system

Abstract

The system according to this embodiment aims to analyze application service specifications and past failure data, and to propose and optimize the most suitable verification items. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an optimization unit. The collection unit collects application service specifications and past failure data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes verification items based on the analysis results obtained by the analysis unit. The optimization unit optimizes the verification items proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, proposals and optimizations of verification items based on the specification of an application service and past failure data have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the specification of an application service and past failure data and propose and optimize optimal verification items.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and an optimization unit. The collection unit collects application service specifications and past failure data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes verification items based on the analysis results obtained by the analysis unit. The optimization unit optimizes the verification items proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze application service specifications and past failure data, and propose and optimize the most suitable verification items. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that learns application service specifications, past failure data, industry best practices, and the latest technology trends, and proposes optimal verification items. By learning application service specifications and past failure data, as well as industry best practices and the latest technology trends, this AI agent system can grasp the latest technological trends and continuously update and optimize verification items. Based on this learned data, the AI ​​agent system automatically proposes optimal verification items. For example, when verifying additional functions, the AI ​​agent system proposes optimal verification items based on specifications, past failure data, and industry best practices. Furthermore, it also considers the execution time of verification items and past verification results to propose verification items of the optimal volume. By using this AI agent system, the reliance on specific individuals in the quality assurance process can be eliminated, ensuring stable application quality without depending on specific members. In addition, by constantly incorporating the latest technology trends, verification items are prevented from becoming outdated. This leads to increased operational efficiency and reduced risk. Furthermore, the AI ​​agent system immediately proposes verification items that correspond to the latest specifications and trends at the timing of the application verification phase. This allows for pre-confirmation of planned verification items before entering the development phase, enabling efficient development by proactively identifying areas for consideration. The AI ​​agent system then collects and analyzes application service specifications and past failure data, proposes verification items, and optimizes them, resulting in more efficient verification.

[0029] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an optimization unit. The data collection unit collects application service specifications and past failure data. For example, the data collection unit can collect application service technical specifications and functional specifications. The data collection unit can also collect past failure data. For example, the data collection unit collects data such as the date and time of failure, the type of failure, and the scope of impact. Furthermore, the data collection unit can also collect industry best practices and the latest technology trends. For example, the data collection unit collects standard methods, success stories, new technologies, and innovative methods. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining techniques. Furthermore, the analysis unit can analyze the data using statistical analysis techniques. Furthermore, the analysis unit can analyze the data using machine learning techniques. For example, the analysis unit extracts data patterns and obtains information for proposing verification items. The proposal unit proposes verification items based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose test cases and checklists. Furthermore, the proposal unit can also propose an optimal volume of verification items, taking into account the time required to perform the verification items and past verification results. For example, the proposal unit analyzes past verification results and evaluates the importance and risk level of the verification items. The optimization unit optimizes the verification items proposed by the proposal unit. The optimization unit can optimize the verification items using, for example, performance optimization techniques. The optimization unit can also optimize the verification items using resource optimization techniques. For example, the optimization unit selects the optimal verification method to shorten the time required to perform the verification items. As a result, the AI ​​agent system according to the embodiment can perform efficient verification by collecting and analyzing application service specifications and past failure data, proposing verification items, and optimizing them.

[0030] The data collection department collects application service specifications and historical failure data. Specifically, it can collect application service technical specifications and functional specifications. Technical specifications include the application architecture, the technology stack used, API details, and database design. Functional specifications include detailed descriptions of each application function, user interface design, user stories, and use cases. By collecting these specifications, the department can grasp the overall picture of the application and use it to formulate verification items. The data collection department can also collect historical failure data. Failure data includes the date and time of the failure, the type of failure, the scope of impact, the cause of the failure, and countermeasures. By collecting this data, the department can understand past problems and formulate verification items to prevent recurrence. Furthermore, the data collection department can collect industry best practices and the latest technology trends. For example, it can collect standard methods, success stories, new technologies, and innovative methods. This allows the department to grasp the latest technology trends and reflect them in the formulation of verification items. The data collection department can centrally manage this data and collaborate with other departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, it can analyze data using data mining techniques. By using data mining techniques, useful patterns and relationships can be extracted from large amounts of data. For example, by analyzing past failure data, patterns such as the frequency of failures, causes, and scope of impact can be extracted. It can also analyze data using statistical analysis techniques. By using statistical analysis techniques, the distribution, trends, and correlations of data can be revealed. For example, by analyzing application usage data, the frequency of use of specific functions and user behavior patterns can be understood. Furthermore, the analysis unit can also analyze data using machine learning techniques. By using machine learning techniques, predictive models and classification models can be built by automatically learning from data. For example, a failure prediction model can be built using past failure data to predict the risk of future failures. The analysis unit combines these techniques to analyze data from multiple perspectives and obtain information to propose verification items. For example, it extracts data patterns and provides information to determine the priority of verification items. The analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to quickly and accurately analyze the collected data and use it to formulate verification items.

[0032] The proposal department proposes verification items based on the analysis results obtained by the analysis department. Specifically, it can propose test cases and checklists. Test cases show specific test procedures for each function and scenario of the application, while checklists are lists of items to be verified and points to be checked. By proposing these test cases and checklists, the proposal department supports efficient and effective verification. The proposal department can also propose verification items of an optimal volume, taking into account the time required to perform the verification items and past verification results. For example, it can analyze past verification results and evaluate the importance and risk level of the verification items. By proposing verification items that focus on high-importance and high-risk items, it is possible to effectively utilize limited resources and achieve efficient verification. Furthermore, the proposal department can also propose verification items considering industry best practices and the latest technology trends. For example, it can propose verification items that incorporate standard methods, success stories, new technologies, and innovative methods. In this way, the proposal department can always provide high-quality verification items based on the latest information and contribute to improving the quality of the application.

[0033] The optimization unit optimizes the verification items proposed by the proposal unit. Specifically, it can optimize verification items using performance optimization techniques. By using performance optimization techniques, the execution time of verification items can be shortened, enabling efficient verification. For example, by executing multiple verification items simultaneously using parallel processing techniques, verification time can be significantly reduced. It can also optimize verification items using resource optimization techniques. By using resource optimization techniques, limited resources can be effectively utilized, enabling efficient verification. For example, by dynamically allocating cloud resources, necessary resources can be flexibly secured, improving verification throughput. Furthermore, the optimization unit can also optimize the execution order of verification items. For example, by prioritizing the execution of dependent verification items, efficient verification can be achieved. As a result, the optimization unit can efficiently and effectively execute the verification items proposed by the proposal unit, contributing to the improvement of application quality. In addition, the optimization unit can provide feedback on the verification results and continuously improve the verification process in cooperation with the proposal unit and analysis unit. As a result, the optimization unit can always provide high-quality verification based on the latest information, contributing to the improvement of application quality.

[0034] The AI ​​agent system includes a learning unit that learns industry best practices and the latest technology trends. The learning unit can, for example, learn industry standard methods and success stories. For example, it can collect industry best practices and learn based on them. For example, it can analyze industry success stories and obtain information to propose optimal validation items. The learning unit can also learn the latest technology trends. For example, it can collect new technologies and innovative methods and learn based on them. For example, it can analyze the latest technology trends and obtain information to propose optimal validation items. This improves the accuracy of the validation items by learning industry best practices and the latest technology trends. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input industry best practices and the latest technology trends into a generative AI and allow the generative AI to learn.

[0035] The AI ​​agent system includes a consideration unit that takes into account the execution time of verification items and past verification results. The consideration unit can, for example, take into account the execution time of verification items. The consideration unit evaluates the execution time of verification items based on, for example, the unit of measurement of execution time. For example, the consideration unit can adjust the verification items with the aim of shortening the execution time. The consideration unit can also, for example, take into account past verification results. For example, the consideration unit analyzes the success rate and failure causes of past verification results to improve the accuracy of the verification items. For example, the consideration unit evaluates the importance and risk level of verification items based on past verification results. This makes it possible to optimize the verification items by taking into account the execution time of verification items and past verification results. Some or all of the above processing in the consideration unit may be performed using, for example, AI, or not using AI. For example, the consideration unit can input past verification results into AI and have the AI ​​analyze them.

[0036] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of reliable data. For example, the data collection unit can prioritize the collection of information from reliable data sources. For example, the data collection unit can verify the source of the data and exclude unreliable data. For example, the data collection unit can score the reliability of the data and prioritize the collection of data with high scores. This enables reliable verification by evaluating the reliability of the data and prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and prioritize the collection of reliable data.

[0037] The data collection unit can prioritize collecting the latest data, taking into account the data update frequency during collection. For example, the data collection unit can prioritize collecting information from data sources that are updated regularly. For example, the data collection unit can check the last update date and time of the data and prioritize collecting the latest data. For example, the data collection unit can prioritize collecting data sources with a high update frequency. This allows for verification based on the latest information by prioritizing the collection of the latest data, taking into account the data update frequency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the data update frequency and prioritize collecting the latest data.

[0038] The data collection unit can prioritize the collection of data from specific regions, taking into account the geographical distribution of the data during collection. For example, the data collection unit can prioritize the collection of data from specific regions. For example, the data collection unit can collect data without geographical bias. For example, the data collection unit can collect data from each region in a balanced manner. This makes it possible to perform verification tailored to regional characteristics by prioritizing the collection of data from specific regions, taking into account the geographical distribution of the data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the geographical distribution of the data and prioritize the collection of data from specific regions.

[0039] The data collection unit can collect highly relevant data by referring to relevant literature during the collection process. For example, the data collection unit can refer to relevant literature and collect highly relevant data. For example, the data collection unit can evaluate the relevance of the data and prioritize the collection of highly relevant data. For example, the data collection unit can collect highly relevant data by referring to the number of citations of relevant literature. This enables highly accurate verification by referring to relevant literature and collecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the relevance of the data and prioritize the collection of highly relevant data.

[0040] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. For example, the analysis unit can improve the accuracy of the analysis by analyzing the correlations between data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. As a result, by improving the accuracy of the analysis by considering the interrelationships between data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can have AI evaluate the interrelationships between data to improve the accuracy of the analysis.

[0041] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply different analysis algorithms to each data category. For example, the analysis unit can apply analysis algorithms that are appropriate to the characteristics of each category. For example, the analysis unit can select the optimal analysis algorithm according to the data category. This enables efficient analysis by applying different analysis algorithms to each data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply different analysis algorithms to AI for each data category.

[0042] The analysis unit can determine the priority of analysis based on the data submission date during analysis. For example, the analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of data submitted earlier. For example, the analysis unit can postpone the analysis of data submitted later. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI evaluate the data submission date and determine the priority of analysis.

[0043] The analysis unit can improve the accuracy of its analysis by referring to relevant market data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant market data. For example, the analysis unit can improve the accuracy of its analysis by considering trends in market data. For example, the analysis unit can improve the accuracy of its analysis by analyzing correlations in market data. This makes it possible to perform highly accurate analysis by improving the accuracy of the analysis by referring to relevant market data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can have AI evaluate the relevant market data to improve the accuracy of its analysis.

[0044] The proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can make detailed proposals for high-importance verification items. For example, the proposal unit can make concise proposals for low-importance verification items. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the verification items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI evaluate the importance of the verification items and adjust the level of detail of the proposal.

[0045] The proposal unit can apply different proposal algorithms depending on the category of the verification item during the proposal process. For example, the proposal unit can apply different proposal algorithms depending on the category of the verification item. For example, the proposal unit can apply proposal algorithms that are appropriate to the characteristics of each category. For example, the proposal unit can select the optimal proposal algorithm based on the category of the verification item. This enables efficient proposals by applying different proposal algorithms depending on the category of the verification item. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI apply different proposal algorithms depending on the category of the verification item.

[0046] The proposal department can determine the priority of proposals based on the submission timing of the verification items. For example, the proposal department can prioritize proposals based on the submission timing of the verification items. For example, the proposal department can prioritize proposals for verification items that are submitted earlier. For example, the proposal department can postpone proposals for verification items that are submitted later. This allows for efficient proposals by prioritizing proposals based on the submission timing of the verification items. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have AI evaluate the submission timing of the verification items and determine the priority of proposals.

[0047] The proposal unit can adjust the order of proposals based on the relevance of the verification items during the proposal process. For example, the proposal unit can adjust the order of proposals based on the relevance of the verification items. For example, the proposal unit can prioritize proposing highly relevant verification items. For example, the proposal unit can postpone proposing less relevant verification items. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the verification items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI evaluate the relevance of the verification items and adjust the order of proposals.

[0048] The optimization unit can optimize the optimization algorithm by referring to past optimization results during the optimization process. For example, the optimization unit can optimize the optimization algorithm by referring to past optimization results. For example, the optimization unit can analyze the history of optimization results and improve the optimization algorithm. For example, the optimization unit can adjust the optimization algorithm based on past optimization results. This enables efficient optimization by optimizing the optimization algorithm by referring to past optimization results. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate past optimization results and optimize the optimization algorithm.

[0049] The optimization unit can improve the accuracy of optimization by taking into account the execution time of the verification items during optimization. For example, the optimization unit can improve the accuracy of optimization by taking into account the execution time of the verification items. For example, the optimization unit can adjust the optimization algorithm to shorten the execution time. For example, the optimization unit can change the optimization method according to the length of the execution time. This makes efficient optimization possible by improving the accuracy of optimization by taking into account the execution time of the verification items. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate the execution time of the verification items to improve the accuracy of optimization.

[0050] The optimization unit can perform optimization while considering the geographical distribution of the verification items. For example, the optimization unit can perform optimization without geographical bias. For example, the optimization unit can prioritize the optimization of data from a specific region. For example, the optimization unit can perform optimization while considering the characteristics of each region. This makes efficient optimization possible by considering the geographical distribution of the verification items. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can have AI evaluate the geographical distribution of the verification items and then perform optimization.

[0051] The optimization unit can improve the accuracy of optimization by referring to relevant literature for the verification items during optimization. For example, the optimization unit can improve the accuracy of optimization by referring to relevant literature. For example, the optimization unit can improve the accuracy of optimization by referring to the number of citations of the literature. For example, the optimization unit can improve the accuracy of optimization by analyzing the content of the relevant literature. This makes efficient optimization possible by improving the accuracy of optimization by referring to relevant literature for the verification items. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate the relevant literature to improve the accuracy of optimization.

[0052] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit can improve the learning algorithm by analyzing the history of learning data. For example, the learning unit can adjust the learning algorithm based on past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can have AI evaluate past learning data to optimize the learning algorithm.

[0053] The learning unit can improve the accuracy of its learning by referring to industry best practices during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to industry best practices. For example, the learning unit can improve the accuracy of its learning by analyzing examples of best practices. For example, the learning unit can adjust its learning algorithm based on industry best practices. This enables efficient learning by improving the accuracy of learning by referring to industry best practices. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can have AI evaluate industry best practices to improve the accuracy of its learning.

[0054] The learning unit can weight the training data by referring to the latest technology trends during training. For example, the learning unit can weight the training data by referring to the latest technology trends. For example, the learning unit can evaluate the impact of technology trends and weight the training data. For example, the learning unit can adjust the weighting of the training data based on the latest technology trends. This enables efficient training by weighting the training data by referring to the latest technology trends. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can have AI evaluate the latest technology trends and weight the training data.

[0055] The learning unit can improve the accuracy of learning by referring to relevant market data during the learning process. For example, the learning unit can improve the accuracy of learning by referring to relevant market data. For example, the learning unit can improve the accuracy of learning by considering trends in market data. For example, the learning unit can improve the accuracy of learning by analyzing correlations in market data. This enables efficient learning by improving the accuracy of learning by referring to relevant market data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can have AI evaluate the relevant market data to improve the accuracy of learning.

[0056] The consideration unit can improve the accuracy of the consideration items by referring to past verification results during the consideration process. For example, the consideration unit can improve the accuracy of the consideration items by referring to past verification results. For example, the consideration unit can improve the accuracy of the consideration items by analyzing the history of verification results. For example, the consideration unit can adjust the accuracy of the consideration items based on past verification results. This enables efficient consideration by improving the accuracy of the consideration items by referring to past verification results. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate past verification results to improve the accuracy of the consideration items.

[0057] The consideration unit can improve the accuracy of its considerations by taking into account the execution time of the verification items during the consideration process. For example, the consideration unit can improve the accuracy of its considerations by taking into account the execution time of the verification items. For example, the consideration unit can adjust the items to be considered in order to shorten the execution time. For example, the consideration unit can change the method of consideration according to the length of the execution time. This makes efficient consideration possible by improving the accuracy of considerations by taking into account the execution time of the verification items. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate the execution time of the verification items to improve the accuracy of its considerations.

[0058] The consideration unit can perform considerations while taking into account the geographical distribution of the verification items. For example, the consideration unit can perform considerations without geographical bias. For example, the consideration unit can prioritize the consideration of data from a specific region. For example, the consideration unit can perform considerations while taking into account the characteristics of each region. This makes efficient consideration possible by considering the geographical distribution of the verification items. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate the geographical distribution of the verification items and then perform considerations.

[0059] The consideration unit can improve the accuracy of its considerations by referring to relevant literature for the verification items during the consideration process. For example, the consideration unit can improve the accuracy of its considerations by referring to relevant literature. For example, the consideration unit can improve the accuracy of its considerations by referring to the number of citations of the literature. For example, the consideration unit can improve the accuracy of its considerations by analyzing the content of the relevant literature. This makes efficient consideration possible by improving the accuracy of considerations by referring to relevant literature for the verification items. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate the relevant literature to improve the accuracy of its considerations.

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

[0061] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of reliable data. For example, the data collection unit can prioritize the collection of information from reliable data sources. For example, the data collection unit can verify the source of the data and exclude unreliable data. For example, the data collection unit can score the reliability of the data and prioritize the collection of data with high scores. This enables reliable verification by evaluating the reliability of the data and prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and prioritize the collection of reliable data.

[0062] The optimization unit can optimize the optimization algorithm by referring to past optimization results during the optimization process. For example, the optimization unit can optimize the optimization algorithm by referring to past optimization results. For example, the optimization unit can analyze the history of optimization results and improve the optimization algorithm. For example, the optimization unit can adjust the optimization algorithm based on past optimization results. This enables efficient optimization by optimizing the optimization algorithm by referring to past optimization results. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate past optimization results and optimize the optimization algorithm.

[0063] The consideration unit can improve the accuracy of the consideration items by referring to past verification results during the consideration process. For example, the consideration unit can improve the accuracy of the consideration items by referring to past verification results. For example, the consideration unit can improve the accuracy of the consideration items by analyzing the history of verification results. For example, the consideration unit can adjust the accuracy of the consideration items based on past verification results. This enables efficient consideration by improving the accuracy of the consideration items by referring to past verification results. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate past verification results to improve the accuracy of the consideration items.

[0064] The data collection unit can prioritize collecting the latest data, taking into account the data update frequency during collection. For example, the data collection unit can prioritize collecting information from data sources that are updated regularly. For example, the data collection unit can check the last update date and time of the data and prioritize collecting the latest data. For example, the data collection unit can prioritize collecting data sources with a high update frequency. This allows for verification based on the latest information by prioritizing the collection of the latest data, taking into account the data update frequency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the data update frequency and prioritize collecting the latest data.

[0065] The proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can make detailed proposals for high-importance verification items. For example, the proposal unit can make concise proposals for low-importance verification items. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the verification items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI evaluate the importance of the verification items and adjust the level of detail of the proposal.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects application service specifications and historical failure data. For example, it collects data such as application service technical specifications, functional specifications, the date and time of failure, the type of failure, and the scope of impact. It also collects industry best practices, the latest technology trends, standard methods and success stories, and new technologies and innovative methods. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using data mining techniques, statistical analysis techniques, and machine learning techniques to extract data patterns and obtain information to propose verification items. Step 3: The proposal team proposes verification items based on the analysis results obtained by the analysis team. For example, they propose test cases and checklists, and suggest the optimal volume of verification items considering the implementation time for the verification items and past verification results. They analyze past verification results and evaluate the importance and risk level of the verification items. Step 4: The optimization unit optimizes the verification items proposed by the proposal unit. For example, it optimizes the verification items using performance optimization techniques and resource optimization techniques, and selects the optimal verification method to shorten the time required to perform the verification items.

[0068] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that learns application service specifications, past failure data, industry best practices, and the latest technology trends, and proposes optimal verification items. By learning application service specifications and past failure data, as well as industry best practices and the latest technology trends, this AI agent system can grasp the latest technological trends and continuously update and optimize verification items. Based on this learned data, the AI ​​agent system automatically proposes optimal verification items. For example, when verifying additional functions, the AI ​​agent system proposes optimal verification items based on specifications, past failure data, and industry best practices. Furthermore, it also considers the execution time of verification items and past verification results to propose verification items of the optimal volume. By using this AI agent system, the reliance on specific individuals in the quality assurance process can be eliminated, ensuring stable application quality without depending on specific members. In addition, by constantly incorporating the latest technology trends, verification items are prevented from becoming outdated. This leads to increased operational efficiency and reduced risk. Furthermore, the AI ​​agent system immediately proposes verification items that correspond to the latest specifications and trends at the timing of the application verification phase. This allows for pre-confirmation of planned verification items before entering the development phase, enabling efficient development by proactively identifying areas for consideration. The AI ​​agent system then collects and analyzes application service specifications and past failure data, proposes verification items, and optimizes them, resulting in more efficient verification.

[0069] The AI ​​agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and an optimization unit. The data collection unit collects application service specifications and past failure data. For example, the data collection unit can collect application service technical specifications and functional specifications. The data collection unit can also collect past failure data. For example, the data collection unit collects data such as the date and time of failure, the type of failure, and the scope of impact. Furthermore, the data collection unit can also collect industry best practices and the latest technology trends. For example, the data collection unit collects standard methods, success stories, new technologies, and innovative methods. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining techniques. Furthermore, the analysis unit can analyze the data using statistical analysis techniques. Furthermore, the analysis unit can analyze the data using machine learning techniques. For example, the analysis unit extracts data patterns and obtains information for proposing verification items. The proposal unit proposes verification items based on the analysis results obtained by the analysis unit. For example, the proposal unit can propose test cases and checklists. Furthermore, the proposal unit can also propose an optimal volume of verification items, taking into account the time required to perform the verification items and past verification results. For example, the proposal unit analyzes past verification results and evaluates the importance and risk level of the verification items. The optimization unit optimizes the verification items proposed by the proposal unit. The optimization unit can optimize the verification items using, for example, performance optimization techniques. The optimization unit can also optimize the verification items using resource optimization techniques. For example, the optimization unit selects the optimal verification method to shorten the time required to perform the verification items. As a result, the AI ​​agent system according to the embodiment can perform efficient verification by collecting and analyzing application service specifications and past failure data, proposing verification items, and optimizing them.

[0070] The data collection department collects application service specifications and historical failure data. Specifically, it can collect application service technical specifications and functional specifications. Technical specifications include the application architecture, the technology stack used, API details, and database design. Functional specifications include detailed descriptions of each application function, user interface design, user stories, and use cases. By collecting these specifications, the department can grasp the overall picture of the application and use it to formulate verification items. The data collection department can also collect historical failure data. Failure data includes the date and time of the failure, the type of failure, the scope of impact, the cause of the failure, and countermeasures. By collecting this data, the department can understand past problems and formulate verification items to prevent recurrence. Furthermore, the data collection department can collect industry best practices and the latest technology trends. For example, it can collect standard methods, success stories, new technologies, and innovative methods. This allows the department to grasp the latest technology trends and reflect them in the formulation of verification items. The data collection department can centrally manage this data and collaborate with other departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis department and the proposal department. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes the data collected by the collection unit. Specifically, it can analyze data using data mining techniques. By using data mining techniques, useful patterns and relationships can be extracted from large amounts of data. For example, by analyzing past failure data, patterns such as the frequency of failures, causes, and scope of impact can be extracted. It can also analyze data using statistical analysis techniques. By using statistical analysis techniques, the distribution, trends, and correlations of data can be revealed. For example, by analyzing application usage data, the frequency of use of specific functions and user behavior patterns can be understood. Furthermore, the analysis unit can also analyze data using machine learning techniques. By using machine learning techniques, predictive models and classification models can be built by automatically learning from data. For example, a failure prediction model can be built using past failure data to predict the risk of future failures. The analysis unit combines these techniques to analyze data from multiple perspectives and obtain information to propose verification items. For example, it extracts data patterns and provides information to determine the priority of verification items. The analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. This allows the analysis unit to quickly and accurately analyze the collected data and use it to formulate verification items.

[0072] The proposal department proposes verification items based on the analysis results obtained by the analysis department. Specifically, it can propose test cases and checklists. Test cases show specific test procedures for each function and scenario of the application, while checklists are lists of items to be verified and points to be checked. By proposing these test cases and checklists, the proposal department supports efficient and effective verification. The proposal department can also propose verification items of an optimal volume, taking into account the time required to perform the verification items and past verification results. For example, it can analyze past verification results and evaluate the importance and risk level of the verification items. By proposing verification items that focus on high-importance and high-risk items, it is possible to effectively utilize limited resources and achieve efficient verification. Furthermore, the proposal department can also propose verification items considering industry best practices and the latest technology trends. For example, it can propose verification items that incorporate standard methods, success stories, new technologies, and innovative methods. In this way, the proposal department can always provide high-quality verification items based on the latest information and contribute to improving the quality of the application.

[0073] The optimization unit optimizes the verification items proposed by the proposal unit. Specifically, it can optimize verification items using performance optimization techniques. By using performance optimization techniques, the execution time of verification items can be shortened, enabling efficient verification. For example, by executing multiple verification items simultaneously using parallel processing techniques, verification time can be significantly reduced. It can also optimize verification items using resource optimization techniques. By using resource optimization techniques, limited resources can be effectively utilized, enabling efficient verification. For example, by dynamically allocating cloud resources, necessary resources can be flexibly secured, improving verification throughput. Furthermore, the optimization unit can also optimize the execution order of verification items. For example, by prioritizing the execution of dependent verification items, efficient verification can be achieved. As a result, the optimization unit can efficiently and effectively execute the verification items proposed by the proposal unit, contributing to the improvement of application quality. In addition, the optimization unit can provide feedback on the verification results and continuously improve the verification process in cooperation with the proposal unit and analysis unit. As a result, the optimization unit can always provide high-quality verification based on the latest information, contributing to the improvement of application quality.

[0074] The AI ​​agent system includes a learning unit that learns industry best practices and the latest technology trends. The learning unit can, for example, learn industry standard methods and success stories. For example, it can collect industry best practices and learn based on them. For example, it can analyze industry success stories and obtain information to propose optimal validation items. The learning unit can also learn the latest technology trends. For example, it can collect new technologies and innovative methods and learn based on them. For example, it can analyze the latest technology trends and obtain information to propose optimal validation items. This improves the accuracy of the validation items by learning industry best practices and the latest technology trends. Some or all of the above processing in the learning unit may be performed using, for example, generative AI, or without generative AI. For example, the learning unit can input industry best practices and the latest technology trends into a generative AI and allow the generative AI to learn.

[0075] The AI ​​agent system includes a consideration unit that takes into account the execution time of verification items and past verification results. The consideration unit can, for example, take into account the execution time of verification items. The consideration unit evaluates the execution time of verification items based on, for example, the unit of measurement of execution time. For example, the consideration unit can adjust the verification items with the aim of shortening the execution time. The consideration unit can also, for example, take into account past verification results. For example, the consideration unit analyzes the success rate and failure causes of past verification results to improve the accuracy of the verification items. For example, the consideration unit evaluates the importance and risk level of verification items based on past verification results. This makes it possible to optimize the verification items by taking into account the execution time of verification items and past verification results. Some or all of the above processing in the consideration unit may be performed using, for example, AI, or not using AI. For example, the consideration unit can input past verification results into AI and have the AI ​​analyze them.

[0076] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting historical failure data. For example, if the user is relaxed, the data collection unit may prioritize collecting industry best practices. For example, if the user is in a hurry, the data collection unit may prioritize collecting the latest technology trends. This enables efficient data collection by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of reliable data. For example, the data collection unit can prioritize the collection of information from reliable data sources. For example, the data collection unit can verify the source of the data and exclude unreliable data. For example, the data collection unit can score the reliability of the data and prioritize the collection of data with high scores. This enables reliable verification by evaluating the reliability of the data and prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and prioritize the collection of reliable data.

[0078] The data collection unit can prioritize collecting the latest data, taking into account the data update frequency during collection. For example, the data collection unit can prioritize collecting information from data sources that are updated regularly. For example, the data collection unit can check the last update date and time of the data and prioritize collecting the latest data. For example, the data collection unit can prioritize collecting data sources with a high update frequency. This allows for verification based on the latest information by prioritizing the collection of the latest data, taking into account the data update frequency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the data update frequency and prioritize collecting the latest data.

[0079] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting past failure data. For example, if the user is relaxed, the data collection unit may prioritize collecting industry best practices. For example, if the user is in a hurry, the data collection unit may prioritize collecting the latest technology trends. This allows for efficient data collection by adjusting the types of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The data collection unit can prioritize the collection of data from specific regions, taking into account the geographical distribution of the data during collection. For example, the data collection unit can prioritize the collection of data from specific regions. For example, the data collection unit can collect data without geographical bias. For example, the data collection unit can collect data from each region in a balanced manner. This makes it possible to perform verification tailored to regional characteristics by prioritizing the collection of data from specific regions, taking into account the geographical distribution of the data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the geographical distribution of the data and prioritize the collection of data from specific regions.

[0081] The data collection unit can collect highly relevant data by referring to relevant literature during the collection process. For example, the data collection unit can refer to relevant literature and collect highly relevant data. For example, the data collection unit can evaluate the relevance of the data and prioritize the collection of highly relevant data. For example, the data collection unit can collect highly relevant data by referring to the number of citations of relevant literature. This enables highly accurate verification by referring to relevant literature and collecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the relevance of the data and prioritize the collection of highly relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method. For example, if the user is relaxed, the analysis unit can apply a detailed analysis method. For example, if the user is in a hurry, the analysis unit can apply a rapid analysis method. This allows for efficient analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can improve the accuracy of the analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. For example, the analysis unit can improve the accuracy of the analysis by analyzing the correlations between data. For example, the analysis unit can improve the accuracy of the analysis by considering the interrelationships between data. As a result, by improving the accuracy of the analysis by considering the interrelationships between data, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can have AI evaluate the interrelationships between data to improve the accuracy of the analysis.

[0084] The analysis unit can apply different analysis algorithms to each data category during analysis. For example, the analysis unit can apply different analysis algorithms to each data category. For example, the analysis unit can apply analysis algorithms that are appropriate to the characteristics of each category. For example, the analysis unit can select the optimal analysis algorithm according to the data category. This enables efficient analysis by applying different analysis algorithms to each data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply different analysis algorithms to AI for each data category.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple display method. For example, if the user is relaxed, the analysis unit can provide a detailed display method. For example, if the user is in a hurry, the analysis unit can provide a rapid display method. This allows for efficient display of analysis results by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on the data submission date during analysis. For example, the analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit can prioritize the analysis of data submitted earlier. For example, the analysis unit can postpone the analysis of data submitted later. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI evaluate the data submission date and determine the priority of analysis.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant market data during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant market data. For example, the analysis unit can improve the accuracy of its analysis by considering trends in market data. For example, the analysis unit can improve the accuracy of its analysis by analyzing correlations in market data. This makes it possible to perform highly accurate analysis by improving the accuracy of the analysis by referring to relevant market data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can have AI evaluate the relevant market data to improve the accuracy of its analysis.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the user is stressed, the suggestion unit can apply a simple expression. For example, if the user is relaxed, the suggestion unit can apply a detailed expression. For example, if the user is in a hurry, the suggestion unit can apply a rapid expression. This allows for efficient suggestions by adjusting the expression of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can make detailed proposals for high-importance verification items. For example, the proposal unit can make concise proposals for low-importance verification items. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the verification items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI evaluate the importance of the verification items and adjust the level of detail of the proposal.

[0090] The proposal unit can apply different proposal algorithms depending on the category of the verification item during the proposal process. For example, the proposal unit can apply different proposal algorithms depending on the category of the verification item. For example, the proposal unit can apply proposal algorithms that are appropriate to the characteristics of each category. For example, the proposal unit can select the optimal proposal algorithm based on the category of the verification item. This enables efficient proposals by applying different proposal algorithms depending on the category of the verification item. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI apply different proposal algorithms depending on the category of the verification item.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit can make a short suggestion. For example, if the user is relaxed, the suggestion unit can make a detailed suggestion. For example, if the user is in a hurry, the suggestion unit can make a quick suggestion. This allows for efficient suggestions by adjusting the length of the suggestion based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The proposal department can determine the priority of proposals based on the submission timing of the verification items. For example, the proposal department can prioritize proposals based on the submission timing of the verification items. For example, the proposal department can prioritize proposals for verification items that are submitted earlier. For example, the proposal department can postpone proposals for verification items that are submitted later. This allows for efficient proposals by prioritizing proposals based on the submission timing of the verification items. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can have AI evaluate the submission timing of the verification items and determine the priority of proposals.

[0093] The proposal unit can adjust the order of proposals based on the relevance of the verification items during the proposal process. For example, the proposal unit can adjust the order of proposals based on the relevance of the verification items. For example, the proposal unit can prioritize proposing highly relevant verification items. For example, the proposal unit can postpone proposing less relevant verification items. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the verification items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI evaluate the relevance of the verification items and adjust the order of proposals.

[0094] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is stressed, the optimization unit can apply a simple optimization method. For example, if the user is relaxed, the optimization unit can apply a detailed optimization method. For example, if the user is in a hurry, the optimization unit can apply a rapid optimization method. This allows for efficient optimization by adjusting the optimization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The optimization unit can optimize the optimization algorithm by referring to past optimization results during the optimization process. For example, the optimization unit can optimize the optimization algorithm by referring to past optimization results. For example, the optimization unit can analyze the history of optimization results and improve the optimization algorithm. For example, the optimization unit can adjust the optimization algorithm based on past optimization results. This enables efficient optimization by optimizing the optimization algorithm by referring to past optimization results. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate past optimization results and optimize the optimization algorithm.

[0096] The optimization unit can improve the accuracy of optimization by taking into account the execution time of the verification items during optimization. For example, the optimization unit can improve the accuracy of optimization by taking into account the execution time of the verification items. For example, the optimization unit can adjust the optimization algorithm to shorten the execution time. For example, the optimization unit can change the optimization method according to the length of the execution time. This makes efficient optimization possible by improving the accuracy of optimization by taking into account the execution time of the verification items. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate the execution time of the verification items to improve the accuracy of optimization.

[0097] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit will prioritize optimizing high-priority items. For example, if the user is relaxed, the optimization unit can optimize considering the overall balance. For example, if the user is in a hurry, the optimization unit can prioritize optimizing items that require a quick response. This enables efficient optimization by determining optimization priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The optimization unit can perform optimization while considering the geographical distribution of the verification items. For example, the optimization unit can perform optimization without geographical bias. For example, the optimization unit can prioritize the optimization of data from a specific region. For example, the optimization unit can perform optimization while considering the characteristics of each region. This makes efficient optimization possible by considering the geographical distribution of the verification items. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can have AI evaluate the geographical distribution of the verification items and then perform optimization.

[0099] The optimization unit can improve the accuracy of optimization by referring to relevant literature for the verification items during optimization. For example, the optimization unit can improve the accuracy of optimization by referring to relevant literature. For example, the optimization unit can improve the accuracy of optimization by referring to the number of citations of the literature. For example, the optimization unit can improve the accuracy of optimization by analyzing the content of the relevant literature. This makes efficient optimization possible by improving the accuracy of optimization by referring to relevant literature for the verification items. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate the relevant literature to improve the accuracy of optimization.

[0100] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit may prioritize learning past failure data. For example, if the user is relaxed, the learning unit may prioritize learning industry best practices. For example, if the user is in a hurry, the learning unit may prioritize learning the latest technology trends. This enables efficient learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit can improve the learning algorithm by analyzing the history of learning data. For example, the learning unit can adjust the learning algorithm based on past learning data. This enables efficient learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can have AI evaluate past learning data to optimize the learning algorithm.

[0102] The learning unit can improve the accuracy of its learning by referring to industry best practices during the learning process. For example, the learning unit can improve the accuracy of its learning by referring to industry best practices. For example, the learning unit can improve the accuracy of its learning by analyzing examples of best practices. For example, the learning unit can adjust its learning algorithm based on industry best practices. This enables efficient learning by improving the accuracy of learning by referring to industry best practices. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can have AI evaluate industry best practices to improve the accuracy of its learning.

[0103] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit may lower the learning frequency. For example, if the user is relaxed, the learning unit may increase the learning frequency. For example, if the user is in a hurry, the learning unit may adjust the learning frequency. This allows for efficient learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The learning unit can weight the training data by referring to the latest technology trends during training. For example, the learning unit can weight the training data by referring to the latest technology trends. For example, the learning unit can evaluate the impact of technology trends and weight the training data. For example, the learning unit can adjust the weighting of the training data based on the latest technology trends. This enables efficient training by weighting the training data by referring to the latest technology trends. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can have AI evaluate the latest technology trends and weight the training data.

[0105] The learning unit can improve the accuracy of learning by referring to relevant market data during the learning process. For example, the learning unit can improve the accuracy of learning by referring to relevant market data. For example, the learning unit can improve the accuracy of learning by considering trends in market data. For example, the learning unit can improve the accuracy of learning by analyzing correlations in market data. This enables efficient learning by improving the accuracy of learning by referring to relevant market data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can have AI evaluate the relevant market data to improve the accuracy of learning.

[0106] The consideration unit can estimate the user's emotions and determine the priority of items to consider based on the estimated user emotions. For example, if the user is stressed, the consideration unit will prioritize items of high importance. For example, if the user is relaxed, the consideration unit can consider the overall balance. For example, if the user is in a hurry, the consideration unit can prioritize items that require immediate attention. This enables efficient consideration by determining the priority of items to consider based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consideration unit may be performed using AI or not using AI. For example, the consideration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The consideration unit can improve the accuracy of the consideration items by referring to past verification results during the consideration process. For example, the consideration unit can improve the accuracy of the consideration items by referring to past verification results. For example, the consideration unit can improve the accuracy of the consideration items by analyzing the history of verification results. For example, the consideration unit can adjust the accuracy of the consideration items based on past verification results. This enables efficient consideration by improving the accuracy of the consideration items by referring to past verification results. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate past verification results to improve the accuracy of the consideration items.

[0108] The consideration unit can improve the accuracy of its considerations by taking into account the execution time of the verification items during the consideration process. For example, the consideration unit can improve the accuracy of its considerations by taking into account the execution time of the verification items. For example, the consideration unit can adjust the items to be considered in order to shorten the execution time. For example, the consideration unit can change the method of consideration according to the length of the execution time. This makes efficient consideration possible by improving the accuracy of considerations by taking into account the execution time of the verification items. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate the execution time of the verification items to improve the accuracy of its considerations.

[0109] The consideration unit can estimate the user's emotions and adjust the display method of the items to be considered based on the estimated user emotions. For example, if the user is stressed, the consideration unit can provide a simple display method. For example, if the user is relaxed, the consideration unit can provide a detailed display method. For example, if the user is in a hurry, the consideration unit can provide a quick display method. This allows for efficient consideration by adjusting the display method of the items to be considered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without AI. For example, the consideration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The consideration unit can perform considerations while taking into account the geographical distribution of the verification items. For example, the consideration unit can perform considerations without geographical bias. For example, the consideration unit can prioritize the consideration of data from a specific region. For example, the consideration unit can perform considerations while taking into account the characteristics of each region. This makes efficient consideration possible by considering the geographical distribution of the verification items. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate the geographical distribution of the verification items and then perform considerations.

[0111] The consideration unit can improve the accuracy of its considerations by referring to relevant literature for the verification items during the consideration process. For example, the consideration unit can improve the accuracy of its considerations by referring to relevant literature. For example, the consideration unit can improve the accuracy of its considerations by referring to the number of citations of the literature. For example, the consideration unit can improve the accuracy of its considerations by analyzing the content of the relevant literature. This makes efficient consideration possible by improving the accuracy of considerations by referring to relevant literature for the verification items. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate the relevant literature to improve the accuracy of its considerations.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method. For example, if the user is relaxed, the analysis unit can apply a detailed analysis method. For example, if the user is in a hurry, the analysis unit can apply a rapid analysis method. This allows for efficient analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of reliable data. For example, the data collection unit can prioritize the collection of information from reliable data sources. For example, the data collection unit can verify the source of the data and exclude unreliable data. For example, the data collection unit can score the reliability of the data and prioritize the collection of data with high scores. This enables reliable verification by evaluating the reliability of the data and prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and prioritize the collection of reliable data.

[0115] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. For example, if the user is stressed, the suggestion unit can apply a simple expression. For example, if the user is relaxed, the suggestion unit can apply a detailed expression. For example, if the user is in a hurry, the suggestion unit can apply a rapid expression. This allows for efficient suggestions by adjusting the expression of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The optimization unit can optimize the optimization algorithm by referring to past optimization results during the optimization process. For example, the optimization unit can optimize the optimization algorithm by referring to past optimization results. For example, the optimization unit can analyze the history of optimization results and improve the optimization algorithm. For example, the optimization unit can adjust the optimization algorithm based on past optimization results. This enables efficient optimization by optimizing the optimization algorithm by referring to past optimization results. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can have AI evaluate past optimization results and optimize the optimization algorithm.

[0117] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit may prioritize learning past failure data. For example, if the user is relaxed, the learning unit may prioritize learning industry best practices. For example, if the user is in a hurry, the learning unit may prioritize learning the latest technology trends. This enables efficient learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The consideration unit can improve the accuracy of the consideration items by referring to past verification results during the consideration process. For example, the consideration unit can improve the accuracy of the consideration items by referring to past verification results. For example, the consideration unit can improve the accuracy of the consideration items by analyzing the history of verification results. For example, the consideration unit can adjust the accuracy of the consideration items based on past verification results. This enables efficient consideration by improving the accuracy of the consideration items by referring to past verification results. Some or all of the above processing in the consideration unit may be performed using AI, for example, or without using AI. For example, the consideration unit can have AI evaluate past verification results to improve the accuracy of the consideration items.

[0119] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting past failure data. For example, if the user is relaxed, the data collection unit may prioritize collecting industry best practices. For example, if the user is in a hurry, the data collection unit may prioritize collecting the latest technology trends. This allows for efficient data collection by adjusting the types of data collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The data collection unit can prioritize collecting the latest data, taking into account the data update frequency during collection. For example, the data collection unit can prioritize collecting information from data sources that are updated regularly. For example, the data collection unit can check the last update date and time of the data and prioritize collecting the latest data. For example, the data collection unit can prioritize collecting data sources with a high update frequency. This allows for verification based on the latest information by prioritizing the collection of the latest data, taking into account the data update frequency. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the data update frequency and prioritize collecting the latest data.

[0121] The proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can adjust the level of detail of a proposal based on the importance of the verification items. For example, the proposal unit can make detailed proposals for high-importance verification items. For example, the proposal unit can make concise proposals for low-importance verification items. This allows for efficient proposals by adjusting the level of detail of a proposal based on the importance of the verification items. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can have AI evaluate the importance of the verification items and adjust the level of detail of the proposal.

[0122] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is stressed, the optimization unit can apply a simple optimization method. For example, if the user is relaxed, the optimization unit can apply a detailed optimization method. For example, if the user is in a hurry, the optimization unit can apply a rapid optimization method. This allows for efficient optimization by adjusting the optimization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects application service specifications and historical failure data. For example, it collects data such as application service technical specifications, functional specifications, the date and time of failure, the type of failure, and the scope of impact. It also collects industry best practices, the latest technology trends, standard methods and success stories, and new technologies and innovative methods. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the data using data mining techniques, statistical analysis techniques, and machine learning techniques to extract data patterns and obtain information to propose verification items. Step 3: The proposal team proposes verification items based on the analysis results obtained by the analysis team. For example, they propose test cases and checklists, and suggest the optimal volume of verification items considering the implementation time for the verification items and past verification results. They analyze past verification results and evaluate the importance and risk level of the verification items. Step 4: The optimization unit optimizes the verification items proposed by the proposal unit. For example, it optimizes the verification items using performance optimization techniques and resource optimization techniques, and selects the optimal verification method to shorten the time required to perform the verification items.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, optimization unit, learning unit, and consideration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects application service specifications and past failure data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes verification items based on the analysis results. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the proposed verification items. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns industry best practices and the latest technology trends. The consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and considers the implementation time of the verification items and past verification results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, optimization unit, learning unit, and consideration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects application service specifications and past failure data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes verification items based on the analysis results. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the proposed verification items. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns industry best practices and the latest technology trends. The consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and considers the implementation time of the verification items and past verification results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, optimization unit, learning unit, and consideration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects application service specifications and past failure data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes verification items based on the analysis results. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the proposed verification items. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns industry best practices and the latest technology trends. The consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and considers the implementation time of the verification items and past verification results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, optimization unit, learning unit, and consideration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects application service specifications and past failure data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes verification items based on the analysis results. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and optimizes the proposed verification items. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns industry best practices and the latest technology trends. The consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and considers the implementation time of the verification items and past verification results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) The collection unit collects application service specifications and past failure data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes verification items based on the analysis results obtained by the aforementioned analysis unit, The system includes an optimization unit that optimizes the verification items proposed by the proposal unit. A system characterized by the following features. (Note 2) It includes a learning section where you can learn industry best practices and the latest technology trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a consideration unit that takes into account the time required to perform the verification items and past verification results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is During data collection, the reliability of the data is evaluated, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, the most recent data is collected first, taking into account the frequency of data updates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the geographical distribution of the data is taken into consideration, and data from specific regions is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, refer to relevant literature to collect highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, we refer to relevant market data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the verification items. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, apply different proposal algorithms depending on the category of the verification item. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the timing of submission of the verification items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the order of the proposals based on the relevance of the verification items. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, the optimization algorithm is optimized by referring to past optimization results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, During optimization, we improve the accuracy of the optimization by taking into account the time required to perform the verification items. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, During optimization, the geographical distribution of the verification items is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The optimization unit, During optimization, we improve the accuracy of the optimization by referring to relevant literature for the validation items. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned learning unit, When learning, refer to industry best practices to improve the accuracy of your learning. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the training data is weighted by referencing the latest technology trends. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned learning unit, During training, relevant market data is referenced to improve the accuracy of the learning process. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned consideration section is, It estimates the user's emotions and determines the priority of items to consider based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned consideration section is, When considering factors, refer to past verification results to improve the accuracy of the consideration items. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned consideration section is, When considering the results, improve the accuracy of the considerations by taking into account the time required to perform the verification items. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned consideration section is, We estimate the user's emotions and adjust how the items to be considered are displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned consideration section is, When considering the options, take into account the geographical distribution of the verification items. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned consideration section is, When considering the factors, refer to relevant literature for the verification items to improve the accuracy of the considerations. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection unit collects application service specifications and past failure data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes verification items based on the analysis results obtained by the aforementioned analysis unit, The system includes an optimization unit that optimizes the verification items proposed by the proposal unit. A system characterized by the following features.

2. It includes a learning section where you can learn industry best practices and the latest technology trends. The system according to feature 1.

3. It includes a consideration unit that takes into account the time required to perform the verification items and past verification results. The system according to feature 1.

4. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is During data collection, the reliability of the data is evaluated, and reliable data is prioritized for collection. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, the most recent data is collected first, taking into account the frequency of data updates. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the geographical distribution of the data is taken into consideration, and data from specific regions is prioritized for collection. The system according to feature 1.

9. The aforementioned collection unit is During data collection, refer to relevant literature to collect highly relevant data. The system according to feature 1.

Images