taking the geographical distribution of users into consideration. Some or all of the above-described processing in the simulation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the simulation unit can input user geographical distribution data into the generation AI and cause the generation AI to adjust the content of the simulation.

[0046] The simulating unit can improve the accuracy of the simulation by referring to related literature during the simulation. For example, the simulating unit can refer to related literature and improve the simulation algorithm. The simulating unit can also improve the accuracy of the simulation based on data from the related literature. Furthermore, the simulating unit can refer to related literature in real time and reflect the results in the simulation. For example, the simulating unit can improve the simulation algorithm by referring to related literature. The simulating unit can also improve the accuracy of the simulation based on data from the related literature. In this way, the accuracy of the simulation can be improved by referring to related literature. Some or all of the above-mentioned processing in the simulating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the simulating unit can input related literature into the generating AI and cause the generating AI to improve the accuracy of the simulation.

[0047] During monitoring, the monitoring unit can optimize the current monitoring criteria by referring to past monitoring data. The monitoring unit, for example, adjusts the current monitoring criteria based on the past monitoring data. The monitoring unit can also optimize the anomaly detection criteria by referring to the past monitoring data. The monitoring unit can also analyze the past monitoring data and dynamically adjust the current monitoring criteria. For example, the monitoring unit can adjust the current monitoring criteria based on the past monitoring data. The monitoring unit can also optimize the anomaly detection criteria by referring to the past monitoring data. In this way, by referring to the past monitoring data, the current monitoring criteria can be optimized and more accurate monitoring can be performed. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input past monitoring data into the generation AI and cause the generation AI to optimize the current monitoring criteria.

[0048] The monitoring unit can apply different monitoring methods to different data categories during monitoring. For example, the monitoring unit can apply a monitoring method using natural language processing to text data. The monitoring unit can also apply a monitoring method using image recognition to image data. The monitoring unit can also apply a monitoring method using statistical analysis to numerical data. For example, the monitoring unit can apply a monitoring method using natural language processing to text data to monitor the content of the text. The monitoring unit can also apply a monitoring method using image recognition to image data to monitor the content of the images. This allows for more accurate monitoring by applying different monitoring methods to different data categories. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input the data category into the generation AI and cause the generation AI to select the monitoring method to be applied.

[0049] During monitoring, the monitoring unit can analyze changes in monitoring based on the time when the data was collected. For example, the monitoring unit prioritizes monitoring the most recent data and analyzes changes. The monitoring unit can also analyze changes in the most recent data while referring to past data. Furthermore, the monitoring unit can dynamically analyze changes in monitoring depending on the time when the data was collected. For example, the monitoring unit can prioritize monitoring the most recent data and analyze changes. The monitoring unit can also analyze changes in the most recent data while referring to past data. In this way, by analyzing changes in monitoring based on the time when the data was collected, monitoring can be performed based on the most recent information. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the time when the data was collected into the generation AI and have the generation AI analyze changes in monitoring.

[0050] During monitoring, the monitoring unit can analyze the monitoring by referring to relevant market data. For example, the monitoring unit can refer to the relevant market data and adjust the monitoring criteria. The monitoring unit can also improve the accuracy of the monitoring based on the relevant market data. Furthermore, the monitoring unit can refer to the relevant market data in real time and reflect it in the monitoring. For example, the monitoring unit can refer to the relevant market data and adjust the monitoring criteria. The monitoring unit can also improve the accuracy of the monitoring based on the relevant market data. In this way, the accuracy of the monitoring can be improved by referring to the relevant market data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the relevant market data into the generation AI and have the generation AI perform the monitoring analysis.

[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] When collecting a user's operation log or behavioral data, the collection unit can adjust the data collection method based on the type of device and usage environment of the user. For example, if the user is using a smartphone, the collection unit can prioritize collection of touch operation data. Also, if the user is using a desktop PC, the collection unit can prioritize collection of mouse and keyboard operation data. Furthermore, if the user is using the app in a public place, the collection unit can reduce the frequency of data collection in consideration of privacy. This allows the optimal data collection method to be selected according to the user's device and usage environment, and data to be collected efficiently.

[0053] When analyzing collected data, the analysis unit compares the user's past behavioral patterns with their current behavioral patterns to detect abnormal behavior. For example, if the user performs an operation that is not normally performed, the analysis unit can detect that operation as abnormal. In addition, if the user performs an operation that is different from normal during a specific time period, the analysis unit can detect the behavior during that time period as abnormal. Furthermore, if the user shows an abnormal reaction to a specific event (e.g., the release of a new feature), the analysis unit can detect that reaction as abnormal. This makes it possible to detect changes in the user's behavioral patterns in real time and respond quickly.

[0054] The monitoring unit can take into account the user's geographical location information when monitoring the performance of an app or web service based on the simulated user behavior patterns. For example, if the user is in a specific area, the monitoring unit can monitor performance taking into account the network conditions of that area. Also, if the user is traveling, the monitoring unit can monitor performance taking into account the network conditions of the area to which the user is traveling. Furthermore, if the user is overseas, the monitoring unit can monitor performance taking into account the local network conditions. In this way, more accurate performance monitoring can be achieved by taking into account the user's geographical location information.

[0055] When collecting user operation logs and behavioral data, the collection unit can analyze the user's social media activities and prioritize collection of related data. For example, the collection unit can collect related operation logs based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. Furthermore, the collection unit can analyze the user's operations on social media and collect related data. This makes it possible to efficiently collect related data by analyzing the user's social media activities.

[0056] When simulating a user's behavioral patterns based on the learned data, the simulator can perform the simulation taking into account the type of device and usage environment of the user. For example, if the user is using a smartphone, the simulator can simulate touch operations. Also, if the user is using a desktop PC, the simulator can simulate mouse and keyboard operations. Furthermore, if the user is using the app in a public place, the simulator can perform a simulation that is tailored to the environment. This allows for a more realistic simulation to be performed that is tailored to the user's device and usage environment.

[0057] When monitoring the performance of an app or web service based on simulated user behavior patterns, the monitoring unit can adjust the monitoring criteria by referring to the user's past behavior patterns. For example, if the user has had problems with a particular operation in the past, the monitoring unit can set stricter monitoring criteria for that operation. Also, if the user has performed an operation without problems in the past, the monitoring unit can relax the monitoring criteria for that operation. Furthermore, the monitoring unit can analyze the user's past behavior patterns and dynamically adjust the monitoring criteria. This allows the monitoring criteria to be adjusted by referring to the user's past behavior patterns, enabling more appropriate performance monitoring.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects user operation logs and behavioral data. The user operation logs and behavioral data include, for example, clicks, page views, and input operations. The collection unit can collect data in real time, as well as in batch processing or when a specific event occurs. Step 2: The analysis unit analyzes the data collected by the collection unit and learns user behavior patterns. The analysis unit analyzes the data using data mining techniques and machine learning algorithms, and can use techniques such as supervised learning, unsupervised learning, and reinforcement learning. Step 3: The simulator simulates the behavioral patterns learned by the analyzer. The simulator simulates the behavioral patterns using Monte Carlo simulation or agent-based simulation, and can reproduce the behavioral patterns of users of various ages and genders. Step 4: The monitoring unit monitors the performance of the app or web service based on the behavioral patterns simulated by the simulation unit. The monitoring unit measures page loading speeds and operation response times, discovers potential problems users may be experiencing, and corrects these problems to improve the quality of the service.

[0060] (Example 2) A system according to an embodiment of the present invention uses a generative AI to reproduce user behavior and improve the quality of applications and services. In this system, the generative AI learns how actual users use apps and web services, simulates the learned user behavior patterns, and identifies areas for improvement in the service. Furthermore, the generative AI automatically tests and monitors performance to identify and correct potential user issues. This mechanism enables the simulation of user behavior patterns across various age groups and genders, reducing testing efforts, identifying daily service improvements, and providing better service to users. For example, the generative AI learns how actual users use apps and web services. During this process, the generative AI collects and analyzes user operation logs and behavioral data. For example, detailed data such as which buttons users clicked, which pages they viewed, and what operations they performed is collected. This allows the generative AI to understand user behavior patterns. Next, the generative AI simulates the learned user behavior patterns. Based on the learned data, the generative AI reproduces user behavior patterns across various age groups and genders. For example, it simulates how young users interact with apps and how older people use web services. This makes it possible to identify areas for improvement in the service. Furthermore, the generative AI automatically performs tests and monitors performance. The generative AI monitors the performance of apps and web services based on simulated user behavior patterns. For example, it measures page loading speeds and operation response times to identify potential problems users may be experiencing. This makes it possible to correct problems and improve service quality. This system reduces verification efforts and makes it possible to identify areas for improvement in the service on a daily basis. Because the generative AI automatically performs tests, manual testing is no longer necessary, making it possible to efficiently improve service quality. Furthermore, simulating user behavior patterns makes it possible to provide services that are suited to a variety of users.For example, it can improve the interface for younger users or add operation guides for older users. This will improve the user experience and provide better services. By collecting, analyzing, simulating, and monitoring user operation logs and behavioral data, the system can improve the quality of applications and services.

[0061] The system according to the embodiment includes a collection unit, an analysis unit, a simulation unit, and a monitoring unit. The collection unit collects user operation logs and behavioral data. The user operation logs and behavioral data include, but are not limited to, clicks, page views, and input operations. The collection unit can, for example, collect data in real time. The collection unit can also collect data using batch processing. The collection unit can also collect data when a specific event occurs. For example, the collection unit can collect data when a user clicks a specific button. The analysis unit analyzes the data collected by the collection unit to learn user behavioral patterns. The analysis unit can, for example, analyze data using data mining techniques. The analysis unit can also analyze data using machine learning algorithms. The analysis unit can also analyze data using techniques such as supervised learning, unsupervised learning, and reinforcement learning. For example, the analysis unit can learn user behavioral patterns using supervised learning. The simulation unit simulates the behavioral patterns learned by the analysis unit. The simulation unit can, for example, simulate the behavioral patterns using Monte Carlo simulation. The simulator can also simulate behavioral patterns using agent-based simulation. Furthermore, the simulator can reproduce user behavioral patterns of various ages and genders based on the learned data. For example, the simulator can simulate how young users operate an app. The monitor can monitor the performance of the app or web service based on the behavioral patterns simulated by the simulator. The monitor can measure, for example, page loading speed and operation response time. The monitor can also detect potential problems users may be experiencing. Furthermore, the monitor can correct the problems and improve the quality of the service. For example, if a page loading speed is slow, the monitor can identify and correct the cause.As a result, the system according to the embodiment can improve the quality of applications and services by collecting, analyzing, simulating, and monitoring user operation logs and behavioral data.

[0062] The collection unit can collect detailed data such as which buttons the user clicked, which pages the user viewed, and what operations the user performed. The detailed data includes, but is not limited to, the click location, the time spent on the page, and the operation sequence. For example, the collection unit can record the location of the button the user clicked. The collection unit can also record the time the user spent on the page. Furthermore, the collection unit can record the order of the operations performed by the user. For example, the collection unit can record the order of the first button the user clicked, the next button the user clicked, and the last button the user clicked. This allows for more accurate analysis of behavioral patterns by collecting detailed user operation data. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's operation log into the generation AI and have the generation AI collect detailed data.

[0063] The analysis unit can analyze the collected data and learn the user's behavioral patterns. Examples of behavioral patterns include, but are not limited to, frequency analysis and sequence pattern analysis. The analysis unit can analyze the user's behavioral patterns using, for example, data mining technology. The analysis unit can also learn the user's behavioral patterns using a machine learning algorithm. Furthermore, the analysis unit can learn the user's behavioral patterns using techniques such as supervised learning, unsupervised learning, and reinforcement learning. For example, the analysis unit can learn the user's behavioral patterns using supervised learning. In this way, by analyzing the collected data, the user's behavioral patterns can be learned and the accuracy of the simulation can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the behavioral patterns.

[0064] The simulator can reproduce user behavior patterns for multiple age groups and genders based on the learned data. Examples of multiple age groups and genders include, but are not limited to, teenagers, people in their twenties, men, and women. The simulator can, for example, simulate how a young user operates an app. The simulator can also simulate how an elderly user uses a web service. The simulator can also simulate how a male user operates an app. For example, the simulator can simulate how a female user uses a web service. This allows for the reproduction of user behavior patterns for various age groups and genders, thereby improving services to accommodate a wider variety of users. Some or all of the above-described processing in the simulator can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the simulator can input learned data into the generation AI and cause the generation AI to simulate behavior patterns.

[0065] The monitoring unit can measure page load speeds, operation response times, and the like based on simulated user behavior patterns to discover potential problems the user may be experiencing. Examples of page load speeds and operation response times include, but are not limited to, measurements in milliseconds and average response times. For example, the monitoring unit can measure page load speeds in milliseconds. The monitoring unit can also measure operation response times as average response times. Furthermore, the monitoring unit can discover potential problems the user may be experiencing. For example, if the page load speed is slow, the monitoring unit can identify and correct the cause. This can discover potential problems the user may be experiencing and improve service quality. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input simulated user behavior patterns into the generation AI and have the generation AI measure page load speeds and operation response times.

[0066] The monitoring unit can correct problems and improve the quality of the service. Problems include, but are not limited to, bugs, performance degradation, and usability issues. The monitoring unit can, for example, correct bugs. The monitoring unit can also identify and correct causes of performance degradation. The monitoring unit can also discover and improve usability issues. For example, the monitoring unit can improve an interface that is difficult for users to operate. This can improve the quality of the service by correcting the problems. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input discovered problems into a generation AI and have the generation AI execute a suggested correction method.

[0067] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection so as not to interfere with the user's operations. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection and acquire a detailed operation log. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important operations and quickly acquire data. For example, the collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the timing of data collection to be adjusted according to the user's emotions, allowing data to be collected without interfering with the user's operations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 may input user emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0068] The collection unit can analyze the user's past operation history and select the optimal data collection method. For example, the collection unit prioritizes collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and collect data during that time period. The collection unit can also analyze the user's past operation history and select the most efficient data collection method. For example, the collection unit can prioritize collection of data on buttons that the user frequently clicked in the past. The collection unit can also predict operations that the user performed during a specific time period in the past and collect data during that time period. In this way, by analyzing the user's past operation history, the optimal data collection method can be selected and data can be collected efficiently. Some or all of the above-described processing in the collection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the collection unit can input the user's past operation history into the generation AI and cause the generation AI to select the optimal data collection method.

[0069] When collecting data, the collection unit can perform filtering based on the user's current usage status and areas of interest. For example, the collection unit collects only relevant data based on the content of the page the user is currently viewing. The collection unit can also prioritize collecting specific operation logs based on the user's areas of interest. Furthermore, the collection unit can analyze the user's current usage status in real time and collect only necessary data. For example, the collection unit can analyze the content of the page the user is currently viewing and collect only relevant data. The collection unit can also prioritize collecting specific operation logs based on the user's areas of interest. This allows filtering based on the user's current usage status and areas of interest to efficiently collect only necessary data. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's current usage status and areas of interest into the generation AI and have the generation AI perform filtering.

[0070] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit can prioritize collecting only important operation logs. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed operation logs. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data that can be collected quickly. For example, the collection unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, the collection unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. Thus, by prioritizing the data to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0071] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. The collection unit can also collect area-specific operation logs based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the area to which the user is traveling. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. The collection unit can also collect area-specific operation logs based on the user's geographical location information. In this way, highly relevant data can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0072] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related operation logs based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. The collection unit can also analyze operations performed by the user on social media and collect related data. For example, the collection unit can collect related operation logs based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. This allows for efficient collection of related data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activities into the generation AI and cause the generation AI to collect related data.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the key points. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the analysis results to be easily understood by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative 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, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the method of expressing the analysis.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on less important data. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. For example, the analysis unit can perform a detailed analysis on data with a high business impact. The analysis unit can also perform a detailed analysis on data with a high user impact. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a statistical analysis algorithm to numerical data. For example, the analysis unit can apply a natural language processing algorithm to text data to analyze the content of the text. The analysis unit can also apply an image recognition algorithm to image data to analyze the content of the image. In this way, by applying different analysis algorithms depending on the data category, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI select the analysis algorithm to apply.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the analysis unit to adjust the length of the analysis according to the user's emotions and provide optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0077] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the data was collected. For example, the analysis unit can prioritize analysis of real-time data. The analysis unit can also analyze the most recent data while referring to past data. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. 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 time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. Furthermore, the analysis unit can prioritize analysis of important data while leaving less relevant data for later analysis. For example, the analysis unit can evaluate the relevance of data using correlation analysis and prioritize analysis of highly relevant data. The analysis unit can also evaluate the relevance of data using causal analysis and prioritize analysis of highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] The simulator can estimate the user's emotions and adjust the simulation criteria based on the estimated user emotions. For example, if the user is relaxed, the simulator can perform a simulation that proceeds at a leisurely pace. If the user is in a hurry, the simulator can also perform a simulation that emphasizes the shortest route. If the user is excited, the simulator can also perform a simulation that adds visually stimulating effects. For example, the simulator can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The simulator can also record the user's voice and estimate the user's emotions using voice analysis technology. The simulator can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the system to adjust the simulation criteria according to the user's emotions, thereby providing an optimal simulation for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 simulation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the simulation unit may input user emotion data into the generation AI and cause the generation AI to adjust the simulation criteria.

[0080] The simulator can improve the accuracy of the simulation by taking into account user interactions during the simulation. For example, the simulator performs the simulation by taking into account how a user interacts with other users. The simulator can also improve the accuracy of the simulation based on the user interactions. Furthermore, the simulator can analyze the user interactions in real time and reflect them in the simulation. For example, the simulator can evaluate the user interactions using social network analysis and reflect them in the simulation. The simulator can also evaluate the user interactions using co-occurrence analysis and reflect them in the simulation. In this way, the accuracy of the simulation can be improved by taking the user interactions into account. Some or all of the above-described processing in the simulator can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the simulator can input user interaction data into the generation AI and cause the generation AI to improve the accuracy of the simulation.

[0081] The simulator unit can perform the simulation while taking into account the user's attribute information. The simulator unit can adjust the content of the simulation based on, for example, the user's age and gender. The simulator unit can also customize the content of the simulation based on the user's interests and concerns. The simulator unit can also analyze the user's attribute information in real time and reflect the analysis results in the simulation. For example, the simulator unit can adjust the content of the simulation based on the user's age and gender. The simulator unit can also customize the content of the simulation based on the user's interests and concerns. This allows for a more accurate simulation by taking the user's attribute information into consideration. Some or all of the above-described processing in the simulator unit can be performed using, or without, a generation AI. For example, the simulator unit can input the user's attribute information into the generation AI and cause the generation AI to adjust the content of the simulation.

[0082] The simulator can estimate the user's emotions and adjust the order in which the simulation results are displayed based on the estimated user emotions. For example, if the user is nervous, the simulator can display important results first. Furthermore, if the user is relaxed, the simulator can sequentially display detailed results. Furthermore, if the user is in a hurry, the simulator can display key results first. For example, the simulator can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The simulator can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the simulator can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the user to receive optimal information by adjusting the order in which the simulation results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 simulation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the simulation unit may input user emotion data into the generation AI and cause the generation AI to adjust the display order of the simulation results.

[0083] The simulation unit can perform a simulation taking into account the geographical distribution of users. For example, if a user is in a specific area, the simulation unit performs a simulation related to that area. The simulation unit can also adjust the content of the simulation based on the geographical distribution of users. Furthermore, if a user is traveling, the simulation unit can perform a simulation related to the area to which the user is traveling. For example, if a user is in a specific area, the simulation unit can perform a simulation related to that area. The simulation unit can also adjust the content of the simulation based on the geographical distribution of users. This allows for a more accurate simulation by taking the geographical distribution of users into consideration. Some or all of the above-described processing in the simulation unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the simulation unit can input user geographical distribution data into the generation AI and cause the generation AI to adjust the content of the simulation.

[0084] The simulating unit can improve the accuracy of the simulation by referring to related literature during the simulation. For example, the simulating unit can refer to related literature and improve the simulation algorithm. The simulating unit can also improve the accuracy of the simulation based on data from the related literature. Furthermore, the simulating unit can refer to related literature in real time and reflect the results in the simulation. For example, the simulating unit can improve the simulation algorithm by referring to related literature. The simulating unit can also improve the accuracy of the simulation based on data from the related literature. In this way, the accuracy of the simulation can be improved by referring to related literature. Some or all of the above-mentioned processing in the simulating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the simulating unit can input related literature into the generating AI and cause the generating AI to improve the accuracy of the simulation.

[0085] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the monitoring unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that focuses on the main points. For example, the monitoring unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The monitoring unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the monitoring unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the monitoring unit to adjust the display method of the monitoring based on the user's emotions, thereby providing optimal information for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input user emotion data into the generation AI and have the generation AI adjust the monitoring display method.

[0086] During monitoring, the monitoring unit can optimize the current monitoring criteria by referring to past monitoring data. The monitoring unit, for example, adjusts the current monitoring criteria based on the past monitoring data. The monitoring unit can also optimize the anomaly detection criteria by referring to the past monitoring data. The monitoring unit can also analyze the past monitoring data and dynamically adjust the current monitoring criteria. For example, the monitoring unit can adjust the current monitoring criteria based on the past monitoring data. The monitoring unit can also optimize the anomaly detection criteria by referring to the past monitoring data. In this way, by referring to the past monitoring data, the current monitoring criteria can be optimized and more accurate monitoring can be performed. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input past monitoring data into the generation AI and cause the generation AI to optimize the current monitoring criteria.

[0087] The monitoring unit can apply different monitoring methods to different data categories during monitoring. For example, the monitoring unit can apply a monitoring method using natural language processing to text data. The monitoring unit can also apply a monitoring method using image recognition to image data. The monitoring unit can also apply a monitoring method using statistical analysis to numerical data. For example, the monitoring unit can apply a monitoring method using natural language processing to text data to monitor the content of the text. The monitoring unit can also apply a monitoring method using image recognition to image data to monitor the content of the images. This allows for more accurate monitoring by applying different monitoring methods to different data categories. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input the data category into the generation AI and cause the generation AI to select the monitoring method to be applied.

[0088] The monitoring unit can estimate the user's emotions and adjust the importance of monitoring based on the estimated user emotions. For example, if the user is nervous, the monitoring unit can prioritize and display important monitoring items. Furthermore, if the user is relaxed, the monitoring unit can display detailed monitoring items. Furthermore, if the user is in a hurry, the monitoring unit can display monitoring items that focus on the essentials. For example, the monitoring unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The monitoring unit can also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the monitoring unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the system to adjust the importance of monitoring based on the user's emotions, thereby providing optimal information for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the monitoring unit may input user emotion data into the generation AI and have the generation AI adjust the importance of monitoring.

[0089] During monitoring, the monitoring unit can analyze changes in monitoring based on the time when the data was collected. For example, the monitoring unit prioritizes monitoring the most recent data and analyzes changes. The monitoring unit can also analyze changes in the most recent data while referring to past data. Furthermore, the monitoring unit can dynamically analyze changes in monitoring depending on the time when the data was collected. For example, the monitoring unit can prioritize monitoring the most recent data and analyze changes. The monitoring unit can also analyze changes in the most recent data while referring to past data. In this way, by analyzing changes in monitoring based on the time when the data was collected, monitoring can be performed based on the most recent information. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input the time when the data was collected into the generation AI and have the generation AI analyze changes in monitoring.

[0090] During monitoring, the monitoring unit can analyze the monitoring by referring to relevant market data. For example, the monitoring unit can refer to the relevant market data and adjust the monitoring criteria. The monitoring unit can also improve the accuracy of the monitoring based on the relevant market data. Furthermore, the monitoring unit can refer to the relevant market data in real time and reflect it in the monitoring. For example, the monitoring unit can refer to the relevant market data and adjust the monitoring criteria. The monitoring unit can also improve the accuracy of the monitoring based on the relevant market data. In this way, the accuracy of the monitoring can be improved by referring to the relevant market data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the monitoring unit can input the relevant market data into the generation AI and have the generation AI perform the monitoring analysis. === Hard Collateral 1-1 === Each of the above-described elements, including the collection unit, analysis unit, simulation unit, monitoring unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user operation logs and behavioral data 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, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the user's behavioral patterns. The simulation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, simulates user behavior based on the learned behavioral patterns. The monitoring unit, implemented, for example, by the control unit 46A of the smart device 14, monitors the performance of apps and web services based on the simulated behavioral patterns. The emotion estimation function, implemented, for example, by the specific processing unit 290 of the data processing device 12, estimates the user's emotions and adjusts the timing of data collection. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, simulation unit, monitoring unit, and emotion estimation function, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user operation logs and behavioral data 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, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the user's behavioral patterns. The simulation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, simulates user behavior based on the learned behavioral patterns. The monitoring unit, implemented, for example, by the control unit 46A of the smart glasses 214, monitors the performance of apps and web services based on the simulated behavioral patterns. The emotion estimation function, implemented, for example, by the specific processing unit 290 of the data processing device 12, estimates the user's emotions and adjusts the timing of data collection. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, simulation unit, monitoring unit, and emotion estimation function, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user operation logs and behavioral data using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the user's behavioral patterns. The simulation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, simulates user behavior based on the learned behavioral patterns. The monitoring unit, implemented, for example, by the control unit 46A of the headset-type terminal 314, monitors the performance of apps and web services based on the simulated behavioral patterns. The emotion estimation function, implemented, for example, by the specific processing unit 290 of the data processing device 12, estimates the user's emotions and adjusts the timing of data collection. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, simulation unit, monitoring unit, and emotion estimation function, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user operation logs and behavioral data 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, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and learns the user's behavioral patterns. The simulation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, simulates user behavior based on the learned behavioral patterns. The monitoring unit, implemented, for example, by the control unit 46A of the robot 414, monitors the performance of apps and web services based on the simulated behavioral patterns. The emotion estimation function, implemented, for example, by the specific processing unit 290 of the data processing device 12, estimates the user's emotions and adjusts the timing of data collection.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When collecting a user's operation log or behavioral data, the collection unit can adjust the data collection method based on the type of device and usage environment of the user. For example, if the user is using a smartphone, the collection unit can prioritize collection of touch operation data. Also, if the user is using a desktop PC, the collection unit can prioritize collection of mouse and keyboard operation data. Furthermore, if the user is using the app in a public place, the collection unit can reduce the frequency of data collection in consideration of privacy. This allows the optimal data collection method to be selected according to the user's device and usage environment, and data to be collected efficiently.

[0093] When analyzing collected data, the analysis unit compares the user's past behavioral patterns with their current behavioral patterns to detect abnormal behavior. For example, if the user performs an operation that is not normally performed, the analysis unit can detect that operation as abnormal. In addition, if the user performs an operation that is different from normal during a specific time period, the analysis unit can detect the behavior during that time period as abnormal. Furthermore, if the user shows an abnormal reaction to a specific event (e.g., the release of a new feature), the analysis unit can detect that reaction as abnormal. This makes it possible to detect changes in the user's behavioral patterns in real time and respond quickly.

[0094] When simulating the user's behavioral patterns based on the learned data, the simulator can perform the simulation by taking into account the user's past emotional data. For example, the simulator can recreate a situation in which the user felt stressed in the past and simulate the behavioral patterns in that situation. The simulator can also recreate a situation in which the user felt relaxed in the past and simulate the behavioral patterns in that situation. Furthermore, the simulator can also recreate a situation in which the user felt excited in the past and simulate the behavioral patterns in that situation. This makes it possible to recreate behavioral patterns based on the user's emotions and perform more realistic simulations.

[0095] The monitoring unit can take into account the user's geographical location information when monitoring the performance of an app or web service based on the simulated user behavior patterns. For example, if the user is in a specific area, the monitoring unit can monitor performance taking into account the network conditions of that area. Also, if the user is traveling, the monitoring unit can monitor performance taking into account the network conditions of the area to which the user is traveling. Furthermore, if the user is overseas, the monitoring unit can monitor performance taking into account the local network conditions. In this way, more accurate performance monitoring can be achieved by taking into account the user's geographical location information.

[0096] The monitoring unit can estimate the user's emotions when measuring page loading speeds, operation response times, etc. based on simulated user behavior patterns, and adjust the monitoring standards based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit can set stricter standards for page loading speeds and operation response times. The monitoring unit can also relax the standards if the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring the response times of important operations. This allows the monitoring standards to be adjusted according to the user's emotions, enabling more appropriate performance monitoring.

[0097] When collecting user operation logs and behavioral data, the collection unit can analyze the user's social media activities and prioritize collection of related data. For example, the collection unit can collect related operation logs based on information shared by the user on social media. The collection unit can also collect data related to topics of interest from the user's social media activities. Furthermore, the collection unit can analyze the user's operations on social media and collect related data. This makes it possible to efficiently collect related data by analyzing the user's social media activities.

[0098] When analyzing collected data, the analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing operation logs that cause stress. Also, if the user is relaxed, the analysis unit can perform a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing important operation logs. This allows the analysis priority to be determined according to the user's emotions, allowing for more effective analysis.

[0099] When simulating a user's behavioral patterns based on the learned data, the simulator can perform the simulation taking into account the type of device and usage environment of the user. For example, if the user is using a smartphone, the simulator can simulate touch operations. Also, if the user is using a desktop PC, the simulator can simulate mouse and keyboard operations. Furthermore, if the user is using the app in a public place, the simulator can perform a simulation that is tailored to the environment. This allows for a more realistic simulation to be performed that is tailored to the user's device and usage environment.

[0100] When monitoring the performance of an app or web service based on simulated user behavior patterns, the monitoring unit can adjust the monitoring criteria by referring to the user's past behavior patterns. For example, if the user has had problems with a particular operation in the past, the monitoring unit can set stricter monitoring criteria for that operation. Also, if the user has performed an operation without problems in the past, the monitoring unit can relax the monitoring criteria for that operation. Furthermore, the monitoring unit can analyze the user's past behavior patterns and dynamically adjust the monitoring criteria. This allows the monitoring criteria to be adjusted by referring to the user's past behavior patterns, enabling more appropriate performance monitoring.

[0101] The monitoring unit can estimate the user's emotions when measuring page loading speeds, operation response times, etc. based on simulated user behavior patterns, and adjust the monitoring display method based on the estimated emotions. For example, if the user is nervous, the monitoring unit can provide a simple, highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. In this way, the monitoring display method can be adjusted according to the user's emotions, and optimal information can be provided to the user.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects user operation logs and behavioral data. The user operation logs and behavioral data include, for example, clicks, page views, and input operations. The collection unit can collect data in real time, as well as in batch processing or when a specific event occurs. Step 2: The analysis unit analyzes the data collected by the collection unit and learns user behavior patterns. The analysis unit analyzes the data using data mining techniques and machine learning algorithms, and can use techniques such as supervised learning, unsupervised learning, and reinforcement learning. Step 3: The simulator simulates the behavioral patterns learned by the analyzer. The simulator simulates the behavioral patterns using Monte Carlo simulation or agent-based simulation, and can reproduce the behavioral patterns of users of various ages and genders. Step 4: The monitoring unit monitors the performance of the app or web service based on the behavioral patterns simulated by the simulation unit. The monitoring unit measures page loading speeds and operation response times, discovers potential problems users may be experiencing, and corrects these problems to improve the quality of the service.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 user operation logs and behavior data; an analysis unit that analyzes the data collected by the collection unit and learns user behavior patterns; a simulation unit that simulates the behavioral patterns learned by the analysis unit; a monitoring unit that monitors the performance of an application or a web service based on the behavioral pattern simulated by the simulating unit. A system characterized by:

2. The collecting unit Collect detailed data about which buttons users click, which pages they view, and what actions they take The system of claim 1 .

3. The analysis unit Analyze collected data and learn user behavior patterns The system of claim 1 .

4. The simulating unit Based on the learned data, it reproduces user behavior patterns for multiple age groups and genders. The system of claim 1 .

5. The monitoring unit Measure page load speeds and response times based on simulated user behavior patterns to identify potential user issues. The system of claim 1 .

6. The monitoring unit Fixing issues and improving service quality 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 Analyze users' past operation history and select the appropriate data collection method The system of claim 1 .

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