System

Abstract

An object of a system according to an embodiment is to identify a potential risk before a service is released and propose an appropriate countermeasure.SOLUTION: A system according to an embodiment includes a data collection unit, a risk specification unit, and a countermeasure proposal unit. The data collection unit collects various data of the service. The risk specification unit analyzes the data collected by the data collection unit to specify a potential risk. The countermeasure proposal unit proposes a countermeasure against the risk specified by the risk specification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult to efficiently identify potential risks and propose appropriate countermeasures before a service is released.

[0005] The system according to the embodiment aims to identify potential risks before the release of a service and propose appropriate countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a risk identification unit, and a countermeasure proposal unit. The data collection unit collects various data on services. The risk identification unit analyzes the data collected by the data collection unit to identify potential risks. The countermeasure proposal unit proposes countermeasures for the risks identified by the risk identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify potential risks before the release of a service and propose appropriate countermeasures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A risk assessment system according to an embodiment of the present invention uses AI to assess risks before a service is released, supporting developers. This system uses AI to analyze various data about the service, identify potential risks, and propose countermeasures to address those risks. This enables the risk assessment system to improve service quality and prevent problems after release.

[0029] A risk diagnosis system according to an embodiment includes a data collection unit, a risk identification unit, and a countermeasure proposal unit. The data collection unit collects various data about a service. For example, the data collection unit collects source code, user interface, security settings, performance data, and the like. The data collection unit can also predict risks based on past failures and successes, including developers' past project data and code review histories. The risk identification unit analyzes the data collected by the data collection unit to identify potential risks. For example, the risk identification unit identifies security vulnerabilities, performance bottlenecks, and usability issues with the user interface. The risk identification unit can also monitor developers' work in real time and perform immediate risk analysis every time a code change is made. The countermeasure proposal unit proposes countermeasures for the risks identified by the risk identification unit. For example, the countermeasure proposal unit proposes specific fixes and security enhancement measures for security vulnerabilities. The countermeasure proposal unit can also propose code optimization and system configuration revisions for performance bottlenecks. This allows the risk diagnosis system according to an embodiment to identify potential risks before a service is released and take appropriate countermeasures. For example, proactively fixing security vulnerabilities can prevent post-release security incidents, eliminating performance bottlenecks can improve user experience, and improving user interface issues can increase user satisfaction.

[0030] The data collection unit can predict risks based on past failures and successes, including developers' past project data and code review history. For example, the data collection unit uses AI to collect developers' past project data and predict risks based on past failures and successes. For example, it analyzes the history of bugs and security issues that have occurred in the past and evaluates the likelihood of similar risks recurring. This allows it to predict risks based on past data and prevent recurrence.

[0031] The data collection unit monitors developer work in real time and can immediately perform risk analysis every time a code change is made. For example, the data collection unit uses AI to monitor developer work in real time and can immediately perform risk analysis every time a code change is made. For example, it can immediately analyze the contents of code changes and identify potential risks. This allows risks to be analyzed in real time and a prompt response to be made.

[0032] The data collection unit can collect user feedback or social media comments and reflect user expectations and dissatisfaction in the analysis. The data collection unit, for example, collects user feedback and reflects it in the analysis of service data. For example, it analyzes user reviews and ratings and identifies potential risks. The data collection unit also collects social media comments and reflects user expectations and dissatisfaction in the analysis. For example, it analyzes Twitter tweets and Facebook posts and identifies user expectations and dissatisfaction. In this way, by reflecting user expectations and dissatisfaction in the analysis, it is possible to provide better services.

[0033] The data collection department can promote data sharing between different development teams and automatically incorporate risks discovered in other projects. The data collection department, for example, builds a system that promotes data sharing between different development teams and automatically incorporates risks discovered in other projects. For example, a common database is used to share risk information. This promotes data sharing between different development teams and automatically incorporates risk information, thereby enhancing risk management.

[0034] The risk identification unit can identify the causes of risks in more detail based on the developer's coding style and the team's communication patterns. For example, the risk identification unit uses AI to analyze the developer's coding style and identify the causes of risks. For example, it evaluates the possibility that a specific coding pattern will cause a bug. The risk identification unit also analyzes the team's communication patterns and identifies the causes of risks. For example, it evaluates the possibility that a lack of communication will cause a risk. This allows the causes of risks to be identified in more detail, allowing effective countermeasures to be taken.

[0035] The risk identification department can detect similar problems early based on past bug reports and user complaint data. The risk identification department, for example, builds a system in which AI references past bug reports to detect similar problems early. For example, it analyzes past bug data and evaluates the possibility of similar risks recurring. The risk identification department also references user complaint data to detect similar problems early. For example, it analyzes user complaint data and evaluates the possibility of similar risks recurring. In this way, by referring to past data, similar problems can be detected early and recurrence can be prevented.

[0036] The Risk Identification Department can incorporate risk cases from other industries based on best practices from different industries. For example, the Risk Identification Department can refer to best practices from different industries and build a system to incorporate them when identifying risks. For example, risk cases from the financial and medical industries can be analyzed to identify common risk factors. This allows risk management to be strengthened by incorporating best practices from different industries.

[0037] The risk identification department can share the results of risk identification in real time not only with developers but also with project managers and management, thereby strengthening overall risk management. The risk identification department can, for example, build a system to share the results of risk identification in real time and provide information not only to developers but also to project managers and management. For example, risk information can be displayed on a dashboard. The risk identification department can also share the results of risk identification in real time via email or a notification system. In this way, sharing the results of risk identification in real time can strengthen overall risk management.

[0038] The countermeasure proposal unit can add a function where AI automatically modifies code, reducing the workload of developers. For example, the countermeasure proposal unit can add a function where AI automatically modifies code and build a system that proposes risk countermeasures. For example, automatically fixing security vulnerabilities. The countermeasure proposal unit can also add a function where AI automatically optimizes code. For example, automatically optimizing performance bottlenecks. In this way, AI can automatically modify code, reducing the workload of developers and realizing efficient risk countermeasures.

[0039] The countermeasure proposal unit can reflect user feedback and prioritize improvements requested by users. The countermeasure proposal unit, for example, builds a system that collects user feedback and reflects it in risk countermeasure proposals. For example, it proposes countermeasures based on user reviews and ratings. The countermeasure proposal unit can also collect comments on social media and prioritize improvements requested by users. For example, it can analyze Twitter tweets and Facebook posts to identify user expectations and dissatisfaction. In this way, by reflecting user feedback, improvements requested by users can be prioritized and user satisfaction can be improved.

[0040] The countermeasure proposal department can compile the proposed countermeasures as general guidelines so that they can be applied to other projects and teams. For example, the countermeasure proposal department compiles the proposed countermeasures as general guidelines and builds a system that makes them applicable to other projects and teams. For example, it creates a common risk countermeasure manual. The countermeasure proposal department can also collect application examples in other projects and teams and update the guidelines. For example, it can improve the guidelines based on successful examples in other projects. In this way, by compiling the proposed countermeasures as general guidelines, they can be applied to other projects and teams.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The risk diagnosis system further includes a prediction unit. The prediction unit can predict future risks using AI. For example, the prediction unit analyzes the current development status and market trends to predict future risks. The prediction unit can also predict future risks based on past data. For example, it analyzes past bug data and security incident history to evaluate the possibility of similar risks recurring. This allows the prediction unit to predict future risks in advance and take appropriate measures.

[0043] The risk assessment system further includes an education unit. The education unit can provide risk management education to developers. For example, the education unit can provide online courses on risk management. The education unit can also provide simulations based on past risk cases. For example, past security incidents can be reproduced to allow developers to learn how to respond. In this way, the education unit can improve the risk management capabilities of developers.

[0044] The risk assessment system further includes a communication unit. The communication unit can promote communication within the development team. For example, the communication unit provides a chat function for sharing information about risks. The communication unit can also automatically schedule meetings regarding risks. For example, when a risk is identified, the communication unit sends a notification to relevant members and sets up a meeting. In this way, the communication unit can promote communication within the development team and strengthen risk management.

[0045] The risk assessment system further includes an anomaly detection unit. The anomaly detection unit can detect abnormal behavior using AI. For example, the anomaly detection unit analyzes service performance data to detect abnormal behavior. The anomaly detection unit can also analyze security logs to detect abnormal access. For example, it can detect signs of unauthorized access or data leakage. This enables the anomaly detection unit to detect abnormal behavior early and respond quickly.

[0046] The risk assessment system further includes a simulation unit. The simulation unit can simulate the effectiveness of risk countermeasures using AI. For example, the simulation unit executes proposed risk countermeasures in a virtual environment and evaluates their effectiveness. The simulation unit can also compare different risk countermeasure scenarios and select the optimal countermeasure. For example, it can simulate multiple security countermeasures and select the most effective one. This allows the simulation unit to evaluate the effectiveness of risk countermeasures in advance and implement the optimal countermeasure.

[0047] The risk diagnosis system further includes a reporting unit. The reporting unit can automatically generate the results of the risk diagnosis as a report. For example, the reporting unit can generate a detailed report based on the results of the risk diagnosis and provide it to developers and project managers. The reporting unit can also track the progress of risk countermeasures and update the report regularly. For example, the implementation status and effects of risk countermeasures are reflected in the report. In this way, the reporting unit can strengthen risk management by automatically generating the results of the risk diagnosis as a report and providing it to relevant parties.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The data collection unit collects various data about the service. For example, the data collection unit collects source code, user interface, security settings, performance data, etc. The data collection unit can also predict risks based on past failures and successes, including data from developers' past projects and code review history. Step 2: The risk identification department analyzes the data collected by the data collection department to identify potential risks. For example, the risk identification department identifies security vulnerabilities, performance bottlenecks, user interface usability issues, etc. The risk identification department can also monitor developer work in real time and perform immediate risk analysis every time a code change is made. Step 3: The Countermeasure Proposal Department proposes countermeasures for the risks identified by the Risk Identification Department. For example, the Countermeasure Proposal Department proposes specific fixes for security vulnerabilities and measures to strengthen security. The Countermeasure Proposal Department can also propose code optimization or system configuration revisions to address performance bottlenecks.

[0050] (Example 2) A risk assessment system according to an embodiment of the present invention uses AI to assess risks before a service is released, supporting developers. This system uses AI to analyze various data about the service, identify potential risks, and propose countermeasures to address those risks. This enables the risk assessment system to improve service quality and prevent problems after release.

[0051] A risk diagnosis system according to an embodiment includes a data collection unit, a risk identification unit, and a countermeasure proposal unit. The data collection unit collects various data about a service. For example, the data collection unit collects source code, user interface, security settings, performance data, and the like. The data collection unit can also predict risks based on past failures and successes, including developers' past project data and code review histories. The risk identification unit analyzes the data collected by the data collection unit to identify potential risks. For example, the risk identification unit identifies security vulnerabilities, performance bottlenecks, and usability issues with the user interface. The risk identification unit can also monitor developers' work in real time and perform immediate risk analysis every time a code change is made. The countermeasure proposal unit proposes countermeasures for the risks identified by the risk identification unit. For example, the countermeasure proposal unit proposes specific fixes and security enhancement measures for security vulnerabilities. The countermeasure proposal unit can also propose code optimization and system configuration revisions for performance bottlenecks. This allows the risk diagnosis system according to an embodiment to identify potential risks before a service is released and take appropriate countermeasures. For example, proactively fixing security vulnerabilities can prevent post-release security incidents, eliminating performance bottlenecks can improve user experience, and improving user interface issues can increase user satisfaction.

[0052] The data collection unit can predict risks based on past failures and successes, including developers' past project data and code review history. For example, the data collection unit uses AI to collect developers' past project data and predict risks based on past failures and successes. For example, it analyzes the history of bugs and security issues that have occurred in the past and evaluates the likelihood of similar risks recurring. This allows it to predict risks based on past data and prevent recurrence.

[0053] The data collection unit monitors developer work in real time and can immediately perform risk analysis every time a code change is made. For example, the data collection unit uses AI to monitor developer work in real time and can immediately perform risk analysis every time a code change is made. For example, it can immediately analyze the contents of code changes and identify potential risks. This allows risks to be analyzed in real time and a prompt response to be made.

[0054] The data collection unit can use the emotion estimation function to analyze the developer's stress level and concentration level, and adjust the accuracy of risk diagnosis according to the developer's state. The data collection unit, for example, uses the emotion estimation function to analyze the developer's stress level and adjust the accuracy of risk diagnosis. For example, when the developer is in a high stress state, risk diagnosis is performed more rigorously. The data collection unit also uses the emotion estimation function to analyze the developer's concentration level and adjust the accuracy of risk diagnosis. For example, when the developer is in a low concentration state, risk diagnosis is performed in more detail. This makes it possible to adjust the accuracy of risk diagnosis according to the developer's state.

[0055] The data collection unit can collect user feedback or social media comments and reflect user expectations and dissatisfaction in the analysis. The data collection unit, for example, collects user feedback and reflects it in the analysis of service data. For example, it analyzes user reviews and ratings and identifies potential risks. The data collection unit also collects social media comments and reflects user expectations and dissatisfaction in the analysis. For example, it analyzes Twitter tweets and Facebook posts and identifies user expectations and dissatisfaction. In this way, by reflecting user expectations and dissatisfaction in the analysis, it is possible to provide better services.

[0056] The data collection department can promote data sharing between different development teams and automatically incorporate risks discovered in other projects. The data collection department, for example, builds a system that promotes data sharing between different development teams and automatically incorporates risks discovered in other projects. For example, a common database is used to share risk information. This promotes data sharing between different development teams and automatically incorporates risk information, thereby enhancing risk management.

[0057] The risk identification unit can identify the causes of risks in more detail based on the developer's coding style and the team's communication patterns. For example, the risk identification unit uses AI to analyze the developer's coding style and identify the causes of risks. For example, it evaluates the possibility that a specific coding pattern will cause a bug. The risk identification unit also analyzes the team's communication patterns and identifies the causes of risks. For example, it evaluates the possibility that a lack of communication will cause a risk. This allows the causes of risks to be identified in more detail, allowing effective countermeasures to be taken.

[0058] The risk identification department can detect similar problems early based on past bug reports and user complaint data. The risk identification department, for example, builds a system in which AI references past bug reports to detect similar problems early. For example, it analyzes past bug data and evaluates the possibility of similar risks recurring. The risk identification department also references user complaint data to detect similar problems early. For example, it analyzes user complaint data and evaluates the possibility of similar risks recurring. In this way, by referring to past data, similar problems can be detected early and recurrence can be prevented.

[0059] The risk identification unit can use the emotion estimation function to identify parts where the user's negative emotions are strongly expressed and prioritize analysis of risks related to those parts. The risk identification unit, for example, uses the emotion estimation function to build a system that identifies parts where the user's negative emotions are strongly expressed. For example, it analyzes the user's facial expressions and voice and calculates a negative emotion score. The risk identification unit also prioritizes analysis of risks related to parts where negative emotions are strongly expressed. For example, it prioritizes analysis of security risks and performance risks in parts where negative emotions are strongly expressed. In this way, by identifying parts where the user's negative emotions are strongly expressed and prioritize analysis of risks related to those parts, user satisfaction is improved.

[0060] The Risk Identification Department can incorporate risk cases from other industries based on best practices from different industries. For example, the Risk Identification Department can refer to best practices from different industries and build a system to incorporate them when identifying risks. For example, risk cases from the financial and medical industries can be analyzed to identify common risk factors. This allows risk management to be strengthened by incorporating best practices from different industries.

[0061] The risk identification department can share the results of risk identification in real time not only with developers but also with project managers and management, thereby strengthening overall risk management. The risk identification department can, for example, build a system to share the results of risk identification in real time and provide information not only to developers but also to project managers and management. For example, risk information can be displayed on a dashboard. The risk identification department can also share the results of risk identification in real time via email or a notification system. In this way, sharing the results of risk identification in real time can strengthen overall risk management.

[0062] The risk identification unit uses the emotion estimation function to analyze emotional fluctuations within the development team and can discover a tendency for specific risks to increase during periods when team morale is low. The risk identification unit, for example, uses the emotion estimation function to build a system that analyzes emotional fluctuations within the development team and discovers a tendency for specific risks to increase during periods when morale is low. For example, it analyzes the facial expressions and voices of team members and calculates an emotion score. The risk identification unit also analyzes the correlation between emotional fluctuations and the tendency for risks to occur. For example, it discovers a tendency for security risks and performance risks to increase during periods when morale is low. This makes it possible to strengthen risk management by analyzing emotional fluctuations within the development team and discovering a tendency for specific risks to increase during periods when morale is low.

[0063] The countermeasure proposal unit can add a function where AI automatically modifies code, reducing the workload of developers. For example, the countermeasure proposal unit can add a function where AI automatically modifies code and build a system that proposes risk countermeasures. For example, automatically fixing security vulnerabilities. The countermeasure proposal unit can also add a function where AI automatically optimizes code. For example, automatically optimizing performance bottlenecks. In this way, AI can automatically modify code, reducing the workload of developers and realizing efficient risk countermeasures.

[0064] The countermeasure suggestion unit can use the emotion estimation function to suggest countermeasures according to the emotional state of the developer, including a method for reducing stress. The countermeasure suggestion unit, for example, uses the emotion estimation function to build a system that suggests countermeasures according to the emotional state of the developer. For example, it simplifies risk countermeasures when stress is high. The countermeasure suggestion unit can also use the emotion estimation function to adjust the priority of risk countermeasures according to the emotional state of the developer. For example, it suggests complex risk countermeasures when the developer is highly concentrated. In this way, by suggesting countermeasures according to the emotional state of the developer, stress is reduced and efficient risk countermeasures are realized.

[0065] The countermeasure proposal unit can reflect user feedback and prioritize improvements requested by users. The countermeasure proposal unit, for example, builds a system that collects user feedback and reflects it in risk countermeasure proposals. For example, it proposes countermeasures based on user reviews and ratings. The countermeasure proposal unit can also collect comments on social media and prioritize improvements requested by users. For example, it can analyze Twitter tweets and Facebook posts to identify user expectations and dissatisfaction. In this way, by reflecting user feedback, improvements requested by users can be prioritized and user satisfaction can be improved.

[0066] The countermeasure proposal department can compile the proposed countermeasures as general guidelines so that they can be applied to other projects and teams. For example, the countermeasure proposal department compiles the proposed countermeasures as general guidelines and builds a system that makes them applicable to other projects and teams. For example, it creates a common risk countermeasure manual. The countermeasure proposal department can also collect application examples in other projects and teams and update the guidelines. For example, it can improve the guidelines based on successful examples in other projects. In this way, by compiling the proposed countermeasures as general guidelines, they can be applied to other projects and teams.

[0067] The countermeasure suggestion unit can use the emotion estimation function to preferentially suggest countermeasures that will elicit the most positive response based on the user's emotional response. The countermeasure suggestion unit, for example, uses the emotion estimation function to build a system that preferentially suggests countermeasures that will elicit the most positive response based on the user's emotional response. For example, the countermeasure suggestion unit analyzes the user's facial expressions and voice and calculates an emotion score. The countermeasure suggestion unit can also suggest countermeasures to elicit a positive emotional response. For example, the countermeasure suggestion unit proposes improvements to the user interface or the addition of new functions. This improves user satisfaction by preferentially suggesting countermeasures that will elicit the most positive response based on the user's emotional response.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The risk diagnosis system further includes a prediction unit. The prediction unit can predict future risks using AI. For example, the prediction unit analyzes the current development status and market trends to predict future risks. The prediction unit can also predict future risks based on past data. For example, it analyzes past bug data and security incident history to evaluate the possibility of similar risks recurring. This allows the prediction unit to predict future risks in advance and take appropriate measures.

[0070] The risk assessment system further includes an education unit. The education unit can provide risk management education to developers. For example, the education unit can provide online courses on risk management. The education unit can also provide simulations based on past risk cases. For example, past security incidents can be reproduced to allow developers to learn how to respond. In this way, the education unit can improve the risk management capabilities of developers.

[0071] The risk assessment system further includes a communication unit. The communication unit can promote communication within the development team. For example, the communication unit provides a chat function for sharing information about risks. The communication unit can also automatically schedule meetings regarding risks. For example, when a risk is identified, the communication unit sends a notification to relevant members and sets up a meeting. In this way, the communication unit can promote communication within the development team and strengthen risk management.

[0072] The risk diagnosis system can also use the emotion estimation function to analyze a developer's motivation and suggest appropriate measures if their motivation is declining. For example, the emotion estimation function can be used to calculate a developer's motivation score and, if their motivation is declining, suggest taking a break. The emotion estimation function can also be used to provide feedback to improve a developer's motivation. For example, it can send positive feedback or encouraging messages. This helps maintain developer motivation and achieve efficient risk management.

[0073] The risk diagnosis system can further use an emotion estimation function to analyze the user's emotions and propose risk countermeasures based on the user's emotions. For example, the emotion estimation function can be used to identify areas where the user is highly dissatisfied and prioritize risk countermeasures for those areas. The emotion estimation function can also be used to identify areas where the user is highly satisfied and propose countermeasures to strengthen those areas. For example, the system can propose improvements to the user interface or the addition of new functions. This makes it possible to propose risk countermeasures based on the user's emotions and improve user satisfaction.

[0074] The risk diagnosis system can further use the emotion estimation function to analyze emotional fluctuations within the development team and propose risk countermeasures based on the emotional fluctuations. For example, the emotion estimation function can be used to discover a tendency for specific risks to increase during periods when morale in the development team is low, and measures can be strengthened at that time. The emotion estimation function can also be used to efficiently implement risk countermeasures during periods when morale in the development team is high. For example, complex risk countermeasures can be proposed during periods when morale is high. This makes it possible to propose risk countermeasures based on emotional fluctuations within the development team and achieve efficient risk management.

[0075] The risk diagnosis system can further use an emotion estimation function to preferentially suggest measures that will elicit the most positive emotional response based on the user's emotional response. For example, the emotion estimation function can be used to analyze the user's facial expressions and voice and calculate an emotion score. The emotion estimation function can also be used to suggest measures to elicit a positive emotional response. For example, it can suggest improvements to the user interface or the addition of new functions. This makes it possible to improve user satisfaction by preferentially suggesting measures that will elicit the most positive response based on the user's emotional response.

[0076] The risk assessment system further includes an anomaly detection unit. The anomaly detection unit can detect abnormal behavior using AI. For example, the anomaly detection unit analyzes service performance data to detect abnormal behavior. The anomaly detection unit can also analyze security logs to detect abnormal access. For example, it can detect signs of unauthorized access or data leakage. This enables the anomaly detection unit to detect abnormal behavior early and respond quickly.

[0077] The risk assessment system further includes a simulation unit. The simulation unit can simulate the effectiveness of risk countermeasures using AI. For example, the simulation unit executes proposed risk countermeasures in a virtual environment and evaluates their effectiveness. The simulation unit can also compare different risk countermeasure scenarios and select the optimal countermeasure. For example, it can simulate multiple security countermeasures and select the most effective one. This allows the simulation unit to evaluate the effectiveness of risk countermeasures in advance and implement the optimal countermeasure.

[0078] The risk diagnosis system further includes a reporting unit. The reporting unit can automatically generate the results of the risk diagnosis as a report. For example, the reporting unit can generate a detailed report based on the results of the risk diagnosis and provide it to developers and project managers. The reporting unit can also track the progress of risk countermeasures and update the report regularly. For example, the implementation status and effects of risk countermeasures are reflected in the report. In this way, the reporting unit can strengthen risk management by automatically generating the results of the risk diagnosis as a report and providing it to relevant parties.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The data collection unit collects various data about the service. For example, the data collection unit collects source code, user interface, security settings, performance data, etc. The data collection unit can also predict risks based on past failures and successes, including data from developers' past projects and code review history. Step 2: The risk identification department analyzes the data collected by the data collection department to identify potential risks. For example, the risk identification department identifies security vulnerabilities, performance bottlenecks, user interface usability issues, etc. The risk identification department can also monitor developer work in real time and perform immediate risk analysis every time a code change is made. Step 3: The Countermeasure Proposal Department proposes countermeasures for the risks identified by the Risk Identification Department. For example, the Countermeasure Proposal Department proposes specific fixes for security vulnerabilities and measures to strengthen security. The Countermeasure Proposal Department can also propose code optimization or system configuration revisions to address performance bottlenecks.

[0081] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0087] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0088] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0091] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0092] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0096] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0131] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0137] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0138] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0139] 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.

[0140] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0141] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0142] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0143] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0144] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0145] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0146] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects various data on the service; a risk identification unit that analyzes the data collected by the data collection unit and identifies potential risks; a countermeasure proposal unit that proposes countermeasures for the risks identified by the risk identification unit. A system characterized by:

2. The data collection unit Monitor developer work in real time and perform immediate risk analysis after each code change 2. The system of claim 1.

3. The risk identification unit Identify risk sources in greater detail based on developer coding styles and team communication patterns 2. The system of claim 1.

4. The measure proposal unit Reflect user feedback and prioritize improvements requested by users 2. The system of claim 1.

5. The data collection unit Analyzes the developer's stress level and concentration using emotion estimation functionality, and adjusts the accuracy of risk diagnosis according to the developer's condition.

2. The system of claim 1.

6. The risk identification unit Using emotion estimation functionality, we identify areas where users' negative emotions are most pronounced, and prioritize the analysis of risks related to those areas.

2. The system of claim 1.

7. The measure proposal unit Uses emotion estimation to suggest measures based on the developer's emotional state, including ways to reduce stress 2. The system of claim 1.

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