system

Abstract

We provide the system. [Solution] Error log collection method, User behavior data analysis method, A term definition generation means using natural language processing means, Dynamic layout change proposal means, A means of displaying specialized terminology used in payment operations as visual information, A means of notifying the user when an error occurs and providing a solution, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention aims to improve the usability for users with respect to a plurality of information providing sites scattered within an enterprise or an organization, and particularly aims to solve the confusion of users due to access errors and excessive use of technical terms. Further, in order to efficiently manage and operate these sites with limited personnel and resources, an object is to provide a system capable of improving the usage experience while reducing the labor.

Means for Solving the Problems

[0005] This invention includes an error log collection means for collecting and analyzing error logs to identify problems. Furthermore, it includes a data analysis means for analyzing user behavior data to reduce user confusion on pages. It also includes a terminology explanation generation means that utilizes natural language processing to support the understanding of technical terms, thereby aiding user comprehension. Moreover, it provides means for dynamically proposing layout changes based on the analysis results of user behavior data and error logs, thereby presenting improvement suggestions to site administrators. Finally, it provides means for providing specific resolution guides when errors occur, supporting users in resolving problems immediately.

[0006] An "error log collection device" is a component that has the function of recording error information such as broken links and access permission errors that occur within the system and saving it to a database.

[0007] A "user behavior data analysis tool" is a component that analyzes user behavior data such as the number of clicks and time spent on a page, and has the function of identifying user navigation patterns and problems.

[0008] A "term explanation generation means using natural language processing means" is a component that has the function of automatically generating tooltips and explanatory texts using natural language processing to understand specialized terminology used within a company and providing them to the user.

[0009] The "dynamic layout change proposal mechanism" is a component that automatically generates layout modification proposals to improve usability based on the analysis results of user behavior data and error logs, and proposes them to administrators.

[0010] An "error resolution guide provisioning mechanism" is a component that has the function of analyzing the cause of an access error when it occurs and generating and providing specific resolution procedures and guides so that users can quickly resolve the problem. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0015] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] In implementing this invention, the server first uses an error log collection means to collect and record various error information that occurs when users use the company's portal site or help pages. This information is stored in a database and used for later analysis. The terminal also collects user behavior data in real time and sends information such as the number of clicks and time spent on pages to the server. The server analyzes this information to identify where users tend to get lost and which pages require improvement.

[0033] Based on the generated analysis data, the server uses natural language processing to provide supplementary information for understanding internal jargon. Specifically, it automatically generates tooltips for difficult terms on the page, and the terminal displays them visually to the user. This function enables users to smoothly understand and confirm information.

[0034] Furthermore, the server generates suggestions for dynamically changing the interface layout based on this data and error logs. These suggestions aim to improve usability and are communicated to the administrator. The administrator utilizes these suggestions to adjust the site layout and provide the optimal environment for end users.

[0035] As a concrete example, suppose a user accesses the help page of a project management tool. In this case, the device records the visit history and click patterns of the page being viewed, and if a broken link or permission error occurs, it sends that information to the server. The server analyzes this information and configures itself to automatically explain terms that the user deems difficult to understand. Furthermore, if it determines that more users than usual are confused on the same page, it dynamically generates suggestions for improving the layout and notifies the administrator. The administrator evaluates the suggestions and updates the page layout as needed to improve user satisfaction.

[0036] In this way, the present invention provides a method for assisting in the understanding of technical terms, supporting the rapid resolution of errors, and efficiently managing a site with limited resources.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The device collects behavioral data in real time, such as the number of clicks, page transitions, and time spent on the site, while the user is using the company's internal portal site. This information is sent to the server as data packets.

[0040] Step 2:

[0041] The server stores the received behavioral data in a database and starts the data analysis process. This analysis specifically identifies points on the page where users are likely to get lost and frequently occurring behavioral patterns.

[0042] Step 3:

[0043] If a user encounters a link error or access permission error on a help page, the device records the error information and immediately sends it to the server as error log data.

[0044] Step 4:

[0045] The server analyzes the collected error logs to identify areas where problems occur frequently. This reveals areas where improvements are needed on specific pages or features.

[0046] Step 5:

[0047] The server applies natural language processing algorithms to learn the technical terms used by the user on the page and generates tooltips to make them easier to understand. This information is provided to the device when the page is displayed, as needed.

[0048] Step 6:

[0049] Based on instructions from the server, the terminal displays tooltips to the user related to technical terms that may be difficult to understand on the page they are viewing. This makes it easier for the user to understand the content of the page.

[0050] Step 7:

[0051] The server generates suggestions for dynamically improving the site layout based on user behavior data and error log analysis results. This optimization suggestion is then communicated to the administrator.

[0052] Step 8:

[0053] The administrator receives layout improvement suggestions from the server and evaluates their validity. If necessary, the suggestions are adopted and the site layout is updated to improve usability.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] Errors and technical jargon that users encounter when using websites and systems degrade the user experience. Furthermore, while efficiently identifying these problems and proposing improvements is necessary to enhance usability, current methods are insufficient to address this issue.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for collecting and recording error information, means for analyzing user behavior data and identifying problems, and means for generating auxiliary information for terminology using natural language processing. This enables the rapid resolution of errors encountered by users, support for understanding technical terms, and dynamic interface improvement suggestions for improved usability.

[0059] "Error information" refers to detailed data about abnormalities or malfunctions that occur while using a system or service.

[0060] "User behavior data" refers to data that records user interactions, such as their operation history, click patterns, and time spent on a site.

[0061] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language.

[0062] "Supplementary information" refers to additional explanations or explanatory information provided to make things easier for the user to understand.

[0063] A "proposal to dynamically change the layout" is an improvement suggestion to optimize the interface based on user actions and behavioral patterns.

[0064] An "interface improvement proposal" is a suggestion to modify the layout and functionality of a system in order to improve its usability and ease of use.

[0065] This invention is based on an information processing system in which a server handles the primary processing and interacts with terminals. First, the server uses error log collection means to collect error information that occurs while a user is using a website or software, and records it in a database. This data is later used for analysis to improve the user experience.

[0066] Next, the device collects user behavior data, particularly information such as click counts and page dwell time, and sends this data to the server in real time. The server uses advanced algorithms, including natural language processing, to analyze this data. This analysis allows the server to identify areas where users are experiencing difficulties on specific pages or features, and to pinpoint which technical terms are difficult to understand.

[0067] The server uses natural language processing to generate auxiliary information to help understand technical terms, and automatically creates this information as tooltips. The terminal then displays these tooltips to the user when they hover over the relevant terms, thereby aiding user understanding.

[0068] Furthermore, the server generates dynamic interface improvement suggestions based on collected error and behavioral data. These suggestions aim to improve usability and are communicated to administrators. Administrators review the suggestions and improve the interface layout and usability as needed.

[0069] As a concrete example, consider a scenario where a user accesses the help page of a project management tool. In this case, the terminal collects the user's operation history and promptly reports any problems, such as broken links or permission errors, to the server. The server analyzes this information and, if it determines that the term "project management" is difficult to understand, generates a tooltip providing an explanation related to that term.

[0070] Examples of prompts include: "Identify terms that users are confused about in the project management tool and generate visual aids," or "Analyze the error logs and create suggestions for interface improvements to enhance usability."

[0071] This invention provides a method for efficiently improving interfaces to enhance the user experience by using a generative AI model.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The server uses error logging to collect and record error information in real time that occurs while users are using the website or software. The input is the error event caused by user actions, and the output is an error log containing metadata such as the time of the error, type, and user ID. Specifically, when the server detects an error, it quickly saves the information to a database for later analysis.

[0075] Step 2:

[0076] The device monitors user behavior data in real time and sends details such as click counts, page transitions, and time spent on a page to the server. The input is the user's operation history, and the output is a log of organized behavioral data. The device tracks user actions in real time, recording in detail which links the user clicks and which pages they spend time on.

[0077] Step 3:

[0078] The server receives the collected error logs and behavioral data. The input is the error logs and behavioral data obtained in the previous step, and the output is the analysis results. The server analyzes the data using a generative AI model to identify which pages are confusing to users and which technical terms are difficult to understand. This process uses natural language processing and pattern recognition techniques to automatically extract problematic pages and terms.

[0079] Step 4:

[0080] Based on the analysis results, the server generates supplementary information for difficult terms and sends this information to the terminal for display as a tooltip. The input is the difficult term identified in the analysis step, and the output is an explanatory text related to that term. Specifically, the server uses natural language processing to generate a definition of the term and configures it to be displayed when the user mouses over it.

[0081] Step 5:

[0082] The server generates dynamic interface improvement suggestions based on the analyzed data and notifies the administrator. The input is analysis results based on error data and behavioral data, and the output is specific layout change proposals aimed at improving usability. The server generates suggestions for button placement changes and navigation menu improvements to enhance usability, and the administrator can adjust the website interface based on these suggestions.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] There is a need to improve the usability of electronic payments. In particular, the rapid resolution of errors and insufficient support for understanding terminology are factors that cause confusion among users. The problem is that conventional systems do not provide efficient solutions to these issues.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes error log collection means, user behavior data analysis means, terminology explanation generation means using natural language processing means, dynamic layout change suggestion means, means for displaying specialized terminology in payment operations as visual information, and means for notifying the user when an error occurs and presenting a solution. This makes it possible to quickly collect and analyze errors that occur during electronic payments, provide prompt guidance to the user, and support them in understanding terminology.

[0088] An "error log collection system" is a system that has the function of recording errors that occur during electronic payments and saving them in a database.

[0089] A "user behavior data analysis system" is a system that identifies problems and patterns faced by users by collecting and analyzing their operation history and behavioral data.

[0090] "Terminology explanation generation means using natural language processing means" refers to a technology that analyzes technical terms, automatically generates their meanings and explanations, and provides them to users.

[0091] A "dynamic layout change proposal means" is a means of analyzing the usage status of a user interface and proposing layout improvement plans to enhance usability.

[0092] "Means for displaying specialized terminology in payment operations as visual information" refers to an interface function that visually explains the specialized terminology used on the payment screen in a way that is easy for users to understand.

[0093] "A means of notifying the user and providing solutions when an error occurs" refers to a system that immediately informs the user of an error when it occurs and provides appropriate solutions.

[0094] To implement this invention, a server and terminal included in an electronic payment system are used. First, the server uses an error log collection means to record error information that occurs during the electronic payment process. This allows detailed data regarding the frequency and type of errors to be stored in a database. The server analyzes this data and utilizes a user behavior data analysis means to identify the situations in which users are likely to encounter errors.

[0095] Next, natural language processing is used to analyze the technical terms displayed on the payment screen. The meaning of these terms is automatically generated and can be displayed on the terminal as visual information, such as a tooltip. This makes it easier for the user to understand the technical terms and allows the payment process to proceed smoothly.

[0096] Furthermore, the server uses a dynamic layout change suggestion mechanism to generate layout improvement proposals for screens that confuse many users. This allows system administrators to instantly optimize the user interface. In the event of an error, a mechanism is activated to notify the user and present possible solutions, enabling users to quickly resolve problems.

[0097] As a concrete example, consider a scenario where a user attempts to purchase goods online using a credit card. If the user makes a mistake entering the security code, the terminal immediately notifies the server of the error. Based on the error log, the server then presents the user with a common solution to the error. Furthermore, in response to the user's question, such as "What is a security code?", a tooltip explanation is immediately displayed using natural language processing, allowing the user to resolve their question on the spot.

[0098] An example of a prompt message to a generative AI model would be, "What explanation should be given to the user in response to a payment error 'Invalid security code'?" The model could then generate an appropriate response.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server receives error information from the terminal using an error log collection mechanism. The input is error information that occurs during electronic payment. The server stores this information in a database for later analysis. This data includes information such as the type of error, the time of occurrence, and the user ID.

[0102] Step 2:

[0103] The server analyzes user behavior data to identify behavioral patterns. The input is user operation logs (e.g., click patterns, page transition history). The server uses data analysis algorithms to analyze the situations in which users are most likely to encounter problems, and generates a report of these trends as output.

[0104] Step 3:

[0105] The server uses natural language processing to generate explanations for technical terms on the payment screen. The input is a list of technical terms. The server uses a natural language processing engine to generate explanatory text for each term and sends the listed explanations to the terminal as output. This allows the user to review the information presented as a tooltip.

[0106] Step 4:

[0107] When a user encounters an error on the payment screen, the server provides a solution based on the error log and analysis results. The input consists of error information and analysis data. The server references similar past error cases from the database and generates the corresponding guidance as output to notify the user of the solution.

[0108] Step 5:

[0109] The server activates a dynamic layout change suggestion mechanism to generate suggestions for improving the user interface. The input consists of user behavior analysis data and error logs. The server analyzes this data and notifies the service administrator of UI change suggestions as output. This allows the administrator to quickly optimize the UI.

[0110] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0111] This invention combines an information processing system aimed at improving usability with an emotion engine that takes user emotions into account, and describes its embodiments. The server collects error logs that occur when users use the company's internal portal site and stores them in a database. This stored information is used to identify problems faced by users and to analyze the error logs.

[0112] The device continuously collects user behavioral data, such as the number of clicks and time spent on a page, and sends this data to the server. The server uses this data to analyze points of user confusion and difficulty in understanding parts of the page and its operation. To begin with, to help users understand technical terms, the device generates tooltips explaining terms using natural language processing, and presents these to the user, thereby improving user convenience.

[0113] The emotion engine analyzes and recognizes user emotions based on data obtained from user input and actions. Specifically, it detects in real time if a user is experiencing frustration or anxiety during operation. The emotion engine works in conjunction with the server to generate customized feedback and support information based on the user's emotional data, and displays support messages to the user. Furthermore, it dynamically adjusts the interface design and user interaction according to the emotional state, providing a more comfortable operating environment.

[0114] For example, when a user uses a new project management tool for the first time, the emotion engine can determine if the user is experiencing stress based on their input speed and the frequency of errors. In this case, the server automatically generates messages to alleviate the user's emotions, and the terminal presents tools that gently guide the user. As a result, the workflow becomes smoother, and the overall user experience is improved.

[0115] This system allows users to comfortably navigate the site even with limited technical knowledge, and enables flexible responses tailored to user emotions. This, in turn, allows for the efficient use of internal resources and enhances the site's effectiveness.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The device collects operational data such as keyboard shortcuts entered by the user while using the portal site, mouse movements, and click patterns, and transmits this data to the server in real time.

[0119] Step 2:

[0120] The server analyzes the operation data received from the terminal and monitors the user's operation speed and error frequency. Based on this, the emotion engine determines how much stress or confusion the user is experiencing during a particular operation.

[0121] Step 3:

[0122] The emotion engine infers the user's emotional state based on the analysis results, and if it detects that stress levels are high, it notifies the server. Upon receiving this notification, the server generates an appropriate support message.

[0123] Step 4:

[0124] The server sends the generated support message to the terminal, including concise explanations and navigation related to operations or technical terms that are particularly difficult to understand.

[0125] Step 5:

[0126] The device displays pop-up tooltips that help the user understand the next steps they should take. These displays are designed with user-centric pacing and visual elements in mind, optimizing the user experience.

[0127] Step 6:

[0128] Users can complete tasks efficiently and comfortably by following the device's instructions and continuing operations through a stress-free interface. This allows users to work in a more relaxed state.

[0129] (Example 2)

[0130] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0131] In information processing systems, it is essential to effectively support users when they encounter complex operations or technical jargon, while taking their emotions into consideration. However, conventional systems have struggled to accurately recognize user emotions and provide appropriate feedback and support. This has led to a decline in user experience and efficiency problems.

[0132] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0133] In this invention, the server includes error data collection means, human behavior information analysis means, term suggestion generation means using natural language processing means, emotion analysis and feedback generation means, and dynamic interface adjustment suggestion means. This makes it possible to analyze the user's emotions during operation, provide appropriate support information, and improve usability.

[0134] An "error data collection means" is a function in an information processing system that acquires and records error information that occurs during user operation.

[0135] A "human behavior information analysis tool" is a function that analyzes user behavior data such as the number of clicks and time spent on a page to identify user behavior patterns and difficulties.

[0136] "Terminology generation means using natural language processing means" refers to a function that uses a generative AI model to generate explanations of technical terms and functions and present them to the user.

[0137] "Emotion analysis and feedback generation means" refers to a function that analyzes emotions from user actions and input data, and generates support information and feedback corresponding to those emotions.

[0138] The "dynamic interface adjustment suggestion means" is a function that makes suggestions for automatically adjusting the interface design and interactions based on the user's usage situation and emotions.

[0139] A description of the embodiment for carrying out the invention will be provided.

[0140] This system incorporates an emotion engine that considers user emotions into an information processing system designed to improve usability. A specific embodiment is shown below.

[0141] The server collects error data and manages it in a database. Specifically, it automatically monitors errors that occur while users are operating the company's internal portal site and saves this information to a storage device such as a relational database. Analysis tools such as Python's Pandas are used to analyze the collected error log data. This provides clues for identifying problems and resolving errors.

[0142] The device collects user behavioral data in real time, such as the number of clicks and time spent on a page, and sends it to the server. Using the collected data, the server analyzes problems the user is experiencing. To address areas where the user frequently makes mistakes or lacks understanding of technical terms, the device uses a generative AI model to generate natural language processing explanations of terms and displays them to the user as tooltips.

[0143] The emotion engine analyzes the user's emotional state based on data obtained from user input and actions. If the server determines that the user is feeling frustrated or anxious during the process, it automatically generates a feedback message. This allows the user to receive support tailored to their situation, thereby reducing stress.

[0144] For example, when a user uses a new project management tool, the emotion engine detects from the user's operation data that they may be experiencing stress. In this case, the server uses a generative AI model to generate a support message, such as "Please suggest a gentle way to guide me through using the new project management tool," and the terminal displays this message to the user.

[0145] This system allows users to navigate the site more comfortably without specialized knowledge and provides appropriate support tailored to their emotions, thereby improving the overall user experience.

[0146] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0147] Step 1:

[0148] The server collects error logs in real time that occur while users are interacting with the company's internal portal site. The input includes error messages and timestamps generated from user actions. This information is stored in a database, providing a foundation for later analysis and problem-solving. The output is structured error data stored in a memory device.

[0149] Step 2:

[0150] The device collects user behavioral data such as the number of clicks and time spent on pages. It takes user actions as input and periodically sends them to the server. Through data transmission, raw data is output that can be used to identify user movements and trends.

[0151] Step 3:

[0152] The server analyzes the collected user behavior data. Using analysis software such as Pandas, it formats the input data to determine which screens users spend the most time on and where click errors are most frequent. The output provides an identification of the problems users are most likely to encounter.

[0153] Step 4:

[0154] The terminal generates tooltips using a generative AI model for technical terms and new features on the screen, and displays them to the user. It receives explanatory text generated from the server as input and presents it visually on the user's screen. As output, it provides an environment that is easier for the user to understand.

[0155] Step 5:

[0156] The emotion engine analyzes the user's emotional state based on their operation speed and frequency of errors. Using this as input, it performs a function to detect signs of stress and anxiety in real time. As output, a report on the user's emotional state is sent to the server.

[0157] Step 6:

[0158] The server generates customized feedback messages based on analysis results from the emotion engine. Using a generation AI model, it automatically generates optimal support content from the input emotion data. As output, a direct support message is sent to the user's device.

[0159] (Application Example 2)

[0160] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0161] This invention aims to improve user convenience in information processing systems. Existing systems often rely on mechanical support for errors and difficult operations, failing to adequately consider the user's psychological state. This increases the likelihood of users experiencing stress and confusion. In particular, it can discourage users unfamiliar with the technology from continuing to use the system. A solution to this problem is needed.

[0162] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0163] In this invention, the server includes error log collection means, user behavior data analysis means, and emotion recognition processing means. This makes it possible to recognize the user's emotional state in real time during operation and to provide support messages and interface adjustments accordingly.

[0164] An "error log collection method" is a means of automatically collecting error information that occurs in a system and using it for databases and analysis processing.

[0165] "User behavior data analysis methods" are means of analyzing user behavior data such as the number of clicks and time spent on pages in order to identify user operation patterns and problems.

[0166] A "term explanation generation method using natural language processing" is a means for generating and presenting explanations of technical terms to users using natural language processing technology, in order to aid in the understanding of technical terms.

[0167] A "dynamic layout change suggestion method" is a means of suggesting adjustments to the interface design and layout according to the user's operation status and emotional state.

[0168] An "error resolution guide provision method" is a means of providing solutions and guides for errors that users encounter, and supporting them in resolving those problems.

[0169] An "emotion recognition processing means" is a means for analyzing and recognizing a user's psychological state and emotions based on their operations and input.

[0170] A "means for generating support messages based on the user's emotional state" refers to a means of displaying appropriate feedback and support messages on the system, taking into account the user's emotions.

[0171] "Means for adjusting the interface in accordance with the user's psychological state" refers to means of dynamically adjusting the interface to reflect the user's emotional state and provide a more comfortable operating environment.

[0172] An embodiment of the present invention is an information processing system that takes into account the user's emotional state. This system has the function of collecting and analyzing user behavior data and error logs on a server. The server records error logs generated within the system in a database using an error log collection means, and identifies the user's problems based on the analysis results. In addition, a user behavior data analysis means is used to analyze which operations are causing the user confusion.

[0173] The server is equipped with emotion recognition processing means to recognize the user's emotional state, thereby enabling real-time understanding of the user's psychological state. Terminology explanation generation means using natural language processing means presents terminology explanations to support user understanding. Furthermore, there is a means to generate support messages based on the user's emotional state, providing personalized feedback. Interface adjustment means that respond to the user's psychological state dynamically changes the operating environment, reducing stress.

[0174] As a concrete example of how users interact with the system, if a user experiences difficulty while operating new software, the emotion recognition processing mechanism detects the user's stress level. As a result, the server generates a message suggesting relaxing music, leading to a more comfortable user experience. Furthermore, as an example of a prompt message for the generating AI model, it is possible to use instructions such as, "Create an AI model that analyzes the emotions from the voice of a user (female, in her 30s) and suggests appropriate actions."

[0175] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0176] Step 1:

[0177] When a user interacts with the system, the terminal collects behavioral data in real time, such as the number of clicks and time spent on pages. This data is sent to the server as input for analyzing user behavior patterns and identifying problems. As output, data showing the user's behavioral characteristics is generated.

[0178] Step 2:

[0179] The server performs user behavior data analysis using the received behavioral data. This analysis diagnoses which operations the user is confused about through data calculations. Based on the behavioral data received as input, it outputs the operations where the user is experiencing difficulty. This output data provides information necessary for subsequent processing.

[0180] Step 3:

[0181] The server uses an error log collection mechanism to record and analyze error logs in a database. The error logs are used as input to identify problems during operation, and the type and frequency of errors are determined based on the analysis results. The output shows the priority of the problems and their frequent locations.

[0182] Step 4:

[0183] The server uses emotion recognition processing to recognize the user's psychological state from their recent actions and voice input. It uses user action data and voice data as input, analyzing, for example, changes in voice tone and input speed. This allows it to identify whether the user is experiencing stress and output the result.

[0184] Step 5:

[0185] The server generates support messages based on the user's emotional state. A generative AI model is used, taking emotion-based behavioral and situational data as input. The output provides customized feedback and recommended action messages that take the user's emotions into account.

[0186] Step 6:

[0187] The server generates tooltips for technical terms as needed using a terminology explanation generation system that employs natural language processing. It takes the content of the page the user is viewing as input and uses natural language processing to output easy-to-understand explanations of the terms.

[0188] Step 7:

[0189] The device adjusts its interface according to the user's psychological state. It inputs the user's emotional state and behavioral data, and dynamically changes the interface design and layout to mitigate the impact on the user's psychology. For example, it adopts stress-reducing colors and layouts, and outputs them in a way that allows for smooth operation for the user.

[0190] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0191] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0192] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0193] [Second Embodiment]

[0194] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0195] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0196] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0197] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0198] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0199] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0200] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0201] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0202] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0203] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0204] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0205] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0206] In implementing this invention, the server first uses an error log collection means to collect and record various error information that occurs when users use the company's portal site or help pages. This information is stored in a database and used for later analysis. The terminal also collects user behavior data in real time and sends information such as the number of clicks and time spent on pages to the server. The server analyzes this information to identify where users tend to get lost and which pages require improvement.

[0207] Based on the generated analysis data, the server uses natural language processing to provide supplementary information for understanding internal jargon. Specifically, it automatically generates tooltips for difficult terms on the page, and the terminal displays them visually to the user. This function enables users to smoothly understand and confirm information.

[0208] Furthermore, the server generates suggestions for dynamically changing the interface layout based on this data and error logs. These suggestions aim to improve usability and are communicated to the administrator. The administrator utilizes these suggestions to adjust the site layout and provide the optimal environment for end users.

[0209] As a concrete example, suppose a user accesses the help page of a project management tool. In this case, the device records the visit history and click patterns of the page being viewed, and if a broken link or permission error occurs, it sends that information to the server. The server analyzes this information and configures itself to automatically explain terms that the user deems difficult to understand. Furthermore, if it determines that more users than usual are confused on the same page, it dynamically generates suggestions for improving the layout and notifies the administrator. The administrator evaluates the suggestions and updates the page layout as needed to improve user satisfaction.

[0210] In this way, the present invention provides a method for assisting in the understanding of technical terms, supporting the rapid resolution of errors, and efficiently managing a site with limited resources.

[0211] The following describes the processing flow.

[0212] Step 1:

[0213] The device collects behavioral data in real time, such as the number of clicks, page transitions, and time spent on the site, while the user is using the company's internal portal site. This information is sent to the server as data packets.

[0214] Step 2:

[0215] The server stores the received behavioral data in a database and starts the data analysis process. This analysis specifically identifies points on the page where users are likely to get lost and frequently occurring behavioral patterns.

[0216] Step 3:

[0217] If a user encounters a link error or access permission error on a help page, the device records the error information and immediately sends it to the server as error log data.

[0218] Step 4:

[0219] The server analyzes the collected error logs to identify areas where problems occur frequently. This reveals areas where improvements are needed on specific pages or features.

[0220] Step 5:

[0221] The server applies natural language processing algorithms to learn the technical terms used by the user on the page and generates tooltips to make them easier to understand. This information is provided to the device when the page is displayed, as needed.

[0222] Step 6:

[0223] Based on instructions from the server, the terminal displays tooltips to the user related to technical terms that may be difficult to understand on the page they are viewing. This makes it easier for the user to understand the content of the page.

[0224] Step 7:

[0225] The server generates suggestions for dynamically improving the site layout based on user behavior data and error log analysis results. This optimization suggestion is then communicated to the administrator.

[0226] Step 8:

[0227] The administrator receives layout improvement suggestions from the server and evaluates their validity. If necessary, the suggestions are adopted and the site layout is updated to improve usability.

[0228] (Example 1)

[0229] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] Errors and technical jargon that users encounter when using websites and systems degrade the user experience. Furthermore, while efficiently identifying these problems and proposing improvements is necessary to enhance usability, current methods are insufficient to address this issue.

[0231] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0232] In this invention, the server includes means for collecting and recording error information, means for analyzing user behavior data and identifying problems, and means for generating auxiliary information for terminology using natural language processing. This enables the rapid resolution of errors encountered by users, support for understanding technical terms, and dynamic interface improvement suggestions for improved usability.

[0233] "Error information" refers to detailed data about abnormalities or malfunctions that occur while using a system or service.

[0234] "User behavior data" refers to data that records user interactions, such as their operation history, click patterns, and time spent on a site.

[0235] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language.

[0236] "Supplementary information" refers to additional explanations or explanatory information provided to make things easier for the user to understand.

[0237] A "proposal to dynamically change the layout" is an improvement suggestion to optimize the interface based on user actions and behavioral patterns.

[0238] An "interface improvement proposal" is a suggestion to modify the layout and functionality of a system in order to improve its usability and ease of use.

[0239] This invention is based on an information processing system in which a server handles the primary processing and interacts with terminals. First, the server uses error log collection means to collect error information that occurs while a user is using a website or software, and records it in a database. This data is later used for analysis to improve the user experience.

[0240] Next, the device collects user behavior data, particularly information such as click counts and page dwell time, and sends this data to the server in real time. The server uses advanced algorithms, including natural language processing, to analyze this data. This analysis allows the server to identify areas where users are experiencing difficulties on specific pages or features, and to pinpoint which technical terms are difficult to understand.

[0241] The server uses natural language processing to generate auxiliary information to help understand technical terms, and automatically creates this information as tooltips. The terminal then displays these tooltips to the user when they hover over the relevant terms, thereby aiding user understanding.

[0242] Furthermore, the server generates dynamic interface improvement suggestions based on collected error and behavioral data. These suggestions aim to improve usability and are communicated to administrators. Administrators review the suggestions and improve the interface layout and usability as needed.

[0243] As a concrete example, consider a scenario where a user accesses the help page of a project management tool. In this case, the terminal collects the user's operation history and promptly reports any problems, such as broken links or permission errors, to the server. The server analyzes this information and, if it determines that the term "project management" is difficult to understand, generates a tooltip providing an explanation related to that term.

[0244] Examples of prompts include: "Identify terms that users are confused about in the project management tool and generate visual aids," or "Analyze the error logs and create suggestions for interface improvements to enhance usability."

[0245] This invention provides a method for efficiently improving interfaces to enhance the user experience by using a generative AI model.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The server uses error logging to collect and record error information in real time that occurs while users are using the website or software. The input is the error event caused by user actions, and the output is an error log containing metadata such as the time of the error, type, and user ID. Specifically, when the server detects an error, it quickly saves the information to a database for later analysis.

[0249] Step 2:

[0250] The device monitors user behavior data in real time and sends details such as click counts, page transitions, and time spent on a page to the server. The input is the user's operation history, and the output is a log of organized behavioral data. The device tracks user actions in real time, recording in detail which links the user clicks and which pages they spend time on.

[0251] Step 3:

[0252] The server receives the collected error logs and behavioral data. The input is the error logs and behavioral data obtained in the previous step, and the output is the analysis results. The server analyzes the data using a generative AI model to identify which pages are confusing to users and which technical terms are difficult to understand. This process uses natural language processing and pattern recognition techniques to automatically extract problematic pages and terms.

[0253] Step 4:

[0254] Based on the analysis results, the server generates supplementary information for difficult terms and sends this information to the terminal for display as a tooltip. The input is the difficult term identified in the analysis step, and the output is an explanatory text related to that term. Specifically, the server uses natural language processing to generate a definition of the term and configures it to be displayed when the user mouses over it.

[0255] Step 5:

[0256] The server generates dynamic interface improvement suggestions based on the analyzed data and notifies the administrator. The input is analysis results based on error data and behavioral data, and the output is specific layout change proposals aimed at improving usability. The server generates suggestions for button placement changes and navigation menu improvements to enhance usability, and the administrator can adjust the website interface based on these suggestions.

[0257] (Application Example 1)

[0258] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0259] There is a need to improve the usability of electronic payments. In particular, the rapid resolution of errors and insufficient support for understanding terminology are factors that cause confusion among users. The problem is that conventional systems do not provide efficient solutions to these issues.

[0260] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0261] In this invention, the server includes error log collection means, user behavior data analysis means, terminology explanation generation means using natural language processing means, dynamic layout change suggestion means, means for displaying specialized terminology in payment operations as visual information, and means for notifying the user when an error occurs and presenting a solution. This makes it possible to quickly collect and analyze errors that occur during electronic payments, provide prompt guidance to the user, and support them in understanding terminology.

[0262] An "error log collection system" is a system that has the function of recording errors that occur during electronic payments and saving them in a database.

[0263] A "user behavior data analysis system" is a system that identifies problems and patterns faced by users by collecting and analyzing their operation history and behavioral data.

[0264] "Terminology explanation generation means using natural language processing means" refers to a technology that analyzes technical terms, automatically generates their meanings and explanations, and provides them to users.

[0265] A "dynamic layout change proposal means" is a means of analyzing the usage status of a user interface and proposing layout improvement plans to enhance usability.

[0266] "Means for displaying specialized terminology in payment operations as visual information" refers to an interface function that visually explains the specialized terminology used on the payment screen in a way that is easy for users to understand.

[0267] "A means of notifying the user and providing solutions when an error occurs" refers to a system that immediately informs the user of an error when it occurs and provides appropriate solutions.

[0268] To implement this invention, a server and terminal included in an electronic payment system are used. First, the server uses an error log collection means to record error information that occurs during the electronic payment process. This allows detailed data regarding the frequency and type of errors to be stored in a database. The server analyzes this data and utilizes a user behavior data analysis means to identify the situations in which users are likely to encounter errors.

[0269] Next, natural language processing is used to analyze the technical terms displayed on the payment screen. The meaning of these terms is automatically generated and can be displayed on the terminal as visual information, such as a tooltip. This makes it easier for the user to understand the technical terms and allows the payment process to proceed smoothly.

[0270] Furthermore, the server uses a dynamic layout change suggestion mechanism to generate layout improvement proposals for screens that confuse many users. This allows system administrators to instantly optimize the user interface. In the event of an error, a mechanism is activated to notify the user and present possible solutions, enabling users to quickly resolve problems.

[0271] As a concrete example, consider a scenario where a user attempts to purchase goods online using a credit card. If the user makes a mistake entering the security code, the terminal immediately notifies the server of the error. Based on the error log, the server then presents the user with a common solution to the error. Furthermore, in response to the user's question, such as "What is a security code?", a tooltip explanation is immediately displayed using natural language processing, allowing the user to resolve their question on the spot.

[0272] An example of a prompt message to a generative AI model would be, "What explanation should be given to the user in response to a payment error 'Invalid security code'?" The model could then generate an appropriate response.

[0273] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0274] Step 1:

[0275] The server receives error information from the terminal using an error log collection mechanism. The input is error information that occurs during electronic payment. The server stores this information in a database for later analysis. This data includes information such as the type of error, the time of occurrence, and the user ID.

[0276] Step 2:

[0277] The server analyzes user behavior data to identify behavior patterns. The input is the user's operation log (e.g., click pattern, page transition history). The server uses a data analysis algorithm to analyze in what situations the user is likely to face problems, and generates a report on that tendency as the output.

[0278] Step 3:

[0279] The server uses natural language processing means to generate explanations for the technical terms on the payment screen. The input is a list of technical terms. The server uses a natural language processing engine to generate an explanatory sentence for each term, and transmits the list of explanatory sentences as the output to the terminal. Thereby, the user can check the information presented as a tooltip.

[0280] Step 4:

[0281] When the user encounters an error on the payment screen, the server presents a solution based on the error log and analysis results. The input is error information and analysis data. The server refers to similar past error cases from the database and generates the corresponding guidance as the output to notify the user of the solution.

[0282] Step 5:

[0283] The server activates the dynamic layout change proposal means to generate improvement proposals for the user interface. The input is user behavior analysis data and error logs. The server analyzes these and notifies the service administrator of the UI change proposals as the output. Thereby, the administrator can quickly optimize the UI.

[0284] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0285] The present invention is a form that combines an emotion engine that takes into account the emotions of users in an information processing system aiming to improve user usability, and its embodiments will be described. The server collects error logs generated while the user is using the company's portal site and stores them in a database. This stored information is used to identify the problems faced by the user and to analyze the error logs.

[0286] The terminal continuously collects behavioral data such as the number of clicks and page stay time of the user, and transmits this data to the server. The server uses this data to analyze the confusing points and difficult-to-understand parts of the user in terms of pages and operations. First, in order to assist in the understanding of technical terms, a tooltip for term explanation is generated by natural language processing means, and the terminal presents this to the user to improve user convenience.

[0287] The emotion engine has a function of analyzing and recognizing the emotions of the user based on the data obtained from the user's input and operations. Specifically, when the user feels irritation or anxiety during operation, the emotion state is detected in real time. The emotion engine cooperates with the server to generate customized feedback and support information based on the user's emotion data, and displays a support message to the user. Furthermore, according to the emotion state, the design of the interface and user interaction are dynamically adjusted to provide a more comfortable operation environment.

[0288] For example, when the user first uses a new project management tool, the emotion engine judges from the user's input speed and the frequency of incorrect operations that the user may be feeling stressed. At this time, the server automatically generates a message to soothe the emotion, and the terminal presents a tool to gently guide the user. As a result, the operation flow can be made smoother and the overall user experience can be improved.

[0289] This system allows users to comfortably navigate the site even with limited technical knowledge, and enables flexible responses tailored to user emotions. This, in turn, allows for the efficient use of internal resources and enhances the site's effectiveness.

[0290] The following describes the processing flow.

[0291] Step 1:

[0292] The device collects operational data such as keyboard shortcuts entered by the user while using the portal site, mouse movements, and click patterns, and transmits this data to the server in real time.

[0293] Step 2:

[0294] The server analyzes the operation data received from the terminal and monitors the user's operation speed and error frequency. Based on this, the emotion engine determines how much stress or confusion the user is experiencing during a particular operation.

[0295] Step 3:

[0296] The emotion engine infers the user's emotional state based on the analysis results, and if it detects that stress levels are high, it notifies the server. Upon receiving this notification, the server generates an appropriate support message.

[0297] Step 4:

[0298] The server sends the generated support message to the terminal, including concise explanations and navigation related to operations or technical terms that are particularly difficult to understand.

[0299] Step 5:

[0300] The device displays pop-up tooltips that help the user understand the next steps they should take. These displays are designed with user-centric pacing and visual elements in mind, optimizing the user experience.

[0301] Step 6:

[0302] The user can efficiently and comfortably complete the task by following the instructions of the terminal and continuing the operation through an interface that is less likely to cause stress. As a result, the user can proceed with the work in a more relaxed state.

[0303] (Example 2)

[0304] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0305] In an information processing system, when a user faces complex operations or technical terms, it is required to effectively support the user while considering the user's feelings. However, in the conventional system, it has been difficult to accurately recognize the user's feelings and provide appropriate feedback or support. As a result, there have been problems such as a decline in the user experience and efficiency.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0307] In this invention, the server includes an error data collection means, a human behavior information analysis means, a term presentation generation means using natural language processing means, an emotion analysis and feedback generation means, and a dynamic interface adjustment proposal means. Thereby, it is possible to analyze the emotion during the user's operation, provide accurate support information, and improve the operability.

[0308] The "error data collection means" is a function for obtaining and recording error information generated during the operation of the user in the information processing system.

[0309] A "human behavior information analysis tool" is a function that analyzes user behavior data such as the number of clicks and time spent on a page to identify user behavior patterns and difficulties.

[0310] "Terminology generation means using natural language processing means" refers to a function that uses a generative AI model to generate explanations of technical terms and functions and present them to the user.

[0311] "Emotion analysis and feedback generation means" refers to a function that analyzes emotions from user actions and input data, and generates support information and feedback corresponding to those emotions.

[0312] The "dynamic interface adjustment suggestion means" is a function that makes suggestions for automatically adjusting the interface design and interactions based on the user's usage situation and emotions.

[0313] A description of the embodiment for carrying out the invention will be provided.

[0314] This system incorporates an emotion engine that considers user emotions into an information processing system designed to improve usability. A specific embodiment is shown below.

[0315] The server collects error data and manages it in a database. Specifically, it automatically monitors errors that occur while users are operating the company's internal portal site and saves this information to a storage device such as a relational database. Analysis tools such as Python's Pandas are used to analyze the collected error log data. This provides clues for identifying problems and resolving errors.

[0316] The device collects user behavioral data in real time, such as the number of clicks and time spent on a page, and sends it to the server. Using the collected data, the server analyzes problems the user is experiencing. To address areas where the user frequently makes mistakes or lacks understanding of technical terms, the device uses a generative AI model to generate natural language processing explanations of terms and displays them to the user as tooltips.

[0317] The emotion engine analyzes the user's emotional state based on data obtained from user input and actions. If the server determines that the user is feeling frustrated or anxious during the process, it automatically generates a feedback message. This allows the user to receive support tailored to their situation, thereby reducing stress.

[0318] For example, when a user uses a new project management tool, the emotion engine detects from the user's operation data that they may be experiencing stress. In this case, the server uses a generative AI model to generate a support message, such as "Please suggest a gentle way to guide me through using the new project management tool," and the terminal displays this message to the user.

[0319] This system allows users to navigate the site more comfortably without specialized knowledge and provides appropriate support tailored to their emotions, thereby improving the overall user experience.

[0320] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0321] Step 1:

[0322] The server collects error logs in real time that occur while users are interacting with the company's internal portal site. The input includes error messages and timestamps generated from user actions. This information is stored in a database, providing a foundation for later analysis and problem-solving. The output is structured error data stored in a memory device.

[0323] Step 2:

[0324] The device collects user behavioral data such as the number of clicks and time spent on pages. It takes user actions as input and periodically sends them to the server. Through data transmission, raw data is output that can be used to identify user movements and trends.

[0325] Step 3:

[0326] The server analyzes the collected user behavior data. Using analysis software such as Pandas, it formats the input data to determine which screens users spend the most time on and where click errors are most frequent. The output provides an identification of the problems users are most likely to encounter.

[0327] Step 4:

[0328] The terminal generates tooltips using a generative AI model for technical terms and new features on the screen, and displays them to the user. It receives explanatory text generated from the server as input and presents it visually on the user's screen. As output, it provides an environment that is easier for the user to understand.

[0329] Step 5:

[0330] The emotion engine analyzes the user's emotional state based on their operation speed and frequency of errors. Using this as input, it performs a function to detect signs of stress and anxiety in real time. As output, a report on the user's emotional state is sent to the server.

[0331] Step 6:

[0332] The server generates customized feedback messages based on analysis results from the emotion engine. Using a generation AI model, it automatically generates optimal support content from the input emotion data. As output, a direct support message is sent to the user's device.

[0333] (Application Example 2)

[0334] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0335] This invention aims to improve user convenience in information processing systems. Existing systems often rely on mechanical support for errors and difficult operations, failing to adequately consider the user's psychological state. This increases the likelihood of users experiencing stress and confusion. In particular, it can discourage users unfamiliar with the technology from continuing to use the system. A solution to this problem is needed.

[0336] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0337] In this invention, the server includes error log collection means, user behavior data analysis means, and emotion recognition processing means. This makes it possible to recognize the user's emotional state in real time during operation and to provide support messages and interface adjustments accordingly.

[0338] An "error log collection method" is a means of automatically collecting error information that occurs in a system and using it for databases and analysis processing.

[0339] "User behavior data analysis methods" are means of analyzing user behavior data such as the number of clicks and time spent on pages in order to identify user operation patterns and problems.

[0340] A "term explanation generation method using natural language processing" is a means for generating and presenting explanations of technical terms to users using natural language processing technology, in order to aid in the understanding of technical terms.

[0341] A "dynamic layout change suggestion method" is a means of suggesting adjustments to the interface design and layout according to the user's operation status and emotional state.

[0342] An "error resolution guide provision method" is a means of providing solutions and guides for errors that users encounter, and supporting them in resolving those problems.

[0343] An "emotion recognition processing means" is a means for analyzing and recognizing a user's psychological state and emotions based on their operations and input.

[0344] A "means for generating support messages based on the user's emotional state" refers to a means of displaying appropriate feedback and support messages on the system, taking into account the user's emotions.

[0345] "Means for adjusting the interface in accordance with the user's psychological state" refers to means of dynamically adjusting the interface to reflect the user's emotional state and provide a more comfortable operating environment.

[0346] An embodiment of the present invention is an information processing system that takes into account the user's emotional state. This system has the function of collecting and analyzing user behavior data and error logs on a server. The server records error logs generated within the system in a database using an error log collection means, and identifies the user's problems based on the analysis results. In addition, a user behavior data analysis means is used to analyze which operations are causing the user confusion.

[0347] The server is equipped with emotion recognition processing means to recognize the user's emotional state, thereby enabling real-time understanding of the user's psychological state. Terminology explanation generation means using natural language processing means presents terminology explanations to support user understanding. Furthermore, there is a means to generate support messages based on the user's emotional state, providing personalized feedback. Interface adjustment means that respond to the user's psychological state dynamically changes the operating environment, reducing stress.

[0348] As a concrete example of how users interact with the system, if a user experiences difficulty while operating new software, the emotion recognition processing mechanism detects the user's stress level. As a result, the server generates a message suggesting relaxing music, leading to a more comfortable user experience. Furthermore, as an example of a prompt message for the generating AI model, it is possible to use instructions such as, "Create an AI model that analyzes the emotions from the voice of a user (female, in her 30s) and suggests appropriate actions."

[0349] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0350] Step 1:

[0351] When a user interacts with the system, the terminal collects behavioral data in real time, such as the number of clicks and time spent on pages. This data is sent to the server as input for analyzing user behavior patterns and identifying problems. As output, data showing the user's behavioral characteristics is generated.

[0352] Step 2:

[0353] The server performs user behavior data analysis using the received behavioral data. This analysis diagnoses which operations the user is confused about through data calculations. Based on the behavioral data received as input, it outputs the operations where the user is experiencing difficulty. This output data provides information necessary for subsequent processing.

[0354] Step 3:

[0355] The server uses an error log collection mechanism to record and analyze error logs in a database. The error logs are used as input to identify problems during operation, and the type and frequency of errors are determined based on the analysis results. The output shows the priority of the problems and their frequent locations.

[0356] Step 4:

[0357] The server uses emotion recognition processing to recognize the user's psychological state from their recent actions and voice input. It uses user action data and voice data as input, analyzing, for example, changes in voice tone and input speed. This allows it to identify whether the user is experiencing stress and output the result.

[0358] Step 5:

[0359] The server generates support messages based on the user's emotional state. A generative AI model is used, taking emotion-based behavioral and situational data as input. The output provides customized feedback and recommended action messages that take the user's emotions into account.

[0360] Step 6:

[0361] The server generates tooltips for technical terms as needed using a terminology explanation generation system that employs natural language processing. It takes the content of the page the user is viewing as input and uses natural language processing to output easy-to-understand explanations of the terms.

[0362] Step 7:

[0363] The device adjusts its interface according to the user's psychological state. It inputs the user's emotional state and behavioral data, and dynamically changes the interface design and layout to mitigate the impact on the user's psychology. For example, it adopts stress-reducing colors and layouts, and outputs them in a way that allows for smooth operation for the user.

[0364] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0365] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0366] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0367] [Third Embodiment]

[0368] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0369] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0370] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0371] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0372] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0373] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0374] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0375] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0376] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0377] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0378] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0379] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0380] In implementing this invention, the server first uses an error log collection means to collect and record various error information that occurs when users use the company's portal site or help pages. This information is stored in a database and used for later analysis. The terminal also collects user behavior data in real time and sends information such as the number of clicks and time spent on pages to the server. The server analyzes this information to identify where users tend to get lost and which pages require improvement.

[0381] Based on the generated analysis data, the server uses natural language processing to provide supplementary information for understanding internal jargon. Specifically, it automatically generates tooltips for difficult terms on the page, and the terminal displays them visually to the user. This function enables users to smoothly understand and confirm information.

[0382] Furthermore, the server generates suggestions for dynamically changing the interface layout based on this data and error logs. These suggestions aim to improve usability and are communicated to the administrator. The administrator utilizes these suggestions to adjust the site layout and provide the optimal environment for end users.

[0383] As a concrete example, suppose a user accesses the help page of a project management tool. In this case, the device records the visit history and click patterns of the page being viewed, and if a broken link or permission error occurs, it sends that information to the server. The server analyzes this information and configures itself to automatically explain terms that the user deems difficult to understand. Furthermore, if it determines that more users than usual are confused on the same page, it dynamically generates suggestions for improving the layout and notifies the administrator. The administrator evaluates the suggestions and updates the page layout as needed to improve user satisfaction.

[0384] In this way, the present invention provides a method for assisting in the understanding of technical terms, supporting the rapid resolution of errors, and efficiently managing a site with limited resources.

[0385] The following describes the processing flow.

[0386] Step 1:

[0387] The device collects behavioral data in real time, such as the number of clicks, page transitions, and time spent on the site, while the user is using the company's internal portal site. This information is sent to the server as data packets.

[0388] Step 2:

[0389] The server stores the received behavioral data in a database and starts the data analysis process. This analysis specifically identifies points on the page where users are likely to get lost and frequently occurring behavioral patterns.

[0390] Step 3:

[0391] If a user encounters a link error or access permission error on a help page, the device records the error information and immediately sends it to the server as error log data.

[0392] Step 4:

[0393] The server analyzes the collected error logs to identify areas where problems occur frequently. This reveals areas where improvements are needed on specific pages or features.

[0394] Step 5:

[0395] The server applies natural language processing algorithms to learn the technical terms used by the user on the page and generates tooltips to make them easier to understand. This information is provided to the device when the page is displayed, as needed.

[0396] Step 6:

[0397] Based on instructions from the server, the terminal displays tooltips to the user related to technical terms that may be difficult to understand on the page they are viewing. This makes it easier for the user to understand the content of the page.

[0398] Step 7:

[0399] The server generates suggestions for dynamically improving the site layout based on user behavior data and error log analysis results. This optimization suggestion is then communicated to the administrator.

[0400] Step 8:

[0401] The administrator receives layout improvement suggestions from the server and evaluates their validity. If necessary, the suggestions are adopted and the site layout is updated to improve usability.

[0402] (Example 1)

[0403] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0404] Errors and technical jargon that users encounter when using websites and systems degrade the user experience. Furthermore, while efficiently identifying these problems and proposing improvements is necessary to enhance usability, current methods are insufficient to address this issue.

[0405] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0406] In this invention, the server includes means for collecting and recording error information, means for analyzing user behavior data and identifying problems, and means for generating auxiliary information for terminology using natural language processing. This enables the rapid resolution of errors encountered by users, support for understanding technical terms, and dynamic interface improvement suggestions for improved usability.

[0407] "Error information" refers to detailed data about abnormalities or malfunctions that occur while using a system or service.

[0408] "User behavior data" refers to data that records user interactions, such as their operation history, click patterns, and time spent on a site.

[0409] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language.

[0410] "Supplementary information" refers to additional explanations or explanatory information provided to make things easier for the user to understand.

[0411] A "proposal to dynamically change the layout" is an improvement suggestion to optimize the interface based on user actions and behavioral patterns.

[0412] An "interface improvement proposal" is a suggestion to modify the layout and functionality of a system in order to improve its usability and ease of use.

[0413] This invention is based on an information processing system in which a server handles the primary processing and interacts with terminals. First, the server uses error log collection means to collect error information that occurs while a user is using a website or software, and records it in a database. This data is later used for analysis to improve the user experience.

[0414] Next, the device collects user behavior data, particularly information such as click counts and page dwell time, and sends this data to the server in real time. The server uses advanced algorithms, including natural language processing, to analyze this data. This analysis allows the server to identify areas where users are experiencing difficulties on specific pages or features, and to pinpoint which technical terms are difficult to understand.

[0415] The server uses natural language processing to generate auxiliary information to help understand technical terms, and automatically creates this information as tooltips. The terminal then displays these tooltips to the user when they hover over the relevant terms, thereby aiding user understanding.

[0416] Furthermore, the server generates dynamic interface improvement suggestions based on collected error and behavioral data. These suggestions aim to improve usability and are communicated to administrators. Administrators review the suggestions and improve the interface layout and usability as needed.

[0417] As a concrete example, consider a scenario where a user accesses the help page of a project management tool. In this case, the terminal collects the user's operation history and promptly reports any problems, such as broken links or permission errors, to the server. The server analyzes this information and, if it determines that the term "project management" is difficult to understand, generates a tooltip providing an explanation related to that term.

[0418] Examples of prompts include: "Identify terms that users are confused about in the project management tool and generate visual aids," or "Analyze the error logs and create suggestions for interface improvements to enhance usability."

[0419] This invention provides a method for efficiently improving interfaces to enhance the user experience by using a generative AI model.

[0420] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0421] Step 1:

[0422] The server uses error logging to collect and record error information in real time that occurs while users are using the website or software. The input is the error event caused by user actions, and the output is an error log containing metadata such as the time of the error, type, and user ID. Specifically, when the server detects an error, it quickly saves the information to a database for later analysis.

[0423] Step 2:

[0424] The device monitors user behavior data in real time and sends details such as click counts, page transitions, and time spent on a page to the server. The input is the user's operation history, and the output is a log of organized behavioral data. The device tracks user actions in real time, recording in detail which links the user clicks and which pages they spend time on.

[0425] Step 3:

[0426] The server receives the collected error logs and behavioral data. The input is the error logs and behavioral data obtained in the previous step, and the output is the analysis results. The server analyzes the data using a generative AI model to identify which pages are confusing to users and which technical terms are difficult to understand. This process uses natural language processing and pattern recognition techniques to automatically extract problematic pages and terms.

[0427] Step 4:

[0428] Based on the analysis results, the server generates supplementary information for difficult terms and sends this information to the terminal for display as a tooltip. The input is the difficult term identified in the analysis step, and the output is an explanatory text related to that term. Specifically, the server uses natural language processing to generate a definition of the term and configures it to be displayed when the user mouses over it.

[0429] Step 5:

[0430] The server generates dynamic interface improvement suggestions based on the analyzed data and notifies the administrator. The input is analysis results based on error data and behavioral data, and the output is specific layout change proposals aimed at improving usability. The server generates suggestions for button placement changes and navigation menu improvements to enhance usability, and the administrator can adjust the website interface based on these suggestions.

[0431] (Application Example 1)

[0432] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0433] There is a need to improve the usability of electronic payments. In particular, the rapid resolution of errors and insufficient support for understanding terminology are factors that cause confusion among users. The problem is that conventional systems do not provide efficient solutions to these issues.

[0434] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0435] In this invention, the server includes error log collection means, user behavior data analysis means, terminology explanation generation means using natural language processing means, dynamic layout change suggestion means, means for displaying specialized terminology in payment operations as visual information, and means for notifying the user when an error occurs and presenting a solution. This makes it possible to quickly collect and analyze errors that occur during electronic payments, provide prompt guidance to the user, and support them in understanding terminology.

[0436] An "error log collection system" is a system that has the function of recording errors that occur during electronic payments and saving them in a database.

[0437] A "user behavior data analysis system" is a system that identifies problems and patterns faced by users by collecting and analyzing their operation history and behavioral data.

[0438] "Terminology explanation generation means using natural language processing means" refers to a technology that analyzes technical terms, automatically generates their meanings and explanations, and provides them to users.

[0439] A "dynamic layout change proposal means" is a means of analyzing the usage status of a user interface and proposing layout improvement plans to enhance usability.

[0440] "Means for displaying specialized terminology in payment operations as visual information" refers to an interface function that visually explains the specialized terminology used on the payment screen in a way that is easy for users to understand.

[0441] "A means of notifying the user and providing solutions when an error occurs" refers to a system that immediately informs the user of an error when it occurs and provides appropriate solutions.

[0442] To implement this invention, a server and terminal included in an electronic payment system are used. First, the server uses an error log collection means to record error information that occurs during the electronic payment process. This allows detailed data regarding the frequency and type of errors to be stored in a database. The server analyzes this data and utilizes a user behavior data analysis means to identify the situations in which users are likely to encounter errors.

[0443] Next, natural language processing is used to analyze the technical terms displayed on the payment screen. The meaning of these terms is automatically generated and can be displayed on the terminal as visual information, such as a tooltip. This makes it easier for the user to understand the technical terms and allows the payment process to proceed smoothly.

[0444] Furthermore, the server uses a dynamic layout change suggestion mechanism to generate layout improvement proposals for screens that confuse many users. This allows system administrators to instantly optimize the user interface. In the event of an error, a mechanism is activated to notify the user and present possible solutions, enabling users to quickly resolve problems.

[0445] As a concrete example, consider a scenario where a user attempts to purchase goods online using a credit card. If the user makes a mistake entering the security code, the terminal immediately notifies the server of the error. Based on the error log, the server then presents the user with a common solution to the error. Furthermore, in response to the user's question, such as "What is a security code?", a tooltip explanation is immediately displayed using natural language processing, allowing the user to resolve their question on the spot.

[0446] An example of a prompt message to a generative AI model would be, "What explanation should be given to the user in response to a payment error 'Invalid security code'?" The model could then generate an appropriate response.

[0447] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0448] Step 1:

[0449] The server receives error information from the terminal using an error log collection mechanism. The input is error information that occurs during electronic payment. The server stores this information in a database for later analysis. This data includes information such as the type of error, the time of occurrence, and the user ID.

[0450] Step 2:

[0451] The server analyzes user behavior data to identify behavioral patterns. The input is user operation logs (e.g., click patterns, page transition history). The server uses data analysis algorithms to analyze the situations in which users are most likely to encounter problems, and generates a report of these trends as output.

[0452] Step 3:

[0453] The server uses natural language processing to generate explanations for technical terms on the payment screen. The input is a list of technical terms. The server uses a natural language processing engine to generate explanatory text for each term and sends the listed explanations to the terminal as output. This allows the user to review the information presented as a tooltip.

[0454] Step 4:

[0455] When a user encounters an error on the payment screen, the server provides a solution based on the error log and analysis results. The input consists of error information and analysis data. The server references similar past error cases from the database and generates the corresponding guidance as output to notify the user of the solution.

[0456] Step 5:

[0457] The server activates a dynamic layout change suggestion mechanism to generate suggestions for improving the user interface. The input consists of user behavior analysis data and error logs. The server analyzes this data and notifies the service administrator of UI change suggestions as output. This allows the administrator to quickly optimize the UI.

[0458] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0459] This invention combines an information processing system aimed at improving usability with an emotion engine that takes user emotions into account, and describes its embodiments. The server collects error logs that occur when users use the company's internal portal site and stores them in a database. This stored information is used to identify problems faced by users and to analyze the error logs.

[0460] The device continuously collects user behavioral data, such as the number of clicks and time spent on a page, and sends this data to the server. The server uses this data to analyze points of user confusion and difficulty in understanding parts of the page and its operation. To begin with, to help users understand technical terms, the device generates tooltips explaining terms using natural language processing, and presents these to the user, thereby improving user convenience.

[0461] The emotion engine analyzes and recognizes user emotions based on data obtained from user input and actions. Specifically, it detects in real time if a user is experiencing frustration or anxiety during operation. The emotion engine works in conjunction with the server to generate customized feedback and support information based on the user's emotional data, and displays support messages to the user. Furthermore, it dynamically adjusts the interface design and user interaction according to the emotional state, providing a more comfortable operating environment.

[0462] For example, when a user uses a new project management tool for the first time, the emotion engine can determine if the user is experiencing stress based on their input speed and the frequency of errors. In this case, the server automatically generates messages to alleviate the user's emotions, and the terminal presents tools that gently guide the user. As a result, the workflow becomes smoother, and the overall user experience is improved.

[0463] This system allows users to comfortably navigate the site even with limited technical knowledge, and enables flexible responses tailored to user emotions. This, in turn, allows for the efficient use of internal resources and enhances the site's effectiveness.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The device collects operational data such as keyboard shortcuts entered by the user while using the portal site, mouse movements, and click patterns, and transmits this data to the server in real time.

[0467] Step 2:

[0468] The server analyzes the operation data received from the terminal and monitors the user's operation speed and error frequency. Based on this, the emotion engine determines how much stress or confusion the user is experiencing during a particular operation.

[0469] Step 3:

[0470] The emotion engine infers the user's emotional state based on the analysis results, and if it detects that stress levels are high, it notifies the server. Upon receiving this notification, the server generates an appropriate support message.

[0471] Step 4:

[0472] The server sends the generated support message to the terminal, including concise explanations and navigation related to operations or technical terms that are particularly difficult to understand.

[0473] Step 5:

[0474] The device displays pop-up tooltips that help the user understand the next steps they should take. These displays are designed with user-centric pacing and visual elements in mind, optimizing the user experience.

[0475] Step 6:

[0476] Users can complete tasks efficiently and comfortably by following the device's instructions and continuing operations through a stress-free interface. This allows users to work in a more relaxed state.

[0477] (Example 2)

[0478] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0479] In information processing systems, it is essential to effectively support users when they encounter complex operations or technical jargon, while taking their emotions into consideration. However, conventional systems have struggled to accurately recognize user emotions and provide appropriate feedback and support. This has led to a decline in user experience and efficiency problems.

[0480] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0481] In this invention, the server includes error data collection means, human behavior information analysis means, term suggestion generation means using natural language processing means, emotion analysis and feedback generation means, and dynamic interface adjustment suggestion means. This makes it possible to analyze the user's emotions during operation, provide appropriate support information, and improve usability.

[0482] An "error data collection means" is a function in an information processing system that acquires and records error information that occurs during user operation.

[0483] A "human behavior information analysis tool" is a function that analyzes user behavior data such as the number of clicks and time spent on a page to identify user behavior patterns and difficulties.

[0484] "Terminology generation means using natural language processing means" refers to a function that uses a generative AI model to generate explanations of technical terms and functions and present them to the user.

[0485] "Emotion analysis and feedback generation means" refers to a function that analyzes emotions from user actions and input data, and generates support information and feedback corresponding to those emotions.

[0486] The "dynamic interface adjustment suggestion means" is a function that makes suggestions for automatically adjusting the interface design and interactions based on the user's usage situation and emotions.

[0487] A description of the embodiment for carrying out the invention will be provided.

[0488] This system incorporates an emotion engine that considers user emotions into an information processing system designed to improve usability. A specific embodiment is shown below.

[0489] The server collects error data and manages it in a database. Specifically, it automatically monitors errors that occur while users are operating the company's internal portal site and saves this information to a storage device such as a relational database. Analysis tools such as Python's Pandas are used to analyze the collected error log data. This provides clues for identifying problems and resolving errors.

[0490] The device collects user behavioral data in real time, such as the number of clicks and time spent on a page, and sends it to the server. Using the collected data, the server analyzes problems the user is experiencing. To address areas where the user frequently makes mistakes or lacks understanding of technical terms, the device uses a generative AI model to generate natural language processing explanations of terms and displays them to the user as tooltips.

[0491] The emotion engine analyzes the user's emotional state based on data obtained from user input and actions. If the server determines that the user is feeling frustrated or anxious during the process, it automatically generates a feedback message. This allows the user to receive support tailored to their situation, thereby reducing stress.

[0492] For example, when a user uses a new project management tool, the emotion engine detects from the user's operation data that they may be experiencing stress. In this case, the server uses a generative AI model to generate a support message, such as "Please suggest a gentle way to guide me through using the new project management tool," and the terminal displays this message to the user.

[0493] This system allows users to navigate the site more comfortably without specialized knowledge and provides appropriate support tailored to their emotions, thereby improving the overall user experience.

[0494] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0495] Step 1:

[0496] The server collects error logs in real time that occur while users are interacting with the company's internal portal site. The input includes error messages and timestamps generated from user actions. This information is stored in a database, providing a foundation for later analysis and problem-solving. The output is structured error data stored in a memory device.

[0497] Step 2:

[0498] The device collects user behavioral data such as the number of clicks and time spent on pages. It takes user actions as input and periodically sends them to the server. Through data transmission, raw data is output that can be used to identify user movements and trends.

[0499] Step 3:

[0500] The server analyzes the collected user behavior data. Using analysis software such as Pandas, it formats the input data to determine which screens users spend the most time on and where click errors are most frequent. The output provides an identification of the problems users are most likely to encounter.

[0501] Step 4:

[0502] The terminal generates tooltips using a generative AI model for technical terms and new features on the screen, and displays them to the user. It receives explanatory text generated from the server as input and presents it visually on the user's screen. As output, it provides an environment that is easier for the user to understand.

[0503] Step 5:

[0504] The emotion engine analyzes the user's emotional state based on their operation speed and frequency of errors. Using this as input, it performs a function to detect signs of stress and anxiety in real time. As output, a report on the user's emotional state is sent to the server.

[0505] Step 6:

[0506] The server generates customized feedback messages based on analysis results from the emotion engine. Using a generation AI model, it automatically generates optimal support content from the input emotion data. As output, a direct support message is sent to the user's device.

[0507] (Application Example 2)

[0508] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0509] This invention aims to improve user convenience in information processing systems. Existing systems often rely on mechanical support for errors and difficult operations, failing to adequately consider the user's psychological state. This increases the likelihood of users experiencing stress and confusion. In particular, it can discourage users unfamiliar with the technology from continuing to use the system. A solution to this problem is needed.

[0510] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0511] In this invention, the server includes error log collection means, user behavior data analysis means, and emotion recognition processing means. This makes it possible to recognize the user's emotional state in real time during operation and to provide support messages and interface adjustments accordingly.

[0512] An "error log collection method" is a means of automatically collecting error information that occurs in a system and using it for databases and analysis processing.

[0513] "User behavior data analysis methods" are means of analyzing user behavior data such as the number of clicks and time spent on pages in order to identify user operation patterns and problems.

[0514] A "term explanation generation method using natural language processing" is a means for generating and presenting explanations of technical terms to users using natural language processing technology, in order to aid in the understanding of technical terms.

[0515] A "dynamic layout change suggestion method" is a means of suggesting adjustments to the interface design and layout according to the user's operation status and emotional state.

[0516] An "error resolution guide provision method" is a means of providing solutions and guides for errors that users encounter, and supporting them in resolving those problems.

[0517] An "emotion recognition processing means" is a means for analyzing and recognizing a user's psychological state and emotions based on their operations and input.

[0518] A "means for generating support messages based on the user's emotional state" refers to a means of displaying appropriate feedback and support messages on the system, taking into account the user's emotions.

[0519] "Means for adjusting the interface in accordance with the user's psychological state" refers to means of dynamically adjusting the interface to reflect the user's emotional state and provide a more comfortable operating environment.

[0520] An embodiment of the present invention is an information processing system that takes into account the user's emotional state. This system has the function of collecting and analyzing user behavior data and error logs on a server. The server records error logs generated within the system in a database using an error log collection means, and identifies the user's problems based on the analysis results. In addition, a user behavior data analysis means is used to analyze which operations are causing the user confusion.

[0521] The server is equipped with emotion recognition processing means to recognize the user's emotional state, thereby enabling real-time understanding of the user's psychological state. Terminology explanation generation means using natural language processing means presents terminology explanations to support user understanding. Furthermore, there is a means to generate support messages based on the user's emotional state, providing personalized feedback. Interface adjustment means that respond to the user's psychological state dynamically changes the operating environment, reducing stress.

[0522] As a concrete example of how users interact with the system, if a user experiences difficulty while operating new software, the emotion recognition processing mechanism detects the user's stress level. As a result, the server generates a message suggesting relaxing music, leading to a more comfortable user experience. Furthermore, as an example of a prompt message for the generating AI model, it is possible to use instructions such as, "Create an AI model that analyzes the emotions from the voice of a user (female, in her 30s) and suggests appropriate actions."

[0523] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0524] Step 1:

[0525] When a user interacts with the system, the terminal collects behavioral data in real time, such as the number of clicks and time spent on pages. This data is sent to the server as input for analyzing user behavior patterns and identifying problems. As output, data showing the user's behavioral characteristics is generated.

[0526] Step 2:

[0527] The server performs user behavior data analysis using the received behavioral data. This analysis diagnoses which operations the user is confused about through data calculations. Based on the behavioral data received as input, it outputs the operations where the user is experiencing difficulty. This output data provides information necessary for subsequent processing.

[0528] Step 3:

[0529] The server uses an error log collection mechanism to record and analyze error logs in a database. The error logs are used as input to identify problems during operation, and the type and frequency of errors are determined based on the analysis results. The output shows the priority of the problems and their frequent locations.

[0530] Step 4:

[0531] The server uses emotion recognition processing to recognize the user's psychological state from their recent actions and voice input. It uses user action data and voice data as input, analyzing, for example, changes in voice tone and input speed. This allows it to identify whether the user is experiencing stress and output the result.

[0532] Step 5:

[0533] The server generates support messages based on the user's emotional state. A generative AI model is used, taking emotion-based behavioral and situational data as input. The output provides customized feedback and recommended action messages that take the user's emotions into account.

[0534] Step 6:

[0535] The server generates tooltips for technical terms as needed using a terminology explanation generation system that employs natural language processing. It takes the content of the page the user is viewing as input and uses natural language processing to output easy-to-understand explanations of the terms.

[0536] Step 7:

[0537] The device adjusts its interface according to the user's psychological state. It inputs the user's emotional state and behavioral data, and dynamically changes the interface design and layout to mitigate the impact on the user's psychology. For example, it adopts stress-reducing colors and layouts, and outputs them in a way that allows for smooth operation for the user.

[0538] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0539] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0540] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0541] [Fourth Embodiment]

[0542] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0543] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0544] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0545] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0546] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0547] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0548] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0549] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0550] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0551] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0552] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0553] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0554] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0555] In implementing this invention, the server first uses an error log collection means to collect and record various error information that occurs when users use the company's portal site or help pages. This information is stored in a database and used for later analysis. The terminal also collects user behavior data in real time and sends information such as the number of clicks and time spent on pages to the server. The server analyzes this information to identify where users tend to get lost and which pages require improvement.

[0556] Based on the generated analysis data, the server uses natural language processing to provide supplementary information for understanding internal jargon. Specifically, it automatically generates tooltips for difficult terms on the page, and the terminal displays them visually to the user. This function enables users to smoothly understand and confirm information.

[0557] Furthermore, the server generates suggestions for dynamically changing the interface layout based on this data and error logs. These suggestions aim to improve usability and are communicated to the administrator. The administrator utilizes these suggestions to adjust the site layout and provide the optimal environment for end users.

[0558] As a concrete example, suppose a user accesses the help page of a project management tool. In this case, the device records the visit history and click patterns of the page being viewed, and if a broken link or permission error occurs, it sends that information to the server. The server analyzes this information and configures itself to automatically explain terms that the user deems difficult to understand. Furthermore, if it determines that more users than usual are confused on the same page, it dynamically generates suggestions for improving the layout and notifies the administrator. The administrator evaluates the suggestions and updates the page layout as needed to improve user satisfaction.

[0559] In this way, the present invention provides a method for assisting in the understanding of technical terms, supporting the rapid resolution of errors, and efficiently managing a site with limited resources.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] The device collects behavioral data in real time, such as the number of clicks, page transitions, and time spent on the site, while the user is using the company's internal portal site. This information is sent to the server as data packets.

[0563] Step 2:

[0564] The server stores the received behavioral data in a database and starts the data analysis process. This analysis specifically identifies points on the page where users are likely to get lost and frequently occurring behavioral patterns.

[0565] Step 3:

[0566] If a user encounters a link error or access permission error on a help page, the device records the error information and immediately sends it to the server as error log data.

[0567] Step 4:

[0568] The server analyzes the collected error logs to identify areas where problems occur frequently. This reveals areas where improvements are needed on specific pages or features.

[0569] Step 5:

[0570] The server applies natural language processing algorithms to learn the technical terms used by the user on the page and generates tooltips to make them easier to understand. This information is provided to the device when the page is displayed, as needed.

[0571] Step 6:

[0572] Based on instructions from the server, the terminal displays tooltips to the user related to technical terms that may be difficult to understand on the page they are viewing. This makes it easier for the user to understand the content of the page.

[0573] Step 7:

[0574] The server generates suggestions for dynamically improving the site layout based on user behavior data and error log analysis results. This optimization suggestion is then communicated to the administrator.

[0575] Step 8:

[0576] The administrator receives layout improvement suggestions from the server and evaluates their validity. If necessary, the suggestions are adopted and the site layout is updated to improve usability.

[0577] (Example 1)

[0578] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0579] Errors and technical jargon that users encounter when using websites and systems degrade the user experience. Furthermore, while efficiently identifying these problems and proposing improvements is necessary to enhance usability, current methods are insufficient to address this issue.

[0580] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0581] In this invention, the server includes means for collecting and recording error information, means for analyzing user behavior data and identifying problems, and means for generating auxiliary information for terminology using natural language processing. This enables the rapid resolution of errors encountered by users, support for understanding technical terms, and dynamic interface improvement suggestions for improved usability.

[0582] "Error information" refers to detailed data about abnormalities or malfunctions that occur while using a system or service.

[0583] "User behavior data" refers to data that records user interactions, such as their operation history, click patterns, and time spent on a site.

[0584] "Natural language processing" is a technology that uses computers to analyze, understand, and generate human language.

[0585] "Supplementary information" refers to additional explanations or explanatory information provided to make things easier for the user to understand.

[0586] A "proposal to dynamically change the layout" is an improvement suggestion to optimize the interface based on user actions and behavioral patterns.

[0587] An "interface improvement proposal" is a suggestion to modify the layout and functionality of a system in order to improve its usability and ease of use.

[0588] This invention is based on an information processing system in which a server handles the primary processing and interacts with terminals. First, the server uses error log collection means to collect error information that occurs while a user is using a website or software, and records it in a database. This data is later used for analysis to improve the user experience.

[0589] Next, the device collects user behavior data, particularly information such as click counts and page dwell time, and sends this data to the server in real time. The server uses advanced algorithms, including natural language processing, to analyze this data. This analysis allows the server to identify areas where users are experiencing difficulties on specific pages or features, and to pinpoint which technical terms are difficult to understand.

[0590] The server uses natural language processing to generate auxiliary information to help understand technical terms, and automatically creates this information as tooltips. The terminal then displays these tooltips to the user when they hover over the relevant terms, thereby aiding user understanding.

[0591] Furthermore, the server generates dynamic interface improvement suggestions based on collected error and behavioral data. These suggestions aim to improve usability and are communicated to administrators. Administrators review the suggestions and improve the interface layout and usability as needed.

[0592] As a concrete example, consider a scenario where a user accesses the help page of a project management tool. In this case, the terminal collects the user's operation history and promptly reports any problems, such as broken links or permission errors, to the server. The server analyzes this information and, if it determines that the term "project management" is difficult to understand, generates a tooltip providing an explanation related to that term.

[0593] Examples of prompts include: "Identify terms that users are confused about in the project management tool and generate visual aids," or "Analyze the error logs and create suggestions for interface improvements to enhance usability."

[0594] This invention provides a method for efficiently improving interfaces to enhance the user experience by using a generative AI model.

[0595] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0596] Step 1:

[0597] The server uses error logging to collect and record error information in real time that occurs while users are using the website or software. The input is the error event caused by user actions, and the output is an error log containing metadata such as the time of the error, type, and user ID. Specifically, when the server detects an error, it quickly saves the information to a database for later analysis.

[0598] Step 2:

[0599] The device monitors user behavior data in real time and sends details such as click counts, page transitions, and time spent on a page to the server. The input is the user's operation history, and the output is a log of organized behavioral data. The device tracks user actions in real time, recording in detail which links the user clicks and which pages they spend time on.

[0600] Step 3:

[0601] The server receives the collected error logs and behavioral data. The input is the error logs and behavioral data obtained in the previous step, and the output is the analysis results. The server analyzes the data using a generative AI model to identify which pages are confusing to users and which technical terms are difficult to understand. This process uses natural language processing and pattern recognition techniques to automatically extract problematic pages and terms.

[0602] Step 4:

[0603] Based on the analysis results, the server generates supplementary information for difficult terms and sends this information to the terminal for display as a tooltip. The input is the difficult term identified in the analysis step, and the output is an explanatory text related to that term. Specifically, the server uses natural language processing to generate a definition of the term and configures it to be displayed when the user mouses over it.

[0604] Step 5:

[0605] The server generates dynamic interface improvement suggestions based on the analyzed data and notifies the administrator. The input is analysis results based on error data and behavioral data, and the output is specific layout change proposals aimed at improving usability. The server generates suggestions for button placement changes and navigation menu improvements to enhance usability, and the administrator can adjust the website interface based on these suggestions.

[0606] (Application Example 1)

[0607] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0608] There is a need to improve the usability of electronic payments. In particular, the rapid resolution of errors and insufficient support for understanding terminology are factors that cause confusion among users. The problem is that conventional systems do not provide efficient solutions to these issues.

[0609] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0610] In this invention, the server includes error log collection means, user behavior data analysis means, terminology explanation generation means using natural language processing means, dynamic layout change suggestion means, means for displaying specialized terminology in payment operations as visual information, and means for notifying the user when an error occurs and presenting a solution. This makes it possible to quickly collect and analyze errors that occur during electronic payments, provide prompt guidance to the user, and support them in understanding terminology.

[0611] An "error log collection system" is a system that has the function of recording errors that occur during electronic payments and saving them in a database.

[0612] A "user behavior data analysis system" is a system that identifies problems and patterns faced by users by collecting and analyzing their operation history and behavioral data.

[0613] "Terminology explanation generation means using natural language processing means" refers to a technology that analyzes technical terms, automatically generates their meanings and explanations, and provides them to users.

[0614] A "dynamic layout change proposal means" is a means of analyzing the usage status of a user interface and proposing layout improvement plans to enhance usability.

[0615] "Means for displaying specialized terminology in payment operations as visual information" refers to an interface function that visually explains the specialized terminology used on the payment screen in a way that is easy for users to understand.

[0616] "A means of notifying the user and providing solutions when an error occurs" refers to a system that immediately informs the user of an error when it occurs and provides appropriate solutions.

[0617] To implement this invention, a server and terminal included in an electronic payment system are used. First, the server uses an error log collection means to record error information that occurs during the electronic payment process. This allows detailed data regarding the frequency and type of errors to be stored in a database. The server analyzes this data and utilizes a user behavior data analysis means to identify the situations in which users are likely to encounter errors.

[0618] Next, natural language processing is used to analyze the technical terms displayed on the payment screen. The meaning of these terms is automatically generated and can be displayed on the terminal as visual information, such as a tooltip. This makes it easier for the user to understand the technical terms and allows the payment process to proceed smoothly.

[0619] Furthermore, the server uses a dynamic layout change suggestion mechanism to generate layout improvement proposals for screens that confuse many users. This allows system administrators to instantly optimize the user interface. In the event of an error, a mechanism is activated to notify the user and present possible solutions, enabling users to quickly resolve problems.

[0620] As a concrete example, consider a scenario where a user attempts to purchase goods online using a credit card. If the user makes a mistake entering the security code, the terminal immediately notifies the server of the error. Based on the error log, the server then presents the user with a common solution to the error. Furthermore, in response to the user's question, such as "What is a security code?", a tooltip explanation is immediately displayed using natural language processing, allowing the user to resolve their question on the spot.

[0621] An example of a prompt message to a generative AI model would be, "What explanation should be given to the user in response to a payment error 'Invalid security code'?" The model could then generate an appropriate response.

[0622] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0623] Step 1:

[0624] The server receives error information from the terminal using an error log collection mechanism. The input is error information that occurs during electronic payment. The server stores this information in a database for later analysis. This data includes information such as the type of error, the time of occurrence, and the user ID.

[0625] Step 2:

[0626] The server analyzes user behavior data to identify behavioral patterns. The input is user operation logs (e.g., click patterns, page transition history). The server uses data analysis algorithms to analyze the situations in which users are most likely to encounter problems, and generates a report of these trends as output.

[0627] Step 3:

[0628] The server uses natural language processing to generate explanations for technical terms on the payment screen. The input is a list of technical terms. The server uses a natural language processing engine to generate explanatory text for each term and sends the listed explanations to the terminal as output. This allows the user to review the information presented as a tooltip.

[0629] Step 4:

[0630] When a user encounters an error on the payment screen, the server provides a solution based on the error log and analysis results. The input consists of error information and analysis data. The server references similar past error cases from the database and generates the corresponding guidance as output to notify the user of the solution.

[0631] Step 5:

[0632] The server activates a dynamic layout change suggestion mechanism to generate suggestions for improving the user interface. The input consists of user behavior analysis data and error logs. The server analyzes this data and notifies the service administrator of UI change suggestions as output. This allows the administrator to quickly optimize the UI.

[0633] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0634] This invention combines an information processing system aimed at improving usability with an emotion engine that takes user emotions into account, and describes its embodiments. The server collects error logs that occur when users use the company's internal portal site and stores them in a database. This stored information is used to identify problems faced by users and to analyze the error logs.

[0635] The device continuously collects user behavioral data, such as the number of clicks and time spent on a page, and sends this data to the server. The server uses this data to analyze points of user confusion and difficulty in understanding parts of the page and its operation. To begin with, to help users understand technical terms, the device generates tooltips explaining terms using natural language processing, and presents these to the user, thereby improving user convenience.

[0636] The emotion engine analyzes and recognizes user emotions based on data obtained from user input and actions. Specifically, it detects in real time if a user is experiencing frustration or anxiety during operation. The emotion engine works in conjunction with the server to generate customized feedback and support information based on the user's emotional data, and displays support messages to the user. Furthermore, it dynamically adjusts the interface design and user interaction according to the emotional state, providing a more comfortable operating environment.

[0637] For example, when a user uses a new project management tool for the first time, the emotion engine can determine if the user is experiencing stress based on their input speed and the frequency of errors. In this case, the server automatically generates messages to alleviate the user's emotions, and the terminal presents tools that gently guide the user. As a result, the workflow becomes smoother, and the overall user experience is improved.

[0638] This system allows users to comfortably navigate the site even with limited technical knowledge, and enables flexible responses tailored to user emotions. This, in turn, allows for the efficient use of internal resources and enhances the site's effectiveness.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The device collects operational data such as keyboard shortcuts entered by the user while using the portal site, mouse movements, and click patterns, and transmits this data to the server in real time.

[0642] Step 2:

[0643] The server analyzes the operation data received from the terminal and monitors the user's operation speed and error frequency. Based on this, the emotion engine determines how much stress or confusion the user is experiencing during a particular operation.

[0644] Step 3:

[0645] The emotion engine infers the user's emotional state based on the analysis results, and if it detects that stress levels are high, it notifies the server. Upon receiving this notification, the server generates an appropriate support message.

[0646] Step 4:

[0647] The server sends the generated support message to the terminal, including concise explanations and navigation related to operations or technical terms that are particularly difficult to understand.

[0648] Step 5:

[0649] The device displays pop-up tooltips that help the user understand the next steps they should take. These displays are designed with user-centric pacing and visual elements in mind, optimizing the user experience.

[0650] Step 6:

[0651] Users can complete tasks efficiently and comfortably by following the device's instructions and continuing operations through a stress-free interface. This allows users to work in a more relaxed state.

[0652] (Example 2)

[0653] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] In information processing systems, it is essential to effectively support users when they encounter complex operations or technical jargon, while taking their emotions into consideration. However, conventional systems have struggled to accurately recognize user emotions and provide appropriate feedback and support. This has led to a decline in user experience and efficiency problems.

[0655] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0656] In this invention, the server includes error data collection means, human behavior information analysis means, term suggestion generation means using natural language processing means, emotion analysis and feedback generation means, and dynamic interface adjustment suggestion means. This makes it possible to analyze the user's emotions during operation, provide appropriate support information, and improve usability.

[0657] An "error data collection means" is a function in an information processing system that acquires and records error information that occurs during user operation.

[0658] A "human behavior information analysis tool" is a function that analyzes user behavior data such as the number of clicks and time spent on a page to identify user behavior patterns and difficulties.

[0659] "Terminology generation means using natural language processing means" refers to a function that uses a generative AI model to generate explanations of technical terms and functions and present them to the user.

[0660] "Emotion analysis and feedback generation means" refers to a function that analyzes emotions from user actions and input data, and generates support information and feedback corresponding to those emotions.

[0661] The "dynamic interface adjustment suggestion means" is a function that makes suggestions for automatically adjusting the interface design and interactions based on the user's usage situation and emotions.

[0662] A description of the embodiment for carrying out the invention will be provided.

[0663] This system incorporates an emotion engine that considers user emotions into an information processing system designed to improve usability. A specific embodiment is shown below.

[0664] The server collects error data and manages it in a database. Specifically, it automatically monitors errors that occur while users are operating the company's internal portal site and saves this information to a storage device such as a relational database. Analysis tools such as Python's Pandas are used to analyze the collected error log data. This provides clues for identifying problems and resolving errors.

[0665] The device collects user behavioral data in real time, such as the number of clicks and time spent on a page, and sends it to the server. Using the collected data, the server analyzes problems the user is experiencing. To address areas where the user frequently makes mistakes or lacks understanding of technical terms, the device uses a generative AI model to generate natural language processing explanations of terms and displays them to the user as tooltips.

[0666] The emotion engine analyzes the user's emotional state based on data obtained from user input and actions. If the server determines that the user is feeling frustrated or anxious during the process, it automatically generates a feedback message. This allows the user to receive support tailored to their situation, thereby reducing stress.

[0667] For example, when a user uses a new project management tool, the emotion engine detects from the user's operation data that they may be experiencing stress. In this case, the server uses a generative AI model to generate a support message, such as "Please suggest a gentle way to guide me through using the new project management tool," and the terminal displays this message to the user.

[0668] This system allows users to navigate the site more comfortably without specialized knowledge and provides appropriate support tailored to their emotions, thereby improving the overall user experience.

[0669] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0670] Step 1:

[0671] The server collects error logs in real time that occur while users are interacting with the company's internal portal site. The input includes error messages and timestamps generated from user actions. This information is stored in a database, providing a foundation for later analysis and problem-solving. The output is structured error data stored in a memory device.

[0672] Step 2:

[0673] The device collects user behavioral data such as the number of clicks and time spent on pages. It takes user actions as input and periodically sends them to the server. Through data transmission, raw data is output that can be used to identify user movements and trends.

[0674] Step 3:

[0675] The server analyzes the collected user behavior data. Using analysis software such as Pandas, it formats the input data to determine which screens users spend the most time on and where click errors are most frequent. The output provides an identification of the problems users are most likely to encounter.

[0676] Step 4:

[0677] The terminal generates tooltips using a generative AI model for technical terms and new features on the screen, and displays them to the user. It receives explanatory text generated from the server as input and presents it visually on the user's screen. As output, it provides an environment that is easier for the user to understand.

[0678] Step 5:

[0679] The emotion engine analyzes the user's emotional state based on their operation speed and frequency of errors. Using this as input, it performs a function to detect signs of stress and anxiety in real time. As output, a report on the user's emotional state is sent to the server.

[0680] Step 6:

[0681] The server generates customized feedback messages based on analysis results from the emotion engine. Using a generation AI model, it automatically generates optimal support content from the input emotion data. As output, a direct support message is sent to the user's device.

[0682] (Application Example 2)

[0683] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0684] This invention aims to improve user convenience in information processing systems. Existing systems often rely on mechanical support for errors and difficult operations, failing to adequately consider the user's psychological state. This increases the likelihood of users experiencing stress and confusion. In particular, it can discourage users unfamiliar with the technology from continuing to use the system. A solution to this problem is needed.

[0685] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0686] In this invention, the server includes error log collection means, user behavior data analysis means, and emotion recognition processing means. This makes it possible to recognize the user's emotional state in real time during operation and to provide support messages and interface adjustments accordingly.

[0687] An "error log collection method" is a means of automatically collecting error information that occurs in a system and using it for databases and analysis processing.

[0688] "User behavior data analysis methods" are means of analyzing user behavior data such as the number of clicks and time spent on pages in order to identify user operation patterns and problems.

[0689] A "term explanation generation method using natural language processing" is a means for generating and presenting explanations of technical terms to users using natural language processing technology, in order to aid in the understanding of technical terms.

[0690] A "dynamic layout change suggestion method" is a means of suggesting adjustments to the interface design and layout according to the user's operation status and emotional state.

[0691] An "error resolution guide provision method" is a means of providing solutions and guides for errors that users encounter, and supporting them in resolving those problems.

[0692] An "emotion recognition processing means" is a means for analyzing and recognizing a user's psychological state and emotions based on their operations and input.

[0693] A "means for generating support messages based on the user's emotional state" refers to a means of displaying appropriate feedback and support messages on the system, taking into account the user's emotions.

[0694] "Means for adjusting the interface in accordance with the user's psychological state" refers to means of dynamically adjusting the interface to reflect the user's emotional state and provide a more comfortable operating environment.

[0695] An embodiment of the present invention is an information processing system that takes into account the user's emotional state. This system has the function of collecting and analyzing user behavior data and error logs on a server. The server records error logs generated within the system in a database using an error log collection means, and identifies the user's problems based on the analysis results. In addition, a user behavior data analysis means is used to analyze which operations are causing the user confusion.

[0696] The server is equipped with emotion recognition processing means to recognize the user's emotional state, thereby enabling real-time understanding of the user's psychological state. Terminology explanation generation means using natural language processing means presents terminology explanations to support user understanding. Furthermore, there is a means to generate support messages based on the user's emotional state, providing personalized feedback. Interface adjustment means that respond to the user's psychological state dynamically changes the operating environment, reducing stress.

[0697] As a concrete example of how users interact with the system, if a user experiences difficulty while operating new software, the emotion recognition processing mechanism detects the user's stress level. As a result, the server generates a message suggesting relaxing music, leading to a more comfortable user experience. Furthermore, as an example of a prompt message for the generating AI model, it is possible to use instructions such as, "Create an AI model that analyzes the emotions from the voice of a user (female, in her 30s) and suggests appropriate actions."

[0698] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0699] Step 1:

[0700] When a user interacts with the system, the terminal collects behavioral data in real time, such as the number of clicks and time spent on pages. This data is sent to the server as input for analyzing user behavior patterns and identifying problems. As output, data showing the user's behavioral characteristics is generated.

[0701] Step 2:

[0702] The server performs user behavior data analysis using the received behavioral data. This analysis diagnoses which operations the user is confused about through data calculations. Based on the behavioral data received as input, it outputs the operations where the user is experiencing difficulty. This output data provides information necessary for subsequent processing.

[0703] Step 3:

[0704] The server uses an error log collection mechanism to record and analyze error logs in a database. The error logs are used as input to identify problems during operation, and the type and frequency of errors are determined based on the analysis results. The output shows the priority of the problems and their frequent locations.

[0705] Step 4:

[0706] The server uses emotion recognition processing to recognize the user's psychological state from their recent actions and voice input. It uses user action data and voice data as input, analyzing, for example, changes in voice tone and input speed. This allows it to identify whether the user is experiencing stress and output the result.

[0707] Step 5:

[0708] The server generates support messages based on the user's emotional state. A generative AI model is used, taking emotion-based behavioral and situational data as input. The output provides customized feedback and recommended action messages that take the user's emotions into account.

[0709] Step 6:

[0710] The server generates tooltips for technical terms as needed using a terminology explanation generation system that employs natural language processing. It takes the content of the page the user is viewing as input and uses natural language processing to output easy-to-understand explanations of the terms.

[0711] Step 7:

[0712] The device adjusts its interface according to the user's psychological state. It inputs the user's emotional state and behavioral data, and dynamically changes the interface design and layout to mitigate the impact on the user's psychology. For example, it adopts stress-reducing colors and layouts, and outputs them in a way that allows for smooth operation for the user.

[0713] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0714] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0715] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0716] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0717] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0718] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0719] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0720] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0721] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0722] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0723] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0724] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0725] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0726] 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.

[0727] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0728] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0729] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0730] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0731] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0732] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0733] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0734] The following is further disclosed regarding the embodiments described above.

[0735] (Claim 1)

[0736] Error log collection method,

[0737] User behavior data analysis method,

[0738] A term definition generation means using natural language processing means,

[0739] Dynamic layout change proposal means,

[0740] A means of providing error resolution guides,

[0741] An information processing system that includes this.

[0742] (Claim 2)

[0743] An information processing system according to claim 1, which stores error logs in a database and performs analysis.

[0744] (Claim 3)

[0745] The information processing system according to claim 1, which displays explanations of technical terms on each page as tooltips.

[0746] "Example 1"

[0747] (Claim 1)

[0748] A means of collecting and recording error information,

[0749] A means of analyzing user behavior data to identify problems,

[0750] A means for generating auxiliary information about terms using natural language processing,

[0751] A means of making suggestions to dynamically change the layout based on collected data,

[0752] A means of notifying suggestions for interface improvements,

[0753] A system that includes this.

[0754] (Claim 2)

[0755] The system according to claim 1, which stores error information and performs analysis using behavioral data.

[0756] (Claim 3)

[0757] The system according to claim 1, which displays visual auxiliary information for technical terms in the information.

[0758] "Application Example 1"

[0759] (Claim 1)

[0760] Error log collection method,

[0761] User behavior data analysis method,

[0762] A term definition generation means using natural language processing means,

[0763] Dynamic layout change proposal means,

[0764] A means of displaying specialized terminology used in payment operations as visual information,

[0765] A means of notifying the user when an error occurs and providing a solution,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, which saves error logs to a database and performs analysis.

[0769] (Claim 3)

[0770] The system according to claim 1, which displays explanations of technical terms on the payment screen as tooltips.

[0771] "Example 2 of combining an emotion engine"

[0772] (Claim 1)

[0773] Error data collection method,

[0774] Human behavior information analysis method,

[0775] A term generation means using natural language processing means,

[0776] Emotion analysis and feedback generation means,

[0777] Dynamic interface adjustment proposal means,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, which stores error data in a storage device and performs analysis.

[0781] (Claim 3)

[0782] The system according to claim 1, which displays explanations of technical terms on each screen as a visual representation.

[0783] "Application example 2 when combining with an emotional engine"

[0784] (Claim 1)

[0785] Error log collection method,

[0786] User behavior data analysis method,

[0787] A term definition generation means using natural language processing means,

[0788] Dynamic layout change proposal means,

[0789] A means of providing error resolution guides,

[0790] Emotion recognition processing means,

[0791] A means of generating support messages based on the user's emotional state,

[0792] Means for adjusting the interface according to the user's psychological state,

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, which saves error logs to a database and performs analysis.

[0796] (Claim 3)

[0797] The system according to claim 1, which displays explanations of technical terms on each page as tooltips and provides a dynamically adjusted interface that responds to the user's emotions. [Explanation of symbols]

[0798] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Error log collection method, User behavior data analysis method, A term definition generation means using natural language processing means, Dynamic layout change proposal means, A means of displaying specialized terminology used in payment operations as visual information, A means of notifying the user when an error occurs and providing a solution, A system that includes this.

2. The system according to claim 1, which saves error logs to a database and performs analysis.

3. The system according to claim 1, which displays explanations of technical terms on the payment screen as tooltips.

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