System

Abstract

An object of a system according to an embodiment is to analyze a usage pattern and behavior data of a user and to identify and improve a usability problem.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, a proposal generation unit, and a heat map analysis unit. The data collection unit collects usage patterns or behavior data of a user. The analysis unit analyzes the usage pattern and the behavior data of the user collected by the data collection unit to identify a usability problem. The proposal generation unit generates an improvement proposal based on the usability problem specified by the analysis unit. The heat map analysis unit analyzes the heat map data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively analyze user usage patterns and behavioral data, identify usability issues, and provide solutions to them.

[0005] The system according to the embodiment aims to analyze user usage patterns and behavioral data, identify usability problems, and improve them. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a proposal generation unit, and a heat map analysis unit. The data collection unit collects user usage pattern or behavior data. The analysis unit analyzes the user usage pattern and behavior data collected by the data collection unit to identify usability issues. The proposal generation unit generates improvement proposals based on the usability issues identified by the analysis unit. The heat map analysis unit analyzes the heat map data. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user usage patterns and behavioral data to identify and improve usability issues. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The UX Director system according to an embodiment of the present invention collects user usage patterns and behavioral data, and uses a generation AI to identify usability issues and generate improvement proposals. This allows the UX Director system to provide specific improvement proposals to improve the user experience.

[0029] The UX director system according to the embodiment includes a data collection unit, an analysis unit, a proposal generation unit, and a heat map analysis unit. The data collection unit collects user usage patterns and behavioral data. For example, it collects data such as which pages users visited, which links they clicked, and how much time they spent on each page. The data collection unit can also collect user scrolling data and page transition data. For example, it records which parts of the page users scrolled to and which pages they transitioned to. The data collection unit can also collect user operation logs. For example, it records which buttons users clicked and which forms they filled out. The analysis unit analyzes the collected data to identify usability issues. For example, if a particular page has a high user abandonment rate, it determines that the page has a problem. The analysis unit can also analyze user behavior patterns to identify which parts of the page are difficult to use. For example, if a user repeatedly performs a particular operation, it determines that the operation is not intuitive. The analysis unit can also analyze user feedback comments to identify issues. For example, if a user comments, "This page is difficult to use," it determines that the page has a problem. The proposal generation unit generates improvement proposals based on the problems identified by the analysis unit. For example, it makes proposals such as "simplify the navigation menu" or "change the button layout." The proposal generation unit can also make proposals for easier-to-use interfaces based on user behavior data. For example, it makes proposals such as "make this button more prominent" or "shorten this form." The proposal generation unit can also generate improvement proposals based on user feedback comments. For example, it makes proposals such as "provide more detailed explanations on this page" or "add this function." The heat map analysis unit analyzes heat map data. For example, it generates a heat map that visually shows which parts of the page the user is focusing on. The heat map analysis unit can also analyze the user's eye movements to identify which parts are attracting attention. For example, if the user is focusing their gaze on a specific area, it determines that that area is important.Furthermore, the heat map analysis unit can analyze the user's click heat map to identify which parts are being clicked. For example, if a user frequently clicks a particular button, it can determine that the button is important. This allows the UX director system according to the embodiment to provide specific improvement suggestions for improving the user experience. For example, it can provide an interface that users can operate intuitively, attracting their interest in the content. Furthermore, by providing specific improvement suggestions based on user behavior data, it is possible to continuously improve the UX.

[0030] The data collection unit collects the user's biometric information in addition to the user's behavioral data, and analyzes the correlation between the behavior and the biometric responses. For example, the data collection unit builds a system that collects the user's heart rate and electrodermal response while the user is browsing a website. This allows the analysis of the user's biometric responses while viewing specific content, and clarifies the correlation between the behavioral data and the biometric data. The data collection unit can also collect the user's body temperature and respiratory rate while using an app. For example, it analyzes changes in body temperature and respiratory rate when the user is feeling stressed. Furthermore, the data collection unit can collect brain wave data while the user is performing specific operations. For example, it analyzes brain wave data when the user is concentrating. In this way, by collecting the user's behavioral data and biometric information and analyzing the correlation, more detailed usability issues can be identified.

[0031] The data collection unit analyzes user behavior data in real time and provides instant feedback. The data collection unit develops a system that analyzes behavior data in real time when a user is browsing a website, for example, and provides instant feedback. For example, if a user stays on a particular page for a long time, related content is instantly suggested. The data collection unit can also analyze behavior data in real time when a user is using an app and provide instant feedback. For example, when a user is performing a specific operation, suggestions are made to improve the operation method. The data collection unit can also analyze behavior data in real time when a user is performing a specific operation and provide instant feedback. For example, if a user makes an error, the cause of the error and how to solve it are instantly presented. In this way, usability issues can be quickly improved by analyzing user behavior data in real time and providing instant feedback.

[0032] The data collection unit uses a wearable device to collect data on the user's physical movements and analyze the relationship between digital behavior and physical behavior. The data collection unit, for example, uses a wearable device to collect data on the user's physical movements and builds a system that analyzes the relationship between digital behavior and physical behavior. For example, the data collection unit analyzes website browsing patterns when the user is in a specific location. The data collection unit can also use the wearable device to collect physical movement data when the user is using an app and analyze the relationship between digital behavior and physical behavior. For example, the data collection unit analyzes app usage patterns when the user is in a specific location. The data collection unit can also use the wearable device to collect physical movement data when the user is performing a specific operation and analyze the relationship between digital behavior and physical behavior. For example, the data collection unit analyzes operation patterns when the user is in a specific location. In this way, usability can be improved by collecting data on the user's physical movements and analyzing the relationship between digital behavior and physical behavior.

[0033] The data collection unit integrates user behavior data across different devices and analyzes cross-device usage patterns. The data collection unit integrates user behavior data across different devices, such as smartphones, tablets, and PCs, to build a system that analyzes cross-device usage patterns. For example, it analyzes which content a user views on which device. The data collection unit can also integrate behavior data when a user uses different devices and analyze cross-device usage patterns. For example, it analyzes patterns in which a user re-views content viewed on a smartphone on a PC. Furthermore, the data collection unit can integrate behavior data across different devices when a user performs a specific operation and analyze cross-device usage patterns. For example, it analyzes patterns in which a user re-operates on a PC what they operated on a tablet. In this way, usability can be improved by integrating user behavior data across different devices and analyzing cross-device usage patterns.

[0034] The analysis unit analyzes user behavior data in chronological order to identify problems during specific time periods or situations. The analysis unit, for example, analyzes user behavior data in chronological order to build a system that identifies usability problems during specific time periods or situations. For example, it identifies problems with pages that receive a lot of access during specific time periods. The analysis unit can also analyze user behavior data in chronological order to identify problems during specific situations. For example, it can identify problems that occur when a user uses a specific device. Furthermore, the analysis unit can analyze user behavior data in chronological order to identify problems during specific event periods. For example, it can identify problems that occur during a sale period. In this way, usability can be improved by analyzing user behavior data in chronological order to identify problems during specific time periods or situations.

[0035] The analysis unit compares the behavioral data of users from different cultural spheres or regions to identify region-specific problems. The analysis unit, for example, compares the behavioral data of users from different cultural spheres or regions to build a system that identifies region-specific usability problems. For example, the analysis unit compares the behavioral patterns of users from Asia and Europe. The analysis unit can also compare the behavioral data of users from different cultural spheres or regions to identify region-specific problems. For example, the analysis unit compares the behavioral patterns of users from North America and South America. The analysis unit can also compare the behavioral data of users from different cultural spheres or regions to identify region-specific problems. For example, the analysis unit compares the behavioral patterns of users from Japan and South Korea. In this way, by comparing the behavioral data of users from different cultural spheres or regions and identifying region-specific problems, usability can be improved.

[0036] The analysis unit references data from different industries and applications to extract common problems. The analysis unit, for example, references user behavior data from different industries and applications to build a system that extracts common usability problems. For example, it identifies common problems between e-commerce sites and education sites. The analysis unit can also reference data from different industries and applications to extract common problems. For example, it identifies common problems between the financial industry and the medical industry. The analysis unit can also reference data from different industries and applications to extract common problems. For example, it identifies common problems between entertainment and business applications. In this way, by referencing data from different industries and applications and extracting common problems, it is possible to improve usability.

[0037] The analysis unit analyzes user behavior data as well as user comments and reactions on social media to grasp an overall picture of engagement. The analysis unit, for example, integrates user behavior data with comments and reactions on social media to build a system that grasps an overall picture of engagement. For example, it analyzes how users react to specific content. The analysis unit can also analyze user comments and reactions on social media to grasp an overall picture of engagement. For example, it analyzes what opinions users have about specific topics. Furthermore, the analysis unit can integrate user behavior data with comments and reactions on social media to grasp an overall picture of engagement. For example, it analyzes how users react to a specific event. In this way, usability can be improved by analyzing comments and reactions on social media in addition to user behavior data to grasp an overall picture of engagement.

[0038] The analysis unit clusters user behavior data and proposes optimal content for each different user group. The analysis unit, for example, clusters user behavior data and builds a system that proposes optimal content for each different user group. For example, specific content is proposed to a user group with similar interests. The analysis unit can also cluster user behavior data and propose optimal content for each different user group. For example, specific content is proposed to a user group in a similar age group. The analysis unit can also cluster user behavior data and propose optimal content for each different user group. For example, specific content is proposed to a user group with similar usage frequency. In this way, usability can be improved by clustering user behavior data and proposing optimal content for each different user group.

[0039] The analysis unit integrates engagement data across different devices and analyzes cross-device engagement. The analysis unit, for example, integrates engagement data across different devices and builds a system that analyzes cross-device engagement. For example, it integrates engagement data across smartphones and PCs. The analysis unit can also integrate engagement data across different devices and analyze cross-device engagement. For example, it integrates engagement data across tablets and PCs. The analysis unit can also integrate engagement data across different devices and analyze cross-device engagement. For example, it integrates engagement data across smartphones and tablets. In this way, usability can be improved by integrating engagement data across different devices and analyzing cross-device engagement.

[0040] The heat map analysis unit collects user gaze tracking data in addition to the heat map data and analyzes the correlation between gaze movements and the heat map. The heat map analysis unit, for example, collects user gaze tracking data in addition to the heat map data and builds a system that analyzes the correlation between gaze movements and the heat map. For example, it analyzes how a user follows specific content with their eyes. The heat map analysis unit can also collect user gaze tracking data and analyze the correlation between gaze movements and the heat map. For example, if a user focuses their gaze on a specific area, it determines that the area is important. The heat map analysis unit can also collect user gaze tracking data and analyze the correlation between gaze movements and the heat map. For example, if a user frequently follows a specific button with their gaze, it determines that the button is important. Thus, by collecting user gaze tracking data in addition to the heat map data and analyzing the correlation between gaze movements and the heat map, usability can be improved.

[0041] The heat map analysis unit analyzes the heat map data over time to understand changes in the user's gaze movement. The heat map analysis unit, for example, analyzes the heat map data over time to build a system that understands changes in the user's gaze movement. For example, it analyzes which area the user focuses their gaze on during a specific time period. The heat map analysis unit can also analyze the heat map data over time to understand changes in the user's gaze movement. For example, it analyzes which area the user focuses their gaze on during a specific event period. The heat map analysis unit can also analyze the heat map data over time to understand changes in the user's gaze movement. For example, it analyzes which area the user focuses their gaze on during a specific campaign period. In this way, by analyzing the heat map data over time and understanding changes in the user's gaze movement, usability can be improved.

[0042] The heat map analysis unit compares heat map data between different devices and analyzes differences in eye gaze movements for each device. The heat map analysis unit, for example, builds a system that compares heat map data between different devices and analyzes differences in eye gaze movements for each device. For example, it analyzes differences in eye gaze movements between smartphones and PCs. The heat map analysis unit can also compare heat map data between different devices and analyze differences in eye gaze movements for each device. For example, it analyzes differences in eye gaze movements between tablets and PCs. The heat map analysis unit can also compare heat map data between different devices and analyze differences in eye gaze movements for each device. For example, it analyzes differences in eye gaze movements between smartphones and tablets. In this way, by comparing heat map data between different devices and analyzing differences in eye gaze movements for each device, usability can be improved.

[0043] The heat map analysis unit integrates the heat map data with data from different industries and applications to extract common gaze patterns. For example, the heat map analysis unit builds a system that integrates the heat map data with data from different industries and applications to extract common gaze patterns. For example, it identifies common gaze patterns between e-commerce sites and education sites. The heat map analysis unit can also integrate the heat map data with data from different industries and applications to extract common gaze patterns. For example, it can identify common gaze patterns between the financial industry and the medical industry. Furthermore, the heat map analysis unit can also integrate the heat map data with data from different industries and applications to extract common gaze patterns. For example, it can identify common gaze patterns between entertainment and business applications. By integrating the heat map data with data from different industries and applications and extracting common gaze patterns, usability can be improved.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The UX Director System can also collect user purchase history data and include a purchase analysis unit. The purchase analysis unit, for example, analyzes the history of products purchased by a user on a website. This allows the system to understand what products the user is interested in. The purchase analysis unit can also collect purchase history data from the user's use of the app and analyze the user's purchasing patterns. For example, if a user frequently purchases products from a specific category, the system can prioritize and suggest products from that category. Furthermore, the purchase analysis unit can collect purchase history data from the user's specific operations and estimate the user's purchasing intentions. This allows the system to analyze the user's purchase history data and improve usability.

[0046] The UX Director System may also collect user location data and include a location analysis unit. The location analysis unit may collect location data, for example, when a user browses a website, and analyze the relationship between the user's physical location and digital behavior. This may allow the system to understand what content a user is interested in when in a specific location. The location analysis unit may also collect location data when a user uses an app and analyze the user's behavioral patterns. For example, if a user tends to perform a specific operation when in a specific location, the system may suggest ways to simplify that operation. Furthermore, the location analysis unit may collect location data when the user performs a specific operation and predict the user's behavior. This may allow the system to analyze the user's location data and improve usability.

[0047] The UX Director System may further include a purchase intention estimation unit that estimates the user's purchase intention. The purchase intention estimation unit may, for example, analyze behavioral data when the user is browsing a website to estimate the user's purchase intention. This makes it possible to understand when the user will have a purchase intention. The purchase intention estimation unit may also analyze behavioral data when the user is using an app to estimate the user's purchase intention. For example, if the user's purchase intention increases when performing a specific operation, the purchase intention estimation unit may make a suggestion to encourage that operation. Furthermore, the purchase intention estimation unit may analyze behavioral data when the user is performing a specific operation to predict the user's purchase intention. This makes it possible to estimate the user's purchase intention and improve usability.

[0048] The UX Director System may also collect user learning data and include a learning analysis unit. The learning analysis unit may, for example, collect learning data when a user uses an online learning platform and analyze the user's learning patterns. This may allow the system to understand the user's preferred learning methods. The learning analysis unit may also collect learning data when a user uses an app and estimate the user's learning effectiveness. For example, the learning analysis unit may analyze the user's level of understanding when using specific learning content. The learning analysis unit may also collect learning data when a user performs specific operations and estimate the user's motivation to learn. This allows the system to analyze the user's learning data and improve usability.

[0049] The UX Director System may further include a hobby and preference analysis unit that collects user interest data. The hobby and preference analysis unit collects hobby and preference data, for example, when the user browses a website, and analyzes the user's interests. This allows the system to understand the user's interests. The hobby and preference analysis unit may also collect hobby and preference data when the user uses an app, and estimate the user's interests. For example, if the user frequently browses content in a specific category, the system may preferentially suggest content in that category. The hobby and preference analysis unit may also collect hobby and preference data when the user performs a specific operation, and predict the user's interests. This allows the system to analyze the user's hobby and preference data and improve usability.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data collection unit collects user usage patterns and behavioral data. For example, it collects data such as which pages users visit, which links they click, and how much time they spend on each page. It can also collect user scrolling data, page transition data, and operation logs. For example, it records which parts users scrolled, which pages they transitioned to, which buttons they clicked, and which forms they filled out. Step 2: The analysis unit analyzes the collected data to identify usability issues. For example, if a particular page has a high rate of users abandoning the site, it can determine that there is a problem with that page. It can also analyze user behavior patterns to identify which parts are difficult to use. It can also analyze user feedback comments to identify problems. Step 3: The proposal generator generates improvement proposals based on the problems identified by the analysis unit. For example, it may make suggestions such as "simplify the navigation menu" or "change the button layout." It can also propose a more user-friendly interface based on user behavior data. It can also generate improvement proposals based on user feedback comments. Step 4: The heat map analysis unit analyzes the heat map data. For example, it generates a heat map that visually shows which parts of the page users are paying attention to. It can also identify which parts are attracting attention by analyzing the user's eye movements. It can also identify which parts are being clicked by analyzing the user's click heat map.

[0052] (Example 2) The UX Director system according to an embodiment of the present invention collects user usage patterns and behavioral data, and uses a generation AI to identify usability issues and generate improvement proposals. This allows the UX Director system to provide specific improvement proposals to improve the user experience.

[0053] The UX director system according to the embodiment includes a data collection unit, an analysis unit, a proposal generation unit, and a heat map analysis unit. The data collection unit collects user usage patterns and behavioral data. For example, it collects data such as which pages users visited, which links they clicked, and how much time they spent on each page. The data collection unit can also collect user scrolling data and page transition data. For example, it records which parts of the page users scrolled to and which pages they transitioned to. The data collection unit can also collect user operation logs. For example, it records which buttons users clicked and which forms they filled out. The analysis unit analyzes the collected data to identify usability issues. For example, if a particular page has a high user abandonment rate, it determines that the page has a problem. The analysis unit can also analyze user behavior patterns to identify which parts of the page are difficult to use. For example, if a user repeatedly performs a particular operation, it determines that the operation is not intuitive. The analysis unit can also analyze user feedback comments to identify issues. For example, if a user comments, "This page is difficult to use," it determines that the page has a problem. The proposal generation unit generates improvement proposals based on the problems identified by the analysis unit. For example, it makes proposals such as "simplify the navigation menu" or "change the button layout." The proposal generation unit can also make proposals for easier-to-use interfaces based on user behavior data. For example, it makes proposals such as "make this button more prominent" or "shorten this form." The proposal generation unit can also generate improvement proposals based on user feedback comments. For example, it makes proposals such as "provide more detailed explanations on this page" or "add this function." The heat map analysis unit analyzes heat map data. For example, it generates a heat map that visually shows which parts of the page the user is focusing on. The heat map analysis unit can also analyze the user's eye movements to identify which parts are attracting attention. For example, if the user is focusing their gaze on a specific area, it determines that that area is important.Furthermore, the heat map analysis unit can analyze the user's click heat map to identify which parts are being clicked. For example, if a user frequently clicks a particular button, it can determine that the button is important. This allows the UX director system according to the embodiment to provide specific improvement suggestions for improving the user experience. For example, it can provide an interface that users can operate intuitively, attracting their interest in the content. Furthermore, by providing specific improvement suggestions based on user behavior data, it is possible to continuously improve the UX.

[0054] The data collection unit collects the user's biometric information in addition to the user's behavioral data, and analyzes the correlation between the behavior and the biometric responses. For example, the data collection unit builds a system that collects the user's heart rate and electrodermal response while the user is browsing a website. This allows the analysis of the user's biometric responses while viewing specific content, and clarifies the correlation between the behavioral data and the biometric data. The data collection unit can also collect the user's body temperature and respiratory rate while using an app. For example, it analyzes changes in body temperature and respiratory rate when the user is feeling stressed. Furthermore, the data collection unit can collect brain wave data while the user is performing specific operations. For example, it analyzes brain wave data when the user is concentrating. In this way, by collecting the user's behavioral data and biometric information and analyzing the correlation, more detailed usability issues can be identified.

[0055] The data collection unit analyzes user behavior data in real time and provides instant feedback. The data collection unit develops a system that analyzes behavior data in real time when a user is browsing a website, for example, and provides instant feedback. For example, if a user stays on a particular page for a long time, related content is instantly suggested. The data collection unit can also analyze behavior data in real time when a user is using an app and provide instant feedback. For example, when a user is performing a specific operation, suggestions are made to improve the operation method. The data collection unit can also analyze behavior data in real time when a user is performing a specific operation and provide instant feedback. For example, if a user makes an error, the cause of the error and how to solve it are instantly presented. In this way, usability issues can be quickly improved by analyzing user behavior data in real time and providing instant feedback.

[0056] The data collection unit uses the emotion estimation function to estimate the user's emotional state from the user's behavioral data and provide personalized feedback based on the emotion. The data collection unit, for example, analyzes the user's behavioral data and builds a system that estimates the user's emotional state using the emotion estimation function. For example, if the user stays on a particular page for a long time, it is estimated that the user is interested and related content is suggested. The data collection unit can also estimate the user's emotional state using the emotion estimation function while the user is using an app and provide personalized feedback based on the emotion. For example, if the user is feeling stressed, it can suggest content that will help the user relax. Furthermore, the data collection unit can estimate the user's emotional state using the emotion estimation function while the user is performing a specific operation and provide personalized feedback based on the emotion. For example, if the user is confused, it can provide an easy-to-understand explanation of how to operate the app. In this way, usability can be improved by estimating the user's emotional state from the user's behavioral data and providing personalized feedback based on the emotion.

[0057] The data collection unit uses a wearable device to collect data on the user's physical movements and analyze the relationship between digital behavior and physical behavior. The data collection unit, for example, uses a wearable device to collect data on the user's physical movements and builds a system that analyzes the relationship between digital behavior and physical behavior. For example, the data collection unit analyzes website browsing patterns when the user is in a specific location. The data collection unit can also use the wearable device to collect physical movement data when the user is using an app and analyze the relationship between digital behavior and physical behavior. For example, the data collection unit analyzes app usage patterns when the user is in a specific location. The data collection unit can also use the wearable device to collect physical movement data when the user is performing a specific operation and analyze the relationship between digital behavior and physical behavior. For example, the data collection unit analyzes operation patterns when the user is in a specific location. In this way, usability can be improved by collecting data on the user's physical movements and analyzing the relationship between digital behavior and physical behavior.

[0058] The data collection unit integrates user behavior data across different devices and analyzes cross-device usage patterns. The data collection unit integrates user behavior data across different devices, such as smartphones, tablets, and PCs, to build a system that analyzes cross-device usage patterns. For example, it analyzes which content a user views on which device. The data collection unit can also integrate behavior data when a user uses different devices and analyze cross-device usage patterns. For example, it analyzes patterns in which a user re-views content viewed on a smartphone on a PC. Furthermore, the data collection unit can integrate behavior data across different devices when a user performs a specific operation and analyze cross-device usage patterns. For example, it analyzes patterns in which a user re-operates on a PC what they operated on a tablet. In this way, usability can be improved by integrating user behavior data across different devices and analyzing cross-device usage patterns.

[0059] The data collection unit uses the emotion estimation function to estimate the emotion of a user when performing a specific action in real time, and changes the interface according to the emotion. The data collection unit, for example, uses the emotion estimation function to estimate the emotion of a user when performing a specific action in real time, and builds a system that changes the interface according to the emotion. For example, if the user is feeling stressed, the interface is simplified. The data collection unit can also use the emotion estimation function to estimate the emotion of a user when using an app in real time, and change the interface according to the emotion. For example, if the user is excited, the interface is changed to a calm design. The data collection unit can also use the emotion estimation function to estimate the emotion of a user when performing a specific operation in real time, and change the interface according to the emotion. For example, if the user is confused, the interface is made easier to understand. In this way, usability can be improved by estimating the emotion of a user when performing a specific action in real time and changing the interface according to the emotion.

[0060] The analysis unit analyzes user behavior data in chronological order to identify problems during specific time periods or situations. The analysis unit, for example, analyzes user behavior data in chronological order to build a system that identifies usability problems during specific time periods or situations. For example, it identifies problems with pages that receive a lot of access during specific time periods. The analysis unit can also analyze user behavior data in chronological order to identify problems during specific situations. For example, it can identify problems that occur when a user uses a specific device. Furthermore, the analysis unit can analyze user behavior data in chronological order to identify problems during specific event periods. For example, it can identify problems that occur during a sale period. In this way, usability can be improved by analyzing user behavior data in chronological order to identify problems during specific time periods or situations.

[0061] The analysis unit uses the emotion estimation function to analyze the emotion a user feels when encountering a problem and generate an improvement proposal based on the emotion. The analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when encountering a problem and builds a system that generates an improvement proposal based on the emotion. For example, if the user is feeling stressed, the analysis unit makes a proposal to simplify the interface. The analysis unit can also analyze the emotion a user feels when encountering a problem and generate an improvement proposal based on the emotion. For example, if the user is confused, the analysis unit makes a proposal to explain the operation method in an easy-to-understand manner. The analysis unit can also analyze the emotion a user feels when encountering a problem and generate an improvement proposal based on the emotion. For example, if the user is excited, the analysis unit makes a proposal to change the interface to a more subdued design. In this way, usability can be improved by analyzing the emotion a user feels when encountering a problem and generating an improvement proposal based on the emotion.

[0062] The analysis unit compares the behavioral data of users from different cultural spheres or regions to identify region-specific problems. The analysis unit, for example, compares the behavioral data of users from different cultural spheres or regions to build a system that identifies region-specific usability problems. For example, the analysis unit compares the behavioral patterns of users from Asia and Europe. The analysis unit can also compare the behavioral data of users from different cultural spheres or regions to identify region-specific problems. For example, the analysis unit compares the behavioral patterns of users from North America and South America. The analysis unit can also compare the behavioral data of users from different cultural spheres or regions to identify region-specific problems. For example, the analysis unit compares the behavioral patterns of users from Japan and South Korea. In this way, by comparing the behavioral data of users from different cultural spheres or regions and identifying region-specific problems, usability can be improved.

[0063] The analysis unit references data from different industries and applications to extract common problems. The analysis unit, for example, references user behavior data from different industries and applications to build a system that extracts common usability problems. For example, it identifies common problems between e-commerce sites and education sites. The analysis unit can also reference data from different industries and applications to extract common problems. For example, it identifies common problems between the financial industry and the medical industry. The analysis unit can also reference data from different industries and applications to extract common problems. For example, it identifies common problems between entertainment and business applications. In this way, by referencing data from different industries and applications and extracting common problems, it is possible to improve usability.

[0064] The analysis unit uses the emotion estimation function to monitor the emotions of a user when he or she faces a specific problem in real time and provide an immediate improvement proposal based on the emotion. The analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotions of a user when he or she faces a specific problem in real time and provides an immediate improvement proposal. For example, if the user is feeling stressed, the analysis unit makes a suggestion to simplify the interface. The analysis unit can also monitor the emotions of a user when he or she faces a specific problem in real time and provide an immediate improvement proposal based on the emotion. For example, if the user is confused, the analysis unit makes a suggestion to explain the operation method in an easy-to-understand manner. The analysis unit can also monitor the emotions of a user when he or she faces a specific problem in real time and provide an immediate improvement proposal based on the emotion. For example, if the user is excited, the analysis unit makes a suggestion to change the interface to a calmer design. In this way, usability can be improved by monitoring the emotions of a user when he or she faces a specific problem in real time and providing an immediate improvement proposal based on the emotion.

[0065] The analysis unit analyzes user behavior data as well as user comments and reactions on social media to grasp an overall picture of engagement. The analysis unit, for example, integrates user behavior data with comments and reactions on social media to build a system that grasps an overall picture of engagement. For example, it analyzes how users react to specific content. The analysis unit can also analyze user comments and reactions on social media to grasp an overall picture of engagement. For example, it analyzes what opinions users have about specific topics. Furthermore, the analysis unit can integrate user behavior data with comments and reactions on social media to grasp an overall picture of engagement. For example, it analyzes how users react to a specific event. In this way, usability can be improved by analyzing comments and reactions on social media in addition to user behavior data to grasp an overall picture of engagement.

[0066] The analysis unit clusters user behavior data and proposes optimal content for each different user group. The analysis unit, for example, clusters user behavior data and builds a system that proposes optimal content for each different user group. For example, specific content is proposed to a user group with similar interests. The analysis unit can also cluster user behavior data and propose optimal content for each different user group. For example, specific content is proposed to a user group in a similar age group. The analysis unit can also cluster user behavior data and propose optimal content for each different user group. For example, specific content is proposed to a user group with similar usage frequency. In this way, usability can be improved by clustering user behavior data and proposing optimal content for each different user group.

[0067] The analysis unit uses the emotion estimation function to analyze the emotion a user has toward specific content and suggests content based on the emotion. The analysis unit, for example, uses the emotion estimation function to analyze the emotion a user has toward specific content and builds a system that suggests content based on the emotion. For example, it preferentially suggests content for which the user has positive emotions. The analysis unit can also analyze the emotion a user has toward specific content and suggest content based on the emotion. For example, it can suggest content to avoid content for which the user has negative emotions. The analysis unit can also analyze the emotion a user has toward specific content and suggest content based on the emotion. For example, it can preferentially suggest content that the user is excited about. In this way, usability can be improved by analyzing the emotion a user has toward specific content and suggesting content based on the emotion.

[0068] The analysis unit integrates engagement data across different devices and analyzes cross-device engagement. The analysis unit, for example, integrates engagement data across different devices and builds a system that analyzes cross-device engagement. For example, it integrates engagement data across smartphones and PCs. The analysis unit can also integrate engagement data across different devices and analyze cross-device engagement. For example, it integrates engagement data across tablets and PCs. The analysis unit can also integrate engagement data across different devices and analyze cross-device engagement. For example, it integrates engagement data across smartphones and tablets. In this way, usability can be improved by integrating engagement data across different devices and analyzing cross-device engagement.

[0069] The analysis unit uses the emotion estimation function to monitor the emotions a user has toward specific content in real time and make instant content suggestions based on the emotions. The analysis unit, for example, uses the emotion estimation function to monitor the emotions a user has toward specific content in real time and build a system that makes instant content suggestions. For example, it prioritizes suggesting content that the user enjoys. The analysis unit can also monitor the emotions a user has toward specific content in real time and make instant content suggestions based on the emotions. For example, it prioritizes suggesting content that the user is excited about. The analysis unit can also monitor the emotions a user has toward specific content in real time and make instant content suggestions based on the emotions. For example, it prioritizes suggesting content that the user has positive emotions about. In this way, usability can be improved by monitoring the emotions a user has toward specific content in real time and making instant content suggestions based on the emotions.

[0070] The heat map analysis unit collects user gaze tracking data in addition to the heat map data and analyzes the correlation between gaze movements and the heat map. The heat map analysis unit, for example, collects user gaze tracking data in addition to the heat map data and builds a system that analyzes the correlation between gaze movements and the heat map. For example, it analyzes how a user follows specific content with their eyes. The heat map analysis unit can also collect user gaze tracking data and analyze the correlation between gaze movements and the heat map. For example, if a user focuses their gaze on a specific area, it determines that the area is important. The heat map analysis unit can also collect user gaze tracking data and analyze the correlation between gaze movements and the heat map. For example, if a user frequently follows a specific button with their gaze, it determines that the button is important. Thus, by collecting user gaze tracking data in addition to the heat map data and analyzing the correlation between gaze movements and the heat map, usability can be improved.

[0071] The heat map analysis unit analyzes the heat map data over time to understand changes in the user's gaze movement. The heat map analysis unit, for example, analyzes the heat map data over time to build a system that understands changes in the user's gaze movement. For example, it analyzes which area the user focuses their gaze on during a specific time period. The heat map analysis unit can also analyze the heat map data over time to understand changes in the user's gaze movement. For example, it analyzes which area the user focuses their gaze on during a specific event period. The heat map analysis unit can also analyze the heat map data over time to understand changes in the user's gaze movement. For example, it analyzes which area the user focuses their gaze on during a specific campaign period. In this way, by analyzing the heat map data over time and understanding changes in the user's gaze movement, usability can be improved.

[0072] The heat map analysis unit uses the emotion estimation function to analyze the emotion a user feels when making specific eye movements and proposes a design based on that emotion. The heat map analysis unit, for example, uses the emotion estimation function to analyze the emotion a user feels when making specific eye movements and builds a system that proposes design based on that emotion. For example, a design that emphasizes areas where the user has positive emotions is proposed. The heat map analysis unit can also analyze the emotion a user feels when making specific eye movements and propose a design based on that emotion. For example, a design that avoids areas where the user has negative emotions is proposed. The heat map analysis unit can also analyze the emotion a user feels when making specific eye movements and propose a design based on that emotion. For example, a design that emphasizes areas where the user is excited is proposed. In this way, usability can be improved by analyzing the emotion a user feels when making specific eye movements and proposing a design based on that emotion.

[0073] The heat map analysis unit compares heat map data between different devices and analyzes differences in eye gaze movements for each device. The heat map analysis unit, for example, builds a system that compares heat map data between different devices and analyzes differences in eye gaze movements for each device. For example, it analyzes differences in eye gaze movements between smartphones and PCs. The heat map analysis unit can also compare heat map data between different devices and analyze differences in eye gaze movements for each device. For example, it analyzes differences in eye gaze movements between tablets and PCs. The heat map analysis unit can also compare heat map data between different devices and analyze differences in eye gaze movements for each device. For example, it analyzes differences in eye gaze movements between smartphones and tablets. In this way, by comparing heat map data between different devices and analyzing differences in eye gaze movements for each device, usability can be improved.

[0074] The heat map analysis unit integrates the heat map data with data from different industries and applications to extract common gaze patterns. For example, the heat map analysis unit builds a system that integrates the heat map data with data from different industries and applications to extract common gaze patterns. For example, it identifies common gaze patterns between e-commerce sites and education sites. The heat map analysis unit can also integrate the heat map data with data from different industries and applications to extract common gaze patterns. For example, it can identify common gaze patterns between the financial industry and the medical industry. Furthermore, the heat map analysis unit can also integrate the heat map data with data from different industries and applications to extract common gaze patterns. For example, it can identify common gaze patterns between entertainment and business applications. By integrating the heat map data with data from different industries and applications and extracting common gaze patterns, usability can be improved.

[0075] The heat map analysis unit uses the emotion estimation function to monitor the emotions of a user when making specific gaze movements in real time and make instant design suggestions based on the emotions. The heat map analysis unit, for example, uses the emotion estimation function to build a system that monitors the emotions of a user when making specific gaze movements in real time and makes instant design suggestions. For example, a design that emphasizes areas where the user has positive emotions can be proposed. The heat map analysis unit can also monitor the emotions of a user when making specific gaze movements in real time and make instant design suggestions based on the emotions. For example, a design that avoids areas where the user has negative emotions can be proposed. The heat map analysis unit can also monitor the emotions of a user when making specific gaze movements in real time and make instant design suggestions based on the emotions. For example, a design that emphasizes areas where the user is excited can be proposed. In this way, usability can be improved by monitoring the emotions of a user when making specific gaze movements in real time and making instant design suggestions based on the emotions.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The UX Director System can also collect user voice data and include a voice analysis unit. For example, the voice analysis unit collects voice data while a user is browsing a website and analyzes the content and tone of the user's speech. This allows the system to understand how the user is responding to specific content. The voice analysis unit can also collect voice data while the user is using an app and estimate the user's emotional state. For example, if the user is excited, the system can suggest changing the interface design to a more calming one. Furthermore, the voice analysis unit can collect voice data while the user is performing specific operations and detect the user's confusion or stress. This allows the system to analyze the user's voice data and improve usability.

[0078] The UX Director System may also include a gesture analysis unit that collects user gesture data. The gesture analysis unit may analyze, for example, hand movements and facial expressions while a user is browsing a website. This may allow the system to understand how the user is responding to specific content. The gesture analysis unit may also collect gesture data while the user is using an app and infer the user's intentions. For example, it may analyze the gestures used when the user performs a specific operation. The gesture analysis unit may also collect gesture data while the user is performing a specific operation and detect the user's confusion or stress. This allows the system to analyze user gesture data and improve usability.

[0079] The UX Director System can also collect user purchase history data and include a purchase analysis unit. The purchase analysis unit, for example, analyzes the history of products purchased by a user on a website. This allows the system to understand what products the user is interested in. The purchase analysis unit can also collect purchase history data from the user's use of the app and analyze the user's purchasing patterns. For example, if a user frequently purchases products from a specific category, the system can prioritize and suggest products from that category. Furthermore, the purchase analysis unit can collect purchase history data from the user's specific operations and estimate the user's purchasing intentions. This allows the system to analyze the user's purchase history data and improve usability.

[0080] The UX Director System may also collect user location data and include a location analysis unit. The location analysis unit may collect location data, for example, when a user browses a website, and analyze the relationship between the user's physical location and digital behavior. This may allow the system to understand what content a user is interested in when in a specific location. The location analysis unit may also collect location data when a user uses an app and analyze the user's behavioral patterns. For example, if a user tends to perform a specific operation when in a specific location, the system may suggest ways to simplify that operation. Furthermore, the location analysis unit may collect location data when the user performs a specific operation and predict the user's behavior. This may allow the system to analyze the user's location data and improve usability.

[0081] The UX Director system can also collect users' social media data and include a social media analysis unit. The social media analysis unit, for example, collects content posted by users on social media and analyzes the users' interests. This makes it possible to understand what topics the users are interested in. The social media analysis unit can also collect social media data when the user is using the app and estimate the user's emotional state. For example, if the user is feeling positive, it can suggest content to maintain that emotion. Furthermore, the social media analysis unit can collect social media data when the user is performing certain operations and detect the user's confusion or stress. This allows for the analysis of users' social media data to improve usability.

[0082] The UX Director System may further include a purchase intention estimation unit that estimates the user's purchase intention. The purchase intention estimation unit may, for example, analyze behavioral data when the user is browsing a website to estimate the user's purchase intention. This makes it possible to understand when the user will have a purchase intention. The purchase intention estimation unit may also analyze behavioral data when the user is using an app to estimate the user's purchase intention. For example, if the user's purchase intention increases when performing a specific operation, the purchase intention estimation unit may make a suggestion to encourage that operation. Furthermore, the purchase intention estimation unit may analyze behavioral data when the user is performing a specific operation to predict the user's purchase intention. This makes it possible to estimate the user's purchase intention and improve usability.

[0083] The UX Director System may also collect user learning data and include a learning analysis unit. The learning analysis unit may, for example, collect learning data when a user uses an online learning platform and analyze the user's learning patterns. This may allow the system to understand the user's preferred learning methods. The learning analysis unit may also collect learning data when a user uses an app and estimate the user's learning effectiveness. For example, the learning analysis unit may analyze the user's level of understanding when using specific learning content. The learning analysis unit may also collect learning data when a user performs specific operations and estimate the user's motivation to learn. This allows the system to analyze the user's learning data and improve usability.

[0084] The UX Director System may further collect user health data and include a health analysis unit. The health analysis unit, for example, collects health data when the user uses a wearable device and analyzes the user's health condition. This allows the user's health condition to be understood. The health analysis unit may also collect health data when the user uses an app and estimate the user's stress level. For example, if the user feels stressed when performing a specific operation, the health analysis unit may suggest ways to simplify that operation. The health analysis unit may also collect health data when the user performs a specific operation and estimate the user's fatigue level. This allows the user's health data to be analyzed and usability to be improved.

[0085] The UX Director System may further include a hobby and preference analysis unit that collects user interest data. The hobby and preference analysis unit collects hobby and preference data, for example, when the user browses a website, and analyzes the user's interests. This allows the system to understand the user's interests. The hobby and preference analysis unit may also collect hobby and preference data when the user uses an app, and estimate the user's interests. For example, if the user frequently browses content in a specific category, the system may preferentially suggest content in that category. The hobby and preference analysis unit may also collect hobby and preference data when the user performs a specific operation, and predict the user's interests. This allows the system to analyze the user's hobby and preference data and improve usability.

[0086] The UX Director System can further use the user's emotion estimation function to analyze the emotion a user has toward specific content and make content suggestions based on that emotion. For example, it can prioritize content that the user has positive emotions about. The emotion estimation function can also be used to analyze the emotion a user has toward specific content and make content suggestions based on that emotion. For example, it can make suggestions to avoid content that the user has negative emotions about. The emotion estimation function can also be used to analyze the emotion a user has toward specific content and make content suggestions based on that emotion. For example, it can prioritize content that the user is excited about. In this way, by analyzing the emotion a user has toward specific content and making content suggestions based on that emotion, usability can be improved.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The data collection unit collects user usage patterns and behavioral data. For example, it collects data such as which pages users visit, which links they click, and how much time they spend on each page. It can also collect user scrolling data, page transition data, and operation logs. For example, it records which parts users scrolled, which pages they transitioned to, which buttons they clicked, and which forms they filled out. Step 2: The analysis unit analyzes the collected data to identify usability issues. For example, if a particular page has a high rate of users abandoning the site, it can determine that there is a problem with that page. It can also analyze user behavior patterns to identify which parts are difficult to use. It can also analyze user feedback comments to identify problems. Step 3: The proposal generator generates improvement proposals based on the problems identified by the analysis unit. For example, it may make suggestions such as "simplify the navigation menu" or "change the button layout." It can also propose a more user-friendly interface based on user behavior data. It can also generate improvement proposals based on user feedback comments. Step 4: The heat map analysis unit analyzes the heat map data. For example, it generates a heat map that visually shows which parts of the page users are paying attention to. It can also identify which parts are attracting attention by analyzing the user's eye movements. It can also identify which parts are being clicked by analyzing the user's click heat map.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[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 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.

[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. 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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. [Explanation of symbols]

[0156] 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 data collection unit that collects user usage pattern or behavior data; an analysis unit that analyzes the usage patterns and behavioral data of the users collected by the data collection unit to identify usability problems; a proposal generation unit that generates an improvement proposal based on the usability problems identified by the analysis unit; a heat map analysis unit that analyzes the heat map data; A system characterized by:

2. The data collection unit The system of claim 1, wherein a wearable device is used to collect physical movement data of the user and analyze the relationship between digital behavior and physical behavior.

3. The analysis unit 2. The system according to claim 1, wherein in addition to the user behavior data, feedback comments are analyzed using natural language processing to identify the problem from multiple angles.

4. The analysis unit The system of claim 1, wherein in addition to the user's behavioral data, the system analyzes the user's statements or reactions on social media to understand an overall picture of engagement.

5. The heat map analysis unit The system of claim 1 , further comprising: collecting eye-tracking data of the user in addition to the heat map data; and analyzing the correlation between eye movements and the heat map.

6. The data collection unit The system of claim 1 , further comprising: an emotion estimation function for estimating an emotional state of the user from the behavioral data and providing personalized feedback based on the emotion.

Images