System

Abstract

An object of the system according to the embodiment is to enable risks and necessary approvals and tasks to be understood in an interactive manner when a new function is released.SOLUTION: A system according to an embodiment includes a risk input unit, a task input unit, an analysis unit, and an interaction unit. The risk input unit inputs a risk assumed in advance to the generation AI. The task input part inputs an approval or a task required for release to the generation AI. The analysis unit analyzes the information input by the risk input unit and the task input unit. The interaction unit presents the information analyzed by the analysis unit in an interactive form.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] With conventional technology, it was difficult to accurately understand and manage the risks and necessary approvals and tasks when releasing new features.

[0005] The system according to the embodiment aims to enable users to understand, in an interactive manner, the risks and the approval tasks required when releasing new features. [Means for solving the problem]

[0006] The system according to the embodiment includes a risk input unit, a task input unit, an analysis unit, and a dialogue unit. The risk input unit inputs anticipated risks to the generation AI. The task input unit inputs approvals or tasks required for release to the generation AI. The analysis unit analyzes the information input by the risk input unit and the task input unit. The dialogue unit presents the information analyzed by the analysis unit in an interactive format. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to interactively understand the risks and necessary approval tasks involved in releasing new features. [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) The release support system according to an embodiment of the present invention is a system that supports the understanding of risks, approvals, and task completion required when releasing new features. This system uses generative AI to input anticipated risks and the approvals and tasks required for release in advance, allowing users to interactively understand what is required for release. This allows the release support system to interactively understand and learn the matters required for release.

[0029] A release support system according to an embodiment includes a risk input unit, a task input unit, an analysis unit, and a dialogue unit. The risk input unit inputs anticipated risks to the generation AI. For example, it can input technical risks and business risks. The risk input unit can also refer to past release cases and failure cases to improve the accuracy of risk prediction. For example, it can analyze data on past security incidents and legal disputes to predict the possibility of similar risks occurring. The task input unit inputs approvals and tasks required for release to the generation AI. For example, it can input legal approvals and technical tasks. The task input unit can also collect risk and task information from different industries and fields to perform risk analysis from a cross-industry perspective. For example, it can integrate and analyze risk information from the medical and financial industries. The analysis unit analyzes the information input by the risk input unit and task input unit. For example, when the generation AI analyzes the input risks and tasks, it automatically references industry-specific regulations and standards to present more specific risks and tasks. Furthermore, when presenting risks and reasons for approval, the analysis unit can refer to past cases and statistical data to provide specific evidence. For example, the analysis unit can explain the basis for risks based on data from past security incidents. The dialogue unit presents the information analyzed by the analysis unit in an interactive format. For example, if a user asks, "What risks should I be aware of when releasing a new feature?", the generation AI can respond with, "There are security risks. Specifically, there is a possibility of data leakage." The dialogue unit can also use an emotion estimation function to analyze the user's emotions regarding risks and tasks and provide detailed explanations for risks that make the user anxious. For example, it can provide detailed explanations for security risks. This allows the release support system according to the embodiment to understand and learn the matters necessary for release in an interactive format. For example, by receiving a detailed explanation of security risks, the user can acquire knowledge to avoid similar risks in future releases.

[0030] The risk input department can improve the accuracy of risk prediction by referring to past release cases or failure cases. For example, the risk input department inputs past release cases and failure cases into the generation AI as a database to improve the accuracy of risk prediction. For example, it analyzes data on past security incidents and legal troubles to predict the possibility of similar risks occurring. In this way, the accuracy of risk prediction is improved by referring to past cases.

[0031] The analysis unit can automatically refer to industry-specific regulations or standards and present specific risks or tasks. For example, when the generative AI analyzes input risks and tasks, the analysis unit automatically refers to industry-specific regulations and standards. For example, it refers to regulations and standards for the medical industry and presents specific risks and tasks. This makes it possible to present specific risks and tasks by referring to industry-specific regulations and standards.

[0032] The risk input and task input units can collect risk or task information from different industries or fields, and perform risk analysis from a cross-industry perspective. For example, the risk input and task input units can collect risk and task information to be input into the generative AI from different industries or fields, and perform risk analysis from a cross-industry perspective. For example, risk information from the medical and financial industries can be integrated and analyzed. This allows information from different industries and fields to be collected, and risk analysis can be performed from a cross-industry perspective.

[0033] The risk input unit and task input unit can input risks or tasks using multimodal methods such as voice input or image input, improving user convenience. The risk input unit and task input unit, for example, input risks or tasks using voice input, improving user convenience. For example, a user describes risks or tasks using voice, which is then analyzed by the generation AI. This allows risks and tasks to be input using multimodal methods such as voice input or image input, improving user convenience.

[0034] The dialogue unit can refer to the user's past question history and provide individually customized answers. For example, when the generation AI presents risks or tasks in an interactive format, the dialogue unit refers to the user's past question history and provides individually customized answers. For example, more detailed security information can be provided to a user who has previously asked a question about security risks. This allows the dialogue unit to refer to the user's past question history and provide individually customized answers.

[0035] The dialogue unit can automatically link relevant legal or technical documentation when a user requests more information about a risk or a task in the interface. For example, the dialogue unit can provide technical documentation about security risks in the dialogue interface. This allows for automatic linking of relevant legal or technical documentation when a user requests more information about a risk or a task.

[0036] The dialogue unit can adapt the interface to different languages, making it compatible with international users. The dialogue unit, for example, adapts the dialogue interface to different languages, making it compatible with international users. For example, the dialogue unit can adapt to multiple languages, such as English, French, and Chinese. This allows the dialogue interface to adapt to different languages, making it compatible with international users.

[0037] The dialogue unit can add a voice assistant function to the interface and enable dialogue by voice. The dialogue unit can add a voice assistant function to an interactive interface and enable dialogue by voice, for example. For example, a user can ask questions about risks or tasks by voice, and the generation AI can respond by voice. In this way, a voice assistant function can be added to the interactive interface and enable dialogue by voice.

[0038] The analysis unit can automatically link relevant legal regulations and industry standards when presenting risks and reasons for approval, allowing the user to easily refer to detailed information. For example, the analysis unit automatically links relevant legal regulations and industry standards when presenting risks and reasons for approval, allowing the user to easily refer to detailed information. For example, the analysis unit links legal regulations related to security risks. As a result, when presenting risks and reasons for approval, the analysis unit automatically links relevant legal regulations and industry standards, allowing the user to easily refer to detailed information.

[0039] The analysis unit can present the risks and the reasons for approval as visual notes or infographics to make them visually easy to understand. The analysis unit, for example, presents the risks and the reasons for approval as visual notes to make them visually easy to understand. For example, the main points of the risks are shown with diagrams or icons. This makes it possible to present the risks and the reasons for approval as visual notes or infographics to make them visually easy to understand.

[0040] The analysis unit can compare risks and reasons for approval with cases from different industries or fields to provide the user with a multifaceted perspective. The analysis unit, for example, compares risks and reasons for approval with cases from different industries or fields to provide the user with a multifaceted perspective. For example, risk cases from the medical industry and the financial industry are compared. This makes it possible to compare risks and reasons for approval with cases from different industries or fields to provide the user with a multifaceted perspective.

[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 release support system can further include a skill assessment unit that evaluates the user's skill level. The skill assessment unit evaluates the user's past release experience and technical knowledge, and customizes the explanation of tasks and risks required for release according to the user's skill level. For example, it provides a basic explanation of risks to novice users and presents detailed technical risks to experienced users. The skill assessment unit can also provide learning resources to help users improve their skills. For example, it can introduce online courses or webinars to teach countermeasures for specific risks. This allows users to receive information appropriate to their skill level and efficiently proceed with release work.

[0043] The release support system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects feedback from users at each step of the release process and uses it to improve the system. For example, it provides a form for users to enter any problems or improvements they have noticed during the release process. The feedback collection unit can also analyze user feedback and use it to optimize the release process. For example, if many users report the same problem, it can add a new function to solve that problem. In this way, the release support system can be continuously improved according to user needs, making it easier to use.

[0044] The release support system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects feedback from users at each step of the release process and uses it to improve the system. For example, it provides a form for users to enter any problems or improvements they have noticed during the release process. The feedback collection unit can also analyze user feedback and use it to optimize the release process. For example, if many users report the same problem, it can add a new function to solve that problem. In this way, the release support system can be continuously improved according to user needs, making it easier to use.

[0045] The release support system can further include a skill assessment unit that evaluates the user's skill level. The skill assessment unit evaluates the user's past release experience and technical knowledge, and customizes the explanation of tasks and risks required for release according to the user's skill level. For example, it provides a basic explanation of risks to novice users and presents detailed technical risks to experienced users. The skill assessment unit can also provide learning resources to help users improve their skills. For example, it can introduce online courses or webinars to teach countermeasures for specific risks. This allows users to receive information appropriate to their skill level and efficiently proceed with release work.

[0046] The release support system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects feedback from users at each step of the release process and uses it to improve the system. For example, it provides a form for users to enter any problems or improvements they have noticed during the release process. The feedback collection unit can also analyze user feedback and use it to optimize the release process. For example, if many users report the same problem, it can add a new function to solve that problem. In this way, the release support system can be continuously improved according to user needs, making it easier to use.

[0047] The release support system can further include a skill assessment unit that evaluates the user's skill level. The skill assessment unit evaluates the user's past release experience and technical knowledge, and customizes the explanation of tasks and risks required for release according to the user's skill level. For example, it provides a basic explanation of risks to novice users and presents detailed technical risks to experienced users. The skill assessment unit can also provide learning resources to help users improve their skills. For example, it can introduce online courses or webinars to teach countermeasures for specific risks. This allows users to receive information appropriate to their skill level and efficiently proceed with release work.

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

[0049] Step 1: The risk input department inputs anticipated risks into the generation AI. For example, technical risks and business risks can be input. The risk input department can also refer to past release cases and failure cases to improve the accuracy of risk predictions. For example, it can analyze data on past security incidents and legal troubles to predict the possibility of similar risks occurring. Step 2: The task input unit inputs the approvals and tasks required for release into the generation AI. For example, legal approvals and technical tasks can be input. The task input unit can also collect risk and task information from different industries and fields to perform risk analysis from a cross-industry perspective. For example, risk information from the medical and financial industries can be integrated and analyzed. Step 3: The analysis unit analyzes the information input by the risk input unit and task input unit. For example, when the generation AI analyzes the input risks and tasks, it automatically references industry-specific regulations and standards to present more specific risks and tasks. The analysis unit can also refer to past cases and statistical data to provide specific justification when presenting risks and reasons for approval. For example, it can explain the basis for risk based on data from past security incidents. Step 4: The dialogue unit presents the information analyzed by the analysis unit in a dialogue format. For example, if a user asks, "What risks should we be aware of when releasing a new feature?", the generation AI will respond with, "There are security risks. Specifically, there is a possibility of data leakage." The dialogue unit can also use its emotion estimation function to analyze the user's emotions regarding risks and tasks, and provide detailed explanations for risks that make the user feel uneasy. For example, it can provide detailed explanations for security risks.

[0050] (Example 2) The release support system according to an embodiment of the present invention is a system that supports the understanding of risks, approvals, and task completion required when releasing new features. This system uses generative AI to input anticipated risks and the approvals and tasks required for release in advance, allowing users to interactively understand what is required for release. This allows the release support system to interactively understand and learn the matters required for release.

[0051] A release support system according to an embodiment includes a risk input unit, a task input unit, an analysis unit, and a dialogue unit. The risk input unit inputs anticipated risks to the generation AI. For example, it can input technical risks and business risks. The risk input unit can also refer to past release cases and failure cases to improve the accuracy of risk prediction. For example, it can analyze data on past security incidents and legal disputes to predict the possibility of similar risks occurring. The task input unit inputs approvals and tasks required for release to the generation AI. For example, it can input legal approvals and technical tasks. The task input unit can also collect risk and task information from different industries and fields to perform risk analysis from a cross-industry perspective. For example, it can integrate and analyze risk information from the medical and financial industries. The analysis unit analyzes the information input by the risk input unit and task input unit. For example, when the generation AI analyzes the input risks and tasks, it automatically references industry-specific regulations and standards to present more specific risks and tasks. Furthermore, when presenting risks and reasons for approval, the analysis unit can refer to past cases and statistical data to provide specific evidence. For example, the analysis unit can explain the basis for risks based on data from past security incidents. The dialogue unit presents the information analyzed by the analysis unit in an interactive format. For example, if a user asks, "What risks should I be aware of when releasing a new feature?", the generation AI can respond with, "There are security risks. Specifically, there is a possibility of data leakage." The dialogue unit can also use an emotion estimation function to analyze the user's emotions regarding risks and tasks and provide detailed explanations for risks that make the user anxious. For example, it can provide detailed explanations for security risks. This allows the release support system according to the embodiment to understand and learn the matters necessary for release in an interactive format. For example, by receiving a detailed explanation of security risks, the user can acquire knowledge to avoid similar risks in future releases.

[0052] The risk input department can improve the accuracy of risk prediction by referring to past release cases or failure cases. For example, the risk input department inputs past release cases and failure cases into the generation AI as a database to improve the accuracy of risk prediction. For example, it analyzes data on past security incidents and legal troubles to predict the possibility of similar risks occurring. In this way, the accuracy of risk prediction is improved by referring to past cases.

[0053] The analysis unit can automatically refer to industry-specific regulations or standards and present specific risks or tasks. For example, when the generative AI analyzes input risks and tasks, the analysis unit automatically refers to industry-specific regulations and standards. For example, it refers to regulations and standards for the medical industry and presents specific risks and tasks. This makes it possible to present specific risks and tasks by referring to industry-specific regulations and standards.

[0054] The dialogue unit can use the emotion estimation function to analyze the user's emotions regarding risks and tasks, and provide a detailed explanation for risks that make the user feel anxious. The dialogue unit, for example, uses the emotion estimation function to analyze the user's emotions regarding risks and tasks, and provide a detailed explanation for risks that make the user feel anxious. For example, a detailed explanation is provided for security risks. This makes it possible to provide a detailed explanation for risks that make the user feel anxious.

[0055] The risk input and task input units can collect risk or task information from different industries or fields, and perform risk analysis from a cross-industry perspective. For example, the risk input and task input units can collect risk and task information to be input into the generative AI from different industries or fields, and perform risk analysis from a cross-industry perspective. For example, risk information from the medical and financial industries can be integrated and analyzed. This allows information from different industries and fields to be collected, and risk analysis can be performed from a cross-industry perspective.

[0056] The risk input unit and task input unit can input risks or tasks using multimodal methods such as voice input or image input, improving user convenience. The risk input unit and task input unit, for example, input risks or tasks using voice input, improving user convenience. For example, a user describes risks or tasks using voice, which is then analyzed by the generation AI. This allows risks and tasks to be input using multimodal methods such as voice input or image input, improving user convenience.

[0057] The risk input unit and the task input unit can use the emotion estimation function to monitor the user's emotions in real time when inputting risks and tasks, and provide an interface that elicits positive emotions. The risk input unit and the task input unit, for example, use the emotion estimation function to monitor the user's emotions in real time when inputting risks and tasks, and provide an interface that elicits positive emotions. For example, if the user feels anxious, an encouraging message is displayed. In this way, the emotion estimation function is used to monitor the user's emotions in real time when inputting risks and tasks, and provide an interface that elicits positive emotions.

[0058] The dialogue unit can refer to the user's past question history and provide individually customized answers. For example, when the generation AI presents risks or tasks in an interactive format, the dialogue unit refers to the user's past question history and provides individually customized answers. For example, more detailed security information can be provided to a user who has previously asked a question about security risks. This allows the dialogue unit to refer to the user's past question history and provide individually customized answers.

[0059] The dialogue unit can automatically link relevant legal or technical documentation when a user requests more information about a risk or a task in the interface. For example, the dialogue unit can provide technical documentation about security risks in the dialogue interface. This allows for automatic linking of relevant legal or technical documentation when a user requests more information about a risk or a task.

[0060] The dialogue unit can use the emotion estimation function to analyze the user's emotional reaction to the question and adjust the tone and content of the answer so that it is easy for the user to understand. For example, the dialogue unit can use the emotion estimation function to analyze the user's emotional reaction to the question and adjust the tone and content of the answer so that it is easy for the user to understand. For example, if the user is feeling anxious, the dialogue unit can answer in a tone that gives a sense of security. In this way, the dialogue unit can analyze the user's emotional reaction to the question and adjust the tone and content of the answer so that it is easy for the user to understand.

[0061] The dialogue unit can adapt the interface to different languages, making it compatible with international users. The dialogue unit, for example, adapts the dialogue interface to different languages, making it compatible with international users. For example, the dialogue unit can adapt to multiple languages, such as English, French, and Chinese. This allows the dialogue interface to adapt to different languages, making it compatible with international users.

[0062] The dialogue unit can add a voice assistant function to the interface and enable dialogue by voice. The dialogue unit can add a voice assistant function to an interactive interface and enable dialogue by voice, for example. For example, a user can ask questions about risks or tasks by voice, and the generation AI can respond by voice. In this way, a voice assistant function can be added to the interactive interface and enable dialogue by voice.

[0063] The dialogue unit can use the emotion estimation function to monitor the user's emotions in real time during the dialogue, and make suggestions to help the user relax if the user feels stressed. For example, the dialogue unit can use the emotion estimation function to monitor the user's emotions in real time during the dialogue, and make suggestions to help the user relax if the user feels stressed. For example, the dialogue unit can suggest breathing techniques to help the user relax. In this way, the dialogue unit can monitor the user's emotions in real time during the dialogue, and make suggestions to help the user relax if the user feels stressed.

[0064] The analysis unit can automatically link relevant legal regulations and industry standards when presenting risks and reasons for approval, allowing the user to easily refer to detailed information. For example, the analysis unit automatically links relevant legal regulations and industry standards when presenting risks and reasons for approval, allowing the user to easily refer to detailed information. For example, the analysis unit links legal regulations related to security risks. As a result, when presenting risks and reasons for approval, the analysis unit automatically links relevant legal regulations and industry standards, allowing the user to easily refer to detailed information.

[0065] The analysis unit can use the emotion estimation function to provide a detailed explanation for a question when the user has questions about the risk or the reason for approval. For example, the analysis unit can use the emotion estimation function to provide a detailed explanation for a question when the user has questions about the risk or the reason for approval. For example, if the user feels anxious, a detailed explanation of the risk is provided. In this way, if the user has questions about the risk or the reason for approval, a detailed explanation for the question can be provided.

[0066] The analysis unit can present the risks and the reasons for approval as visual notes or infographics to make them visually easy to understand. The analysis unit, for example, presents the risks and the reasons for approval as visual notes to make them visually easy to understand. For example, the main points of the risks are shown with diagrams or icons. This makes it possible to present the risks and the reasons for approval as visual notes or infographics to make them visually easy to understand.

[0067] The analysis unit can compare risks and reasons for approval with cases from different industries or fields to provide the user with a multifaceted perspective. The analysis unit, for example, compares risks and reasons for approval with cases from different industries or fields to provide the user with a multifaceted perspective. For example, risk cases from the medical industry and the financial industry are compared. This makes it possible to compare risks and reasons for approval with cases from different industries or fields to provide the user with a multifaceted perspective.

[0068] The analysis unit can use the emotion estimation function to analyze the user's emotional response to risks and reasons for approval, and provide an explanation that elicits positive emotions. The analysis unit can, for example, use the emotion estimation function to analyze the user's emotional response to risks and reasons for approval, and provide an explanation that elicits positive emotions. For example, the analysis unit can add success stories to risk explanations. This makes it possible to analyze the user's emotional response to risks and reasons for approval, and provide an explanation that elicits positive emotions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The release support system can further include a skill assessment unit that evaluates the user's skill level. The skill assessment unit evaluates the user's past release experience and technical knowledge, and customizes the explanation of tasks and risks required for release according to the user's skill level. For example, it provides a basic explanation of risks to novice users and presents detailed technical risks to experienced users. The skill assessment unit can also provide learning resources to help users improve their skills. For example, it can introduce online courses or webinars to teach countermeasures for specific risks. This allows users to receive information appropriate to their skill level and efficiently proceed with release work.

[0071] The release support system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the release process based on the estimated emotions. The emotion adjustment unit makes suggestions to reduce the stress and anxiety the user feels during the release process. For example, if the user feels anxious, it can play relaxing music. The emotion adjustment unit can also display encouraging messages to encourage the user to have positive emotions. For example, it can display a message such as, "You're doing a great job! Keep it up!" This reduces the stress the user feels during the release process and allows them to proceed with the work in a positive mood.

[0072] The release support system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects feedback from users at each step of the release process and uses it to improve the system. For example, it provides a form for users to enter any problems or improvements they have noticed during the release process. The feedback collection unit can also analyze user feedback and use it to optimize the release process. For example, if many users report the same problem, it can add a new function to solve that problem. In this way, the release support system can be continuously improved according to user needs, making it easier to use.

[0073] The release support system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the release process based on the estimated emotions. The emotion adjustment unit makes suggestions to reduce the stress and anxiety the user feels during the release process. For example, if the user feels anxious, it can play relaxing music. The emotion adjustment unit can also display encouraging messages to encourage the user to have positive emotions. For example, it can display a message such as, "You're doing a great job! Keep it up!" This reduces the stress the user feels during the release process and allows them to proceed with the work in a positive mood.

[0074] The release support system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects feedback from users at each step of the release process and uses it to improve the system. For example, it provides a form for users to enter any problems or improvements they have noticed during the release process. The feedback collection unit can also analyze user feedback and use it to optimize the release process. For example, if many users report the same problem, it can add a new function to solve that problem. In this way, the release support system can be continuously improved according to user needs, making it easier to use.

[0075] The release support system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the release process based on the estimated emotions. The emotion adjustment unit makes suggestions to reduce the stress and anxiety the user feels during the release process. For example, if the user feels anxious, it can play relaxing music. The emotion adjustment unit can also display encouraging messages to encourage the user to have positive emotions. For example, it can display a message such as, "You're doing a great job! Keep it up!" This reduces the stress the user feels during the release process and allows them to proceed with the work in a positive mood.

[0076] The release support system can further include a skill assessment unit that evaluates the user's skill level. The skill assessment unit evaluates the user's past release experience and technical knowledge, and customizes the explanation of tasks and risks required for release according to the user's skill level. For example, it provides a basic explanation of risks to novice users and presents detailed technical risks to experienced users. The skill assessment unit can also provide learning resources to help users improve their skills. For example, it can introduce online courses or webinars to teach countermeasures for specific risks. This allows users to receive information appropriate to their skill level and efficiently proceed with release work.

[0077] The release support system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the release process based on the estimated emotions. The emotion adjustment unit makes suggestions to reduce the stress and anxiety the user feels during the release process. For example, if the user feels anxious, it can play relaxing music. The emotion adjustment unit can also display encouraging messages to encourage the user to have positive emotions. For example, it can display a message such as, "You're doing a great job! Keep it up!" This reduces the stress the user feels during the release process and allows them to proceed with the work in a positive mood.

[0078] The release support system can further include a feedback collection unit that collects user feedback. The feedback collection unit collects feedback from users at each step of the release process and uses it to improve the system. For example, it provides a form for users to enter any problems or improvements they have noticed during the release process. The feedback collection unit can also analyze user feedback and use it to optimize the release process. For example, if many users report the same problem, it can add a new function to solve that problem. In this way, the release support system can be continuously improved according to user needs, making it easier to use.

[0079] The release support system can further include a skill assessment unit that evaluates the user's skill level. The skill assessment unit evaluates the user's past release experience and technical knowledge, and customizes the explanation of tasks and risks required for release according to the user's skill level. For example, it provides a basic explanation of risks to novice users and presents detailed technical risks to experienced users. The skill assessment unit can also provide learning resources to help users improve their skills. For example, it can introduce online courses or webinars to teach countermeasures for specific risks. This allows users to receive information appropriate to their skill level and efficiently proceed with release work.

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

[0081] Step 1: The risk input department inputs anticipated risks into the generation AI. For example, technical risks and business risks can be input. The risk input department can also refer to past release cases and failure cases to improve the accuracy of risk predictions. For example, it can analyze data on past security incidents and legal troubles to predict the possibility of similar risks occurring. Step 2: The task input unit inputs the approvals and tasks required for release into the generation AI. For example, legal approvals and technical tasks can be input. The task input unit can also collect risk and task information from different industries and fields to perform risk analysis from a cross-industry perspective. For example, risk information from the medical and financial industries can be integrated and analyzed. Step 3: The analysis unit analyzes the information input by the risk input unit and task input unit. For example, when the generation AI analyzes the input risks and tasks, it automatically references industry-specific regulations and standards to present more specific risks and tasks. The analysis unit can also refer to past cases and statistical data to provide specific justification when presenting risks and reasons for approval. For example, it can explain the basis for risk based on data from past security incidents. Step 4: The dialogue unit presents the information analyzed by the analysis unit in a dialogue format. For example, if a user asks, "What risks should we be aware of when releasing a new feature?", the generation AI will respond with, "There are security risks. Specifically, there is a possibility of data leakage." The dialogue unit can also use its emotion estimation function to analyze the user's emotions regarding risks and tasks, and provide detailed explanations for risks that make the user feel uneasy. For example, it can provide detailed explanations for security risks.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 system equipped with a generative AI, Risk Input Department and A task input section, an analysis unit; a dialogue unit, The risk input unit Predicted risks are input into the generation AI, The task input unit Input any approvals or tasks required for release into the generation AI, The analysis unit Analyzing information input by the risk input unit and the task input unit; The dialogue unit The information analyzed by the analysis unit is presented in an interactive format. A system characterized by:

2. The risk input unit Refer to past release or failure cases to improve the accuracy of predicting the above risks. The system of claim 1 .

3. The analysis unit Automatically reference industry-specific regulations or standards to present specific risks or tasks The system of claim 1 .

4. The risk input unit and the task input unit Collect information on the risks or tasks from different industries or fields to conduct risk analysis from a cross-industry perspective. The system of claim 1 .

5. The dialogue unit Refer to the user's past question history to provide personalized answers The system of claim 1 .

6. The dialogue unit Using emotion estimation, the app analyzes a user's emotional response to a question and adjusts the tone and content of the answer to make it easier for the user to understand. The system of claim 1 .

7. The analysis unit When presenting the risks and reasons for approval, provide specific evidence by referring to past cases or statistical data. The system of claim 1 .

8. The analysis unit Using an emotion estimation function, the user's emotional response to the risk or the reason for approval is analyzed, and an explanation is provided to elicit positive emotions. The system of claim 1 .

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