System

Abstract

An object of the system according to the embodiment is to make the entire process of test automation efficient.SOLUTION: A system includes an information reception part, an information analysis part, a program generation part, a scenario examination part, and a result analysis part. The information reception unit receives information on a test object from a user. The information analysis unit analyzes the information received by the information reception unit. The program generation unit generates an automation program based on the information analyzed by the information analysis unit. The scenario examination unit examines the test scenario based on the automation program generated by the program generation unit. The result analysis part analyzes a test result on the basis of the test scenario examined by the scenario examination part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of requiring a lot of man-hours to create automation programs, consider test scenarios, and analyze test results.

[0005] The system according to the embodiment aims to streamline the entire process of test automation. [Means for solving the problem]

[0006] The system according to the embodiment includes an information receiving unit, an information analysis unit, a program generation unit, a scenario review unit, and a result analysis unit. The information receiving unit receives information about the test subject from a user. The information analysis unit analyzes the information received by the information receiving unit. The program generation unit generates an automation program based on the information analyzed by the information analysis unit. The scenario review unit reviews a test scenario based on the automation program generated by the program generation unit. The result analysis unit analyzes the test results based on the test scenario reviewed by the scenario review unit. [Effects of the Invention]

[0007] The system according to the embodiment can streamline the entire process of test automation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A complete test automation system according to an embodiment of the present invention accepts test target information from a user, analyzes that information, generates an automation program, considers test scenarios, and analyzes test results. In a complete test automation system, a user inputs information about the system or application under test, and an LLM analyzes that information to generate an automation program. For example, the user inputs information such as system functions, requirements, and test cases. This information is input into an LLM, which analyzes it and generates an appropriate automation program. The generated automation program also automatically considers test scenarios and analyzes test results. For example, an LLM analyzes the results of test case execution, identifies errors and defects, and reports their causes. This significantly reduces the labor required for test automation. Users simply input test target information; the LLM automatically executes tests, analyzes the results, and reports them, enabling hassle-free testing. For example, this system can handle a variety of tests, such as regression testing before software release and regression testing after system updates. This allows the complete test automation system to improve test efficiency and quality. For example, it significantly reduces the labor required for test automation, allowing users to perform tests with minimal effort. In addition, analysis of test results is performed automatically, allowing for quick and accurate feedback.

[0029] A complete test automation system according to an embodiment includes an information receiving unit, an information analysis unit, a program generation unit, a scenario review unit, and a result analysis unit. The information receiving unit receives test target information from a user. The test target information from the user includes, but is not limited to, system functions, requirements, and test cases. The information receiving unit receives information, for example, through an online form. The information receiving unit can also receive information offline. For example, the information receiving unit can receive documents and files provided by the user. The information analysis unit analyzes the information received by the information receiving unit using an LLM. The information analysis unit understands the information entered by the user and analyzes the system functions, requirements, test cases, and the like, for example, using natural language processing technology. For example, the LLM analyzes the information entered by the user using a text generation AI (e.g., an LLM). The information analysis unit can also analyze multiple modalities using a multimodal generation AI. The program generation unit generates an automation program based on the information analyzed by the information analysis unit. The program generation unit, for example, automatically generates test cases based on the system's functions and creates an automation program based on them. For example, the program generation unit automatically generates test cases using a text generation AI. The program generation unit can also analyze multiple modals using a multimodal generation AI to generate an automation program. The scenario review unit reviews test scenarios based on the automation program generated by the program generation unit. For example, the scenario review unit automatically generates test scenarios based on the generated automation program. For example, the scenario review unit automatically generates test scenarios using a text generation AI. The scenario review unit can also analyze multiple modals using a multimodal generation AI to generate test scenarios. The result analysis unit analyzes test results based on the test scenarios reviewed by the scenario review unit. For example, the result analysis unit analyzes the execution results of the test cases, identifies errors and malfunctions, and reports their causes. For example, the result analysis unit analyzes the test results using a text generation AI.The result analysis unit can also use a multimodal generation AI to analyze multiple modalities and report test results. This allows the complete test automation system according to the embodiment to efficiently accept and analyze user test target information, generate automation programs, consider test scenarios, and analyze test results.

[0030] The information accepting unit can analyze the user's past test information input history and select the optimal information accepting method. For example, the information accepting unit can automatically display information that the user has frequently input in the past as candidates. The information accepting unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The information accepting unit can also predict and suggest information that will be used during a specific time period based on the user's past input history. In this way, the optimal information accepting method can be selected by analyzing the user's past test information input history. Some or all of the above-mentioned processing in the information accepting unit can be performed using, or without, AI, for example. For example, the information accepting unit can input the user's past input history data into a generation AI and have the generation AI select the optimal information accepting method.

[0031] When receiving information, the information receiving unit can filter the information based on the user's current project or area of ​​interest. For example, the information receiving unit preferentially receives only information related to the project the user is currently working on. The information receiving unit can also filter and receive highly relevant information based on the user's area of ​​interest. The information receiving unit can also filter related information by referring to the user's past project history. In this way, by filtering information based on the user's current project or area of ​​interest, highly relevant information can be preferentially received. Some or all of the above-described processing in the information receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the information receiving unit can input the user's project information to a generation AI and cause the generation AI to filter the related information.

[0032] When receiving information, the information receiving unit can select an appropriate receiving means depending on the user's input method. For example, if the user selects voice input, the information receiving unit can receive information using voice recognition technology. Furthermore, if the user selects text input, the information receiving unit can also receive information using text analysis technology. Furthermore, if the user selects image input, the information receiving unit can also receive information using image analysis technology. This allows information to be received efficiently by selecting the optimal receiving means depending on the user's input method. Some or all of the above-mentioned processing in the information receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the information receiving unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0033] When receiving information, the information receiving unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information receiving unit prioritizes receiving information related to that area. Furthermore, when the user is moving, the information receiving unit can prioritize receiving related information based on the user's current location. Furthermore, when the user is in a specific location, the information receiving unit can prioritize receiving information related to that location. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the information receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the information receiving unit can input the user's location information data into the generation AI and cause the generation AI to select related information.

[0034] The information receiving unit can analyze the user's social media activity and receive related information when receiving information. The information receiving unit can receive related information based on, for example, information shared by the user on social media. The information receiving unit can also analyze the user's social media activity history and receive related information. The information receiving unit can also receive related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information can be preferentially received. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the information receiving unit can input the user's social media data into a generation AI and cause the generation AI to select related information.

[0035] The information receiving unit can customize the reception method by reflecting the user's past feedback when receiving information. The information receiving unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The information receiving unit can also customize the reception interface by reflecting the user's past feedback. The information receiving unit can also optimize the reception procedure by referring to the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the information receiving unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0036] The information analysis unit can adjust the level of detail of the analysis based on the system's importance during information analysis. For example, the information analysis unit performs a detailed analysis for an important system. The information analysis unit can also perform a standard analysis for a general system. The information analysis unit can also perform a simplified analysis for a low-importance system. In this way, by adjusting the level of detail of the analysis based on the system's importance, appropriate analysis results can be provided. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input system importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] When analyzing information, the information analysis unit can apply different analysis algorithms depending on the system category. For example, in the case of a web application, the information analysis unit can apply a specific security analysis algorithm. In addition, in the case of a mobile application, the information analysis unit can also apply a specific performance analysis algorithm. In addition, in the case of a desktop application, the information analysis unit can also apply a specific compatibility analysis algorithm. In this way, by applying an appropriate analysis algorithm depending on the system category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input system category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] When analyzing information, the information analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The information analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The information analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The information analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] When analyzing information, the information analysis unit can determine the priority of analysis based on the submission time of the system. For example, in the case of an emergency system, the information analysis unit performs analysis with the highest priority. In addition, in the case of a normal system, the information analysis unit can also perform analysis with standard priority. In addition, in the case of a low-priority system, the information analysis unit can also perform analysis at a later date. In this way, by determining the priority of analysis based on the submission time of the system, analysis can be performed at an appropriate time. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input system submission time data into the generation AI and have the generation AI determine the analysis priority.

[0040] During information analysis, the information analysis unit can adjust the order of analysis based on the relevance of the systems. For example, the information analysis unit may analyze an important system first. Furthermore, the information analysis unit may also analyze a general system in a standard order. Furthermore, the information analysis unit may also analyze a system with low relevance later. In this way, by adjusting the order of analysis based on the relevance of the systems, important systems can be analyzed preferentially. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit may input system relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0041] During information analysis, the information analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the information analysis unit uses detailed technical terminology for a user with high levels of expertise. The information analysis unit can also use standard technical terminology for a general user. The information analysis unit can also use simple terminology for a user with low levels of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the information analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the information analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0042] When generating a program, the program generation unit can adjust the level of detail of the generation based on the system's functions. For example, the program generation unit generates a detailed program for an important function. The program generation unit can also generate a standard program for a general function. The program generation unit can also generate a simplified program for a low-importance function. In this way, an appropriate program can be generated by adjusting the level of detail of the generation based on the system's functions. Some or all of the above-described processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input system function data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0043] When generating a program, the program generation unit can apply different generation algorithms depending on the system category. For example, in the case of a web application, the program generation unit can apply a specific security program generation algorithm. In addition, in the case of a mobile application, the program generation unit can also apply a specific performance program generation algorithm. In addition, in the case of a desktop application, the program generation unit can also apply a specific compatibility program generation algorithm. In this way, by applying an appropriate generation algorithm depending on the system category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input system category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0044] When generating a program, the program generation unit can improve the accuracy of generation by referring to the user's past generation results. The program generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The program generation unit can also improve the accuracy of generation by referring to the user's past generation results. The program generation unit can also analyze the user's past generation results and propose an optimal generation method. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0045] When generating a program, the program generation unit can determine the generation priority based on the system submission time. For example, in the case of an emergency system, the program generation unit generates a program with the highest priority. In addition, in the case of a normal system, the program generation unit can also generate a program with a standard priority. In addition, in the case of a low-priority system, the program generation unit can also generate a program at a later date. In this way, by determining the generation priority based on the system submission time, it is possible to generate a program at an appropriate time. Some or all of the above-mentioned processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input system submission time data into the generation AI and have the generation AI determine the generation priority.

[0046] When generating a program, the program generation unit can adjust the order of generation based on the relevance of the systems. For example, in the case of an important system, the program generation unit generates a program first. Furthermore, in the case of a general system, the program generation unit can also generate a program in a standard order. Furthermore, in the case of a system with low relevance, the program generation unit can postpone the generation of a program. In this way, by adjusting the order of generation based on the relevance of the systems, important systems can be generated with priority. Some or all of the above-described processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit inputs system relevance data into the generation AI and causes the generation AI to adjust the order of generation.

[0047] When generating a program, the program generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the program generation unit uses detailed technical terminology for a user with high levels of expertise. The program generation unit can also use standard technical terminology for a general user. The program generation unit can also use simple terminology for a user with low levels of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, a more understandable program can be generated. Some or all of the above-described processing in the program generation unit may be performed using AI, for example, or may be performed without using AI. For example, the program generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0048] When considering a scenario, the scenario consideration unit can adjust the level of detail of the consideration based on the system's functions. For example, the scenario consideration unit considers a detailed scenario for an important function. The scenario consideration unit can also consider a standard scenario for a general function. The scenario consideration unit can also consider a simplified scenario for a low-importance function. In this way, by adjusting the level of detail of the consideration based on the system's functions, an appropriate scenario can be considered. Some or all of the above-mentioned processing in the scenario consideration unit may be performed using, or without, AI, for example. For example, the scenario consideration unit can input system function data into a generation AI and have the generation AI adjust the level of detail of the consideration.

[0049] When considering a scenario, the scenario consideration unit can apply different consideration algorithms depending on the system category. For example, in the case of a web application, the scenario consideration unit can apply a specific security scenario consideration algorithm. In addition, in the case of a mobile application, the scenario consideration unit can also apply a specific performance scenario consideration algorithm. In addition, in the case of a desktop application, the scenario consideration unit can also apply a specific compatibility scenario consideration algorithm. This improves the accuracy of the consideration by applying an appropriate consideration algorithm depending on the system category. Some or all of the above-mentioned processing in the scenario consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario consideration unit can input system category data into the generation AI and cause the generation AI to apply the consideration algorithm.

[0050] When considering a scenario, the scenario review unit can improve the accuracy of the review by referring to the user's past review results. The scenario review unit, for example, adjusts the review algorithm based on the user's past review results. The scenario review unit can also improve the accuracy of the review by referring to the user's past review results. The scenario review unit can also analyze the user's past review results and propose an optimal review method. In this way, the accuracy of the review is improved by referring to the user's past review results. Some or all of the above-mentioned processing in the scenario review unit may be performed, for example, using AI or may be performed without using AI. For example, the scenario review unit can input the user's past review result data into the generation AI and have the generation AI improve the accuracy of the review.

[0051] When reviewing a scenario, the scenario review unit can determine the priority of the review based on the submission time of the system. For example, in the case of an urgent system, the scenario review unit reviews the scenario with the highest priority. In addition, in the case of a normal system, the scenario review unit can also review the scenario with standard priority. In addition, in the case of a low-priority system, the scenario review unit can also review the scenario at a later date. In this way, by determining the priority of the review based on the submission time of the system, scenarios can be reviewed at an appropriate time. Some or all of the above-mentioned processing in the scenario review unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario review unit can input system submission time data into the generation AI and have the generation AI determine the priority of the review.

[0052] When considering a scenario, the scenario review unit can adjust the order of consideration based on the relevance of the systems. For example, in the case of an important system, the scenario review unit will consider the scenario first. Furthermore, in the case of a general system, the scenario review unit can also consider scenarios in a standard order. Furthermore, in the case of a system with low relevance, the scenario review unit can postpone the consideration of scenarios. In this way, by adjusting the order of consideration based on the relevance of the systems, important systems can be given priority for consideration. Some or all of the above-mentioned processing in the scenario review unit may be performed, for example, using AI, or may be performed without using AI. For example, the scenario review unit can input system relevance data into the generation AI and have the generation AI adjust the order of consideration.

[0053] When considering a scenario, the scenario review unit can adjust the use of technical terminology in the review depending on the user's level of expertise. For example, the scenario review unit uses detailed technical terminology for a user with high levels of expertise. The scenario review unit can also use standard technical terminology for a general user. The scenario review unit can also use simple terminology for a user with low levels of expertise. In this way, by adjusting the use of technical terminology in the review depending on the user's level of expertise, a more understandable scenario can be considered. Some or all of the above-mentioned processing in the scenario review unit may be performed using AI, for example, or may be performed without using AI. For example, the scenario review unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0054] The result analysis unit can adjust the level of detail of the analysis based on the system's functions during result analysis. For example, the result analysis unit performs a detailed analysis for important functions. The result analysis unit can also perform a standard analysis for general functions. The result analysis unit can also perform a simplified analysis for functions of low importance. In this way, by adjusting the level of detail of the analysis based on the system's functions, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input system function data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0055] The result analysis unit can apply different analysis algorithms depending on the system category when analyzing the results. For example, the result analysis unit can apply a specific security analysis algorithm to a web application. The result analysis unit can also apply a specific performance analysis algorithm to a mobile application. The result analysis unit can also apply a specific compatibility analysis algorithm to a desktop application. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the system category. Some or all of the above-mentioned processing in the result analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the result analysis unit can input system category data to a generation AI and cause the generation AI to apply the analysis algorithm.

[0056] During result analysis, the result analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The result analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The result analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The result analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0057] When analyzing results, the result analysis unit can determine the priority of analysis based on the system submission time. For example, in the case of an urgent system, the result analysis unit performs analysis with the highest priority. In addition, in the case of a normal system, the result analysis unit can also perform analysis with standard priority. In addition, in the case of a low-priority system, the result analysis unit can also perform analysis at a later date. In this way, by determining the priority of analysis based on the system submission time, analysis can be performed at an appropriate time. Some or all of the above-mentioned processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input system submission time data into the generation AI and have the generation AI determine the analysis priority.

[0058] The result analysis unit can adjust the order of analysis based on the relevance of the systems during result analysis. For example, the result analysis unit may analyze an important system first. The result analysis unit can also analyze a general system in a standard order. The result analysis unit can also postpone the analysis of a system with low relevance. In this way, by adjusting the order of analysis based on the relevance of the systems, important systems can be analyzed preferentially. Some or all of the above-described processing in the result analysis unit may be performed using, or without, AI, for example. For example, the result analysis unit can input system relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0059] During the result analysis, the result analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the result analysis unit uses detailed technical terminology for a user with high levels of expertise. The result analysis unit can also use standard technical terminology for a general user. The result analysis unit can also use simple terminology for a user with low levels of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The information accepting unit can learn the user's past input patterns and dynamically provide the optimal input interface. For example, if the user has frequently used voice input in the past, the voice input interface can be displayed preferentially. Also, if the user frequently inputs information during a specific time period, the interface can be optimized to suit that time period. Furthermore, related input fields can be automatically suggested based on the type of information the user has input in the past. This makes it possible to accept information more efficiently by utilizing the user's past input patterns.

[0062] The information receiving unit can analyze the user's past input data and provide feedback to improve input accuracy. For example, if the user has frequently made input errors in the past, guidelines to prevent those errors can be displayed. Also, if the user takes a long time to input data into a particular field, input assistance for that field can be provided. Furthermore, an auto-completion function can be provided based on the data the user has previously input. This makes it possible to improve input accuracy and efficiency by utilizing the user's past input data.

[0063] The information accepting unit can provide customized templates to improve input efficiency based on the user's past input history. For example, if the user has previously input information in a specific format, a template based on that format can be automatically generated. It can also provide information that the user frequently inputs in a pre-entered state. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. This makes it possible to improve input efficiency by utilizing the user's past input history.

[0064] The information receiving unit can dynamically add region-specific input fields based on the user's geographical location information. For example, if the user is in a specific region, a field for inputting information related to that region can be automatically displayed. Also, if the user is moving, related information can be preferentially received based on the user's current location. Furthermore, if the user is in a specific location, information related to that location can be filtered and received. This makes it possible to receive more relevant information by utilizing the user's geographical location information.

[0065] The information receiving unit can analyze the user's social media activity and automatically suggest related information. For example, it can automatically display related input fields based on information the user has shared on social media. It can also analyze the user's social media activity history and preferentially accept related information. It can also suggest related information based on the activities of the user's friends on social media. This makes it possible to accept more relevant information by utilizing the user's social media activity.

[0066] The information receiving unit can dynamically customize the input interface based on the user's past feedback. For example, it can propose an optimal input interface based on the user's past feedback. It can also adjust the layout and design of input fields by reflecting the user's past feedback. It can also optimize the input procedure by referring to the user's past feedback. In this way, it is possible to provide an easier-to-use input interface by utilizing the user's past feedback.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The information receiving unit receives information about the test subject from the user. The information about the test subject from the user includes system functions, requirements, test cases, etc. The information receiving unit can receive information through an online form or offline documents or files. Step 2: The information analysis unit analyzes the information received by the information reception unit. Using LLM and natural language processing technology, the information analysis unit understands the information entered by the user and analyzes the system's functions, requirements, test cases, etc. It can also analyze multiple modalities using multimodal generation AI. Step 3: The program generation unit generates an automation program based on the information analyzed by the information analysis unit. The program generation unit automatically generates test cases based on the system's functions and creates an automation program based on them. It is also possible to use text generation AI or multimodal generation AI to analyze multiple modalities and generate an automation program. Step 4: The scenario review unit reviews test scenarios based on the automation program generated by the program generation unit. The scenario review unit automatically generates test scenarios based on the generated automation program. It can also generate test scenarios by analyzing multiple modalities using text generation AI or multimodal generation AI. Step 5: The Result Analysis Unit analyzes the test results based on the test scenarios reviewed by the Scenario Review Unit. The Result Analysis Unit analyzes the test case execution results, identifies errors and defects, and reports their causes. It can also use text generation AI or multimodal generation AI to analyze multiple modalities and report the test results.

[0069] (Example 2) A complete test automation system according to an embodiment of the present invention accepts test target information from a user, analyzes that information, generates an automation program, considers test scenarios, and analyzes test results. In a complete test automation system, a user inputs information about the system or application under test, and an LLM analyzes that information to generate an automation program. For example, the user inputs information such as system functions, requirements, and test cases. This information is input into an LLM, which analyzes it and generates an appropriate automation program. The generated automation program also automatically considers test scenarios and analyzes test results. For example, an LLM analyzes the results of test case execution, identifies errors and defects, and reports their causes. This significantly reduces the labor required for test automation. Users simply input test target information; the LLM automatically executes tests, analyzes the results, and reports them, enabling hassle-free testing. For example, this system can handle a variety of tests, such as regression testing before software release and regression testing after system updates. This allows the complete test automation system to improve test efficiency and quality. For example, it significantly reduces the labor required for test automation, allowing users to perform tests with minimal effort. In addition, analysis of test results is performed automatically, allowing for quick and accurate feedback.

[0070] A complete test automation system according to an embodiment includes an information receiving unit, an information analysis unit, a program generation unit, a scenario review unit, and a result analysis unit. The information receiving unit receives test target information from a user. The test target information from the user includes, but is not limited to, system functions, requirements, and test cases. The information receiving unit receives information, for example, through an online form. The information receiving unit can also receive information offline. For example, the information receiving unit can receive documents and files provided by the user. The information analysis unit analyzes the information received by the information receiving unit using an LLM. The information analysis unit understands the information entered by the user and analyzes the system functions, requirements, test cases, and the like, for example, using natural language processing technology. For example, the LLM analyzes the information entered by the user using a text generation AI (e.g., an LLM). The information analysis unit can also analyze multiple modalities using a multimodal generation AI. The program generation unit generates an automation program based on the information analyzed by the information analysis unit. The program generation unit, for example, automatically generates test cases based on the system's functions and creates an automation program based on them. For example, the program generation unit automatically generates test cases using a text generation AI. The program generation unit can also analyze multiple modals using a multimodal generation AI to generate an automation program. The scenario review unit reviews test scenarios based on the automation program generated by the program generation unit. For example, the scenario review unit automatically generates test scenarios based on the generated automation program. For example, the scenario review unit automatically generates test scenarios using a text generation AI. The scenario review unit can also analyze multiple modals using a multimodal generation AI to generate test scenarios. The result analysis unit analyzes test results based on the test scenarios reviewed by the scenario review unit. For example, the result analysis unit analyzes the execution results of the test cases, identifies errors and malfunctions, and reports their causes. For example, the result analysis unit analyzes the test results using a text generation AI.The result analysis unit can also use a multimodal generation AI to analyze multiple modalities and report test results. This allows the complete test automation system according to the embodiment to efficiently accept and analyze user test target information, generate automation programs, consider test scenarios, and analyze test results.

[0071] The information receiving unit can estimate the user's emotions and adjust the timing of information reception based on the estimated user emotions. For example, if the user is feeling stressed, the information receiving unit can temporarily delay information reception to provide time for the user to relax. Furthermore, if the user is concentrating, the information receiving unit can immediately receive information and efficiently collect information. Furthermore, if the user is tired, the information receiving unit can start with simple questions and gradually request more detailed information. This allows the timing of information reception to be adjusted according to the user's emotions, allowing information to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information receiving unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0072] The information accepting unit can analyze the user's past test information input history and select the optimal information accepting method. For example, the information accepting unit can automatically display information that the user has frequently input in the past as candidates. The information accepting unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The information accepting unit can also predict and suggest information that will be used during a specific time period based on the user's past input history. In this way, the optimal information accepting method can be selected by analyzing the user's past test information input history. Some or all of the above-mentioned processing in the information accepting unit can be performed using, or without, AI, for example. For example, the information accepting unit can input the user's past input history data into a generation AI and have the generation AI select the optimal information accepting method.

[0073] When receiving information, the information receiving unit can filter the information based on the user's current project or area of ​​interest. For example, the information receiving unit preferentially receives only information related to the project the user is currently working on. The information receiving unit can also filter and receive highly relevant information based on the user's area of ​​interest. The information receiving unit can also filter related information by referring to the user's past project history. In this way, by filtering information based on the user's current project or area of ​​interest, highly relevant information can be preferentially received. Some or all of the above-described processing in the information receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the information receiving unit can input the user's project information to a generation AI and cause the generation AI to filter the related information.

[0074] When receiving information, the information receiving unit can select an appropriate receiving means depending on the user's input method. For example, if the user selects voice input, the information receiving unit can receive information using voice recognition technology. Furthermore, if the user selects text input, the information receiving unit can also receive information using text analysis technology. Furthermore, if the user selects image input, the information receiving unit can also receive information using image analysis technology. This allows information to be received efficiently by selecting the optimal receiving means depending on the user's input method. Some or all of the above-mentioned processing in the information receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the information receiving unit can input voice data to a generation AI and have the generation AI perform voice recognition.

[0075] The information receiving unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user's emotions. For example, when the user is nervous, the information receiving unit can prioritize receiving important information. Furthermore, when the user is relaxed, the information receiving unit can prioritize receiving detailed information. Furthermore, when the user is in a hurry, the information receiving unit can prioritize receiving information that can be processed quickly. Thus, by determining the priority of information according to the user's emotions, important information can be preferentially received. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information receiving unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] When receiving information, the information receiving unit can prioritize receiving highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information receiving unit prioritizes receiving information related to that area. Furthermore, when the user is moving, the information receiving unit can prioritize receiving related information based on the user's current location. Furthermore, when the user is in a specific location, the information receiving unit can prioritize receiving information related to that location. In this way, by taking the user's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the information receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the information receiving unit can input the user's location information data into the generation AI and cause the generation AI to select related information.

[0077] The information receiving unit can analyze the user's social media activity and receive related information when receiving information. The information receiving unit can receive related information based on, for example, information shared by the user on social media. The information receiving unit can also analyze the user's social media activity history and receive related information. The information receiving unit can also receive related information by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, related information can be preferentially received. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the information receiving unit can input the user's social media data into a generation AI and cause the generation AI to select related information.

[0078] The information receiving unit can customize the reception method by reflecting the user's past feedback when receiving information. The information receiving unit can, for example, propose an optimal reception method based on feedback provided by the user in the past. The information receiving unit can also customize the reception interface by reflecting the user's past feedback. The information receiving unit can also optimize the reception procedure by referring to the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the information receiving unit can be performed using, for example, AI, or can be performed without using AI. For example, the information receiving unit can input the user's feedback data into the generation AI and have the generation AI customize the reception method.

[0079] The information analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the information analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the information analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the information analysis unit can provide analysis results that focus on the main points. By adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information analysis unit can be performed using, for example, an AI, or without an AI. For example, the information analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] The information analysis unit can adjust the level of detail of the analysis based on the system's importance during information analysis. For example, the information analysis unit performs a detailed analysis for an important system. The information analysis unit can also perform a standard analysis for a general system. The information analysis unit can also perform a simplified analysis for a low-importance system. In this way, by adjusting the level of detail of the analysis based on the system's importance, appropriate analysis results can be provided. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input system importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0081] When analyzing information, the information analysis unit can apply different analysis algorithms depending on the system category. For example, in the case of a web application, the information analysis unit can apply a specific security analysis algorithm. In addition, in the case of a mobile application, the information analysis unit can also apply a specific performance analysis algorithm. In addition, in the case of a desktop application, the information analysis unit can also apply a specific compatibility analysis algorithm. In this way, by applying an appropriate analysis algorithm depending on the system category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input system category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0082] When analyzing information, the information analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The information analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The information analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The information analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0083] The information analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the information analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the information analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the information analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information analysis unit can be performed using, for example, an AI, or without an AI. For example, the information analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] When analyzing information, the information analysis unit can determine the priority of analysis based on the submission time of the system. For example, in the case of an emergency system, the information analysis unit performs analysis with the highest priority. In addition, in the case of a normal system, the information analysis unit can also perform analysis with standard priority. In addition, in the case of a low-priority system, the information analysis unit can also perform analysis at a later date. In this way, by determining the priority of analysis based on the submission time of the system, analysis can be performed at an appropriate time. Some or all of the above-mentioned processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit can input system submission time data into the generation AI and have the generation AI determine the analysis priority.

[0085] During information analysis, the information analysis unit can adjust the order of analysis based on the relevance of the systems. For example, the information analysis unit may analyze an important system first. Furthermore, the information analysis unit may also analyze a general system in a standard order. Furthermore, the information analysis unit may also analyze a system with low relevance later. In this way, by adjusting the order of analysis based on the relevance of the systems, important systems can be analyzed preferentially. Some or all of the above-described processing in the information analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the information analysis unit may input system relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0086] During information analysis, the information analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the information analysis unit uses detailed technical terminology for a user with high levels of expertise. The information analysis unit can also use standard technical terminology for a general user. The information analysis unit can also use simple terminology for a user with low levels of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the information analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the information analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0087] The program generation unit can estimate the user's emotions and adjust the program generation method based on the estimated user's emotions. For example, if the user is relaxed, the program generation unit can generate a program that proceeds at a leisurely pace. If the user is in a hurry, the program generation unit can also generate a program that emphasizes the shortest route. If the user is excited, the program generation unit can also generate a program that adds visually stimulating effects. By adjusting the program generation method according to the user's emotions, a more appropriate program can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the program generation unit can be performed using, for example, an AI, or without an AI. For example, the program generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0088] When generating a program, the program generation unit can adjust the level of detail of the generation based on the system's functions. For example, the program generation unit generates a detailed program for an important function. The program generation unit can also generate a standard program for a general function. The program generation unit can also generate a simplified program for a low-importance function. In this way, an appropriate program can be generated by adjusting the level of detail of the generation based on the system's functions. Some or all of the above-described processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input system function data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0089] When generating a program, the program generation unit can apply different generation algorithms depending on the system category. For example, in the case of a web application, the program generation unit can apply a specific security program generation algorithm. In addition, in the case of a mobile application, the program generation unit can also apply a specific performance program generation algorithm. In addition, in the case of a desktop application, the program generation unit can also apply a specific compatibility program generation algorithm. In this way, by applying an appropriate generation algorithm depending on the system category, the accuracy of generation is improved. Some or all of the above-mentioned processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input system category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0090] When generating a program, the program generation unit can improve the accuracy of generation by referring to the user's past generation results. The program generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The program generation unit can also improve the accuracy of generation by referring to the user's past generation results. The program generation unit can also analyze the user's past generation results and propose an optimal generation method. In this way, the accuracy of generation is improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0091] The program generation unit can estimate the user's emotions and determine the priority of programs to be generated based on the estimated user emotions. For example, if the user is nervous, the program generation unit can prioritize generating important programs. Furthermore, if the user is relaxed, the program generation unit can also prioritize generating detailed programs. Furthermore, if the user is in a hurry, the program generation unit can prioritize generating programs that can be generated quickly. Thus, by determining the priority of programs according to the user's emotions, important programs can be generated preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the program generation unit can be performed using, for example, an AI, or without an AI. For example, the program generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0092] When generating a program, the program generation unit can determine the generation priority based on the system submission time. For example, in the case of an emergency system, the program generation unit generates a program with the highest priority. In addition, in the case of a normal system, the program generation unit can also generate a program with a standard priority. In addition, in the case of a low-priority system, the program generation unit can also generate a program at a later date. In this way, by determining the generation priority based on the system submission time, it is possible to generate a program at an appropriate time. Some or all of the above-mentioned processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit can input system submission time data into the generation AI and have the generation AI determine the generation priority.

[0093] When generating a program, the program generation unit can adjust the order of generation based on the relevance of the systems. For example, in the case of an important system, the program generation unit generates a program first. Furthermore, in the case of a general system, the program generation unit can also generate a program in a standard order. Furthermore, in the case of a system with low relevance, the program generation unit can postpone the generation of a program. In this way, by adjusting the order of generation based on the relevance of the systems, important systems can be generated with priority. Some or all of the above-described processing in the program generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the program generation unit inputs system relevance data into the generation AI and causes the generation AI to adjust the order of generation.

[0094] When generating a program, the program generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the program generation unit uses detailed technical terminology for a user with high levels of expertise. The program generation unit can also use standard technical terminology for a general user. The program generation unit can also use simple terminology for a user with low levels of expertise. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, a more understandable program can be generated. Some or all of the above-described processing in the program generation unit may be performed using AI, for example, or may be performed without using AI. For example, the program generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0095] The scenario review unit can estimate the user's emotions and adjust the scenario review method based on the estimated user emotions. For example, if the user is relaxed, the scenario review unit can review a detailed scenario. Furthermore, if the user is in a hurry, the scenario review unit can review a scenario that focuses on the main points. Furthermore, if the user is excited, the scenario review unit can review a visually stimulating scenario. By adjusting the scenario review method according to the user's emotions, a more appropriate scenario can be developed. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the scenario review unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scenario review unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0096] When considering a scenario, the scenario consideration unit can adjust the level of detail of the consideration based on the system's functions. For example, the scenario consideration unit considers a detailed scenario for an important function. The scenario consideration unit can also consider a standard scenario for a general function. The scenario consideration unit can also consider a simplified scenario for a low-importance function. In this way, by adjusting the level of detail of the consideration based on the system's functions, an appropriate scenario can be considered. Some or all of the above-mentioned processing in the scenario consideration unit may be performed using, or without, AI, for example. For example, the scenario consideration unit can input system function data into a generation AI and have the generation AI adjust the level of detail of the consideration.

[0097] When considering a scenario, the scenario consideration unit can apply different consideration algorithms depending on the system category. For example, in the case of a web application, the scenario consideration unit can apply a specific security scenario consideration algorithm. In addition, in the case of a mobile application, the scenario consideration unit can also apply a specific performance scenario consideration algorithm. In addition, in the case of a desktop application, the scenario consideration unit can also apply a specific compatibility scenario consideration algorithm. This improves the accuracy of the consideration by applying an appropriate consideration algorithm depending on the system category. Some or all of the above-mentioned processing in the scenario consideration unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario consideration unit can input system category data into the generation AI and cause the generation AI to apply the consideration algorithm.

[0098] When considering a scenario, the scenario review unit can improve the accuracy of the review by referring to the user's past review results. The scenario review unit, for example, adjusts the review algorithm based on the user's past review results. The scenario review unit can also improve the accuracy of the review by referring to the user's past review results. The scenario review unit can also analyze the user's past review results and propose an optimal review method. In this way, the accuracy of the review is improved by referring to the user's past review results. Some or all of the above-mentioned processing in the scenario review unit may be performed, for example, using AI or may be performed without using AI. For example, the scenario review unit can input the user's past review result data into the generation AI and have the generation AI improve the accuracy of the review.

[0099] The scenario review unit can estimate the user's emotions and determine the priority of scenarios to be considered based on the estimated user emotions. For example, if the user is nervous, the scenario review unit can prioritize important scenarios. Furthermore, if the user is relaxed, the scenario review unit can prioritize detailed scenarios. Furthermore, if the user is in a hurry, the scenario review unit can prioritize scenarios that can be quickly reviewed. Thus, by determining the priority of scenarios according to the user's emotions, important scenarios can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the scenario review unit can be performed using, for example, an AI, or without an AI. For example, the scenario review unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] When reviewing a scenario, the scenario review unit can determine the priority of the review based on the submission time of the system. For example, in the case of an urgent system, the scenario review unit reviews the scenario with the highest priority. In addition, in the case of a normal system, the scenario review unit can also review the scenario with standard priority. In addition, in the case of a low-priority system, the scenario review unit can also review the scenario at a later date. In this way, by determining the priority of the review based on the submission time of the system, scenarios can be reviewed at an appropriate time. Some or all of the above-mentioned processing in the scenario review unit may be performed using, for example, AI, or may be performed without using AI. For example, the scenario review unit can input system submission time data into the generation AI and have the generation AI determine the priority of the review.

[0101] When considering a scenario, the scenario review unit can adjust the order of consideration based on the relevance of the systems. For example, in the case of an important system, the scenario review unit will consider the scenario first. Furthermore, in the case of a general system, the scenario review unit can also consider scenarios in a standard order. Furthermore, in the case of a system with low relevance, the scenario review unit can postpone the consideration of scenarios. In this way, by adjusting the order of consideration based on the relevance of the systems, important systems can be given priority for consideration. Some or all of the above-mentioned processing in the scenario review unit may be performed, for example, using AI, or may be performed without using AI. For example, the scenario review unit can input system relevance data into the generation AI and have the generation AI adjust the order of consideration.

[0102] When considering a scenario, the scenario review unit can adjust the use of technical terminology in the review depending on the user's level of expertise. For example, the scenario review unit uses detailed technical terminology for a user with high levels of expertise. The scenario review unit can also use standard technical terminology for a general user. The scenario review unit can also use simple terminology for a user with low levels of expertise. In this way, by adjusting the use of technical terminology in the review depending on the user's level of expertise, a more understandable scenario can be considered. Some or all of the above-mentioned processing in the scenario review unit may be performed using AI, for example, or may be performed without using AI. For example, the scenario review unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0103] The result analysis unit can estimate the user's emotions and adjust the result analysis method based on the estimated user emotions. For example, if the user is nervous, the result analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the result analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the result analysis unit can provide analysis results that focus on the main points. By adjusting the result analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the result analysis unit can be performed using, for example, AI, or without AI. For example, the result analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0104] The result analysis unit can adjust the level of detail of the analysis based on the system's functions during result analysis. For example, the result analysis unit performs a detailed analysis for important functions. The result analysis unit can also perform a standard analysis for general functions. The result analysis unit can also perform a simplified analysis for functions of low importance. In this way, by adjusting the level of detail of the analysis based on the system's functions, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input system function data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0105] The result analysis unit can apply different analysis algorithms depending on the system category when analyzing the results. For example, the result analysis unit can apply a specific security analysis algorithm to a web application. The result analysis unit can also apply a specific performance analysis algorithm to a mobile application. The result analysis unit can also apply a specific compatibility analysis algorithm to a desktop application. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the system category. Some or all of the above-mentioned processing in the result analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the result analysis unit can input system category data to a generation AI and cause the generation AI to apply the analysis algorithm.

[0106] During result analysis, the result analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The result analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The result analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The result analysis unit can also analyze the user's past analysis results and propose an optimal analysis method. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0107] The result analysis unit can estimate the user's emotions and determine the priority of the results to be analyzed based on the estimated user's emotions. For example, if the user is nervous, the result analysis unit can prioritize analyzing important results. Furthermore, if the user is relaxed, the result analysis unit can also prioritize analyzing detailed results. Furthermore, if the user is in a hurry, the result analysis unit can prioritize analyzing results that can be analyzed quickly. Thus, by determining the priority of results according to the user's emotions, important results can be analyzed preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the result analysis unit can be performed using, for example, an AI, or without an AI. For example, the result analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0108] When analyzing results, the result analysis unit can determine the priority of analysis based on the system submission time. For example, in the case of an urgent system, the result analysis unit performs analysis with the highest priority. In addition, in the case of a normal system, the result analysis unit can also perform analysis with standard priority. In addition, in the case of a low-priority system, the result analysis unit can also perform analysis at a later date. In this way, by determining the priority of analysis based on the system submission time, analysis can be performed at an appropriate time. Some or all of the above-mentioned processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input system submission time data into the generation AI and have the generation AI determine the analysis priority.

[0109] The result analysis unit can adjust the order of analysis based on the relevance of the systems during result analysis. For example, the result analysis unit may analyze an important system first. The result analysis unit can also analyze a general system in a standard order. The result analysis unit can also postpone the analysis of a system with low relevance. In this way, by adjusting the order of analysis based on the relevance of the systems, important systems can be analyzed preferentially. Some or all of the above-described processing in the result analysis unit may be performed using, or without, AI, for example. For example, the result analysis unit can input system relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0110] During the result analysis, the result analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the result analysis unit uses detailed technical terminology for a user with high levels of expertise. The result analysis unit can also use standard technical terminology for a general user. The result analysis unit can also use simple terminology for a user with low levels of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the result analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the result analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the information receiving unit, information analysis unit, program generation unit, scenario review unit, and result analysis unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information receiving unit can receive test target information from a user using the reception device 38 of the smart device 14. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using the LLM. The program generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an automation program based on the analyzed information. The scenario review unit is realized by the specific processing unit 290 of the data processing device 12 and reviews a test scenario based on the generated automation program. The result analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes test results based on the test scenario. === Hard Collateral 1-2 === Each of the multiple elements, including the information receiving unit, information analysis unit, program generation unit, scenario review unit, and result analysis unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information receiving unit can receive test target information from a user using the microphone 238 of the smart glasses 214. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using the LLM. The program generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an automation program based on the analyzed information. The scenario review unit is realized by the specific processing unit 290 of the data processing device 12 and reviews a test scenario based on the generated automation program. The result analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes test results based on the test scenario. === Hard Collateral 1-3 === Each of the multiple elements including the information receiving unit, information analysis unit, program generation unit, scenario review unit, and result analysis unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the information receiving unit can receive test target information from a user using the microphone 238 of the headset-type terminal 314. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using the LLM. The program generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an automation program based on the analyzed information. The scenario review unit is realized by the specific processing unit 290 of the data processing device 12 and reviews a test scenario based on the generated automation program. The result analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes test results based on the test scenario. === Hard Collateral 1-4 === Each of the multiple elements, including the information receiving unit, information analysis unit, program generation unit, scenario review unit, and result analysis unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information receiving unit can receive test target information from a user using the microphone 238 of the robot 414. The information analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the received information using the LLM. The program generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an automation program based on the analyzed information. The scenario review unit is realized by the specific processing unit 290 of the data processing device 12 and reviews a test scenario based on the generated automation program. The result analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes test results based on the test scenario.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The information accepting unit can learn the user's past input patterns and dynamically provide the optimal input interface. For example, if the user has frequently used voice input in the past, the voice input interface can be displayed preferentially. Also, if the user frequently inputs information during a specific time period, the interface can be optimized to suit that time period. Furthermore, related input fields can be automatically suggested based on the type of information the user has input in the past. This makes it possible to accept information more efficiently by utilizing the user's past input patterns.

[0113] The information receiving unit can estimate the user's emotions and dynamically change the interface design based on the estimated emotions. For example, if the user is feeling stressed, a simple and calm interface design can be provided. If the user is relaxed, a colorful and interactive design can be provided. Furthermore, if the user is in a hurry, the interface can be simplified so that information can be entered with a minimum of clicks. This makes it possible to provide an optimal interface according to the user's emotions.

[0114] The information receiving unit can analyze the user's past input data and provide feedback to improve input accuracy. For example, if the user has frequently made input errors in the past, guidelines to prevent those errors can be displayed. Also, if the user takes a long time to input data into a particular field, input assistance for that field can be provided. Furthermore, an auto-completion function can be provided based on the data the user has previously input. This makes it possible to improve input accuracy and efficiency by utilizing the user's past input data.

[0115] The information accepting unit can dynamically change the order of input fields based on the user's current emotional state. For example, if the user is nervous, important fields can be displayed first, and if the user is relaxed, detailed fields can be displayed later. Also, if the user is in a hurry, only the most important fields can be displayed, and other fields can be entered later. Furthermore, if the user is excited, visually stimulating fields can be displayed preferentially. This makes it possible to provide an optimal order of input fields according to the user's emotional state.

[0116] The information accepting unit can provide customized templates to improve input efficiency based on the user's past input history. For example, if the user has previously input information in a specific format, a template based on that format can be automatically generated. It can also provide information that the user frequently inputs in a pre-entered state. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. This makes it possible to improve input efficiency by utilizing the user's past input history.

[0117] The information receiving unit can estimate the user's emotions and adjust the difficulty of input based on the estimated emotions. For example, if the user is feeling stressed, the unit can start with simple questions and gradually request more detailed information. If the user is relaxed, the unit can present detailed questions all at once. Furthermore, if the user is in a hurry, the unit can request only the most important information. This makes it possible to provide an optimal level of difficulty for input according to the user's emotions.

[0118] The information receiving unit can dynamically add region-specific input fields based on the user's geographical location information. For example, if the user is in a specific region, a field for inputting information related to that region can be automatically displayed. Also, if the user is moving, related information can be preferentially received based on the user's current location. Furthermore, if the user is in a specific location, information related to that location can be filtered and received. This makes it possible to receive more relevant information by utilizing the user's geographical location information.

[0119] The information receiving unit can analyze the user's social media activity and automatically suggest related information. For example, it can automatically display related input fields based on information the user has shared on social media. It can also analyze the user's social media activity history and preferentially accept related information. It can also suggest related information based on the activities of the user's friends on social media. This makes it possible to accept more relevant information by utilizing the user's social media activity.

[0120] The information receiving unit can dynamically customize the input interface based on the user's past feedback. For example, it can propose an optimal input interface based on the user's past feedback. It can also adjust the layout and design of input fields by reflecting the user's past feedback. It can also optimize the input procedure by referring to the user's past feedback. In this way, it is possible to provide an easier-to-use input interface by utilizing the user's past feedback.

[0121] The information analysis unit can estimate the user's emotions and dynamically change the way the analysis results are presented based on the estimated emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can also provide detailed analysis results. Furthermore, if the user is in a hurry, it can also provide analysis results that focus on the main points. This makes it possible to provide the optimal way to present analysis results according to the user's emotions.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The information receiving unit receives information about the test subject from the user. The information about the test subject from the user includes system functions, requirements, test cases, etc. The information receiving unit can receive information through an online form or offline documents or files. Step 2: The information analysis unit analyzes the information received by the information reception unit. Using LLM and natural language processing technology, the information analysis unit understands the information entered by the user and analyzes the system's functions, requirements, test cases, etc. It can also analyze multiple modalities using multimodal generation AI. Step 3: The program generation unit generates an automation program based on the information analyzed by the information analysis unit. The program generation unit automatically generates test cases based on the system's functions and creates an automation program based on them. It is also possible to use text generation AI or multimodal generation AI to analyze multiple modalities and generate an automation program. Step 4: The scenario review unit reviews test scenarios based on the automation program generated by the program generation unit. The scenario review unit automatically generates test scenarios based on the generated automation program. It can also generate test scenarios by analyzing multiple modalities using text generation AI or multimodal generation AI. Step 5: The Result Analysis Unit analyzes the test results based on the test scenarios reviewed by the Scenario Review Unit. The Result Analysis Unit analyzes the test case execution results, identifies errors and defects, and reports their causes. It can also use text generation AI or multimodal generation AI to analyze multiple modalities and report the test results.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] 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.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information receiving unit that receives information about a test subject from a user; an information analysis unit that analyzes the information received by the information receiving unit; a program generation unit that generates an automation program based on the information analyzed by the information analysis unit; a scenario review unit that reviews a test scenario based on the automation program generated by the program generation unit; a result analysis unit that analyzes test results based on the test scenarios reviewed by the scenario review unit; Equipped with A system characterized by:

2. The information receiving unit Estimates user emotions and adjusts the timing of information reception based on the estimated user emotions.

2. The system of claim 1.

3. The information receiving unit Analyze the user's past exam information input history and select the appropriate information reception method 2. The system of claim 1.

4. The information receiving unit Filter incoming information based on the user's current projects and interests 2. The system of claim 1.

5. The information receiving unit When accepting information, select the appropriate acceptance method according to the user's input method.

2. The system of claim 1.

6. The information receiving unit Estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions.

2. The system of claim 1.

7. The information receiving unit When receiving information, the system prioritizes receiving highly relevant information by taking into account the user's geographical location information.

2. The system of claim 1.

8. The information receiving unit When receiving information, analyze the user's social media activity and receive related information.

2. The system of claim 1.

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