System

Abstract

An object of a system according to an embodiment is to efficiently generate a test scenario for appropriately verifying a required specification and a requirement definition when a new facility or a system is introduced.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit includes a specific method in which the user inputs the required specification and the requirement definition. The analysis unit analyzes the information input by the reception unit and specifies a verification item. The generation unit generates a test scenario based on the item specified by the analysis unit.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 has had the problem of making it difficult to efficiently generate test scenarios to properly verify required specifications and requirement definitions when introducing new equipment or systems.

[0005] The system according to the embodiment aims to efficiently generate test scenarios for appropriately verifying required specifications and requirement definitions when introducing new equipment or systems. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit includes a specific method for a user to input required specifications and requirement definitions. The analysis unit analyzes the information input by the reception unit and identifies verification items. The generation unit generates a test scenario based on the items identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate test scenarios for appropriately verifying required specifications and requirement definitions when introducing new equipment or systems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention generates test scenarios for identifying items and content to be verified and specific error factors when a user inputs requirements specifications and definitions at the conceptual stage when introducing new equipment or systems. In this system, a generation AI identifies items and content to be verified based on vast amounts of learning data on domestic and international equipment and systems, and generates test scenarios for identifying specific error factors. This allows the user to efficiently introduce new equipment or systems. For example, a user inputs requirements specifications and definitions for a new equipment or system. The user must enter detailed specific specifications and requirements. The generation AI then analyzes the input information and identifies items and content to be verified. The generation AI has learned from vast amounts of data on past equipment and systems, and extracts necessary verification items based on similar cases and common verification items. The generation AI then generates test scenarios for identifying specific error factors based on the identified items. This allows the system to automatically identify items and content to be verified and generate test scenarios for identifying specific error factors, thereby smoothly progressing the introduction process. For example, when introducing a new manufacturing line, the performance of each piece of equipment, how it works together, and the operating conditions can be efficiently verified, and error causes can be identified early, improving the success rate of the introduction.

[0029] A verification system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input of required specifications and requirement definitions from a user. Specific methods for user input include, for example, form input, voice input, and text input. The analysis unit analyzes the information input by the reception unit and identifies verification items. The analysis unit performs analysis using, for example, natural language processing, data mining, or a machine learning algorithm. The generation unit generates a test scenario based on the items identified by the analysis unit. The generation unit generates a test scenario using, for example, a scenario template or a generation algorithm. This allows the verification system according to an embodiment to receive input of required specifications and requirement definitions from a user, identify verification items based on the input, and generate a test scenario.

[0030] The verification system includes a collection unit that collects data for the generative AI to learn from. The collection unit collects data for the generative AI to learn from. The data includes, for example, text data, image data, and audio data. The collection unit collects data from public databases on the Internet, for example. The collection unit can also collect data from in-house databases. Furthermore, the collection unit can collect data in real time using sensors. This improves the accuracy of the system by collecting data for the generative AI to learn from.

[0031] The verification system includes a providing unit that provides the generated test scenario to the user. The providing unit provides the generated test scenario to the user. Specific methods of providing the test scenario include, for example, displaying the test scenario through a user interface or sending it by email. The providing unit displays the test scenario through, for example, a web application. The providing unit can also display the test scenario through a mobile application. Furthermore, the providing unit can generate the test scenario in PDF format and allow the user to download it. By providing the generated test scenario to the user, the user can efficiently perform tests.

[0032] The verification system includes an algorithm unit that executes a specific algorithm for identifying an error factor. The algorithm unit executes the specific algorithm for identifying an error factor. Specific algorithms include, for example, regression analysis, clustering, and decision trees. The algorithm unit identifies an error factor using, for example, regression analysis. The algorithm unit can also identify an error factor using clustering. Furthermore, the algorithm unit can also identify an error factor using a decision tree. This makes it easier to identify an error factor by executing the specific algorithm for identifying an error factor.

[0033] The verification system includes a formatting unit that indicates a specific format of information to be input by a user and a specific example of a test scenario to be generated. The formatting unit indicates a specific format of information to be input by a user and a specific example of a test scenario to be generated. The specific format includes, for example, an input form layout and a scenario template. The formatting unit provides a format for a user to input information using, for example, a web form. The formatting unit can also provide a test scenario template to make it easier for the user to understand the generated scenario. Furthermore, the formatting unit enables the user to smoothly perform input work by indicating a specific example of information to be input by a user. Thus, by indicating a format of information to be input by a user and a specific example of a test scenario to be generated, the user can easily understand.

[0034] The verification system has an effect section that shows the specific effects and benefits of introducing the system. The effect section shows the specific effects and benefits of introducing the system. The specific effects and benefits include, for example, time savings, cost reduction, and improved accuracy. The effect section shows, for example, how much time can be saved by introducing the system. The effect section can also show how much cost can be reduced by introducing the system. Furthermore, the effect section can also show how much accuracy can be improved by introducing the system. In this way, by showing the effects and benefits of introducing the system, it becomes easier for users to understand the value of the system.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest similar input methods based on the content the user has input in the past. Furthermore, the reception unit can predict and suggest the input method that will be used in a specific time period from the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, enabling efficient input.

[0036] The reception unit can filter the input content based on the user's current project or area of ​​interest when inputting requirements specifications or requirement definitions. For example, the reception unit preferentially displays input content related to the user's ongoing project. The reception unit can also filter related input content based on the user's area of ​​interest. Furthermore, the reception unit can suggest related input content by referring to the user's past project history. This allows the user to efficiently input highly relevant information by filtering the input content based on the user's current project or area of ​​interest.

[0037] The reception unit can select a specific input means according to the user's input method when inputting required specifications and requirement definitions. For example, if the user selects voice input, the reception unit converts the input content into text using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology. This allows for efficient input by selecting the optimal input means according to the user's input method.

[0038] The reception unit can prioritize input of highly relevant information based on the user's geographical location information when inputting required specifications and requirement definitions. For example, if the user is in a specific area, the reception unit prioritizes input of information related to that area. The reception unit can also filter relevant information based on the user's current location. Furthermore, the reception unit can also suggest relevant information by referring to the user's past location information. This enables efficient input by preferentially inputting highly relevant information in consideration of the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and input related information when inputting requirement specifications and requirement definitions. The reception unit can input related requirement specifications and requirement definitions based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related information. Furthermore, the reception unit can input related information by referring to the activity of the user's friends on social media. This allows for efficient input by analyzing the user's social media activity and inputting related information.

[0040] The reception unit can customize the input method based on the user's past feedback when inputting required specifications and requirement definitions. The reception unit can optimize the input method based on, for example, feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can also customize the input interface by reflecting the user's feedback. This allows for efficient input by customizing the input method by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions. For example, the analysis unit performs a detailed analysis on requirement specifications and requirement definitions with high importance. The analysis unit can also perform a simplified analysis on requirement specifications and requirement definitions with low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the requirement specifications and requirement definitions. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions.

[0042] During analysis, the analysis unit can apply specific analysis algorithms depending on the category of the requirement specifications and requirement definitions. For example, the analysis unit applies a technical analysis algorithm to technical requirement specifications. The analysis unit can also apply a business analysis algorithm to business requirements. Furthermore, the analysis unit can also apply a security analysis algorithm to security requirements. This enables efficient analysis by applying different analysis algorithms depending on the category of the requirement specifications and requirement definitions.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by extracting specific patterns from the user's past analysis results. Furthermore, the analysis unit can also adjust the priority of the analysis by referring to the user's past analysis results. This enables efficient analysis by improving the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis in a specific manner based on the submission dates of the requirement specifications and requirement definitions. For example, the analysis unit prioritizes the analysis of requirement specifications and requirement definitions whose submission deadlines are approaching. The analysis unit can also postpone the analysis of requirement specifications and requirement definitions whose submission deadlines are further away. Furthermore, the analysis unit can adjust the analysis schedule based on the submission dates. This enables efficient analysis by determining the priority of analysis based on the submission dates of the requirement specifications and requirement definitions.

[0045] During analysis, the analysis unit can adjust the order of analysis in a specific manner based on the relevance of the requirement specifications and requirement definitions. For example, the analysis unit prioritizes analysis of highly relevant requirement specifications and requirement definitions. The analysis unit can also postpone analysis of less relevant requirement specifications and requirement definitions. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the requirement specifications and requirement definitions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the requirement specifications and requirement definitions.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis in a specific manner according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0047] When generating a test scenario, the generation unit can adjust the level of detail of the scenario in a specific manner based on the importance of the identified verification items. For example, the generation unit generates a detailed test scenario for a verification item with a high level of importance. The generation unit can also generate a simplified test scenario for a verification item with a low level of importance. Furthermore, the generation unit can adjust the priority of the test scenario according to the importance of the verification item. This allows for efficient generation of test scenarios by adjusting the level of detail of the scenario based on the importance of the identified verification items.

[0048] When generating a test scenario, the generation unit can apply a specific generation algorithm depending on the category of the verification item. For example, the generation unit applies a technical generation algorithm to a technical verification item. The generation unit can also apply a business generation algorithm to a business requirement. Furthermore, the generation unit can also apply a security generation algorithm to a security requirement. This allows for efficient generation of test scenarios by applying different generation algorithms depending on the category of the verification item.

[0049] When generating test scenarios, the generation unit can improve the accuracy of generation based on the user's past test scenario results. The generation unit, for example, optimizes the generation algorithm based on the user's past test scenario results. The generation unit can also improve the accuracy of generation by extracting specific patterns from the user's past test scenario results. Furthermore, the generation unit can adjust the priority of generation by referring to the user's past test scenario results. This enables efficient test scenario generation by improving the accuracy of generation by referring to the user's past test scenario results.

[0050] When generating test scenarios, the generation unit can determine the priority of the scenarios in a specific manner based on the submission dates of the verification items. For example, the generation unit preferentially incorporates verification items with upcoming submission deadlines into the test scenario. The generation unit can also postpone the incorporation of verification items with more distant submission deadlines into the test scenario. Furthermore, the generation unit can adjust the schedule of the test scenarios based on the submission dates. This enables efficient generation of test scenarios by determining the priority of scenarios based on the submission dates of the verification items.

[0051] When generating test scenarios, the generation unit can adjust the order of the scenarios in a specific manner based on the relevance of the verification items. For example, the generation unit preferentially incorporates highly relevant verification items into the test scenario. The generation unit can also incorporate less relevant verification items into the test scenario later. Furthermore, the generation unit can adjust the order of the test scenarios based on the relevance of the verification items. This allows for efficient generation of test scenarios by adjusting the order of the scenarios based on the relevance of the verification items.

[0052] When generating a test scenario, the generation unit can adjust the use of technical terms in the scenario in a specific manner according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a test scenario that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can generate a test scenario that avoids technical terms. Furthermore, the generation unit can adjust the way the test scenario is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the scenario according to the user's level of expertise, it is possible to provide a test scenario that is easy for the user to understand.

[0053] The collection unit can optimize the collection algorithm based on previously collected data during collection. For example, the collection unit optimizes the collection algorithm based on previously collected data. The collection unit can also extract specific patterns from previously collected data to improve collection accuracy. Furthermore, the collection unit can also adjust collection priorities with reference to previously collected data. This enables efficient data collection by optimizing the collection algorithm with reference to previously collected data.

[0054] The collection unit can update the collected data based on user feedback during collection. For example, the collection unit updates the collected data based on user feedback. The collection unit can also prioritize collection of specific data based on user feedback. Furthermore, the collection unit can also optimize the collection algorithm by reflecting user feedback. This allows for efficient data collection by updating the collected data by reflecting user feedback.

[0055] The collection unit can weight the collected data in a specific manner based on the time of data submission during collection. For example, the collection unit prioritizes collection of data with an upcoming submission deadline. The collection unit can also postpone collection of data with a more distant submission deadline. Furthermore, the collection unit can adjust the weighting of the collected data based on the time of submission. This allows for efficient data collection by weighting the collected data based on the time of data submission.

[0056] During collection, the collection unit can integrate information from different data sources in a specific manner to enrich the collected data. For example, the collection unit integrates information from different data sources to enrich the collected data. The collection unit can also optimize the collection algorithm based on the information from the different data sources. Furthermore, the collection unit can adjust the collection priority based on the information from the different data sources. This enables efficient data collection by integrating information from different data sources to enrich the collected data.

[0057] The providing unit can select the optimal display method based on the user's past operation history when providing the display interface. The providing unit selects the optimal display method based on, for example, the user's past operation history. The providing unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the providing unit can also customize the display interface by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a display method that is easy for the user to understand.

[0058] The providing unit can customize the display content in a specific manner according to the user's current task when providing the display content. For example, the providing unit can prioritize displaying information related to the task the user is currently performing. The providing unit can also filter related information based on the user's current task. Furthermore, the providing unit can also customize the display content by referring to the user's current task. In this way, by customizing the display content according to the user's current task, it is possible to provide display content that is easy for the user to understand.

[0059] The providing unit can select the optimal display method based on the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, by selecting the optimal display method in consideration of the user's device information, it is possible to provide a display method that is easy for the user to understand.

[0060] The providing unit can make the display content multilingual in a specific manner according to the user's language setting when providing the display content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can also provide the display content in that language. In this way, by making the display content multilingual according to the user's language setting, it is possible to provide display content that is easy for the user to understand.

[0061] When providing the information, the providing unit can analyze the user's social media activity in a specific manner and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and providing related information, it is possible to provide information that is easy for the user to understand.

[0062] When executing an algorithm, the algorithm unit can optimize the algorithm based on past error cause data. For example, the algorithm unit optimizes the algorithm based on past error cause data. The algorithm unit can also extract specific patterns from past error cause data to improve the accuracy of the algorithm. Furthermore, the algorithm unit can also adjust the priority of the algorithm by referring to past error cause data. In this way, by optimizing the algorithm by referring to past error cause data, it becomes possible to efficiently identify the cause of an error.

[0063] The algorithm unit can update the algorithm based on user feedback when the algorithm is executed. For example, the algorithm unit updates the algorithm based on user feedback. The algorithm unit can also develop an algorithm that preferentially identifies specific error factors based on user feedback. Furthermore, the algorithm unit can reflect user feedback to improve the accuracy of the algorithm. As a result, updating the algorithm to reflect user feedback enables efficient error factor identification.

[0064] When executing the algorithm, the algorithm unit can weight the algorithm in a specific manner based on the submission time of the error factor. For example, the algorithm unit executes an algorithm that prioritizes identifying error factors with an upcoming submission deadline. The algorithm unit can also execute an algorithm that prioritizes identifying error factors with a more distant submission deadline. Furthermore, the algorithm unit can adjust the weighting of the algorithm based on the submission time. Thus, by weighting the algorithm based on the submission time of the error factor, efficient error factor identification is possible.

[0065] The algorithm unit can integrate information from different data sources in a specific manner to enhance the algorithm when the algorithm is executed. For example, the algorithm unit integrates information from different data sources to enhance the algorithm. The algorithm unit can also optimize the algorithm based on information from different data sources. Furthermore, the algorithm unit can adjust the priority of the algorithm by referring to information from different data sources. In this way, by enhancing the algorithm by integrating information from different data sources, efficient error cause identification is possible.

[0066] When displaying a format, the format unit can select the optimal display method based on the user's past operation history. The format unit selects the optimal display method based on, for example, the user's past operation history. The format unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the format unit can also customize the display interface of the format by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a format that is easy for the user to understand.

[0067] When displaying the format, the formatting unit can customize the display content in a specific manner depending on the user's current task. For example, the formatting unit can prioritize displaying information related to the user's current task. The formatting unit can also filter related information based on the user's current task. Furthermore, the formatting unit can customize the display content of the format by referring to the user's current task. This allows the display content to be customized depending on the user's current task, making it possible to provide a format that is easy for the user to understand.

[0068] When displaying a format, the format unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the format unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the format unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the format unit can provide a simple and highly visible display method. This allows the user to be provided with a format that is easy to understand by selecting the optimal display method in consideration of the user's device information.

[0069] When displaying the format, the formatting unit can make the display content multilingual in a specific manner according to the user's language setting. For example, the formatting unit automatically sets the display content based on the language setting of the user's device. The formatting unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the formatting unit can provide the display content in that language. This makes it possible to provide a format that is easy for the user to understand by making the display content multilingual according to the user's language setting.

[0070] The effect unit can select the optimal display method based on the user's past operation history when displaying effects. The effect unit selects the optimal display method based on, for example, the user's past operation history. The effect unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the effect unit can also customize the effect display interface by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a display method that is easy for the user to understand.

[0071] The effect unit can customize the display content in a specific manner depending on the user's current task when displaying the effect. For example, the effect unit can prioritize displaying information related to the user's current task. The effect unit can also filter related information based on the user's current task. Furthermore, the effect unit can customize the content of the effect display by referring to the user's current task. This allows the display content to be easily understood by the user by customizing the display content depending on the user's current task.

[0072] The effect unit can select the optimal display method based on the user's device information when displaying effects. For example, if the user is using a smartphone, the effect unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the effect unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the effect unit can also provide a simple and highly visible display method. In this way, by selecting the optimal display method taking into account the user's device information, it is possible to provide a display method that is easy for the user to understand.

[0073] The effect unit can make the display content multilingual in a specific manner according to the user's language setting when displaying effects. For example, the effect unit automatically sets the display content based on the language setting of the user's device. The effect unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the effect unit can provide the display content in that language. This makes it possible to provide display content that is easy for the user to understand by making the display content multilingual according to the user's language setting.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions. For example, a detailed analysis can be performed for requirement specifications and requirement definitions with high importance. A simplified analysis can also be performed for requirement specifications and requirement definitions with low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the requirement specifications and requirement definitions. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions.

[0076] The collection unit can optimize the collection algorithm based on previously collected data during collection. For example, the collection algorithm is optimized based on previously collected data. It can also extract specific patterns from previously collected data to improve collection accuracy. Furthermore, it can also adjust collection priorities by referring to previously collected data. This makes it possible to collect data efficiently by optimizing the collection algorithm with reference to previously collected data.

[0077] The providing unit can select the optimal display method based on the user's past operation history when providing the display. For example, the optimal display method is selected based on the user's past operation history. Also, it is possible to preferentially suggest a specific display method based on the user's past operation history. Furthermore, it is possible to customize the display interface by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a display method that is easy for the user to understand.

[0078] When generating test scenarios, the generation unit can adjust the level of detail of the scenarios in a specific manner based on the importance of the identified verification items. For example, a detailed test scenario can be generated for a highly important verification item. A simplified test scenario can also be generated for a less important verification item. Furthermore, the priority of the test scenarios can be adjusted according to the importance of the verification items. This allows for efficient generation of test scenarios by adjusting the level of detail of the scenarios based on the importance of the identified verification items.

[0079] When executing an algorithm, the algorithm unit can optimize the algorithm based on past error cause data. For example, the algorithm is optimized based on past error cause data. It is also possible to improve the accuracy of the algorithm by extracting specific patterns from past error cause data. Furthermore, it is also possible to adjust the priority of the algorithm by referring to past error cause data. In this way, by optimizing the algorithm with reference to past error cause data, it is possible to efficiently identify the cause of an error.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The user inputs the required specifications and requirements definitions into the reception unit. Specific methods for user input include form input, voice input, text input, etc. Step 2: The analysis unit analyzes the information input by the reception unit and identifies verification items. The analysis unit performs the analysis using natural language processing, data mining, machine learning algorithms, etc. Step 3: The generator generates test scenarios based on the items identified by the analyzer. The generator generates test scenarios using scenario templates and generation algorithms.

[0082] (Example 2) A system according to an embodiment of the present invention generates test scenarios for identifying items and content to be verified and specific error factors when a user inputs requirements specifications and definitions at the conceptual stage when introducing new equipment or systems. In this system, a generation AI identifies items and content to be verified based on vast amounts of learning data on domestic and international equipment and systems, and generates test scenarios for identifying specific error factors. This allows the user to efficiently introduce new equipment or systems. For example, a user inputs requirements specifications and definitions for a new equipment or system. The user must enter detailed specific specifications and requirements. The generation AI then analyzes the input information and identifies items and content to be verified. The generation AI has learned from vast amounts of data on past equipment and systems, and extracts necessary verification items based on similar cases and common verification items. The generation AI then generates test scenarios for identifying specific error factors based on the identified items. This allows the system to automatically identify items and content to be verified and generate test scenarios for identifying specific error factors, thereby smoothly progressing the introduction process. For example, when introducing a new manufacturing line, the performance of each piece of equipment, how it works together, and the operating conditions can be efficiently verified, and error causes can be identified early, improving the success rate of the introduction.

[0083] A verification system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The reception unit receives input of required specifications and requirement definitions from a user. Specific methods for user input include, for example, form input, voice input, and text input. The analysis unit analyzes the information input by the reception unit and identifies verification items. The analysis unit performs analysis using, for example, natural language processing, data mining, or a machine learning algorithm. The generation unit generates a test scenario based on the items identified by the analysis unit. The generation unit generates a test scenario using, for example, a scenario template or a generation algorithm. This allows the verification system according to an embodiment to receive input of required specifications and requirement definitions from a user, identify verification items based on the input, and generate a test scenario.

[0084] The verification system includes a collection unit that collects data for the generative AI to learn from. The collection unit collects data for the generative AI to learn from. The data includes, for example, text data, image data, and audio data. The collection unit collects data from public databases on the Internet, for example. The collection unit can also collect data from in-house databases. Furthermore, the collection unit can collect data in real time using sensors. This improves the accuracy of the system by collecting data for the generative AI to learn from.

[0085] The verification system includes a providing unit that provides the generated test scenario to the user. The providing unit provides the generated test scenario to the user. Specific methods of providing the test scenario include, for example, displaying the test scenario through a user interface or sending it by email. The providing unit displays the test scenario through, for example, a web application. The providing unit can also display the test scenario through a mobile application. Furthermore, the providing unit can generate the test scenario in PDF format and allow the user to download it. By providing the generated test scenario to the user, the user can efficiently perform tests.

[0086] The verification system includes an algorithm unit that executes a specific algorithm for identifying an error factor. The algorithm unit executes the specific algorithm for identifying an error factor. Specific algorithms include, for example, regression analysis, clustering, and decision trees. The algorithm unit identifies an error factor using, for example, regression analysis. The algorithm unit can also identify an error factor using clustering. Furthermore, the algorithm unit can also identify an error factor using a decision tree. This makes it easier to identify an error factor by executing the specific algorithm for identifying an error factor.

[0087] The verification system includes a formatting unit that indicates a specific format of information to be input by a user and a specific example of a test scenario to be generated. The formatting unit indicates a specific format of information to be input by a user and a specific example of a test scenario to be generated. The specific format includes, for example, an input form layout and a scenario template. The formatting unit provides a format for a user to input information using, for example, a web form. The formatting unit can also provide a test scenario template to make it easier for the user to understand the generated scenario. Furthermore, the formatting unit enables the user to smoothly perform input work by indicating a specific example of information to be input by a user. Thus, by indicating a format of information to be input by a user and a specific example of a test scenario to be generated, the user can easily understand.

[0088] The verification system has an effect section that shows the specific effects and benefits of introducing the system. The effect section shows the specific effects and benefits of introducing the system. The specific effects and benefits include, for example, time savings, cost reduction, and improved accuracy. The effect section shows, for example, how much time can be saved by introducing the system. The effect section can also show how much cost can be reduced by introducing the system. Furthermore, the effect section can also show how much accuracy can be improved by introducing the system. In this way, by showing the effects and benefits of introducing the system, it becomes easier for users to understand the value of the system.

[0089] The reception unit can estimate the user's emotions and adjust the input timing of the request specifications and requirement definitions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide time for the user to relax. Furthermore, if the user is relaxed, the reception unit can also speed up the input timing to allow the user to work efficiently. Furthermore, if the user is in a hurry, the reception unit can optimize the input timing to allow the user to input information quickly. This reduces the user's stress by adjusting the input timing according to the user's emotions, enabling efficient input. 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.

[0090] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest similar input methods based on the content the user has input in the past. Furthermore, the reception unit can predict and suggest the input method that will be used in a specific time period from the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested, enabling efficient input.

[0091] The reception unit can filter the input content based on the user's current project or area of ​​interest when inputting requirements specifications or requirement definitions. For example, the reception unit preferentially displays input content related to the user's ongoing project. The reception unit can also filter related input content based on the user's area of ​​interest. Furthermore, the reception unit can suggest related input content by referring to the user's past project history. This allows the user to efficiently input highly relevant information by filtering the input content based on the user's current project or area of ​​interest.

[0092] The reception unit can select a specific input means according to the user's input method when inputting required specifications and requirement definitions. For example, if the user selects voice input, the reception unit converts the input content into text using voice recognition technology. Also, if the user selects text input, the reception unit can support keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology. This allows for efficient input by selecting the optimal input means according to the user's input method.

[0093] The reception unit can estimate the user's emotions and determine the priority of the required specifications and requirement definitions to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit adjusts the priority by deferring items of lower importance. Furthermore, if the user is relaxed, the reception unit can also prioritize input of items of higher importance. Furthermore, if the user is in a hurry, the reception unit can also prioritize input of the most important items. This enables efficient input by determining the priority of the required specifications and requirement definitions to be input based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] The reception unit can prioritize input of highly relevant information based on the user's geographical location information when inputting required specifications and requirement definitions. For example, if the user is in a specific area, the reception unit prioritizes input of information related to that area. The reception unit can also filter relevant information based on the user's current location. Furthermore, the reception unit can also suggest relevant information by referring to the user's past location information. This enables efficient input by preferentially inputting highly relevant information in consideration of the user's geographical location information.

[0095] The reception unit can analyze the user's social media activity and input related information when inputting requirement specifications and requirement definitions. The reception unit can input related requirement specifications and requirement definitions based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related information. Furthermore, the reception unit can input related information by referring to the activity of the user's friends on social media. This allows for efficient input by analyzing the user's social media activity and inputting related information.

[0096] The reception unit can customize the input method based on the user's past feedback when inputting required specifications and requirement definitions. The reception unit can optimize the input method based on, for example, feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, the reception unit can also customize the input interface by reflecting the user's feedback. This allows for efficient input by customizing the input method by reflecting the user's past feedback.

[0097] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are concise. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0098] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions. For example, the analysis unit performs a detailed analysis on requirement specifications and requirement definitions with high importance. The analysis unit can also perform a simplified analysis on requirement specifications and requirement definitions with low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the requirement specifications and requirement definitions. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions.

[0099] During analysis, the analysis unit can apply specific analysis algorithms depending on the category of the requirement specifications and requirement definitions. For example, the analysis unit applies a technical analysis algorithm to technical requirement specifications. The analysis unit can also apply a business analysis algorithm to business requirements. Furthermore, the analysis unit can also apply a security analysis algorithm to security requirements. This enables efficient analysis by applying different analysis algorithms depending on the category of the requirement specifications and requirement definitions.

[0100] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit optimizes the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by extracting specific patterns from the user's past analysis results. Furthermore, the analysis unit can also adjust the priority of the analysis by referring to the user's past analysis results. This enables efficient analysis by improving the accuracy of the analysis by referring to the user's past analysis results.

[0101] The 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 analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result if the user is excited. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0102] During analysis, the analysis unit can determine the priority of analysis in a specific manner based on the submission dates of the requirement specifications and requirement definitions. For example, the analysis unit prioritizes the analysis of requirement specifications and requirement definitions whose submission deadlines are approaching. The analysis unit can also postpone the analysis of requirement specifications and requirement definitions whose submission deadlines are further away. Furthermore, the analysis unit can adjust the analysis schedule based on the submission dates. This enables efficient analysis by determining the priority of analysis based on the submission dates of the requirement specifications and requirement definitions.

[0103] During analysis, the analysis unit can adjust the order of analysis in a specific manner based on the relevance of the requirement specifications and requirement definitions. For example, the analysis unit prioritizes analysis of highly relevant requirement specifications and requirement definitions. The analysis unit can also postpone analysis of less relevant requirement specifications and requirement definitions. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the requirement specifications and requirement definitions. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the requirement specifications and requirement definitions.

[0104] During analysis, the analysis unit can adjust the use of technical terms in the analysis in a specific manner according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Also, if the user does not have technical expertise, the analysis unit can provide analysis results that avoid technical terms. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0105] The generation unit can estimate the user's emotions and adjust the expression method of the generated test scenario based on the estimated user's emotions. For example, the generation unit provides a detailed test scenario when the user is relaxed. The generation unit can also provide a concise test scenario when the user is in a hurry. Furthermore, the generation unit can provide a visually stimulating test scenario when the user is excited. In this way, by adjusting the expression method of the test scenario according to the user's emotions, it is possible to provide a test scenario that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0106] When generating a test scenario, the generation unit can adjust the level of detail of the scenario in a specific manner based on the importance of the identified verification items. For example, the generation unit generates a detailed test scenario for a verification item with a high level of importance. The generation unit can also generate a simplified test scenario for a verification item with a low level of importance. Furthermore, the generation unit can adjust the priority of the test scenario according to the importance of the verification item. This allows for efficient generation of test scenarios by adjusting the level of detail of the scenario based on the importance of the identified verification items.

[0107] When generating a test scenario, the generation unit can apply a specific generation algorithm depending on the category of the verification item. For example, the generation unit applies a technical generation algorithm to a technical verification item. The generation unit can also apply a business generation algorithm to a business requirement. Furthermore, the generation unit can also apply a security generation algorithm to a security requirement. This allows for efficient generation of test scenarios by applying different generation algorithms depending on the category of the verification item.

[0108] When generating test scenarios, the generation unit can improve the accuracy of generation based on the user's past test scenario results. The generation unit, for example, optimizes the generation algorithm based on the user's past test scenario results. The generation unit can also improve the accuracy of generation by extracting specific patterns from the user's past test scenario results. Furthermore, the generation unit can adjust the priority of generation by referring to the user's past test scenario results. This enables efficient test scenario generation by improving the accuracy of generation by referring to the user's past test scenario results.

[0109] The generation unit can estimate the user's emotions and adjust the length of the generated test scenario based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point test scenario. Furthermore, if the user is relaxed, the generation unit can generate a longer test scenario with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a test scenario with visually stimulating effects. This allows the length of the test scenario to be adjusted according to the user's emotions, providing a test scenario that is easy for the user to understand. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] When generating test scenarios, the generation unit can determine the priority of the scenarios in a specific manner based on the submission dates of the verification items. For example, the generation unit preferentially incorporates verification items with upcoming submission deadlines into the test scenario. The generation unit can also postpone the incorporation of verification items with more distant submission deadlines into the test scenario. Furthermore, the generation unit can adjust the schedule of the test scenarios based on the submission dates. This enables efficient generation of test scenarios by determining the priority of scenarios based on the submission dates of the verification items.

[0111] When generating test scenarios, the generation unit can adjust the order of the scenarios in a specific manner based on the relevance of the verification items. For example, the generation unit preferentially incorporates highly relevant verification items into the test scenario. The generation unit can also incorporate less relevant verification items into the test scenario later. Furthermore, the generation unit can adjust the order of the test scenarios based on the relevance of the verification items. This allows for efficient generation of test scenarios by adjusting the order of the scenarios based on the relevance of the verification items.

[0112] When generating a test scenario, the generation unit can adjust the use of technical terms in the scenario in a specific manner according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a test scenario that uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the generation unit can generate a test scenario that avoids technical terms. Furthermore, the generation unit can adjust the way the test scenario is expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the scenario according to the user's level of expertise, it is possible to provide a test scenario that is easy for the user to understand.

[0113] The collection unit can estimate the user's emotions and adjust the timing of collecting learning data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to provide time for the user to relax. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to efficiently collect data. Furthermore, if the user is in a hurry, the collection unit can optimize the collection timing to quickly collect data. This enables efficient data collection by adjusting the timing of collecting learning data according to the user's emotions. The emotion estimation is realized 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.

[0114] The collection unit can optimize the collection algorithm based on previously collected data during collection. For example, the collection unit optimizes the collection algorithm based on previously collected data. The collection unit can also extract specific patterns from previously collected data to improve collection accuracy. Furthermore, the collection unit can also adjust collection priorities with reference to previously collected data. This enables efficient data collection by optimizing the collection algorithm with reference to previously collected data.

[0115] The collection unit can update the collected data based on user feedback during collection. For example, the collection unit updates the collected data based on user feedback. The collection unit can also prioritize collection of specific data based on user feedback. Furthermore, the collection unit can also optimize the collection algorithm by reflecting user feedback. This allows for efficient data collection by updating the collected data by reflecting user feedback.

[0116] The collection unit can estimate the user's emotions and adjust the collection frequency based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection frequency to provide time for the user to relax. Furthermore, if the user is relaxed, the collection unit can increase the collection frequency to efficiently collect data. Furthermore, if the user is in a hurry, the collection unit can optimize the collection frequency to quickly collect data. This allows for efficient data collection by adjusting the collection frequency according to the user's emotions. The emotion estimation is realized 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.

[0117] The collection unit can weight the collected data in a specific manner based on the time of data submission during collection. For example, the collection unit prioritizes collection of data with an upcoming submission deadline. The collection unit can also postpone collection of data with a more distant submission deadline. Furthermore, the collection unit can adjust the weighting of the collected data based on the time of submission. This allows for efficient data collection by weighting the collected data based on the time of data submission.

[0118] During collection, the collection unit can integrate information from different data sources in a specific manner to enrich the collected data. For example, the collection unit integrates information from different data sources to enrich the collected data. The collection unit can also optimize the collection algorithm based on the information from the different data sources. Furthermore, the collection unit can adjust the collection priority based on the information from the different data sources. This enables efficient data collection by integrating information from different data sources to enrich the collected data.

[0119] The providing unit can estimate the user's emotions and adjust the display method of the test scenario to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the test scenario according to the user's emotions, it is possible to provide a test scenario that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0120] The providing unit can select the optimal display method based on the user's past operation history when providing the display interface. The providing unit selects the optimal display method based on, for example, the user's past operation history. The providing unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the providing unit can also customize the display interface by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a display method that is easy for the user to understand.

[0121] The providing unit can customize the display content in a specific manner according to the user's current task when providing the display content. For example, the providing unit can prioritize displaying information related to the task the user is currently performing. The providing unit can also filter related information based on the user's current task. Furthermore, the providing unit can also customize the display content by referring to the user's current task. In this way, by customizing the display content according to the user's current task, it is possible to provide display content that is easy for the user to understand.

[0122] The providing unit can estimate the user's emotions and adjust the operation procedures of the test scenario to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible operation procedures. Furthermore, if the user is relaxed, the providing unit can also provide operation procedures that include detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide operation procedures that focus on the main points. In this way, by adjusting the operation procedures of the test scenario according to the user's emotions, it is possible to provide operation procedures that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0123] The providing unit can select the optimal display method based on the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, by selecting the optimal display method in consideration of the user's device information, it is possible to provide a display method that is easy for the user to understand.

[0124] The providing unit can make the display content multilingual in a specific manner according to the user's language setting when providing the display content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can also provide the display content in that language. In this way, by making the display content multilingual according to the user's language setting, it is possible to provide display content that is easy for the user to understand.

[0125] When providing the information, the providing unit can analyze the user's social media activity in a specific manner and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and providing related information, it is possible to provide information that is easy for the user to understand.

[0126] The algorithm unit can estimate the user's emotions and adjust the execution timing of the error factor identification algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the algorithm unit can delay the execution timing of the algorithm to provide time for the user to relax. Furthermore, if the user is relaxed, the algorithm unit can also accelerate the execution timing of the algorithm to efficiently identify the error factor. Furthermore, if the user is in a hurry, the algorithm unit can optimize the execution timing of the algorithm to quickly identify the error factor. This enables efficient error factor identification by adjusting the execution timing of the error factor identification algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] When executing an algorithm, the algorithm unit can optimize the algorithm based on past error cause data. For example, the algorithm unit optimizes the algorithm based on past error cause data. The algorithm unit can also extract specific patterns from past error cause data to improve the accuracy of the algorithm. Furthermore, the algorithm unit can also adjust the priority of the algorithm by referring to past error cause data. In this way, by optimizing the algorithm by referring to past error cause data, it becomes possible to efficiently identify the cause of an error.

[0128] The algorithm unit can update the algorithm based on user feedback when the algorithm is executed. For example, the algorithm unit updates the algorithm based on user feedback. The algorithm unit can also develop an algorithm that preferentially identifies specific error factors based on user feedback. Furthermore, the algorithm unit can reflect user feedback to improve the accuracy of the algorithm. As a result, updating the algorithm to reflect user feedback enables efficient error factor identification.

[0129] The algorithm unit can estimate the user's emotions and adjust the frequency of algorithm execution based on the estimated user emotions. For example, if the user is feeling stressed, the algorithm unit can reduce the frequency of algorithm execution to provide time for the user to relax. Furthermore, if the user is relaxed, the algorithm unit can increase the frequency of algorithm execution to efficiently identify the cause of the error. Furthermore, if the user is in a hurry, the algorithm unit can optimize the frequency of algorithm execution to quickly identify the cause of the error. This allows for efficient error cause identification by adjusting the frequency of algorithm execution according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0130] When executing the algorithm, the algorithm unit can weight the algorithm in a specific manner based on the submission time of the error factor. For example, the algorithm unit executes an algorithm that prioritizes identifying error factors with an upcoming submission deadline. The algorithm unit can also execute an algorithm that prioritizes identifying error factors with a more distant submission deadline. Furthermore, the algorithm unit can adjust the weighting of the algorithm based on the submission time. Thus, by weighting the algorithm based on the submission time of the error factor, efficient error factor identification is possible.

[0131] The algorithm unit can integrate information from different data sources in a specific manner to enhance the algorithm when the algorithm is executed. For example, the algorithm unit integrates information from different data sources to enhance the algorithm. The algorithm unit can also optimize the algorithm based on information from different data sources. Furthermore, the algorithm unit can adjust the priority of the algorithm by referring to information from different data sources. In this way, by enhancing the algorithm by integrating information from different data sources, efficient error cause identification is possible.

[0132] The formatting unit can estimate the user's emotions and adjust the display method of the input format based on the estimated user emotions. For example, if the user is nervous, the formatting unit can provide a format with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the formatting unit can provide a format with bright colors to make the input task more enjoyable. Furthermore, if the user is tired, the formatting unit can provide a simple, highly visible format to make the input task easier. This allows the display method of the input format to be adjusted according to the user's emotions, thereby providing a format that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0133] When displaying a format, the format unit can select the optimal display method based on the user's past operation history. The format unit selects the optimal display method based on, for example, the user's past operation history. The format unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the format unit can also customize the display interface of the format by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a format that is easy for the user to understand.

[0134] When displaying the format, the formatting unit can customize the display content in a specific manner depending on the user's current task. For example, the formatting unit can prioritize displaying information related to the user's current task. The formatting unit can also filter related information based on the user's current task. Furthermore, the formatting unit can customize the display content of the format by referring to the user's current task. This allows the display content to be customized depending on the user's current task, making it possible to provide a format that is easy for the user to understand.

[0135] The formatting unit can estimate the user's emotions and adjust the input format operation procedures based on the estimated user emotions. For example, if the user is nervous, the formatting unit can provide simple, highly visible operation procedures. If the user is relaxed, the formatting unit can also provide operation procedures that include detailed information. If the user is in a hurry, the formatting unit can also provide operation procedures that focus on the main points. In this way, by adjusting the input format operation procedures according to the user's emotions, it is possible to provide operation procedures that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0136] When displaying a format, the format unit can select the optimal display method based on the user's device information. For example, if the user is using a smartphone, the format unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the format unit can provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the format unit can provide a simple and highly visible display method. This allows the user to be provided with a format that is easy to understand by selecting the optimal display method in consideration of the user's device information.

[0137] When displaying the format, the formatting unit can make the display content multilingual in a specific manner according to the user's language setting. For example, the formatting unit automatically sets the display content based on the language setting of the user's device. The formatting unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the formatting unit can provide the display content in that language. This makes it possible to provide a format that is easy for the user to understand by making the display content multilingual according to the user's language setting.

[0138] The effect unit can estimate the user's emotions and adjust the display method of the system's effects and benefits based on the estimated user emotions. For example, if the user is nervous, the effect unit can provide a simple, highly visible display method. If the user is relaxed, the effect unit can also provide a display method that includes detailed information. If the user is in a hurry, the effect unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the system's effects and benefits according to the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0139] The effect unit can select the optimal display method based on the user's past operation history when displaying effects. The effect unit selects the optimal display method based on, for example, the user's past operation history. The effect unit can also preferentially suggest a specific display method based on the user's past operation history. Furthermore, the effect unit can also customize the effect display interface by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a display method that is easy for the user to understand.

[0140] The effect unit can customize the display content in a specific manner depending on the user's current task when displaying the effect. For example, the effect unit can prioritize displaying information related to the user's current task. The effect unit can also filter related information based on the user's current task. Furthermore, the effect unit can customize the content of the effect display by referring to the user's current task. This allows the display content to be easily understood by the user by customizing the display content depending on the user's current task.

[0141] The effect unit can estimate the user's emotions and adjust the operation procedures for the system's effects and benefits based on the estimated user emotions. For example, if the user is nervous, the effect unit can provide simple, highly visible operation procedures. If the user is relaxed, the effect unit can also provide operation procedures that include detailed information. If the user is in a hurry, the effect unit can also provide operation procedures that focus on the main points. In this way, by adjusting the operation procedures for the system's effects and benefits according to the user's emotions, it is possible to provide operation procedures that are easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0142] The effect unit can select the optimal display method based on the user's device information when displaying effects. For example, if the user is using a smartphone, the effect unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the effect unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the effect unit can also provide a simple and highly visible display method. In this way, by selecting the optimal display method taking into account the user's device information, it is possible to provide a display method that is easy for the user to understand.

[0143] The effect unit can make the display content multilingual in a specific manner according to the user's language setting when displaying effects. For example, the effect unit automatically sets the display content based on the language setting of the user's device. The effect unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the effect unit can provide the display content in that language. This makes it possible to provide display content that is easy for the user to understand by making the display content multilingual according to the user's language setting. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, collection unit, provision unit, algorithm unit, format unit, and effect unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and a user inputs required specifications and requirement definitions. The analysis unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and analyzes the input information and identifies verification items. The generation unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and generates a test scenario based on the identified items. The collection unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and collects data for the generation AI to learn. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated test scenario to the user. The algorithm unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and executes a specific algorithm for identifying error causes. The format section is realized, for example, by the control unit 46A of the smart device 14, and indicates a specific format of information input by the user and a specific example of a test scenario to be generated. The effect section is realized, for example, by the specific processing unit 290 of the data processing device 12, and indicates specific effects and benefits of introducing the system. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, collection unit, provision unit, algorithm unit, format unit, and effect unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and a user inputs required specifications and requirement definitions. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the input information and identifies verification items. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and generates a test scenario based on the identified items. The collection unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and collects data for the generation AI to learn. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated test scenario to the user. The algorithm unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and executes a specific algorithm for identifying error factors. The format section is realized, for example, by the control unit 46A of the smart glasses 214, and indicates a specific format of information input by the user and a specific example of a test scenario to be generated. The effect section is realized, for example, by the specific processing unit 290 of the data processing device 12, and indicates specific effects and benefits of introducing the system. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, collection unit, provision unit, algorithm unit, format unit, and effect unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and a user inputs required specifications and requirement definitions. The analysis unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and analyzes the input information and identifies verification items. The generation unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and generates a test scenario based on the identified items. The collection unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and collects data for the generation AI to learn. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the generated test scenario to the user. The algorithm unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and executes a specific algorithm for identifying error causes. The format section is realized, for example, by the control unit 46A of the headset terminal 314, and indicates a specific format of information input by the user and a specific example of a test scenario to be generated. The effect section is realized, for example, by the specific processing unit 290 of the data processing device 12, and indicates specific effects and benefits of introducing the system. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, collection unit, provision unit, algorithm unit, format unit, and effect unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and a user inputs required specifications and requirement definitions. The analysis unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and analyzes the input information and identifies verification items. The generation unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and generates a test scenario based on the identified items. The collection unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and collects data for the generation AI to learn. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated test scenario to the user. The algorithm unit is realized, for example, by the specification processing unit 290 of the data processing device 12, and executes a specific algorithm for identifying error causes. The format section is realized, for example, by the control unit 46A of the robot 414, and indicates a specific format of the information input by the user and a specific example of the test scenario to be generated. The effect section is realized, for example, by the specific processing unit 290 of the data processing device 12, and indicates specific effects and benefits of introducing the system.

[0144] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0145] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user 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 are concise. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0146] The collection unit can estimate the user's emotions and adjust the timing of collecting learning data based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to provide time for the user to relax. Also, if the user is relaxed, the collection timing can be advanced to efficiently collect data. Furthermore, if the user is in a hurry, the collection timing can be optimized to quickly collect data. This allows for efficient data collection by adjusting the timing of collecting learning data according to the user's emotions.

[0147] The providing unit can estimate the user's emotions and adjust the display method of the test scenario to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the test scenario according to the user's emotions, it is possible to provide a test scenario that is easy for the user to understand.

[0148] The generation unit can estimate the user's emotions and adjust the expression method of the generated test scenario based on the estimated user's emotions. For example, if the user is relaxed, a detailed test scenario can be provided. If the user is in a hurry, a concise test scenario can be provided. Furthermore, if the user is excited, a visually stimulating test scenario can be provided. In this way, by adjusting the expression method of the test scenario according to the user's emotions, it is possible to provide a test scenario that is easy for the user to understand.

[0149] The formatting unit can estimate the user's emotions and adjust the display method of the input format based on the estimated user's emotions. For example, if the user is nervous, a format with calm colors can be provided to reduce visual stress. If the user is having fun, a format with bright colors can be provided to make input work more enjoyable. Furthermore, if the user is tired, a simple, highly visible format can be provided to make input work easier. In this way, by adjusting the display method of the input format according to the user's emotions, a format that is easy for the user to understand can be provided.

[0150] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions. For example, a detailed analysis can be performed for requirement specifications and requirement definitions with high importance. A simplified analysis can also be performed for requirement specifications and requirement definitions with low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance of the requirement specifications and requirement definitions. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the requirement specifications and requirement definitions.

[0151] The collection unit can optimize the collection algorithm based on previously collected data during collection. For example, the collection algorithm is optimized based on previously collected data. It can also extract specific patterns from previously collected data to improve collection accuracy. Furthermore, it can also adjust collection priorities by referring to previously collected data. This makes it possible to collect data efficiently by optimizing the collection algorithm with reference to previously collected data.

[0152] The providing unit can select the optimal display method based on the user's past operation history when providing the display. For example, the optimal display method is selected based on the user's past operation history. Also, it is possible to preferentially suggest a specific display method based on the user's past operation history. Furthermore, it is possible to customize the display interface by referring to the user's past operation history. In this way, by selecting the optimal display method by referring to the user's past operation history, it is possible to provide a display method that is easy for the user to understand.

[0153] When generating test scenarios, the generation unit can adjust the level of detail of the scenarios in a specific manner based on the importance of the identified verification items. For example, a detailed test scenario can be generated for a highly important verification item. A simplified test scenario can also be generated for a less important verification item. Furthermore, the priority of the test scenarios can be adjusted according to the importance of the verification items. This allows for efficient generation of test scenarios by adjusting the level of detail of the scenarios based on the importance of the identified verification items.

[0154] When executing an algorithm, the algorithm unit can optimize the algorithm based on past error cause data. For example, the algorithm is optimized based on past error cause data. It is also possible to improve the accuracy of the algorithm by extracting specific patterns from past error cause data. Furthermore, it is also possible to adjust the priority of the algorithm by referring to past error cause data. In this way, by optimizing the algorithm with reference to past error cause data, it is possible to efficiently identify the cause of an error.

[0155] The processing flow of the second embodiment will be briefly explained below.

[0156] Step 1: The user inputs the required specifications and requirements definitions into the reception unit. Specific methods for user input include form input, voice input, text input, etc. Step 2: The analysis unit analyzes the information input by the reception unit and identifies verification items. The analysis unit performs the analysis using natural language processing, data mining, machine learning algorithms, etc. Step 3: The generator generates test scenarios based on the items identified by the analyzer. The generator generates test scenarios using scenario templates and generation algorithms.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[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 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.

[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 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.

[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 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.

[0175] 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.

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

[0178] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0194] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0203] The storage 32 stores 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0208] 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.

[0209] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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).

[0214] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0215] 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."

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Explanation of symbols]

[0229] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit including a specific method for a user to input a request specification or a requirement definition; an analysis unit that analyzes the information input by the reception unit and identifies verification items; a generation unit that generates a test scenario based on the items identified by the analysis unit. A system characterized by:

2. Equipped with a collection unit that collects data for the generation AI to learn 2. The system of claim 1.

3. A provision unit is provided that provides the generated test scenario to the user.

2. The system of claim 1.

4. An algorithm section that executes a specific algorithm to identify the cause of the error 2. The system of claim 1.

5. It has a format section that shows the specific format of the information entered by the user and specific examples of the test scenarios to be generated.

2. The system of claim 1.

6. Equipped with an effects section that shows the specific effects and benefits of introducing the system 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the timing of inputting requirements specifications and definitions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

9. The reception unit As you enter your requirements, filter your input in specific ways based on your current project or area of ​​interest.

2. The system of claim 1.

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