system

Abstract

The system according to the embodiment aims to simulate the behavior of a virtual customer and evaluate the UI / UX of a web page. [Solution] A system according to an embodiment includes a generation unit, a simulation unit, and an evaluation unit. The generation unit generates behavioral patterns of virtual customers. The simulation unit simulates the virtual customers generated by the generation unit visiting web pages and simulating page transitions and site applications. The evaluation unit evaluates the results obtained 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 technology does not adequately simulate customer behavior during the UI / UX consideration stage of a web page, leaving room for improvement.

[0005] The system according to the embodiment aims to simulate the behavior of a virtual customer and evaluate the UI / UX of a web page. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a simulation unit, and an evaluation unit. The generation unit generates a behavior pattern of a virtual customer. The simulation unit simulates the virtual customer generated by the generation unit visiting web pages and simulating page transitions and site applications. The evaluation unit evaluates the results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can simulate the behavior of a virtual customer and evaluate the UI / UX of a web page. [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 generation AI to create virtual customers visiting web pages and perform simulations during the UI / UX review stage. In this system, the generation AI generates the behavioral patterns of the virtual customers, and the generated virtual customers visit web pages and simulate page transitions and site subscriptions. This simulation can measure page transition rates and site subscription rates during the UI / UX review stage. For example, measuring the frequency with which virtual customers click specific buttons and the length of time they spend on specific pages can be useful for improving usability. Furthermore, the generation AI can generate multiple virtual customers with different attributes and behavioral patterns, enabling simulations under various scenarios. This allows specific UI / UX improvements to be identified and the actual user experience to be improved. This allows the system to measure page transition rates and site subscription rates during the UI / UX review stage. For example, measuring the frequency with which virtual customers click specific buttons and the length of time they spend on specific pages can be useful for improving usability. Furthermore, the generation AI can generate multiple virtual customers with different attributes and behavioral patterns, enabling simulations under various scenarios. This allows specific UI / UX improvements to be identified and the actual user experience to be improved.

[0029] The system according to the embodiment includes a generation unit, a simulation unit, and an evaluation unit. The generation unit generates a behavior pattern of a virtual customer using a generation AI. For example, the generation AI generates attributes and behavior patterns of the virtual customer. The generation unit can also generate multiple virtual customers with different attributes and behavior patterns. For example, the generation AI generates virtual customers with different attributes, such as age, gender, region, and interests. The simulation unit simulates the virtual customer generated by the generation unit visiting web pages and simulating page transitions and site applications. For example, the simulation unit measures the frequency with which the virtual customer clicks a specific button and the time spent on a specific page. The simulation unit can also simulate the behavior pattern of the virtual customer on different devices (such as a smartphone, tablet, or PC). For example, the simulation unit simulates the operation of a virtual customer using a smartphone, focusing on touch operations. The evaluation unit evaluates the results obtained by the simulation unit. For example, the evaluation unit identifies areas for improvement in UI / UX based on the results of the simulation. The evaluation unit can also change the evaluation conditions based on specific time periods or days of the week based on the results of the simulation. For example, the evaluation unit performs evaluation based on the results of a simulation performed during the daytime on a weekday. As a result, the system according to the embodiment generates behavioral patterns of virtual customers, performs simulations, and evaluates the results, thereby specifically identifying areas for improvement in UI / UX and improving the actual user experience.

[0030] The generation unit can generate multiple virtual customers with different attributes and behavioral patterns. The generation unit generates multiple virtual customers with different attributes and behavioral patterns using, for example, a generation AI. For example, the generation unit generates virtual customers with different attributes such as age, gender, region, and interests. The generation unit can also generate behavioral patterns of the virtual customers using the generation AI. For example, the generation unit generates behavioral patterns such as page transition rules, click frequency, and stay time. This enables simulation of various scenarios by generating multiple virtual customers with different attributes and behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can cause the generation AI to generate different attributes and behavioral patterns.

[0031] The simulation unit can measure the frequency with which a virtual customer clicks a specific button or the time spent on a specific page. For example, the simulation unit measures the frequency with which a virtual customer clicks a specific button. For example, the simulation unit measures the click frequency of specific buttons such as a purchase button, an inquiry button, or a subscribe button. The simulation unit can also measure the time spent on a specific page. For example, the simulation unit measures the time from page load or the time between user actions. This allows for detailed measurement of the virtual customer's behavior to specifically identify areas for improvement in UI / UX. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can have AI measure the click frequency of a specific button or the time spent on a specific page.

[0032] The evaluation unit can identify areas for improvement in UI / UX based on the results of the simulation. The evaluation unit identifies areas for improvement in UI / UX based on, for example, the results of the simulation. For example, the evaluation unit identifies areas for improvement in UI / UX using evaluation criteria such as usability testing, heat map analysis, and user feedback. The evaluation unit can also change the evaluation conditions based on a specific time period or day of the week based on the results of the simulation. For example, the evaluation unit performs evaluation based on the results of a simulation performed during the daytime on a weekday. In this way, by evaluating the results of the simulation, it is possible to specifically identify areas for improvement in UI / UX. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may have AI evaluate the results of the simulation.

[0033] When generating a virtual customer, the generation unit can optimize the behavioral pattern by referring to past user behavior data. The generation unit, for example, uses a generation AI to refer to past user behavior data. For example, the generation unit refers to past user behavior data such as log data, clickstream data, and session data. The generation unit can also optimize the behavioral pattern of the virtual customer based on the past user behavior data. For example, the generation unit sets the route most frequently selected by the virtual customer based on the past user behavior data. The generation unit can also analyze the past user behavior data and predict the behavior of the virtual customer during a specific time period. The generation unit can also optimize the time the virtual customer spends on a specific page by referring to the past user behavior data. In this way, the behavioral pattern of the virtual customer can be optimized by referring to the past user behavior data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to refer to past user behavior data and optimize the behavioral pattern.

[0034] The generation unit can consider behavioral patterns on different devices when generating virtual customers. The generation unit, for example, uses a generation AI to consider behavioral patterns on different devices. For example, the generation unit considers behavioral patterns on different devices, such as smartphones, tablets, and desktops. The generation unit can also generate behavioral patterns of virtual customers based on behavioral patterns on different devices. For example, the generation unit can set behavioral patterns for virtual customers who use smartphones and emphasize touch operation. The generation unit can also set behavioral patterns for virtual customers who use tablets and emphasize operation on large screens. The generation unit can also set behavioral patterns for virtual customers who use PCs and emphasize mouse and keyboard operation. This allows for more diverse simulations by considering behavioral patterns on different devices. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause the generation AI to consider and generate behavioral patterns on different devices.

[0035] When generating a virtual customer, the generation unit can change the behavior pattern based on a specific time period or day of the week. The generation unit, for example, uses a generation AI to change the behavior pattern based on a specific time period or day of the week. For example, if the virtual customer visits during the daytime on a weekday, the generation unit can set a behavior pattern that assumes a user who is working. Furthermore, if the virtual customer visits on a weekend evening, the generation unit can set a behavior pattern that assumes a user who is relaxed. Furthermore, if the virtual customer visits during a specific event, the generation unit can set a behavior pattern related to the event. This enables a more realistic simulation by changing the behavior pattern based on a specific time period or day of the week. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to change the behavior pattern based on a specific time period or day of the week.

[0036] The generation unit can generate behavioral patterns by taking geographical location information into consideration when generating virtual customers. The generation unit, for example, uses a generation AI to consider geographical location information. For example, the generation unit considers geographical location information such as GPS data, IP addresses, and area codes. The generation unit can also generate behavioral patterns of virtual customers based on geographical location information. For example, the generation unit can set behavioral patterns for virtual customers living in urban areas and emphasize the use of public transportation. The generation unit can also set behavioral patterns for virtual customers living in suburban areas and emphasize the use of private cars. The generation unit can also set behavioral patterns for virtual customers living in specific areas and reflect the characteristics of the area. This allows for more realistic simulations by taking geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to consider geographical location information and generate behavioral patterns.

[0037] When generating the virtual customer, the generation unit can generate a behavioral pattern by referring to social media activity data. The generation unit, for example, uses a generation AI to refer to the social media activity data. For example, the generation unit refers to social media activity data such as posting data, the number of likes, and the number of followers. The generation unit can also generate a behavioral pattern of the virtual customer based on the social media activity data. For example, the generation unit sets destinations based on the virtual customer's social media check-in data. The generation unit can also analyze the content of the virtual customer's social media posts and have the virtual customer visit pages that are likely to be of interest. The generation unit can also set a behavioral pattern by referring to the activities of the virtual customer's friends on social media. In this way, referring to the social media activity data enables a more realistic simulation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to refer to the social media activity data and generate a behavioral pattern.

[0038] When generating a virtual customer, the generation unit can customize the behavioral pattern by reflecting the user's past feedback. The generation unit, for example, uses a generation AI to refer to the user's past feedback. For example, the generation unit refers to the user's past feedback, such as survey results, reviews, and comments. The generation unit can also customize the virtual customer's behavioral pattern based on the user's past feedback. For example, the generation unit can optimize the virtual customer's behavioral pattern based on the user's past feedback. The generation unit can also set a behavioral pattern that avoids specific operations based on the user's past feedback. The generation unit can also adjust the time the virtual customer stays on a specific page by referring to the user's past feedback. This enables a more realistic simulation by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to refer to the user's past feedback and customize the behavioral pattern.

[0039] The simulation unit can improve the accuracy of the simulation by referring to the behavioral history of the virtual customer during the simulation. The simulation unit, for example, uses AI to refer to the behavioral history of the virtual customer. For example, the simulation unit refers to the behavioral history of the virtual customer, such as past simulation data and clickstream data. The simulation unit can also improve the accuracy of the simulation based on the behavioral history of the virtual customer. For example, the simulation unit improves the accuracy of the simulation based on past simulation results. The simulation unit can also analyze the behavioral history of the virtual customer and predict specific operations. The simulation unit can also optimize the results of the simulation by referring to the behavioral history of the virtual customer. In this way, the accuracy of the simulation can be improved by referring to the behavioral history of the virtual customer. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can cause AI to refer to the behavioral history of the virtual customer and improve the accuracy of the simulation.

[0040] The simulation unit can simulate operations on different devices during the simulation. The simulation unit, for example, uses AI to simulate operations on different devices. For example, the simulation unit simulates operations on different devices such as smartphones, tablets, and desktops. The simulation unit can also perform simulations based on operations on different devices. For example, the simulation unit can simulate operations of a virtual customer using a smartphone, emphasizing touch operations. The simulation unit can also simulate operations of a virtual customer using a tablet, emphasizing operations on a large screen. The simulation unit can also simulate operations of a virtual customer using a PC, emphasizing mouse and keyboard operations. This enables more diverse simulations by simulating operations on different devices. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI execute simulations of operations on different devices.

[0041] The simulation unit can change the simulation conditions based on a specific time period or day of the week during the simulation. The simulation unit, for example, uses AI to change the simulation conditions based on a specific time period or day of the week. For example, when performing a simulation during the daytime on a weekday, the simulation unit can set conditions that assume a user is at work. Furthermore, when performing a simulation on a weekend night, the simulation unit can set conditions that assume a user is relaxed. Furthermore, when performing a simulation during a specific event period, the simulation unit can set conditions related to the event. In this way, by changing the simulation conditions based on a specific time period or day of the week, a more realistic simulation can be achieved. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or may be performed without using AI. For example, the simulation unit can cause AI to change the simulation conditions based on a specific time period or day of the week.

[0042] The simulation unit can perform a simulation taking geographical location information into consideration during the simulation. The simulation unit, for example, uses AI to consider the geographical location information. For example, the simulation unit considers geographical location information such as GPS data, IP addresses, and area codes. The simulation unit can also perform a simulation based on the geographical location information. For example, the simulation unit can simulate the behavior of virtual customers living in urban areas and emphasize the use of public transportation. The simulation unit can also simulate the behavior of virtual customers living in suburban areas and emphasize the use of private cars. The simulation unit can also simulate the behavior of virtual customers living in a specific area and reflect the characteristics of that area. This allows for a more realistic simulation by taking geographical location information into consideration. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI consider the geographical location information and perform the simulation.

[0043] The simulation unit can perform the simulation by referring to social media activity data during the simulation. The simulation unit, for example, uses AI to refer to the social media activity data. For example, the simulation unit refers to social media activity data such as posting data, number of likes, and number of followers. The simulation unit can also perform the simulation based on the social media activity data. For example, the simulation unit simulates visits based on the virtual customer's social media check-in data. The simulation unit can also analyze the content of the virtual customer's social media posts and have the virtual customer visit pages that are likely to be of interest. The simulation unit can also simulate behavior by referring to the activities of the virtual customer's friends on social media. In this way, referring to the social media activity data enables a more realistic simulation. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI refer to the social media activity data and perform the simulation.

[0044] During the simulation, the simulation unit can customize the simulation conditions by reflecting the user's past feedback. The simulation unit, for example, uses AI to refer to the user's past feedback. For example, the simulation unit can refer to the user's past feedback, such as survey results, reviews, and comments. The simulation unit can also customize the simulation conditions based on the user's past feedback. For example, the simulation unit can optimize the simulation conditions based on the user's past feedback. The simulation unit can also set conditions to avoid specific operations based on the user's past feedback. The simulation unit can also optimize the simulation results by referring to the user's past feedback. This enables a more realistic simulation by reflecting the user's past feedback. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can cause AI to refer to the user's past feedback and customize the simulation conditions.

[0045] The evaluation unit can improve the accuracy of the evaluation by referring to past simulation results during evaluation. The evaluation unit, for example, uses AI to refer to past simulation results. For example, the evaluation unit refers to past simulation results such as log data, clickstream data, and session data. The evaluation unit can also improve the accuracy of the evaluation based on the past simulation results. For example, the evaluation unit improves the accuracy of the evaluation based on the past simulation results. The evaluation unit can also analyze past simulation results and predict specific evaluation items. The evaluation unit can also optimize the evaluation criteria by referring to the past simulation results. In this way, the accuracy of the evaluation can be improved by referring to the past simulation results. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to refer to the past simulation results and improve the accuracy of the evaluation.

[0046] The evaluation unit can perform the evaluation by taking into account operation results on different devices. The evaluation unit, for example, uses AI to consider operation results on different devices. For example, the evaluation unit considers operation results on different devices such as a smartphone, a tablet, and a desktop. The evaluation unit can also perform the evaluation based on operation results on different devices. For example, the evaluation unit can perform the evaluation based on simulation results using a smartphone. The evaluation unit can also perform the evaluation based on simulation results using a tablet. The evaluation unit can also perform the evaluation based on simulation results using a PC. This allows for more diverse evaluations by considering operation results on different devices. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to consider and evaluate operation results on different devices.

[0047] The evaluation unit can change the evaluation conditions based on a specific time period or day of the week during evaluation. The evaluation unit changes the evaluation conditions based on a specific time period or day of the week using, for example, AI. For example, the evaluation unit performs evaluation based on the results of a simulation performed during the daytime on a weekday. The evaluation unit can also perform evaluation based on the results of a simulation performed on a weekend night. The evaluation unit can also perform evaluation based on the results of a simulation performed during a specific event period. This enables more realistic evaluation by changing the evaluation conditions based on a specific time period or day of the week. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can cause AI to change the evaluation conditions based on a specific time period or day of the week.

[0048] The evaluation unit can perform the evaluation by taking geographical location information into consideration. The evaluation unit, for example, uses AI to consider the geographical location information. For example, the evaluation unit considers geographical location information such as GPS data, IP address, and area code. The evaluation unit can also perform the evaluation based on the geographical location information. For example, the evaluation unit performs the evaluation based on simulation results for users living in urban areas. The evaluation unit can also perform the evaluation based on simulation results for users living in suburban areas. The evaluation unit can also perform the evaluation based on simulation results for users living in specific areas. By taking geographical location information into consideration, a more realistic evaluation is possible. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to consider the geographical location information and perform the evaluation.

[0049] The evaluation unit can perform the evaluation by referring to social media activity data during the evaluation. The evaluation unit, for example, uses AI to refer to the social media activity data. For example, the evaluation unit refers to social media activity data such as posting data, the number of likes, and the number of followers. The evaluation unit can also perform the evaluation based on the social media activity data. For example, the evaluation unit performs the evaluation based on the user's social media check-in data. The evaluation unit can also analyze the content of the user's social media posts and perform the evaluation. The evaluation unit can also perform the evaluation by referring to the activities of the user's friends on social media. In this way, by referring to the social media activity data, a more realistic evaluation is possible. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to refer to the social media activity data and perform the evaluation.

[0050] The evaluation unit can customize the evaluation conditions by reflecting the user's past feedback during evaluation. The evaluation unit, for example, uses AI to refer to the user's past feedback. For example, the evaluation unit can refer to the user's past feedback, such as survey results, reviews, and comments. The evaluation unit can also customize the evaluation conditions based on the user's past feedback. For example, the evaluation unit can optimize the evaluation conditions based on the user's past feedback. The evaluation unit can also set conditions to avoid specific evaluation items based on the user's past feedback. The evaluation unit can also optimize the evaluation criteria by referring to the user's past feedback. This enables more realistic evaluation by reflecting the user's past feedback. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can cause AI to refer to the user's past feedback and customize the evaluation conditions.

[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 generating the behavioral pattern of a virtual customer, the generation unit can optimize the behavioral pattern by referring to the user's past purchase history. For example, the generation unit can increase the probability that the virtual customer will purchase a specific product based on past purchase history data. The generation unit can also set a behavioral pattern in which the virtual customer is interested in products in a specific category based on the past purchase history. Furthermore, the generation unit can analyze the past purchase history and generate a behavioral pattern in which the virtual customer responds to a specific promotion. In this way, by referring to the past purchase history, the behavioral pattern of the virtual customer can be optimized more realistically.

[0053] The simulation unit can simulate the scrolling behavior of a virtual customer on a specific page. For example, the simulation unit can measure how often the virtual customer scrolls from the top to the bottom of a page. The simulation unit can also measure the time it takes the virtual customer to stop scrolling at a specific section. Furthermore, the simulation unit can measure the distance the virtual customer scrolls before clicking a link on the page. By simulating scrolling behavior, it is possible to specifically identify areas for improvement in the page layout and content placement.

[0054] The evaluation unit can identify areas for improvement in UI / UX for a specific user group based on the results of the simulation. For example, the evaluation unit can analyze the simulation results by age group and identify areas for improvement in UI / UX for that specific age group. The evaluation unit can also analyze the simulation results by region and identify areas for improvement in UI / UX for users living in that specific region. Furthermore, the evaluation unit can analyze the simulation results by device and identify areas for improvement in UI / UX for users who use specific devices. This makes it possible to specifically identify areas for improvement in UI / UX for a specific user group.

[0055] The evaluation unit can identify UI / UX improvements for specific time periods and days of the week based on the simulation results. For example, the evaluation unit can analyze the results of a simulation conducted during the daytime on a weekday to identify UI / UX improvements for users who are working. The evaluation unit can also analyze the results of a simulation conducted on a weekend night to identify UI / UX improvements for users who are relaxing. Furthermore, the evaluation unit can analyze the results of a simulation conducted during a specific event period to identify UI / UX improvements related to the event. This makes it possible to specifically identify UI / UX improvements for specific time periods and days of the week.

[0056] When generating the behavioral patterns of the virtual customers, the generation unit can optimize the behavioral patterns by referring to the user's social media activity data. For example, the generation unit analyzes the content of the user's posts on social media and has the user visit pages that are likely to be of interest. The generation unit can also set the behavioral patterns by referring to the activities of the user's friends on social media. Furthermore, the generation unit can also set destinations based on the user's social media check-in data. In this way, by referring to the social media activity data, the behavioral patterns of the virtual customers can be optimized more realistically.

[0057] The evaluation unit can identify areas for improvement in UI / UX for specific devices based on the results of the simulation. For example, the evaluation unit can analyze the results of a simulation using a smartphone and identify areas for improvement in UI / UX for smartphone users. The evaluation unit can also analyze the results of a simulation using a tablet and identify areas for improvement in UI / UX for tablet users. Furthermore, the evaluation unit can analyze the results of a simulation using a desktop and identify areas for improvement in UI / UX for desktop users. This makes it possible to specifically identify areas for improvement in UI / UX for specific devices.

[0058] When generating the behavioral patterns of the virtual customers, the generation unit can customize the behavioral patterns by reflecting the user's past feedback. For example, the generation unit optimizes the behavioral patterns of the virtual customers based on the user's past feedback. The generation unit can also set a behavioral pattern that avoids specific operations based on the user's past feedback. Furthermore, the generation unit can also adjust the amount of time the virtual customers stay on specific pages by referring to the user's past feedback. In this way, the behavioral patterns of the virtual customers can be customized more realistically by reflecting the user's past feedback.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The generation unit generates the behavioral patterns of virtual customers using the generation AI. For example, the generation AI generates the attributes and behavioral patterns of virtual customers. The generation unit can also generate multiple virtual customers with different attributes and behavioral patterns. For example, the generation AI generates virtual customers with different attributes such as age, gender, region, and interests. Step 2: The simulation unit simulates the virtual customer generated by the generation unit visiting web pages and simulating page transitions and site applications. For example, the simulation unit measures how often the virtual customer clicks a specific button or how long they stay on a specific page. The simulation unit can also simulate the behavioral patterns of the virtual customer on different devices (smartphones, tablets, PCs, etc.). For example, the simulation unit simulates the operations of a virtual customer using a smartphone, focusing on touch operations. Step 3: The evaluation unit evaluates the results obtained by the simulation unit. For example, the evaluation unit identifies areas for improvement in UI / UX based on the results of the simulation. The evaluation unit can also change the evaluation conditions based on specific time periods or days of the week based on the results of the simulation. For example, the evaluation unit evaluates the results of a simulation conducted during the daytime on a weekday.

[0061] (Example 2) A system according to an embodiment of the present invention uses a generation AI to create virtual customers visiting web pages and perform simulations during the UI / UX review stage. In this system, the generation AI generates the behavioral patterns of the virtual customers, and the generated virtual customers visit web pages and simulate page transitions and site subscriptions. This simulation can measure page transition rates and site subscription rates during the UI / UX review stage. For example, measuring the frequency with which virtual customers click specific buttons and the length of time they spend on specific pages can be useful for improving usability. Furthermore, the generation AI can generate multiple virtual customers with different attributes and behavioral patterns, enabling simulations under various scenarios. This allows specific UI / UX improvements to be identified and the actual user experience to be improved. This allows the system to measure page transition rates and site subscription rates during the UI / UX review stage. For example, measuring the frequency with which virtual customers click specific buttons and the length of time they spend on specific pages can be useful for improving usability. Furthermore, the generation AI can generate multiple virtual customers with different attributes and behavioral patterns, enabling simulations under various scenarios. This allows specific UI / UX improvements to be identified and the actual user experience to be improved.

[0062] The system according to the embodiment includes a generation unit, a simulation unit, and an evaluation unit. The generation unit generates a behavior pattern of a virtual customer using a generation AI. For example, the generation AI generates attributes and behavior patterns of the virtual customer. The generation unit can also generate multiple virtual customers with different attributes and behavior patterns. For example, the generation AI generates virtual customers with different attributes, such as age, gender, region, and interests. The simulation unit simulates the virtual customer generated by the generation unit visiting web pages and simulating page transitions and site applications. For example, the simulation unit measures the frequency with which the virtual customer clicks a specific button and the time spent on a specific page. The simulation unit can also simulate the behavior pattern of the virtual customer on different devices (such as a smartphone, tablet, or PC). For example, the simulation unit simulates the operation of a virtual customer using a smartphone, focusing on touch operations. The evaluation unit evaluates the results obtained by the simulation unit. For example, the evaluation unit identifies areas for improvement in UI / UX based on the results of the simulation. The evaluation unit can also change the evaluation conditions based on specific time periods or days of the week based on the results of the simulation. For example, the evaluation unit performs evaluation based on the results of a simulation performed during the daytime on a weekday. As a result, the system according to the embodiment generates behavioral patterns of virtual customers, performs simulations, and evaluates the results, thereby specifically identifying areas for improvement in UI / UX and improving the actual user experience.

[0063] The generation unit can generate multiple virtual customers with different attributes and behavioral patterns. The generation unit generates multiple virtual customers with different attributes and behavioral patterns using, for example, a generation AI. For example, the generation unit generates virtual customers with different attributes such as age, gender, region, and interests. The generation unit can also generate behavioral patterns of the virtual customers using the generation AI. For example, the generation unit generates behavioral patterns such as page transition rules, click frequency, and stay time. This enables simulation of various scenarios by generating multiple virtual customers with different attributes and behavioral patterns. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can cause the generation AI to generate different attributes and behavioral patterns.

[0064] The simulation unit can measure the frequency with which a virtual customer clicks a specific button or the time spent on a specific page. For example, the simulation unit measures the frequency with which a virtual customer clicks a specific button. For example, the simulation unit measures the click frequency of specific buttons such as a purchase button, an inquiry button, or a subscribe button. The simulation unit can also measure the time spent on a specific page. For example, the simulation unit measures the time from page load or the time between user actions. This allows for detailed measurement of the virtual customer's behavior to specifically identify areas for improvement in UI / UX. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI. For example, the simulation unit can have AI measure the click frequency of a specific button or the time spent on a specific page.

[0065] The evaluation unit can identify areas for improvement in UI / UX based on the results of the simulation. The evaluation unit identifies areas for improvement in UI / UX based on, for example, the results of the simulation. For example, the evaluation unit identifies areas for improvement in UI / UX using evaluation criteria such as usability testing, heat map analysis, and user feedback. The evaluation unit can also change the evaluation conditions based on a specific time period or day of the week based on the results of the simulation. For example, the evaluation unit performs evaluation based on the results of a simulation performed during the daytime on a weekday. In this way, by evaluating the results of the simulation, it is possible to specifically identify areas for improvement in UI / UX. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may have AI evaluate the results of the simulation.

[0066] The generation unit can estimate the user's emotions and adjust the virtual customer's behavior pattern based on the estimated user's emotions. The generation unit estimates the user's emotions using, for example, a generation AI. For example, the generation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The generation unit can also adjust the virtual customer's behavior pattern based on the estimated user's emotions. For example, if the user is stressed, the generation unit can simplify the virtual customer's behavior pattern and avoid complex operations. If the user is relaxed, the generation unit can make the virtual customer's behavior pattern more detailed and try multiple options. If the user is in a hurry, the generation unit can speed up the virtual customer's behavior pattern and select the shortest route. This enables a more realistic simulation by adjusting the virtual customer's behavior pattern according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit may cause the generation AI to estimate the user's emotions and adjust the behavioral patterns.

[0067] When generating a virtual customer, the generation unit can optimize the behavioral pattern by referring to past user behavior data. The generation unit, for example, uses a generation AI to refer to past user behavior data. For example, the generation unit refers to past user behavior data such as log data, clickstream data, and session data. The generation unit can also optimize the behavioral pattern of the virtual customer based on the past user behavior data. For example, the generation unit sets the route most frequently selected by the virtual customer based on the past user behavior data. The generation unit can also analyze the past user behavior data and predict the behavior of the virtual customer during a specific time period. The generation unit can also optimize the time the virtual customer spends on a specific page by referring to the past user behavior data. In this way, the behavioral pattern of the virtual customer can be optimized by referring to the past user behavior data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to refer to past user behavior data and optimize the behavioral pattern.

[0068] The generation unit can consider behavioral patterns on different devices when generating virtual customers. The generation unit, for example, uses a generation AI to consider behavioral patterns on different devices. For example, the generation unit considers behavioral patterns on different devices, such as smartphones, tablets, and desktops. The generation unit can also generate behavioral patterns of virtual customers based on behavioral patterns on different devices. For example, the generation unit can set behavioral patterns for virtual customers who use smartphones and emphasize touch operation. The generation unit can also set behavioral patterns for virtual customers who use tablets and emphasize operation on large screens. The generation unit can also set behavioral patterns for virtual customers who use PCs and emphasize mouse and keyboard operation. This allows for more diverse simulations by considering behavioral patterns on different devices. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can cause the generation AI to consider and generate behavioral patterns on different devices.

[0069] When generating a virtual customer, the generation unit can change the behavior pattern based on a specific time period or day of the week. The generation unit, for example, uses a generation AI to change the behavior pattern based on a specific time period or day of the week. For example, if the virtual customer visits during the daytime on a weekday, the generation unit can set a behavior pattern that assumes a user who is working. Furthermore, if the virtual customer visits on a weekend evening, the generation unit can set a behavior pattern that assumes a user who is relaxed. Furthermore, if the virtual customer visits during a specific event, the generation unit can set a behavior pattern related to the event. This enables a more realistic simulation by changing the behavior pattern based on a specific time period or day of the week. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to change the behavior pattern based on a specific time period or day of the week.

[0070] The generation unit can estimate the user's emotions and adjust the attributes of the virtual customer based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, a generation AI. For example, the generation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The generation unit can also adjust the attributes of the virtual customer based on the estimated user emotions. For example, if the user is stressed, the generation unit simplifies the attributes of the virtual customer to avoid complex operations. If the user is relaxed, the generation unit can provide detailed attributes of the virtual customer and allow multiple options to be explored. If the user is in a hurry, the generation unit can speed up the attributes of the virtual customer and select the shortest route. This enables a more realistic simulation by adjusting the attributes of the virtual customer according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generation AI with an emotion estimation function. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can cause the generation AI to estimate the user's emotions and adjust the attributes.

[0071] The generation unit can generate behavioral patterns by taking geographical location information into consideration when generating virtual customers. The generation unit, for example, uses a generation AI to consider geographical location information. For example, the generation unit considers geographical location information such as GPS data, IP addresses, and area codes. The generation unit can also generate behavioral patterns of virtual customers based on geographical location information. For example, the generation unit can set behavioral patterns for virtual customers living in urban areas and emphasize the use of public transportation. The generation unit can also set behavioral patterns for virtual customers living in suburban areas and emphasize the use of private cars. The generation unit can also set behavioral patterns for virtual customers living in specific areas and reflect the characteristics of the area. This allows for more realistic simulations by taking geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to consider geographical location information and generate behavioral patterns.

[0072] When generating the virtual customer, the generation unit can generate a behavioral pattern by referring to social media activity data. The generation unit, for example, uses a generation AI to refer to the social media activity data. For example, the generation unit refers to social media activity data such as posting data, the number of likes, and the number of followers. The generation unit can also generate a behavioral pattern of the virtual customer based on the social media activity data. For example, the generation unit sets destinations based on the virtual customer's social media check-in data. The generation unit can also analyze the content of the virtual customer's social media posts and have the virtual customer visit pages that are likely to be of interest. The generation unit can also set a behavioral pattern by referring to the activities of the virtual customer's friends on social media. In this way, referring to the social media activity data enables a more realistic simulation. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to refer to the social media activity data and generate a behavioral pattern.

[0073] When generating a virtual customer, the generation unit can customize the behavioral pattern by reflecting the user's past feedback. The generation unit, for example, uses a generation AI to refer to the user's past feedback. For example, the generation unit refers to the user's past feedback, such as survey results, reviews, and comments. The generation unit can also customize the virtual customer's behavioral pattern based on the user's past feedback. For example, the generation unit can optimize the virtual customer's behavioral pattern based on the user's past feedback. The generation unit can also set a behavioral pattern that avoids specific operations based on the user's past feedback. The generation unit can also adjust the time the virtual customer stays on a specific page by referring to the user's past feedback. This enables a more realistic simulation by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can cause the generation AI to refer to the user's past feedback and customize the behavioral pattern.

[0074] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated user's emotions. The simulation unit estimates the user's emotions using, for example, AI. For example, the simulation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The simulation unit can also adjust the simulation scenario based on the estimated user's emotions. For example, if the user is stressed, the simulation unit can set a simple scenario and avoid complicated operations. If the user is relaxed, the simulation unit can set a detailed scenario and try multiple options. If the user is in a hurry, the simulation unit can set a quick scenario and select the shortest route. This enables a more realistic simulation by adjusting the simulation scenario according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can have the AI ​​estimate the user's emotions and adjust the scenario.

[0075] The simulation unit can improve the accuracy of the simulation by referring to the behavioral history of the virtual customer during the simulation. The simulation unit, for example, uses AI to refer to the behavioral history of the virtual customer. For example, the simulation unit refers to the behavioral history of the virtual customer, such as past simulation data and clickstream data. The simulation unit can also improve the accuracy of the simulation based on the behavioral history of the virtual customer. For example, the simulation unit improves the accuracy of the simulation based on past simulation results. The simulation unit can also analyze the behavioral history of the virtual customer and predict specific operations. The simulation unit can also optimize the results of the simulation by referring to the behavioral history of the virtual customer. In this way, the accuracy of the simulation can be improved by referring to the behavioral history of the virtual customer. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can cause AI to refer to the behavioral history of the virtual customer and improve the accuracy of the simulation.

[0076] The simulation unit can simulate operations on different devices during the simulation. The simulation unit, for example, uses AI to simulate operations on different devices. For example, the simulation unit simulates operations on different devices such as smartphones, tablets, and desktops. The simulation unit can also perform simulations based on operations on different devices. For example, the simulation unit can simulate operations of a virtual customer using a smartphone, emphasizing touch operations. The simulation unit can also simulate operations of a virtual customer using a tablet, emphasizing operations on a large screen. The simulation unit can also simulate operations of a virtual customer using a PC, emphasizing mouse and keyboard operations. This enables more diverse simulations by simulating operations on different devices. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI execute simulations of operations on different devices.

[0077] The simulation unit can change the simulation conditions based on a specific time period or day of the week during the simulation. The simulation unit, for example, uses AI to change the simulation conditions based on a specific time period or day of the week. For example, when performing a simulation during the daytime on a weekday, the simulation unit can set conditions that assume a user is at work. Furthermore, when performing a simulation on a weekend night, the simulation unit can set conditions that assume a user is relaxed. Furthermore, when performing a simulation during a specific event period, the simulation unit can set conditions related to the event. In this way, by changing the simulation conditions based on a specific time period or day of the week, a more realistic simulation can be achieved. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or may be performed without using AI. For example, the simulation unit can cause AI to change the simulation conditions based on a specific time period or day of the week.

[0078] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated user's emotions. The simulation unit, for example, uses AI to estimate the user's emotions. For example, the simulation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The simulation unit can also adjust the display method of the simulation results based on the estimated user's emotions. For example, the simulation unit can provide a simple, highly visible display method when the user is stressed. The simulation unit can also provide a display method including detailed information when the user is relaxed. The simulation unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows for more appropriate display by adjusting the display method of the simulation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or without AI. For example, the simulation unit can have the AI ​​estimate the user's emotions and adjust how they are displayed.

[0079] The simulation unit can perform a simulation taking geographical location information into consideration during the simulation. The simulation unit, for example, uses AI to consider the geographical location information. For example, the simulation unit considers geographical location information such as GPS data, IP addresses, and area codes. The simulation unit can also perform a simulation based on the geographical location information. For example, the simulation unit can simulate the behavior of virtual customers living in urban areas and emphasize the use of public transportation. The simulation unit can also simulate the behavior of virtual customers living in suburban areas and emphasize the use of private cars. The simulation unit can also simulate the behavior of virtual customers living in a specific area and reflect the characteristics of that area. This allows for a more realistic simulation by taking geographical location information into consideration. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI consider the geographical location information and perform the simulation.

[0080] The simulation unit can perform the simulation by referring to social media activity data during the simulation. The simulation unit, for example, uses AI to refer to the social media activity data. For example, the simulation unit refers to social media activity data such as posting data, number of likes, and number of followers. The simulation unit can also perform the simulation based on the social media activity data. For example, the simulation unit simulates visits based on the virtual customer's social media check-in data. The simulation unit can also analyze the content of the virtual customer's social media posts and have the virtual customer visit pages that are likely to be of interest. The simulation unit can also simulate behavior by referring to the activities of the virtual customer's friends on social media. In this way, referring to the social media activity data enables a more realistic simulation. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI refer to the social media activity data and perform the simulation.

[0081] During the simulation, the simulation unit can customize the simulation conditions by reflecting the user's past feedback. The simulation unit, for example, uses AI to refer to the user's past feedback. For example, the simulation unit can refer to the user's past feedback, such as survey results, reviews, and comments. The simulation unit can also customize the simulation conditions based on the user's past feedback. For example, the simulation unit can optimize the simulation conditions based on the user's past feedback. The simulation unit can also set conditions to avoid specific operations based on the user's past feedback. The simulation unit can also optimize the simulation results by referring to the user's past feedback. This enables a more realistic simulation by reflecting the user's past feedback. Some or all of the above-described processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can cause AI to refer to the user's past feedback and customize the simulation conditions.

[0082] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. The evaluation unit, for example, uses AI to estimate the user's emotions. For example, the evaluation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The evaluation unit can also adjust the evaluation criteria based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can set simple evaluation criteria and avoid complex evaluation. If the user is relaxed, the evaluation unit can set detailed evaluation criteria and try multiple evaluation items. If the user is in a hurry, the evaluation unit can set quick evaluation criteria and perform evaluation in the shortest time. This enables more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can have the AI ​​estimate the user's emotions and adjust the evaluation criteria.

[0083] The evaluation unit can improve the accuracy of the evaluation by referring to past simulation results during evaluation. The evaluation unit, for example, uses AI to refer to past simulation results. For example, the evaluation unit refers to past simulation results such as log data, clickstream data, and session data. The evaluation unit can also improve the accuracy of the evaluation based on the past simulation results. For example, the evaluation unit improves the accuracy of the evaluation based on the past simulation results. The evaluation unit can also analyze past simulation results and predict specific evaluation items. The evaluation unit can also optimize the evaluation criteria by referring to the past simulation results. In this way, the accuracy of the evaluation can be improved by referring to the past simulation results. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to refer to the past simulation results and improve the accuracy of the evaluation.

[0084] The evaluation unit can perform the evaluation by taking into account operation results on different devices. The evaluation unit, for example, uses AI to consider operation results on different devices. For example, the evaluation unit considers operation results on different devices such as a smartphone, a tablet, and a desktop. The evaluation unit can also perform the evaluation based on operation results on different devices. For example, the evaluation unit can perform the evaluation based on simulation results using a smartphone. The evaluation unit can also perform the evaluation based on simulation results using a tablet. The evaluation unit can also perform the evaluation based on simulation results using a PC. This allows for more diverse evaluations by considering operation results on different devices. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to consider and evaluate operation results on different devices.

[0085] The evaluation unit can change the evaluation conditions based on a specific time period or day of the week during evaluation. The evaluation unit changes the evaluation conditions based on a specific time period or day of the week using, for example, AI. For example, the evaluation unit performs evaluation based on the results of a simulation performed during the daytime on a weekday. The evaluation unit can also perform evaluation based on the results of a simulation performed on a weekend night. The evaluation unit can also perform evaluation based on the results of a simulation performed during a specific event period. This enables more realistic evaluation by changing the evaluation conditions based on a specific time period or day of the week. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can cause AI to change the evaluation conditions based on a specific time period or day of the week.

[0086] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. The evaluation unit, for example, uses AI to estimate the user's emotions. For example, the evaluation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. The evaluation unit can also adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit can provide a simple, highly visible display method. If the user is relaxed, the evaluation unit can provide a display method that includes detailed information. If the user is in a hurry, the evaluation unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can have the AI ​​estimate the user's emotions and adjust how the evaluation results are displayed.

[0087] The evaluation unit can perform the evaluation by taking geographical location information into consideration. The evaluation unit, for example, uses AI to consider the geographical location information. For example, the evaluation unit considers geographical location information such as GPS data, IP address, and area code. The evaluation unit can also perform the evaluation based on the geographical location information. For example, the evaluation unit performs the evaluation based on simulation results for users living in urban areas. The evaluation unit can also perform the evaluation based on simulation results for users living in suburban areas. The evaluation unit can also perform the evaluation based on simulation results for users living in specific areas. By taking geographical location information into consideration, a more realistic evaluation is possible. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to consider the geographical location information and perform the evaluation.

[0088] The evaluation unit can perform the evaluation by referring to social media activity data during the evaluation. The evaluation unit, for example, uses AI to refer to the social media activity data. For example, the evaluation unit refers to social media activity data such as posting data, the number of likes, and the number of followers. The evaluation unit can also perform the evaluation based on the social media activity data. For example, the evaluation unit performs the evaluation based on the user's social media check-in data. The evaluation unit can also analyze the content of the user's social media posts and perform the evaluation. The evaluation unit can also perform the evaluation by referring to the activities of the user's friends on social media. In this way, by referring to the social media activity data, a more realistic evaluation is possible. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can cause AI to refer to the social media activity data and perform the evaluation.

[0089] The evaluation unit can customize the evaluation conditions by reflecting the user's past feedback during evaluation. The evaluation unit, for example, uses AI to refer to the user's past feedback. For example, the evaluation unit can refer to the user's past feedback, such as survey results, reviews, and comments. The evaluation unit can also customize the evaluation conditions based on the user's past feedback. For example, the evaluation unit can optimize the evaluation conditions based on the user's past feedback. The evaluation unit can also set conditions to avoid specific evaluation items based on the user's past feedback. The evaluation unit can also optimize the evaluation criteria by referring to the user's past feedback. This enables more realistic evaluation by reflecting the user's past feedback. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can cause AI to refer to the user's past feedback and customize the evaluation conditions. === Hard Collateral 1-1 === Each of the multiple elements including the generation unit, simulation unit, and evaluation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates behavioral patterns of virtual customers using a generation AI. The simulation unit is realized, for example, by the control unit 46A of the smart device 14 and simulates the generated virtual customers visiting web pages and page transitions and site applications. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in UI / UX based on the results of the simulation. === Hard Collateral 1-2 === Each of the multiple elements including the generation unit, simulation unit, and evaluation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a behavior pattern of a virtual customer using a generation AI. The simulation unit is realized, for example, by the control unit 46A of the smart glasses 214 and simulates the generated virtual customer visiting a web page and page transitions and site applications. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in the UI / UX based on the results of the simulation. === Hard Collateral 1-3 === Each of the multiple elements including the generation unit, simulation unit, and evaluation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates behavioral patterns of virtual customers using a generation AI. The simulation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and simulates the generated virtual customers visiting web pages and page transitions and site applications. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in the UI / UX based on the results of the simulation. === Hard Collateral 1-4 === Each of the multiple elements including the generation unit, simulation unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates behavioral patterns of virtual customers using a generation AI. The simulation unit is realized, for example, by the control unit 46A of the robot 414 and simulates the generated virtual customers visiting web pages and page transitions and site applications. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in the UI / UX based on the results of the simulation.

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

[0091] When generating the behavioral pattern of a virtual customer, the generation unit can optimize the behavioral pattern by referring to the user's past purchase history. For example, the generation unit can increase the probability that the virtual customer will purchase a specific product based on past purchase history data. The generation unit can also set a behavioral pattern in which the virtual customer is interested in products in a specific category based on the past purchase history. Furthermore, the generation unit can analyze the past purchase history and generate a behavioral pattern in which the virtual customer responds to a specific promotion. In this way, by referring to the past purchase history, the behavioral pattern of the virtual customer can be optimized more realistically.

[0092] The simulation unit can simulate the scrolling behavior of a virtual customer on a specific page. For example, the simulation unit can measure how often the virtual customer scrolls from the top to the bottom of a page. The simulation unit can also measure the time it takes the virtual customer to stop scrolling at a specific section. Furthermore, the simulation unit can measure the distance the virtual customer scrolls before clicking a link on the page. By simulating scrolling behavior, it is possible to specifically identify areas for improvement in the page layout and content placement.

[0093] The evaluation unit can identify areas for improvement in UI / UX for a specific user group based on the results of the simulation. For example, the evaluation unit can analyze the simulation results by age group and identify areas for improvement in UI / UX for that specific age group. The evaluation unit can also analyze the simulation results by region and identify areas for improvement in UI / UX for users living in that specific region. Furthermore, the evaluation unit can analyze the simulation results by device and identify areas for improvement in UI / UX for users who use specific devices. This makes it possible to specifically identify areas for improvement in UI / UX for a specific user group.

[0094] When generating the behavioral patterns of the virtual customers, the generation unit can estimate the user's emotions and adjust the behavioral patterns based on the estimated emotions. For example, if the user is excited, the generation unit can make the virtual customer's behavioral patterns more proactive and visit multiple pages. Also, if the user is feeling anxious, the generation unit can make the virtual customer's behavioral patterns more cautious and check for more detailed information. Furthermore, if the user is satisfied, the generation unit can make the virtual customer's behavioral patterns more relaxed and stay longer. This allows for more realistic simulations by adjusting the behavioral patterns of the virtual customers according to the user's emotions.

[0095] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is excited, the simulation unit sets a complex scenario and tries multiple options. If the user is anxious, the simulation unit can set a simple scenario and avoid complex operations. Furthermore, if the user is satisfied, the simulation unit can set a detailed scenario and visit multiple pages. This allows for a more realistic simulation by adjusting the simulation scenario according to the user's emotions.

[0096] The evaluation unit can identify UI / UX improvements for specific time periods and days of the week based on the simulation results. For example, the evaluation unit can analyze the results of a simulation conducted during the daytime on a weekday to identify UI / UX improvements for users who are working. The evaluation unit can also analyze the results of a simulation conducted on a weekend night to identify UI / UX improvements for users who are relaxing. Furthermore, the evaluation unit can analyze the results of a simulation conducted during a specific event period to identify UI / UX improvements related to the event. This makes it possible to specifically identify UI / UX improvements for specific time periods and days of the week.

[0097] When generating the behavioral patterns of the virtual customers, the generation unit can optimize the behavioral patterns by referring to the user's social media activity data. For example, the generation unit analyzes the content of the user's posts on social media and has the user visit pages that are likely to be of interest. The generation unit can also set the behavioral patterns by referring to the activities of the user's friends on social media. Furthermore, the generation unit can also set destinations based on the user's social media check-in data. In this way, by referring to the social media activity data, the behavioral patterns of the virtual customers can be optimized more realistically.

[0098] The simulation unit can estimate the user's emotions and adjust the display method of the simulation results based on the estimated emotions. For example, if the user is excited, the simulation unit can provide a display method that includes detailed information. If the user is feeling anxious, the simulation unit can also provide a simple, highly visible display method. Furthermore, if the user is satisfied, the simulation unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the simulation results according to the user's emotions, thereby enabling more appropriate display.

[0099] The evaluation unit can identify areas for improvement in UI / UX for specific devices based on the results of the simulation. For example, the evaluation unit can analyze the results of a simulation using a smartphone and identify areas for improvement in UI / UX for smartphone users. The evaluation unit can also analyze the results of a simulation using a tablet and identify areas for improvement in UI / UX for tablet users. Furthermore, the evaluation unit can analyze the results of a simulation using a desktop and identify areas for improvement in UI / UX for desktop users. This makes it possible to specifically identify areas for improvement in UI / UX for specific devices.

[0100] When generating the behavioral patterns of the virtual customers, the generation unit can customize the behavioral patterns by reflecting the user's past feedback. For example, the generation unit optimizes the behavioral patterns of the virtual customers based on the user's past feedback. The generation unit can also set a behavioral pattern that avoids specific operations based on the user's past feedback. Furthermore, the generation unit can also adjust the amount of time the virtual customers stay on specific pages by referring to the user's past feedback. In this way, the behavioral patterns of the virtual customers can be customized more realistically by reflecting the user's past feedback.

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

[0102] Step 1: The generation unit generates the behavioral patterns of virtual customers using the generation AI. For example, the generation AI generates the attributes and behavioral patterns of virtual customers. The generation unit can also generate multiple virtual customers with different attributes and behavioral patterns. For example, the generation AI generates virtual customers with different attributes such as age, gender, region, and interests. Step 2: The simulation unit simulates the virtual customer generated by the generation unit visiting web pages and simulating page transitions and site applications. For example, the simulation unit measures how often the virtual customer clicks a specific button or how long they stay on a specific page. The simulation unit can also simulate the behavioral patterns of the virtual customer on different devices (smartphones, tablets, PCs, etc.). For example, the simulation unit simulates the operations of a virtual customer using a smartphone, focusing on touch operations. Step 3: The evaluation unit evaluates the results obtained by the simulation unit. For example, the evaluation unit identifies areas for improvement in UI / UX based on the results of the simulation. The evaluation unit can also change the evaluation conditions based on specific time periods or days of the week based on the results of the simulation. For example, the evaluation unit evaluates the results of a simulation conducted during the daytime on a weekday.

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

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

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

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

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

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

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

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

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

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

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

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

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

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

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

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

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

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

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

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

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

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

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

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

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0174] [Explanation of symbols]

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

Claims

1. a generation unit that generates a behavior pattern of a virtual customer; a simulation unit that simulates the virtual customer generated by the generation unit visiting a web page and simulating page transitions and site applications; an evaluation unit that evaluates the results obtained by the simulation unit; A system characterized by:

2. The generation unit Generate multiple virtual customers with different attributes and behavioral patterns 2. The system of claim 1.

3. The simulation unit Measure how often virtual customers click on certain buttons or how long they stay on certain pages 2. The system of claim 1.

4. The evaluation unit Identify areas for improvement in UI / UX based on simulation results 2. The system of claim 1.

5. The generation unit Estimating user emotions and adjusting the behavioral patterns of the virtual customer based on the estimated user emotions 2. The system of claim 1.

6. The generation unit When generating virtual customers, optimize behavioral patterns by referencing past user behavior data 2. The system of claim 1.

7. The generation unit Consider behavioral patterns across different devices when generating virtual customers 2. The system of claim 1.

8. The generation unit When generating virtual customers, change their behavior patterns based on specific times of day or days of the week.

2. The system of claim 1.

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