system

Abstract

The system according to this embodiment aims to enable non-engineers to execute automated tests by giving instructions in natural language. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives natural language test instructions from the user. The generation unit automatically operates a PC browser or smartphone browser to execute the test based on the instructions received by the reception unit. The analysis unit analyzes the test results executed by the generation unit and reports problems and areas for improvement. The provision unit provides specific correction policies and improvements based on the report obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In the prior art, there is a problem that a huge amount of code needs to be written to execute e2e automated tests, and it is difficult for non-engineers to handle.

[0005] The system according to the embodiment aims to enable non-engineers to issue instructions in natural language and execute automated tests.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives natural language test instructions from the user. The generation unit automatically operates a PC browser or smartphone browser to execute the test based on the instructions received by the reception unit. The analysis unit analyzes the test results executed by the generation unit and reports on problems and areas for improvement. The provision unit provides specific correction policies and improvements based on the report obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows even non-engineers to issue instructions in natural language and execute automated tests. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

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

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The end-to-end automated testing system according to an embodiment of the present invention is a system that performs end-to-end automated testing using an AI agent. Conventional end-to-end automated testing required writing a large amount of code and required reconfiguration every time the test environment changed, making it difficult for non-engineers to handle. In the present invention, the AI ​​agent automatically operates a PC browser or smartphone browser and evaluates whether there are any display problems and whether the operation can be performed without problems on the specified page. The user simply inputs in natural language which pages to test and how to test them, and the tests are executed daily. This makes it possible for non-engineers to create and execute end-to-end automated tests. Specifically, it consists of the following steps. First, the user inputs test instructions in natural language. For example, the user inputs an instruction such as "I want you to test the login page." This instruction is input to the AI ​​agent. Next, the AI ​​agent analyzes the input instruction and automatically operates the PC browser or smartphone browser to execute the test. For example, in a login page test, it automatically inputs the username and password and clicks the login button. The test results evaluate whether there are any display problems and whether the operation can be performed without problems. Furthermore, the AI ​​agent analyzes test results daily to identify potential problems in the implementation. For example, if an error message is displayed on the login page, it identifies the cause and suggests a solution. It also provides concrete action plans for quality improvement through the analysis of test results. This further reduces the burden on developers, allowing engineers to focus on their core tasks. This mechanism significantly improves the efficiency of end-to-end automated testing, making it easy for non-engineers to run tests. In addition, the AI ​​agent analyzes test results and automatically reports problems and areas for improvement, thereby improving quality. For example, the test result report includes specific correction strategies and improvement points, allowing developers to respond quickly based on them. As a result, the end-to-end automated testing system automatically runs tests based on user instructions in natural language, analyzes the results, and provides correction strategies and improvement points, making it easy for non-engineers to run tests.

[0029] The e2e automated test system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives natural language test instructions from the user. Natural language test instructions from the user include, but are not limited to, text format or audio format. For example, the reception unit analyzes text format instructions using natural language processing technology and passes them to the generation unit. The reception unit can also convert audio format instructions into text using speech recognition technology and pass them to the generation unit. The generation unit automatically operates a PC browser or smartphone browser to execute tests based on the instructions received by the reception unit. For example, the generation unit operates the browser using an automation tool such as Selenium and evaluates whether there are any display problems on the specified page and whether the operation can be performed without problems. The generation unit can also automatically perform actions such as entering a username and password and clicking the login button. For example, the generation unit automatically tests a login page, enters a username and password, and clicks the login button. The analysis unit analyzes the test results executed by the generation unit and reports problems and areas for improvement. The analysis department, for example, analyzes error logs and evaluates performance to identify areas where there may be problems in the implementation. The analysis department can also automatically analyze test results daily to identify areas where there may be problems in the implementation. The provision department provides specific correction strategies and improvement points based on the reports obtained by the analysis department. The provision department provides specific action plans for quality improvement, such as code correction and UI improvements. As a result, the e2e automated test system according to this embodiment automatically executes tests based on the user's natural language instructions, analyzes the results, and provides correction strategies and improvement points, making it easy for even non-engineers to perform tests.

[0030] The reception unit receives natural language test instructions from users. These natural language test instructions may include, but are not limited to, text or audio formats. For example, the reception unit analyzes text-based instructions using natural language processing technology and passes them to the generation unit. The reception unit can also convert audio-based instructions into text using speech recognition technology and pass them to the generation unit. Specifically, the reception unit analyzes the text instructions entered by the user using natural language processing (NLP) technology to understand the content and purpose of the test. For example, if it receives the instruction "Run the login page test," it uses NLP technology to extract the keywords "login page," "test," and "run" and passes the appropriate instructions to the generation unit. If it receives an audio-based instruction, it uses speech recognition technology to convert the audio into text and then analyzes it using NLP technology. Speech recognition technology includes techniques to analyze the features of speech and recognize phonemes and words. For example, if a user gives the audio instruction "Run the login page test," it uses speech recognition technology to convert the audio into text and then analyzes it using NLP technology. This allows the reception unit to accurately receive diverse forms of instructions from users and pass them on to the generation unit in the appropriate format. Furthermore, the reception unit can utilize past instruction history and user profile information to more accurately understand the user's intent. For example, if similar instructions have been received in the past, it can refer to that history to take appropriate action. It can also provide customized responses based on the user's profile information, tailored to the user's preferences and usage patterns. As a result, the reception unit can respond to diverse user needs and achieve more accurate instruction reception.

[0031] The generation unit automatically operates PC and smartphone browsers to execute tests based on instructions received by the reception unit. For example, the generation unit uses automation tools such as Selenium to control the browser and evaluate whether there are any display problems or if operations can be performed correctly on the specified page. Specifically, the generation unit uses Selenium WebDriver to control the browser and execute the test scenario specified by the user. For example, when testing a login page, the generation unit uses Selenium to launch the browser and access the specified URL. It then automatically enters the username and password and clicks the login button. The generation unit can record these operations as a script and save it in a reusable format. Furthermore, the generation unit detects and logs any errors or anomalies that occur during test execution. For example, if an error message is displayed after clicking the login button, its contents are logged for use in subsequent analysis by the analysis unit. In addition, the generation unit can save the test execution results as screenshots for visual verification. This allows the generation unit to automatically execute tests based on user instructions and provide detailed test results. The generation unit can also support multiple browsers and devices. For example, it can perform the same tests not only on PC browsers but also on smartphone and tablet browsers. This allows the generation unit to verify operation in different environments and achieve broader test coverage. Furthermore, the generation unit can set a test execution schedule and run tests regularly. For example, it can automatically run tests every night and provide test results the following morning. This allows the generation unit to continuously perform tests and provide results, contributing to the improvement of system quality.

[0032] The analysis department analyzes the test results performed by the generation department and reports on problems and areas for improvement. For example, the analysis department analyzes error logs and evaluates performance to identify potential problems in the implementation. Specifically, the analysis department analyzes error logs provided by the generation department to identify the cause and location of errors. For instance, if an error message is displayed during a login page test, the analysis department analyzes the message to verify whether the entered username and password are correct and whether there are any problems with server-side processing. The analysis department also evaluates test execution time and resource usage to identify performance bottlenecks. For example, if a specific operation is delayed, the analysis department identifies the cause and proposes improvements. Furthermore, the analysis department provides a dashboard to visually display test results, allowing users to intuitively understand the results. For example, it displays error log content and performance evaluation results as graphs and charts, allowing users to see problems and areas for improvement at a glance. The analysis department can also automatically analyze test results daily to identify potential problems in the implementation. This enables the analysis department to quickly and accurately identify problems and contribute to improving system quality. Furthermore, the analysis department can utilize past test results and historical data to conduct long-term trend analysis and risk assessment. For example, based on past test results, it can analyze the tendency for errors to occur in specific functions or pages and predict future risks. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0033] The service provider will provide specific correction strategies and improvements based on reports obtained by the analysis team. For example, the service provider will provide concrete action plans for quality improvement, such as code fixes and UI improvements. Specifically, based on reports from the analysis team, the service provider will identify the causes of errors and areas for improvement, and propose specific correction strategies to the development team. For example, if an error occurs during testing of the login page, the service provider will identify the cause of the error and propose code fixes and UI improvements. Furthermore, based on performance evaluation results, the service provider will propose system optimizations and efficient resource utilization methods. For example, if a specific operation is delayed, they will identify the cause and propose database query optimizations and caching methods. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the action plans they provide. For example, they can monitor the results after implementing correction strategies and improvements and make additional suggestions as needed. The service provider can also reliably communicate information to the development team using multiple communication channels. For example, they can quickly share correction strategies and improvements using email and chat tools, enabling the development team to respond promptly. This allows the service provider to offer development teams concrete and practical action plans, contributing to improved system quality. Furthermore, the service provider can track the implementation status of corrective policies and improvements, and manage progress. For example, they can regularly check whether corrective policies are being implemented appropriately and whether improvements are having an effect, and take additional action as needed. In this way, the service provider can continuously support the improvement of system quality and increase user satisfaction.

[0034] The generation unit can automatically operate PC and smartphone browsers to perform tests. For example, the generation unit uses automation tools such as Selenium to operate the browser and evaluate whether there are any display problems or whether operations can be performed without problems on a specified page. For example, the generation unit can automatically operate a PC browser to check the display content of a specified page. The generation unit can also automatically operate a smartphone browser to check the usability of a specified page. For example, the generation unit can use a PC browser to check whether links on a specified page work correctly. The generation unit can also use a smartphone browser to check whether form input on a specified page can be performed correctly. In this way, test automation is achieved by automatically operating PC and smartphone browsers. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the operation procedures for a PC or smartphone browser into a generation AI, and the generation AI can automatically execute the operation procedures.

[0035] The analysis unit can automatically analyze test results daily and identify areas where there may be problems in the implementation. For example, the analysis unit can analyze error logs to identify areas where there may be problems in the implementation. The analysis unit can also evaluate performance to identify areas where there may be problems in the implementation. For example, the analysis unit can analyze the error logs of the test results to identify the cause of error messages. The analysis unit can also analyze the performance data of the test results to identify performance bottlenecks. For example, the analysis unit can analyze the error logs of the test results to identify the cause of error messages and suggest solutions. The analysis unit can also analyze the performance data of the test results to identify performance bottlenecks and suggest solutions. This allows for the early detection of implementation problems by automatically analyzing test results daily. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the error logs of the test results into a generative AI, which can identify the cause of error messages and suggest solutions.

[0036] The service provider can provide specific correction strategies and improvements based on the test results report. For example, the service provider can analyze the test results report and present specific correction strategies. The service provider can also analyze the test results report and present specific improvements. For example, the service provider can analyze the test results report and present code correction strategies. The service provider can also analyze the test results report and present UI improvements. For example, the service provider can analyze the test results report, present code correction strategies, and provide specific correction procedures. The service provider can also analyze the test results report, present UI improvements, and provide specific improvement procedures. This improves quality by providing specific correction strategies and improvements. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the test results report into a generative AI, which can then present specific correction strategies and improvements.

[0037] The generation unit can automatically perform the actions of entering a username and password and clicking the login button. For example, the generation unit can automatically test the login page, enter a username and password, and click the login button. For example, the generation unit can automatically test the login page, simulate the actions of entering a username and password and clicking the login button. The generation unit can also automatically test the login page, actually perform the actions of entering a username and password and clicking the login button. For example, the generation unit can automatically test the login page, simulate the actions of entering a username and password and clicking the login button, and check if there are any display problems. The generation unit can also automatically test the login page, actually perform the actions of entering a username and password and clicking the login button, and check if the operation can be performed without problems. This improves the efficiency of testing by automating the testing of the login page. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the test steps for the login page into the generation AI, which can then automatically execute the login steps.

[0038] The analysis unit can identify the cause of an error message displayed on the login page and provide a solution. For example, the analysis unit can analyze the error message on the login page and identify its cause. The analysis unit can also analyze the error message on the login page and provide a solution. For example, the analysis unit can analyze the error message on the login page, identify its cause, and provide a solution. The analysis unit can also analyze the error message on the login page, identify its cause, and provide a solution. The analysis unit can also analyze the error message on the login page, identify its cause, and provide a solution. This allows for quick problem resolution by identifying the cause of the error message and providing a solution. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the error message on the login page into a generation AI, which can then identify the cause and provide a solution.

[0039] The reception unit can analyze the user's past test instruction history and select the optimal reception method. For example, the reception unit can prioritize receiving test instructions that the user has frequently requested in the past. The reception unit can also analyze the user's past test instruction history to determine when they tend to request instructions and process requests during those times. For example, the reception unit can propose the optimal reception method (voice, text, etc.) based on the user's past test instruction history. In this way, the optimal reception method can be selected by analyzing the past test instruction history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's past test instruction history into a generative AI, which can then select the optimal reception method.

[0040] The reception unit can filter test instructions based on the user's current projects and areas of interest when receiving them. For example, the reception unit prioritizes receiving test instructions related to the project the user is currently working on. The reception unit can also filter and receive relevant test instructions based on the user's areas of interest. For example, the reception unit can suggest appropriate test instructions according to the user's project progress. This ensures that appropriate test instructions are received by filtering them based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's project information into a generative AI, which can then filter relevant test instructions.

[0041] The reception unit can prioritize receiving test instructions that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving test instructions related to that region. The reception unit can also suggest optimal test instructions based on the user's geographical location information. For example, if the user is on the move, the reception unit will receive appropriate test instructions based on their current location. This prioritizes receiving highly relevant test instructions by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving relevant test instructions.

[0042] The reception unit can analyze the user's social media activity when receiving test instructions and accept relevant instructions. For example, the reception unit can prioritize receiving test instructions related to the user's current interests based on their social media activity. The reception unit can also accept test instructions related to projects mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and suggest the most suitable test instructions. In this way, it accepts relevant test instructions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, which can then accept relevant test instructions.

[0043] The generation unit can adjust the level of detail generated based on the importance of the test during test generation. For example, the generation unit generates detailed procedures and results for high-importance tests. Conversely, it can also generate concise procedures and results for low-importance tests. The generation unit dynamically adjusts the level of detail of the test according to its importance. This enables efficient test generation by adjusting the level of detail based on the importance of the test. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input test importance data into the generation AI, which can then adjust the level of detail of the test.

[0044] The generation unit can apply different generation algorithms depending on the test category when generating tests. For example, in a login test, the generation unit applies an algorithm that performs input of authentication information and verification of the authentication result. The generation unit can also apply an algorithm that checks the page load time and displayed content in a page display test. For example, in a form input test, the generation unit applies an algorithm that performs validation of input fields and verification of the submission result. By applying different generation algorithms depending on the test category, appropriate test generation becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input test category data into a generation AI, and the generation AI can apply an appropriate generation algorithm.

[0045] The generation unit can determine the priority of test generation based on the test submission deadlines. For example, the generation unit can prioritize generating tests with approaching deadlines. It can also postpone generating tests with ample time for submission. The generation unit can dynamically adjust the test generation order based on the submission deadlines. This enables efficient test generation by determining the priority of generation based on the test submission deadlines. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input test submission deadline data into a generation AI, which can then determine the priority of generation.

[0046] The generation unit can adjust the order of test generation based on the relevance of the tests. For example, the generation unit can prioritize the generation of highly relevant tests. It can also postpone the generation of less relevant tests. The generation unit can dynamically adjust the order of generation based on the relevance of the tests. This allows for efficient test generation by adjusting the order of generation based on the relevance of the tests. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input test relevance data into a generation AI, which can then adjust the order of generation.

[0047] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of test results during the analysis process. For example, the analysis unit can analyze the interrelationships of test results and identify related problems. The analysis unit can also provide highly accurate analysis results by considering the interrelationships of test results. For example, the analysis unit can propose areas for improvement based on the interrelationships of test results. In this way, by considering the interrelationships of test results, it can provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the interrelationship data of test results into a generative AI, which can then improve the accuracy of its analysis by considering the interrelationships.

[0048] The analysis department can perform analysis while considering the attribute information of the test result submitter. For example, the analysis department can perform analysis while considering the position and years of experience of the test result submitter. The analysis department can also improve the accuracy of the analysis based on the past performance of the test result submitter. For example, the analysis department can propose optimal improvements based on the attribute information of the test result submitter. In this way, by considering the attribute information of the submitter, it proposes optimal improvements. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis department can input the attribute information of the test result submitter into a generative AI, and the generative AI can perform analysis while considering the attribute information.

[0049] The analysis unit can perform analysis while considering the geographical distribution of test results. For example, the analysis unit can analyze the geographical distribution of test results and identify problems in each region. The analysis unit can also provide highly accurate analysis results by considering the geographical distribution of test results. For example, the analysis unit can propose areas for improvement in each region based on the geographical distribution of test results. In this way, problems in each region are identified by considering the geographical distribution. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input geographical distribution data of test results into a generative AI, and the generative AI can perform analysis while considering the geographical distribution.

[0050] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the test results during the analysis process. For example, the analysis unit can refer to relevant literature related to the test results to provide highly accurate analysis results. The analysis unit can also propose optimal improvements based on the relevant literature related to the test results. For example, the analysis unit improves the accuracy of its analysis by considering relevant literature related to the test results. This allows it to provide highly accurate analysis results by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevant literature data of the test results into a generative AI, which can then refer to the relevant literature to improve the accuracy of the analysis.

[0051] The service provider can improve the accuracy of its service by considering the interrelationships of test results during the service provision process. For example, the service provider can analyze the interrelationships of test results and provide relevant correction strategies and improvements. The service provider can also provide highly accurate correction strategies and improvements by considering the interrelationships of test results. For example, the service provider can propose optimal correction strategies and improvements based on the interrelationships of test results. This allows for the provision of highly accurate correction strategies and improvements by considering the interrelationships of test results. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input interrelationship data of test results into a generative AI, which can then improve the accuracy of its service by considering the interrelationships.

[0052] The service provider can provide test results while considering the attribute information of the test result submitter. For example, the service provider can provide appropriate correction strategies and improvements by considering the test result submitter's position and years of experience. The service provider can also provide optimal correction strategies and improvements based on the test result submitter's past performance. For example, the service provider can propose optimal correction strategies and improvements based on the test result submitter's attribute information. In this way, by considering the submitter's attribute information, optimal correction strategies and improvements are provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the test result submitter's attribute information into a generative AI, and the generative AI can provide the results while considering the attribute information.

[0053] The service provider can provide test results while considering their geographical distribution. For example, the service provider can analyze the geographical distribution of test results and provide correction strategies and improvements for each region. The service provider can also provide highly accurate correction strategies and improvements while considering the geographical distribution of test results. For example, the service provider can propose optimal correction strategies and improvements for each region based on the geographical distribution of test results. In this way, by considering the geographical distribution, it provides optimal correction strategies and improvements for each region. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input geographical distribution data of test results into a generative AI, and the generative AI can provide the results while considering the geographical distribution.

[0054] The service provider can improve the accuracy of its provision by referring to relevant literature related to the test results at the time of provision. For example, the service provider can refer to relevant literature related to the test results to provide highly accurate correction strategies and improvements. The service provider can also propose optimal correction strategies and improvements based on the relevant literature related to the test results. For example, the service provider improves the accuracy of its provision by considering relevant literature related to the test results. This allows it to provide highly accurate correction strategies and improvements by referring to relevant literature. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the relevant literature data of the test results into a generating AI, and the generating AI can improve the accuracy of its provision by referring to the relevant literature.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The generation unit can adjust the level of detail generated based on the importance of the test during test generation. For example, it can generate detailed procedures and results for high-importance tests, and concise procedures and results for low-importance tests. Furthermore, it can dynamically adjust the level of detail of the test according to its importance. This allows for efficient test generation by adjusting the level of detail based on the importance of the test. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input test importance data into the generation AI, which can then adjust the level of detail of the test.

[0057] The reception unit can analyze the user's past test instruction history and select the optimal reception method. For example, it can prioritize receiving test instructions that the user has frequently requested in the past. It can also analyze the user's past test instruction history to determine when they tend to request instructions and then process requests during those times. Furthermore, it can suggest the optimal reception method (voice, text, etc.) based on the user's past test instruction history. In this way, the optimal reception method can be selected by analyzing the past test instruction history. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's past test instruction history into a generative AI, which can then select the optimal reception method.

[0058] The generation unit can apply different generation algorithms depending on the test category when generating tests. For example, for login tests, an algorithm that checks the input of authentication information and the authentication result can be applied. For page display tests, an algorithm that checks the page load time and displayed content can be applied. Furthermore, for form input tests, an algorithm that checks the input fields and the submission result can be applied. This makes it possible to generate appropriate tests by applying different generation algorithms depending on the test category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input test category data into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0059] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of test results. For example, it can analyze the interrelationships of test results to identify related problems. It can also provide highly accurate analysis results by considering the interrelationships of test results. Furthermore, it can propose areas for improvement based on the interrelationships of test results. In this way, by considering the interrelationships of test results, it is possible to provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the interrelationship data of test results into a generative AI, and the generative AI can improve the accuracy of the analysis by considering the interrelationships.

[0060] The service provider can improve the accuracy of its service by considering the interrelationships of test results during the service provision process. For example, it can analyze the interrelationships of test results and provide relevant correction strategies and improvements. It can also provide highly accurate correction strategies and improvements by considering the interrelationships of test results. Furthermore, it can propose optimal correction strategies and improvements based on the interrelationships of test results. In this way, by considering the interrelationships of test results, it is possible to provide highly accurate correction strategies and improvements. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the interrelationship data of test results into a generation AI, and the generation AI can improve the accuracy of its service by considering the interrelationships.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception unit receives natural language test instructions from the user. These natural language test instructions can be in text or audio format. The reception unit analyzes text-format instructions using natural language processing technology and passes them to the generation unit. It can also convert audio-format instructions into text using speech recognition technology and pass them to the generation unit. Step 2: The generation unit automatically operates PC and smartphone browsers to perform tests based on instructions received by the reception unit. The generation unit uses automation tools such as Selenium to operate the browser and evaluate whether there are any display problems on the specified page and whether operations can be performed without problems. The generation unit can also automatically perform actions such as entering a username and password and clicking the login button. Step 3: The analysis unit analyzes the test results performed by the generation unit and reports on problems and areas for improvement. The analysis unit analyzes error logs and evaluates performance to identify areas where there may be problems with the implementation. Step 4: The service provider will provide specific correction strategies and improvement points based on the report obtained by the analysis department. The service provider will provide specific action plans for quality improvement, such as code corrections and UI improvements.

[0063] (Example of form 2) The end-to-end automated testing system according to an embodiment of the present invention is a system that performs end-to-end automated testing using an AI agent. Conventional end-to-end automated testing required writing a large amount of code and required reconfiguration every time the test environment changed, making it difficult for non-engineers to handle. In the present invention, the AI ​​agent automatically operates a PC browser or smartphone browser and evaluates whether there are any display problems and whether the operation can be performed without problems on the specified page. The user simply inputs in natural language which pages to test and how to test them, and the tests are executed daily. This makes it possible for non-engineers to create and execute end-to-end automated tests. Specifically, it consists of the following steps. First, the user inputs test instructions in natural language. For example, the user inputs an instruction such as "I want you to test the login page." This instruction is input to the AI ​​agent. Next, the AI ​​agent analyzes the input instruction and automatically operates the PC browser or smartphone browser to execute the test. For example, in a login page test, it automatically inputs the username and password and clicks the login button. The test results evaluate whether there are any display problems and whether the operation can be performed without problems. Furthermore, the AI ​​agent analyzes test results daily to identify potential problems in the implementation. For example, if an error message is displayed on the login page, it identifies the cause and suggests a solution. It also provides concrete action plans for quality improvement through the analysis of test results. This further reduces the burden on developers, allowing engineers to focus on their core tasks. This mechanism significantly improves the efficiency of end-to-end automated testing, making it easy for non-engineers to run tests. In addition, the AI ​​agent analyzes test results and automatically reports problems and areas for improvement, thereby improving quality. For example, the test result report includes specific correction strategies and improvement points, allowing developers to respond quickly based on them. As a result, the end-to-end automated testing system automatically runs tests based on user instructions in natural language, analyzes the results, and provides correction strategies and improvement points, making it easy for non-engineers to run tests.

[0064] The e2e automated test system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a provision unit. The reception unit receives natural language test instructions from the user. Natural language test instructions from the user include, but are not limited to, text format or audio format. For example, the reception unit analyzes text format instructions using natural language processing technology and passes them to the generation unit. The reception unit can also convert audio format instructions into text using speech recognition technology and pass them to the generation unit. The generation unit automatically operates a PC browser or smartphone browser to execute tests based on the instructions received by the reception unit. For example, the generation unit operates the browser using an automation tool such as Selenium and evaluates whether there are any display problems on the specified page and whether the operation can be performed without problems. The generation unit can also automatically perform actions such as entering a username and password and clicking the login button. For example, the generation unit automatically tests a login page, enters a username and password, and clicks the login button. The analysis unit analyzes the test results executed by the generation unit and reports problems and areas for improvement. The analysis department, for example, analyzes error logs and evaluates performance to identify areas where there may be problems in the implementation. The analysis department can also automatically analyze test results daily to identify areas where there may be problems in the implementation. The provision department provides specific correction strategies and improvement points based on the reports obtained by the analysis department. The provision department provides specific action plans for quality improvement, such as code correction and UI improvements. As a result, the e2e automated test system according to this embodiment automatically executes tests based on the user's natural language instructions, analyzes the results, and provides correction strategies and improvement points, making it easy for even non-engineers to perform tests.

[0065] The reception unit receives natural language test instructions from users. These natural language test instructions may include, but are not limited to, text or audio formats. For example, the reception unit analyzes text-based instructions using natural language processing technology and passes them to the generation unit. The reception unit can also convert audio-based instructions into text using speech recognition technology and pass them to the generation unit. Specifically, the reception unit analyzes the text instructions entered by the user using natural language processing (NLP) technology to understand the content and purpose of the test. For example, if it receives the instruction "Run the login page test," it uses NLP technology to extract the keywords "login page," "test," and "run" and passes the appropriate instructions to the generation unit. If it receives an audio-based instruction, it uses speech recognition technology to convert the audio into text and then analyzes it using NLP technology. Speech recognition technology includes techniques to analyze the features of speech and recognize phonemes and words. For example, if a user gives the audio instruction "Run the login page test," it uses speech recognition technology to convert the audio into text and then analyzes it using NLP technology. This allows the reception unit to accurately receive diverse forms of instructions from users and pass them on to the generation unit in the appropriate format. Furthermore, the reception unit can utilize past instruction history and user profile information to more accurately understand the user's intent. For example, if similar instructions have been received in the past, it can refer to that history to take appropriate action. It can also provide customized responses based on the user's profile information, tailored to the user's preferences and usage patterns. As a result, the reception unit can respond to diverse user needs and achieve more accurate instruction reception.

[0066] The generation unit automatically operates PC and smartphone browsers to execute tests based on instructions received by the reception unit. For example, the generation unit uses automation tools such as Selenium to control the browser and evaluate whether there are any display problems or if operations can be performed correctly on the specified page. Specifically, the generation unit uses Selenium WebDriver to control the browser and execute the test scenario specified by the user. For example, when testing a login page, the generation unit uses Selenium to launch the browser and access the specified URL. It then automatically enters the username and password and clicks the login button. The generation unit can record these operations as a script and save it in a reusable format. Furthermore, the generation unit detects and logs any errors or anomalies that occur during test execution. For example, if an error message is displayed after clicking the login button, its contents are logged for use in subsequent analysis by the analysis unit. In addition, the generation unit can save the test execution results as screenshots for visual verification. This allows the generation unit to automatically execute tests based on user instructions and provide detailed test results. The generation unit can also support multiple browsers and devices. For example, it can perform the same tests not only on PC browsers but also on smartphone and tablet browsers. This allows the generation unit to verify operation in different environments and achieve broader test coverage. Furthermore, the generation unit can set a test execution schedule and run tests regularly. For example, it can automatically run tests every night and provide test results the following morning. This allows the generation unit to continuously perform tests and provide results, contributing to the improvement of system quality.

[0067] The analysis department analyzes the test results performed by the generation department and reports on problems and areas for improvement. For example, the analysis department analyzes error logs and evaluates performance to identify potential problems in the implementation. Specifically, the analysis department analyzes error logs provided by the generation department to identify the cause and location of errors. For instance, if an error message is displayed during a login page test, the analysis department analyzes the message to verify whether the entered username and password are correct and whether there are any problems with server-side processing. The analysis department also evaluates test execution time and resource usage to identify performance bottlenecks. For example, if a specific operation is delayed, the analysis department identifies the cause and proposes improvements. Furthermore, the analysis department provides a dashboard to visually display test results, allowing users to intuitively understand the results. For example, it displays error log content and performance evaluation results as graphs and charts, allowing users to see problems and areas for improvement at a glance. The analysis department can also automatically analyze test results daily to identify potential problems in the implementation. This enables the analysis department to quickly and accurately identify problems and contribute to improving system quality. Furthermore, the analysis department can utilize past test results and historical data to conduct long-term trend analysis and risk assessment. For example, based on past test results, it can analyze the tendency for errors to occur in specific functions or pages and predict future risks. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0068] The service provider will provide specific correction strategies and improvements based on reports obtained by the analysis team. For example, the service provider will provide concrete action plans for quality improvement, such as code fixes and UI improvements. Specifically, based on reports from the analysis team, the service provider will identify the causes of errors and areas for improvement, and propose specific correction strategies to the development team. For example, if an error occurs during testing of the login page, the service provider will identify the cause of the error and propose code fixes and UI improvements. Furthermore, based on performance evaluation results, the service provider will propose system optimizations and efficient resource utilization methods. For example, if a specific operation is delayed, they will identify the cause and propose database query optimizations and caching methods. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the action plans they provide. For example, they can monitor the results after implementing correction strategies and improvements and make additional suggestions as needed. The service provider can also reliably communicate information to the development team using multiple communication channels. For example, they can quickly share correction strategies and improvements using email and chat tools, enabling the development team to respond promptly. This allows the service provider to offer development teams concrete and practical action plans, contributing to improved system quality. Furthermore, the service provider can track the implementation status of corrective policies and improvements, and manage progress. For example, they can regularly check whether corrective policies are being implemented appropriately and whether improvements are having an effect, and take additional action as needed. In this way, the service provider can continuously support the improvement of system quality and increase user satisfaction.

[0069] The generation unit can automatically operate PC and smartphone browsers to perform tests. For example, the generation unit uses automation tools such as Selenium to operate the browser and evaluate whether there are any display problems or whether operations can be performed without problems on a specified page. For example, the generation unit can automatically operate a PC browser to check the display content of a specified page. The generation unit can also automatically operate a smartphone browser to check the usability of a specified page. For example, the generation unit can use a PC browser to check whether links on a specified page work correctly. The generation unit can also use a smartphone browser to check whether form input on a specified page can be performed correctly. In this way, test automation is achieved by automatically operating PC and smartphone browsers. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the operation procedures for a PC or smartphone browser into a generation AI, and the generation AI can automatically execute the operation procedures.

[0070] The analysis unit can automatically analyze test results daily and identify areas where there may be problems in the implementation. For example, the analysis unit can analyze error logs to identify areas where there may be problems in the implementation. The analysis unit can also evaluate performance to identify areas where there may be problems in the implementation. For example, the analysis unit can analyze the error logs of the test results to identify the cause of error messages. The analysis unit can also analyze the performance data of the test results to identify performance bottlenecks. For example, the analysis unit can analyze the error logs of the test results to identify the cause of error messages and suggest solutions. The analysis unit can also analyze the performance data of the test results to identify performance bottlenecks and suggest solutions. This allows for the early detection of implementation problems by automatically analyzing test results daily. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the error logs of the test results into a generative AI, which can identify the cause of error messages and suggest solutions.

[0071] The service provider can provide specific correction strategies and improvements based on the test results report. For example, the service provider can analyze the test results report and present specific correction strategies. The service provider can also analyze the test results report and present specific improvements. For example, the service provider can analyze the test results report and present code correction strategies. The service provider can also analyze the test results report and present UI improvements. For example, the service provider can analyze the test results report, present code correction strategies, and provide specific correction procedures. The service provider can also analyze the test results report, present UI improvements, and provide specific improvement procedures. This improves quality by providing specific correction strategies and improvements. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the test results report into a generative AI, which can then present specific correction strategies and improvements.

[0072] The generation unit can automatically perform the actions of entering a username and password and clicking the login button. For example, the generation unit can automatically test the login page, enter a username and password, and click the login button. For example, the generation unit can automatically test the login page, simulate the actions of entering a username and password and clicking the login button. The generation unit can also automatically test the login page, actually perform the actions of entering a username and password and clicking the login button. For example, the generation unit can automatically test the login page, simulate the actions of entering a username and password and clicking the login button, and check if there are any display problems. The generation unit can also automatically test the login page, actually perform the actions of entering a username and password and clicking the login button, and check if the operation can be performed without problems. This improves the efficiency of testing by automating the testing of the login page. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the test steps for the login page into the generation AI, which can then automatically execute the login steps.

[0073] The analysis unit can identify the cause of an error message displayed on the login page and provide a solution. For example, the analysis unit can analyze the error message on the login page and identify its cause. The analysis unit can also analyze the error message on the login page and provide a solution. For example, the analysis unit can analyze the error message on the login page, identify its cause, and provide a solution. The analysis unit can also analyze the error message on the login page, identify its cause, and provide a solution. The analysis unit can also analyze the error message on the login page, identify its cause, and provide a solution. This allows for quick problem resolution by identifying the cause of the error message and providing a solution. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the error message on the login page into a generation AI, which can then identify the cause and provide a solution.

[0074] The reception unit can estimate the user's emotions and adjust the timing of receiving test instructions based on the estimated emotions. For example, if the user is stressed, the reception unit may delay receiving the test instructions until the user is relaxed. Conversely, if the user is focused, the reception unit can immediately receive the test instructions and start the test quickly. For example, if the user is tired, the reception unit may postpone receiving the test instructions until the next day, after the user has rested. This reduces the user's burden by adjusting the timing of receiving test instructions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the reception timing.

[0075] The reception unit can analyze the user's past test instruction history and select the optimal reception method. For example, the reception unit can prioritize receiving test instructions that the user has frequently requested in the past. The reception unit can also analyze the user's past test instruction history to determine when they tend to request instructions and process requests during those times. For example, the reception unit can propose the optimal reception method (voice, text, etc.) based on the user's past test instruction history. In this way, the optimal reception method can be selected by analyzing the past test instruction history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's past test instruction history into a generative AI, which can then select the optimal reception method.

[0076] The reception unit can filter test instructions based on the user's current projects and areas of interest when receiving them. For example, the reception unit prioritizes receiving test instructions related to the project the user is currently working on. The reception unit can also filter and receive relevant test instructions based on the user's areas of interest. For example, the reception unit can suggest appropriate test instructions according to the user's project progress. This ensures that appropriate test instructions are received by filtering them based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's project information into a generative AI, which can then filter relevant test instructions.

[0077] The reception desk can estimate the user's emotions and determine the priority of test instructions to accept based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone less important test instructions. Conversely, if the user is relaxed, the reception desk may prioritize accepting more important test instructions. For example, if the user is in a hurry, the reception desk will prioritize accepting test instructions that require immediate attention. In this way, important test instructions are prioritized by determining the priority of test instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of test instructions.

[0078] The reception unit can prioritize receiving test instructions that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving test instructions related to that region. The reception unit can also suggest optimal test instructions based on the user's geographical location information. For example, if the user is on the move, the reception unit will receive appropriate test instructions based on their current location. This prioritizes receiving highly relevant test instructions by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, which can then prioritize receiving relevant test instructions.

[0079] The reception unit can analyze the user's social media activity when receiving test instructions and accept relevant instructions. For example, the reception unit can prioritize receiving test instructions related to the user's current interests based on their social media activity. The reception unit can also accept test instructions related to projects mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and suggest the most suitable test instructions. In this way, it accepts relevant test instructions by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit can input the user's social media activity data into a generative AI, which can then accept relevant test instructions.

[0080] The generation unit can estimate the user's emotions and adjust the test presentation based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed test results. It can also provide concise test results if the user is in a hurry. For example, if the user is stressed, the generation unit can provide visually clear test results. This allows for the provision of test results that are easy for the user to understand by adjusting the test presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the test presentation.

[0081] The generation unit can adjust the level of detail generated based on the importance of the test during test generation. For example, the generation unit generates detailed procedures and results for high-importance tests. Conversely, it can also generate concise procedures and results for low-importance tests. The generation unit dynamically adjusts the level of detail of the test according to its importance. This enables efficient test generation by adjusting the level of detail based on the importance of the test. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input test importance data into the generation AI, which can then adjust the level of detail of the test.

[0082] The generation unit can apply different generation algorithms depending on the test category when generating tests. For example, in a login test, the generation unit applies an algorithm that performs input of authentication information and verification of the authentication result. The generation unit can also apply an algorithm that checks the page load time and displayed content in a page display test. For example, in a form input test, the generation unit applies an algorithm that performs validation of input fields and verification of the submission result. By applying different generation algorithms depending on the test category, appropriate test generation becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input test category data into a generation AI, and the generation AI can apply an appropriate generation algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the test it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise test. Conversely, if the user is relaxed, the generation unit can generate a longer test with detailed explanations. If the user is stressed, for example, the generation unit can generate a concise and easy-to-understand test. This allows the system to provide the user with an appropriate test by adjusting the test length according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the test length.

[0084] The generation unit can determine the priority of test generation based on the test submission deadlines. For example, the generation unit can prioritize generating tests with approaching deadlines. It can also postpone generating tests with ample time for submission. The generation unit can dynamically adjust the test generation order based on the submission deadlines. This enables efficient test generation by determining the priority of generation based on the test submission deadlines. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input test submission deadline data into a generation AI, which can then determine the priority of generation.

[0085] The generation unit can adjust the order of test generation based on the relevance of the tests. For example, the generation unit can prioritize the generation of highly relevant tests. It can also postpone the generation of less relevant tests. The generation unit can dynamically adjust the order of generation based on the relevance of the tests. This allows for efficient test generation by adjusting the order of generation based on the relevance of the tests. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input test relevance data into a generation AI, which can then adjust the order of generation.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. It can also provide concise analysis results if the user is in a hurry. For example, if the user is stressed, the analysis unit can provide visually easy-to-understand analysis results. This ensures that the analysis results are easy for the user to understand by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the analysis criteria.

[0087] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of test results during the analysis process. For example, the analysis unit can analyze the interrelationships of test results and identify related problems. The analysis unit can also provide highly accurate analysis results by considering the interrelationships of test results. For example, the analysis unit can propose areas for improvement based on the interrelationships of test results. In this way, by considering the interrelationships of test results, it can provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the interrelationship data of test results into a generative AI, which can then improve the accuracy of its analysis by considering the interrelationships.

[0088] The analysis department can perform analysis while considering the attribute information of the test result submitter. For example, the analysis department can perform analysis while considering the position and years of experience of the test result submitter. The analysis department can also improve the accuracy of the analysis based on the past performance of the test result submitter. For example, the analysis department can propose optimal improvements based on the attribute information of the test result submitter. In this way, by considering the attribute information of the submitter, it proposes optimal improvements. Some or all of the above processing in the analysis department may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis department can input the attribute information of the test result submitter into a generative AI, and the generative AI can perform analysis while considering the attribute information.

[0089] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize displaying detailed analysis results. It can also prioritize displaying important analysis results if the user is in a hurry. For example, if the user is stressed, the analysis unit may prioritize displaying visually easy-to-understand analysis results. This adjusts the display order of the analysis results according to the user's emotions, providing analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the display order of the analysis results.

[0090] The analysis unit can perform analysis while considering the geographical distribution of test results. For example, the analysis unit can analyze the geographical distribution of test results and identify problems in each region. The analysis unit can also provide highly accurate analysis results by considering the geographical distribution of test results. For example, the analysis unit can propose areas for improvement in each region based on the geographical distribution of test results. In this way, problems in each region are identified by considering the geographical distribution. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input geographical distribution data of test results into a generative AI, and the generative AI can perform analysis while considering the geographical distribution.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the test results during the analysis process. For example, the analysis unit can refer to relevant literature related to the test results to provide highly accurate analysis results. The analysis unit can also propose optimal improvements based on the relevant literature related to the test results. For example, the analysis unit improves the accuracy of its analysis by considering relevant literature related to the test results. This allows it to provide highly accurate analysis results by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevant literature data of the test results into a generative AI, which can then refer to the relevant literature to improve the accuracy of the analysis.

[0092] The service provider can estimate the user's emotions and determine the priority of corrective actions and improvements to be provided based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed corrective actions and improvements. If the user is in a hurry, the service provider can also prioritize providing important corrective actions and improvements. For example, if the user is stressed, the service provider can provide visually easy-to-understand corrective actions and improvements. In this way, by determining the priority of corrective actions and improvements according to the user's emotions, the service provider can provide corrective actions and improvements that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of corrective actions and improvements.

[0093] The service provider can improve the accuracy of its service by considering the interrelationships of test results during the service provision process. For example, the service provider can analyze the interrelationships of test results and provide relevant correction strategies and improvements. The service provider can also provide highly accurate correction strategies and improvements by considering the interrelationships of test results. For example, the service provider can propose optimal correction strategies and improvements based on the interrelationships of test results. This allows for the provision of highly accurate correction strategies and improvements by considering the interrelationships of test results. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input interrelationship data of test results into a generative AI, which can then improve the accuracy of its service by considering the interrelationships.

[0094] The service provider can provide test results while considering the attribute information of the test result submitter. For example, the service provider can provide appropriate correction strategies and improvements by considering the test result submitter's position and years of experience. The service provider can also provide optimal correction strategies and improvements based on the test result submitter's past performance. For example, the service provider can propose optimal correction strategies and improvements based on the test result submitter's attribute information. In this way, by considering the submitter's attribute information, optimal correction strategies and improvements are provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the test result submitter's attribute information into a generative AI, and the generative AI can provide the results while considering the attribute information.

[0095] The service provider can estimate the user's emotions and adjust how corrective actions and improvements are displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display detailed corrective actions and improvements. If the user is in a hurry, the service provider can also display concise corrective actions and improvements. If the user is stressed, the service provider can display visually easy-to-understand corrective actions and improvements. By adjusting how corrective actions and improvements are displayed according to the user's emotions, the service provider can provide corrective actions and improvements that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust how corrective actions and improvements are displayed.

[0096] The service provider can provide test results while considering their geographical distribution. For example, the service provider can analyze the geographical distribution of test results and provide correction strategies and improvements for each region. The service provider can also provide highly accurate correction strategies and improvements while considering the geographical distribution of test results. For example, the service provider can propose optimal correction strategies and improvements for each region based on the geographical distribution of test results. In this way, by considering the geographical distribution, it provides optimal correction strategies and improvements for each region. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input geographical distribution data of test results into a generative AI, and the generative AI can provide the results while considering the geographical distribution.

[0097] The service provider can improve the accuracy of its provision by referring to relevant literature related to the test results at the time of provision. For example, the service provider can refer to relevant literature related to the test results to provide highly accurate correction strategies and improvements. The service provider can also propose optimal correction strategies and improvements based on the relevant literature related to the test results. For example, the service provider improves the accuracy of its provision by considering relevant literature related to the test results. This allows it to provide highly accurate correction strategies and improvements by referring to relevant literature. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the relevant literature data of the test results into a generating AI, and the generating AI can improve the accuracy of its provision by referring to the relevant literature.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception unit can estimate the user's emotions and adjust the timing of receiving test instructions based on the estimated emotions. For example, if the user is stressed, the reception of test instructions can be delayed until the user is relaxed. Conversely, if the user is focused, the test instructions can be accepted immediately, allowing for a quick start to the test. Furthermore, if the user is tired, the reception of test instructions can be postponed until the next day, allowing the user to rest. This reduces the user's burden by adjusting the timing of test instruction acceptance according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using generative AI or not. For example, the reception unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the reception timing.

[0100] The generation unit can adjust the level of detail generated based on the importance of the test during test generation. For example, it can generate detailed procedures and results for high-importance tests, and concise procedures and results for low-importance tests. Furthermore, it can dynamically adjust the level of detail of the test according to its importance. This allows for efficient test generation by adjusting the level of detail based on the importance of the test. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input test importance data into the generation AI, which can then adjust the level of detail of the test.

[0101] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. Furthermore, if the user is stressed, it can provide visually easy-to-understand analysis results. In this way, by adjusting the analysis criteria according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the analysis criteria.

[0102] The service provider can estimate the user's emotions and determine the priority of corrective measures and improvements to provide based on the estimated emotions. For example, if the user is relaxed, detailed corrective measures and improvements can be provided. If the user is in a hurry, important corrective measures and improvements can be prioritized. Furthermore, if the user is stressed, visually easy-to-understand corrective measures and improvements can be provided. In this way, by determining the priority of corrective measures and improvements according to the user's emotions, corrective measures and improvements that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of corrective measures and improvements.

[0103] The generation unit can estimate the user's emotions and adjust the length of the test generated based on the estimated emotions. For example, if the user is in a hurry, it can generate a short, concise test. If the user is relaxed, it can generate a longer test with detailed explanations. Furthermore, if the user is stressed, it can generate a simple and easy-to-understand test. By adjusting the length of the test according to the user's emotions, it is possible to provide the user with a test that is appropriate for them. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the length of the test.

[0104] The reception unit can analyze the user's past test instruction history and select the optimal reception method. For example, it can prioritize receiving test instructions that the user has frequently requested in the past. It can also analyze the user's past test instruction history to determine when they tend to request instructions and then process requests during those times. Furthermore, it can suggest the optimal reception method (voice, text, etc.) based on the user's past test instruction history. In this way, the optimal reception method can be selected by analyzing the past test instruction history. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's past test instruction history into a generative AI, which can then select the optimal reception method.

[0105] The generation unit can apply different generation algorithms depending on the test category when generating tests. For example, for login tests, an algorithm that checks the input of authentication information and the authentication result can be applied. For page display tests, an algorithm that checks the page load time and displayed content can be applied. Furthermore, for form input tests, an algorithm that checks the input fields and the submission result can be applied. This makes it possible to generate appropriate tests by applying different generation algorithms depending on the test category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input test category data into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0106] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of test results. For example, it can analyze the interrelationships of test results to identify related problems. It can also provide highly accurate analysis results by considering the interrelationships of test results. Furthermore, it can propose areas for improvement based on the interrelationships of test results. In this way, by considering the interrelationships of test results, it is possible to provide highly accurate analysis results. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the interrelationship data of test results into a generative AI, and the generative AI can improve the accuracy of the analysis by considering the interrelationships.

[0107] The service provider can improve the accuracy of its service by considering the interrelationships of test results during the service provision process. For example, it can analyze the interrelationships of test results and provide relevant correction strategies and improvements. It can also provide highly accurate correction strategies and improvements by considering the interrelationships of test results. Furthermore, it can propose optimal correction strategies and improvements based on the interrelationships of test results. In this way, by considering the interrelationships of test results, it is possible to provide highly accurate correction strategies and improvements. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the interrelationship data of test results into a generation AI, and the generation AI can improve the accuracy of its service by considering the interrelationships.

[0108] The service provider can estimate the user's emotions and adjust how corrective measures and improvements are displayed based on the estimated emotions. For example, if the user is relaxed, detailed corrective measures and improvements can be displayed. If the user is in a hurry, concise corrective measures and improvements can be displayed. Furthermore, if the user is stressed, visually easy-to-understand corrective measures and improvements can be displayed. In this way, by adjusting how corrective measures and improvements are displayed according to the user's emotions, corrective measures and improvements that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using a generative AI or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, the generative AI can estimate the emotions, and adjust how corrective measures and improvements are displayed.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception unit receives natural language test instructions from the user. These natural language test instructions can be in text or audio format. The reception unit analyzes text-format instructions using natural language processing technology and passes them to the generation unit. It can also convert audio-format instructions into text using speech recognition technology and pass them to the generation unit. Step 2: The generation unit automatically operates PC and smartphone browsers to perform tests based on instructions received by the reception unit. The generation unit uses automation tools such as Selenium to operate the browser and evaluate whether there are any display problems on the specified page and whether operations can be performed without problems. The generation unit can also automatically perform actions such as entering a username and password and clicking the login button. Step 3: The analysis unit analyzes the test results performed by the generation unit and reports on problems and areas for improvement. The analysis unit analyzes error logs and evaluates performance to identify areas where there may be problems with the implementation. Step 4: The service provider will provide specific correction strategies and improvement points based on the report obtained by the analysis department. The service provider will provide specific action plans for quality improvement, such as code corrections and UI improvements.

[0111] 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.

[0112] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives natural language test instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and executes the test by automatically operating a PC browser or smartphone browser. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the test results and reports problems and areas for improvement. The provision unit is implemented by the control unit 46A of the smart device 14 and provides specific correction policies and areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0120] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] 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.

[0122] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives natural language test instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically operates a PC browser or smartphone browser to execute the test. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the test results to report problems and areas for improvement. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides specific correction policies and areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0136] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] 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.

[0138] 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.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives natural language test instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically operates a PC browser or smartphone browser to execute the test. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the test results to report problems and areas for improvement. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides specific correction policies and areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0152] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] 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.

[0154] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] 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.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives natural language test instructions from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically operates a PC browser or smartphone browser to execute the test. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the test results to report problems and areas for improvement. The provision unit is implemented by the control unit 46A of the robot 414 and provides specific correction policies and areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] Figure 9 shows the 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.

[0166] 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.

[0167] 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.

[0168] 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, and motorcycles, 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 based, for example, 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.

[0169] 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."

[0170] 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.

[0171] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] 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 other things 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.

[0181] 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.

[0182] (Note 1) A reception desk that receives natural language testing instructions from users, A generation unit that automatically operates a PC browser or smartphone browser to execute a test based on instructions received by the reception unit, An analysis unit analyzes the test results performed by the generation unit and reports problems and areas for improvement. The system includes a provisioning unit that provides specific correction policies and improvement points based on the report obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The generating unit is The system automatically operates PC and smartphone browsers to run the tests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The system automatically analyzes test results daily to identify potential problems in the implementation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the test results report, we will provide specific correction plans and areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is This system automatically performs the actions of entering a username and password and clicking the login button. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is If an error message is displayed on the login page, identify the cause and provide a solution. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting test requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past test instruction history and select the optimal method for receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving test requests, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of test instructions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving test instructions, the system prioritizes accepting instructions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving test instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust the test presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating tests, adjust the level of detail based on the importance of the tests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating tests, different generation algorithms are applied depending on the test category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the tests generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating tests, the generation priority is determined based on when the tests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating tests, adjust the generation order based on the relevance of the tests. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, consider the interrelationships between test results to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During the analysis, the attribute information of the test result submitters will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which the analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When conducting the analysis, the geographical distribution of the test results should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, we refer to relevant literature related to the test results to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate user sentiment and determine the priority of corrective actions and improvements to provide based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we will improve the accuracy of the service by considering the interrelationships of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the test results, the attribute information of the person who submitted the test results will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We estimate user sentiment and adjust how corrective measures and improvements are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the results, we will take into account the geographical distribution of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the results, we will refer to relevant literature to improve the accuracy of the provided data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives natural language testing instructions from users, A generation unit that automatically operates a PC browser or smartphone browser to execute a test based on instructions received by the reception unit, An analysis unit analyzes the test results performed by the generation unit and reports on problems and areas for improvement. The system includes a provisioning unit that provides specific correction policies and improvement points based on the report obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit is The system automatically analyzes test results daily to identify potential problems in the implementation. The system according to feature 1.

3. The aforementioned supply unit is, Based on the test results report, we will provide specific correction plans and areas for improvement. The system according to feature 1.

4. The generating unit is This automates the process of entering a username and password and clicking the login button. The system according to feature 1.

5. The aforementioned analysis unit is If an error message is displayed on the login page, identify the cause and provide a solution. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of accepting test requests based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past test instruction history and select the optimal method for receiving requests. The system according to feature 1.

Images