system

Abstract

The system according to the embodiment aims to monitor the behavior and output of AI and prevent abnormal behavior and malicious actions before they occur. [Solution] A system according to an embodiment includes a monitoring unit, a countermeasure unit, and a collaboration unit. The monitoring unit monitors the behavior or products of the AI. The countermeasure unit takes measures against abnormal behavior or malicious behavior detected by the monitoring unit. The collaboration unit shares information between the monitoring unit and the countermeasure unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to prevent malicious or abnormal behavior due to insufficient monitoring of AI behavior and outputs.

[0005] The system according to the embodiment aims to monitor the behavior and output of AI and prevent abnormal behavior and malicious actions before they occur. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a countermeasure unit, and a collaboration unit. The monitoring unit monitors the behavior or products of the AI. The countermeasure unit takes measures against abnormal behavior or malicious behavior detected by the monitoring unit. The collaboration unit shares information between the monitoring unit and the countermeasure unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the behavior and output of AI and prevent abnormal behavior and malicious actions before they occur. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A monitoring system according to an embodiment of the present invention monitors AI behavior and outputs, detects abnormal behavior and malicious behavior, and takes countermeasures. The monitoring system monitors AI behavior and outputs, detects abnormal behavior and malicious behavior, and prevents damage. For example, the monitoring system monitors AI responses and actions in real time to detect abnormal behavior and malicious behavior. The monitoring system then takes countermeasures against detected abnormal or malicious behavior, such as temporarily suspending the AI's operation, issuing a warning to the user, or deleting malicious programs. Furthermore, the monitoring system shares information between the monitoring unit and the countermeasure unit in real time, enabling prompt and effective countermeasures. This allows the monitoring system to prevent AI from generating malicious responses, collecting user data, or installing malicious programs. This allows the monitoring system to monitor AI behavior and outputs, detect abnormal or malicious behavior, and take countermeasures, thereby ensuring user safety and improving trust in the use of AI.

[0029] A monitoring system according to an embodiment includes a monitoring unit, a countermeasure unit, and a collaboration unit. The monitoring unit monitors the behavior or products of an AI. For example, the monitoring unit monitors answers and actions generated by the AI ​​in real time to detect abnormal or malicious behavior. The monitoring unit can use sensors and log data to monitor the behavior or products of the AI. For example, the monitoring unit can analyze text data generated by the AI ​​and detect abnormalities based on specific keywords or phrases. The monitoring unit can also learn the behavior patterns of the AI ​​and detect abnormal behavior early. The countermeasure unit takes countermeasures against abnormal or malicious behavior detected by the monitoring unit. For example, the countermeasure unit temporarily suspends the AI's operation, issues a warning to the user, or deletes malicious programs. To control the AI's behavior, the countermeasure unit can take countermeasures taking into account the AI's operating environment (hardware, software). The collaboration unit shares information between the monitoring unit and the countermeasure unit. For example, the collaboration unit transmits abnormal information detected by the monitoring unit to the countermeasure unit in real time and transmits information on countermeasures implemented by the countermeasure unit to the monitoring unit in real time. The collaboration unit can also encrypt and share information securely. This allows the monitoring system according to the embodiment to monitor the behavior and output of the AI, detect abnormal behavior or malicious actions, and take measures to prevent damage.

[0030] The monitoring unit can monitor answers or actions generated by the AI ​​in real time and detect abnormal or malicious behavior. For example, the monitoring unit can analyze text data generated by the AI ​​in real time and detect anomalies based on specific keywords or phrases. For example, the monitoring unit can monitor whether specific keywords are included in text generated by the AI. The monitoring unit can also analyze phrases in text generated by the AI ​​to detect abnormal content. The monitoring unit can also analyze the context of text generated by the AI ​​to detect abnormal content. This makes it possible to monitor AI products and actions in real time and detect abnormalities or malicious behavior early. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input text data generated by the AI ​​into the generation AI and have the generation AI detect abnormal content.

[0031] The countermeasure unit can temporarily suspend the operation of the AI ​​in response to detected abnormal or malicious behavior. The countermeasure unit, for example, controls the AI's operating environment (hardware, software) to temporarily suspend the operation of the AI. For example, the countermeasure unit can temporarily turn off the power to the hardware on which the AI ​​is running. The countermeasure unit can also temporarily suspend the software process on which the AI ​​is running. The countermeasure unit can also temporarily disconnect the network connection on which the AI ​​is running. In this way, when abnormal or malicious behavior is detected, damage can be prevented by temporarily suspending the operation of the AI. Some or all of the above-mentioned processing in the countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure unit can input control of the AI's operating environment to the generation AI and cause the generation AI to temporarily suspend the operation of the AI.

[0032] The countermeasure unit can notify the user of a warning. For example, the countermeasure unit issues a warning to the user when abnormal behavior or malicious behavior is detected. For example, the countermeasure unit can display a warning message on the user's device. The countermeasure unit can also send a warning to the user by email. The countermeasure unit can also notify the user of the warning by voice. In this way, when abnormal behavior or malicious behavior is detected, a warning can be issued to the user to encourage a prompt response. Some or all of the above-mentioned processing in the countermeasure unit may be performed, for example, using AI or may be performed without using AI. For example, the countermeasure unit can input the detection of abnormal behavior or malicious behavior into a generation AI and have the generation AI execute a warning notification.

[0033] The countermeasure unit can remove malicious programs. For example, if a malicious program is detected, the countermeasure unit deletes it. For example, the countermeasure unit can detect a malicious program that the AI ​​is attempting to install and delete that program. The countermeasure unit can also detect malicious programs that have already been installed and delete them. The countermeasure unit can also monitor the AI's operating environment and detect abnormal behavior to prevent the installation of malicious programs. This makes it possible to ensure the security of the system by deleting a malicious program if it is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input the detection of a malicious program to the generation AI and have the generation AI delete the program.

[0034] The coordination unit can instantly share information between the monitoring unit and the countermeasure unit. For example, the coordination unit transmits abnormality information detected by the monitoring unit to the countermeasure unit in real time. For example, the coordination unit can instantly communicate information about abnormal behavior or malicious actions detected by the monitoring unit to the countermeasure unit. The coordination unit can also transmit information about countermeasures implemented by the countermeasure unit to the monitoring unit in real time. For example, the coordination unit can instantly communicate information about AI operation suspension or warning notifications implemented by the countermeasure unit to the monitoring unit. The coordination unit can also synchronize all information shared between the monitoring unit and the countermeasure unit in real time. This allows information to be shared between the monitoring unit and the countermeasure unit in real time, enabling prompt and effective countermeasures to be taken. Some or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI or without AI. For example, the coordination unit can input information shared between the monitoring unit and the countermeasure unit to the generation AI and have the generation AI synchronize the information.

[0035] During monitoring, the monitoring unit can improve the accuracy of anomaly detection by referring to the AI's past operation history. The monitoring unit, for example, improves the accuracy of anomaly detection by referring to the AI's past operation history. For example, the monitoring unit can detect similar patterns based on the timing of past anomalies. The monitoring unit can also identify time periods when anomalies are likely to occur from the past operation history and focus monitoring on those time periods. The monitoring unit can also learn past abnormal operation patterns and detect new abnormal operations early. In this way, by referring to the past operation history, the accuracy of anomaly detection can be improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the AI's past operation history into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0036] The monitoring unit learns the AI's behavior patterns during monitoring and can quickly detect abnormal behavior. The monitoring unit, for example, learns the AI's normal behavior patterns and detects behavior that differs from them as abnormal. For example, the monitoring unit can monitor changes in the AI's behavior patterns in real time and detect abnormalities early. The monitoring unit can also continuously learn the AI's behavior patterns and improve the accuracy of abnormality detection. In this way, by learning the AI's behavior patterns, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the AI's behavior patterns to the generation AI and cause the generation AI to detect abnormal behavior.

[0037] During monitoring, the monitoring unit can analyze the content of the AI's product and detect anomalies based on specific keywords or phrases. The monitoring unit, for example, monitors whether specific keywords are included in text generated by the AI. For example, the monitoring unit can analyze phrases in the text generated by the AI ​​and detect anomalous content. The monitoring unit can also analyze the context of the text generated by the AI ​​and detect anomalous content. In this way, by analyzing the content of the AI's product, anomalies can be detected based on specific keywords or phrases. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input text data generated by the AI ​​into the generation AI and cause the generation AI to detect anomalous content.

[0038] During monitoring, the monitoring unit can detect anomalies based on the operating environment of the AI. For example, the monitoring unit monitors the state of the hardware on which the AI ​​is running and detects anomalies. For example, the monitoring unit can monitor the version of the software on which the AI ​​is running and detect anomalies. The monitoring unit can also monitor the entire environment in which the AI ​​is running and detect anomalies. This allows for improved accuracy in anomaly detection by taking the operating environment of the AI ​​into consideration. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, an AI. For example, the monitoring unit can input operating environment data of the AI ​​to the generation AI and cause the generation AI to perform anomaly detection.

[0039] During monitoring, the monitoring unit monitors the AI's operating speed or resource usage and can identify abnormal resource consumption. The monitoring unit, for example, monitors the AI's CPU usage and detects abnormal resource consumption. For example, the monitoring unit can monitor the AI's memory usage and detect abnormal resource consumption. The monitoring unit can also monitor the AI's disk I / O and detect abnormal resource consumption. In this way, by monitoring the AI's operating speed and resource usage, abnormal resource consumption can be detected early. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the AI's resource usage data to the generation AI and cause the generation AI to detect abnormal resource consumption.

[0040] During monitoring, the monitoring unit can identify anomalies by referring to external data sources related to the operation of the AI. The monitoring unit, for example, monitors the status of external data sources accessed by the AI ​​and detects anomalies. For example, the monitoring unit can monitor the responses of APIs used by the AI ​​and detect anomalies. The monitoring unit can also monitor the status of external services on which the AI ​​depends and detect anomalies. By referring to external data sources, the accuracy of anomaly detection can be improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input information from external data sources to the generation AI and cause the generation AI to perform anomaly detection.

[0041] When taking countermeasures, the countermeasure unit can select the optimal countermeasure by referring to the AI's past abnormal operation history. The countermeasure unit selects the optimal countermeasure, for example, based on countermeasures that were effective in the past. For example, the countermeasure unit can select a countermeasure for a similar abnormality from the past abnormal operation history. The countermeasure unit can also analyze the past countermeasure history and select the most effective countermeasure. In this way, the optimal countermeasure can be selected by referring to the past abnormal operation history. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, or without, AI, for example. For example, the countermeasure unit can input the past abnormal operation history into the generation AI and have the generation AI select the optimal countermeasure.

[0042] When taking countermeasures, the countermeasure unit can analyze the operating environment of the AI ​​and select the optimal countermeasure. For example, the countermeasure unit can analyze the state of the hardware on which the AI ​​is running and select the optimal countermeasure. For example, the countermeasure unit can analyze the version of the software on which the AI ​​is running and select the optimal countermeasure. The countermeasure unit can also analyze the entire environment in which the AI ​​is running and select the optimal countermeasure. In this way, the optimal countermeasure can be selected by analyzing the operating environment of the AI. Some or all of the above-mentioned processing in the countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure unit can input the operating environment data of the AI ​​to the generation AI and have the generation AI select the optimal countermeasure.

[0043] The countermeasure unit can improve the countermeasure method by reflecting user feedback when taking countermeasures. The countermeasure unit improves the countermeasure method, for example, based on user feedback. For example, the countermeasure unit can adjust the priority of countermeasures by reflecting user opinions. The countermeasure unit can also collect user feedback and use it to improve the countermeasure method. In this way, the countermeasure method can be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input user feedback data into a generation AI and have the generation AI improve the countermeasure method.

[0044] When taking countermeasures, the countermeasure unit can select the optimal countermeasure by referring to external data sources related to the behavior of the AI. The countermeasure unit, for example, refers to the status of external data sources accessed by the AI ​​and selects the optimal countermeasure. For example, the countermeasure unit can select the optimal countermeasure by referring to the response of an API used by the AI. The countermeasure unit can also select the optimal countermeasure by referring to the status of external services on which the AI ​​depends. In this way, the optimal countermeasure can be selected by referring to external data sources. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, or without, AI, for example. For example, the countermeasure unit can input information from external data sources into the generation AI and have the generation AI select the optimal countermeasure.

[0045] When taking countermeasures, the countermeasure unit can execute the countermeasure taking into consideration the AI's operating speed and resource usage. The countermeasure unit, for example, takes into consideration the AI's CPU usage rate to execute the optimal countermeasure. For example, the countermeasure unit can take into consideration the AI's memory usage to execute the optimal countermeasure. The countermeasure unit can also take into consideration the AI's disk I / O to execute the optimal countermeasure. This makes it possible to execute the optimal countermeasure by taking into consideration the AI's operating speed and resource usage. Some or all of the above-mentioned processing in the countermeasure unit may be performed using an AI, for example, or may be performed without using an AI. For example, the countermeasure unit can input the AI's resource usage data into the generation AI and have the generation AI execute the optimal countermeasure.

[0046] When taking countermeasures, the countermeasure unit can execute the countermeasures based on the operating environment of the AI. The countermeasure unit, for example, takes into account the state of the hardware on which the AI ​​is running and executes the optimal countermeasure. For example, the countermeasure unit can take into account the version of the software on which the AI ​​is running and execute the optimal countermeasure. The countermeasure unit can also execute the optimal countermeasure by taking into account the entire environment in which the AI ​​is running. In this way, the optimal countermeasure can be executed by taking into account the operating environment of the AI. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, or without, an AI. For example, the countermeasure unit can input operating environment data of the AI ​​into the generation AI and cause the generation AI to execute the optimal countermeasure.

[0047] During collaboration, the collaboration unit can improve the accuracy of information sharing by referring to the past collaboration history between the monitoring unit and the countermeasure unit. The collaboration unit, for example, selects the optimal information sharing method based on the past collaboration history. For example, the collaboration unit can optimize the timing of information sharing from the past collaboration history. The collaboration unit can also analyze the past collaboration history and improve the accuracy of information sharing. In this way, the accuracy of information sharing can be improved by referring to the past collaboration history. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input past collaboration history data into the generation AI and have the generation AI improve the accuracy of information sharing.

[0048] During collaboration, the coordination unit can encrypt information between the monitoring unit and the countermeasure unit to share the information securely. The coordination unit, for example, encrypts and transmits information from the monitoring unit to the countermeasure unit. For example, the coordination unit can encrypt and transmit feedback information from the countermeasure unit to the monitoring unit. The coordination unit can also encrypt and transmit all information shared between the monitoring unit and the countermeasure unit. This allows for secure information sharing by encrypting the information. Some or all of the above-mentioned processing in the coordination unit may be performed using AI, for example, or may be performed without using AI. For example, the coordination unit can input encryption of the information to the generation AI and have the generation AI encrypt and transmit the information.

[0049] During collaboration, the collaboration unit can instantly synchronize information between the monitoring unit and the countermeasure unit. For example, the collaboration unit transmits abnormality information detected by the monitoring unit to the countermeasure unit in real time. For example, the collaboration unit can transmit information on countermeasures taken by the countermeasure unit to the monitoring unit in real time. The collaboration unit can also synchronize all information between the monitoring unit and the countermeasure unit in real time. This allows for quick and effective countermeasures to be taken by synchronizing information in real time. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input information synchronization to the generation AI and have the generation AI perform real-time synchronization of information.

[0050] During collaboration, the coordination unit can compress information between the monitoring unit and the countermeasure unit to efficiently share information. For example, the coordination unit compresses and transmits information from the monitoring unit to the countermeasure unit, thereby reducing communication volume. For example, the coordination unit can compress and transmit feedback information from the countermeasure unit to the monitoring unit, thereby improving communication speed. The coordination unit can also compress and transmit all information shared between the monitoring unit and the countermeasure unit, thereby achieving efficient information sharing. In this way, information can be shared efficiently by compressing the information. Some or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI, or may be performed without using AI. For example, the coordination unit can input information compression to the generation AI and have the generation AI compress and transmit the information.

[0051] During collaboration, the collaboration unit can filter information between the monitoring unit and the countermeasure unit to share only necessary information. For example, the collaboration unit transmits only important information from among the anomaly information detected by the monitoring unit to the countermeasure unit. For example, the collaboration unit can transmit only necessary information from among the countermeasure information implemented by the countermeasure unit to the monitoring unit. The collaboration unit can also filter all information between the monitoring unit and the countermeasure unit to share only necessary information. This enables efficient information sharing by sharing only necessary information. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information filtering to the generation AI and have the generation AI select and share the necessary information.

[0052] During collaboration, the collaboration unit can share information between the monitoring unit and the countermeasure unit in multiple languages. For example, the collaboration unit can transmit information from the monitoring unit to the countermeasure unit in multiple languages, enabling international responses. For example, the collaboration unit can transmit feedback information from the countermeasure unit to the monitoring unit in multiple languages, enabling global collaboration. The collaboration unit can also transmit all information shared between the monitoring unit and the countermeasure unit in multiple languages, enabling efficient information sharing. This enables international responses through multilingual support. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the multilingual translation of the information into a generation AI and have the generation AI translate and share the information.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When monitoring AI behavior, the monitoring unit can also evaluate the reliability of the data generated by the AI. For example, the monitoring unit can check the source of the data generated by the AI ​​and filter out unreliable data. The monitoring unit can also check the consistency of the data generated by the AI ​​and issue a warning if there are any inconsistencies. Furthermore, the monitoring unit can evaluate the accuracy of the data generated by the AI ​​and suggest corrections if there are large errors. This helps ensure the reliability of the data generated by the AI ​​and provides more accurate information.

[0055] The countermeasures department can also temporarily restrict the AI's operations if abnormal or malicious behavior is detected. For example, the countermeasures department can temporarily prohibit the AI ​​from using certain functions. The countermeasures department can also limit the databases that the AI ​​can access, preventing access to important data. Furthermore, the countermeasures department can restrict the AI's communication with external parties to prevent information leaks. In this way, damage can be minimized by restricting the AI's operations if abnormal or malicious behavior is detected.

[0056] The coordination unit can also set the priority of information when sharing information between the monitoring unit and the countermeasures unit. For example, the coordination unit can prioritize sending information with a high level of urgency to the countermeasures unit. The coordination unit can also prioritize sending information with a high level of importance to the monitoring unit to encourage a quick response. Furthermore, the coordination unit can adjust the frequency of information sharing depending on the importance of the information. In this way, by setting the priority of information, quick and effective countermeasures can be taken.

[0057] The countermeasures department can also provide a detailed report to the user when abnormal or malicious behavior is detected. For example, the countermeasures department can analyze the cause of the abnormal or malicious behavior and report it to the user. The countermeasures department can also record the implementation status of the countermeasures in detail and report it to the user. Furthermore, the countermeasures department can formulate a future countermeasure plan and propose it to the user. In this way, by providing the user with a detailed report, transparency can be ensured and reliability can be improved.

[0058] When monitoring the behavior of AI, the monitoring unit can also evaluate the ethics of the data generated by AI. For example, the monitoring unit can check whether the text data generated by AI contains discriminatory content. The monitoring unit can also evaluate whether the image data generated by AI contains inappropriate content. Furthermore, the monitoring unit can check whether the audio data generated by AI contains violent content. In this way, by evaluating the ethics of the data generated by AI, it is possible to provide socially acceptable content.

[0059] The coordination department can also evaluate the reliability of information when sharing it between the monitoring department and the countermeasures department. For example, the coordination department can check the source of information sent from the monitoring department and filter out unreliable information. The coordination department can also check the consistency of information sent from the countermeasures department and issue a warning if there are any inconsistencies. Furthermore, the coordination department can evaluate the accuracy of the information and suggest corrections if there are large errors. This allows for more accurate information sharing by evaluating the reliability of the information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The monitoring unit monitors the AI's behavior or products. For example, the monitoring unit monitors the answers and actions generated by the AI ​​in real time and detects abnormal or malicious behavior. The monitoring unit can use sensors and log data to monitor the AI's behavior and products. For example, the monitoring unit can analyze text data generated by the AI ​​and detect abnormalities based on specific keywords or phrases. The monitoring unit can also learn the AI's behavior patterns and detect abnormal behavior early. Step 2: The countermeasures department takes measures against abnormal or malicious behavior detected by the monitoring department. For example, the countermeasures department may temporarily stop the AI's operation, issue a warning to the user, or delete malicious programs. To control the AI's behavior, the countermeasures department can take into account the AI's operating environment (hardware, software) when implementing countermeasures. Step 3: The coordination unit shares information between the monitoring unit and the countermeasures unit. For example, the coordination unit sends information about abnormalities detected by the monitoring unit to the countermeasures unit in real time, and also sends information about countermeasures taken by the countermeasures unit to the monitoring unit in real time. The coordination unit can also encrypt information to share it securely.

[0062] (Example 2) A monitoring system according to an embodiment of the present invention monitors AI behavior and outputs, detects abnormal behavior and malicious behavior, and takes countermeasures. The monitoring system monitors AI behavior and outputs, detects abnormal behavior and malicious behavior, and prevents damage. For example, the monitoring system monitors AI responses and actions in real time to detect abnormal behavior and malicious behavior. The monitoring system then takes countermeasures against detected abnormal or malicious behavior, such as temporarily suspending the AI's operation, issuing a warning to the user, or deleting malicious programs. Furthermore, the monitoring system shares information between the monitoring unit and the countermeasure unit in real time, enabling prompt and effective countermeasures. This allows the monitoring system to prevent AI from generating malicious responses, collecting user data, or installing malicious programs. This allows the monitoring system to monitor AI behavior and outputs, detect abnormal or malicious behavior, and take countermeasures, thereby ensuring user safety and improving trust in the use of AI.

[0063] A monitoring system according to an embodiment includes a monitoring unit, a countermeasure unit, and a collaboration unit. The monitoring unit monitors the behavior or products of an AI. For example, the monitoring unit monitors answers and actions generated by the AI ​​in real time to detect abnormal or malicious behavior. The monitoring unit can use sensors and log data to monitor the behavior or products of the AI. For example, the monitoring unit can analyze text data generated by the AI ​​and detect abnormalities based on specific keywords or phrases. The monitoring unit can also learn the behavior patterns of the AI ​​and detect abnormal behavior early. The countermeasure unit takes countermeasures against abnormal or malicious behavior detected by the monitoring unit. For example, the countermeasure unit temporarily suspends the AI's operation, issues a warning to the user, or deletes malicious programs. To control the AI's behavior, the countermeasure unit can take countermeasures taking into account the AI's operating environment (hardware, software). The collaboration unit shares information between the monitoring unit and the countermeasure unit. For example, the collaboration unit transmits abnormal information detected by the monitoring unit to the countermeasure unit in real time and transmits information on countermeasures implemented by the countermeasure unit to the monitoring unit in real time. The collaboration unit can also encrypt and share information securely. This allows the monitoring system according to the embodiment to monitor the behavior and output of the AI, detect abnormal behavior or malicious actions, and take measures to prevent damage.

[0064] The monitoring unit can monitor answers or actions generated by the AI ​​in real time and detect abnormal or malicious behavior. For example, the monitoring unit can analyze text data generated by the AI ​​in real time and detect anomalies based on specific keywords or phrases. For example, the monitoring unit can monitor whether specific keywords are included in text generated by the AI. The monitoring unit can also analyze phrases in text generated by the AI ​​to detect abnormal content. The monitoring unit can also analyze the context of text generated by the AI ​​to detect abnormal content. This makes it possible to monitor AI products and actions in real time and detect abnormalities or malicious behavior early. Some or all of the above-mentioned processing in the monitoring unit can be performed using AI, for example, or without AI. For example, the monitoring unit can input text data generated by the AI ​​into the generation AI and have the generation AI detect abnormal content.

[0065] The countermeasure unit can temporarily suspend the operation of the AI ​​in response to detected abnormal or malicious behavior. The countermeasure unit, for example, controls the AI's operating environment (hardware, software) to temporarily suspend the operation of the AI. For example, the countermeasure unit can temporarily turn off the power to the hardware on which the AI ​​is running. The countermeasure unit can also temporarily suspend the software process on which the AI ​​is running. The countermeasure unit can also temporarily disconnect the network connection on which the AI ​​is running. In this way, when abnormal or malicious behavior is detected, damage can be prevented by temporarily suspending the operation of the AI. Some or all of the above-mentioned processing in the countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure unit can input control of the AI's operating environment to the generation AI and cause the generation AI to temporarily suspend the operation of the AI.

[0066] The countermeasure unit can notify the user of a warning. For example, the countermeasure unit issues a warning to the user when abnormal behavior or malicious behavior is detected. For example, the countermeasure unit can display a warning message on the user's device. The countermeasure unit can also send a warning to the user by email. The countermeasure unit can also notify the user of the warning by voice. In this way, when abnormal behavior or malicious behavior is detected, a warning can be issued to the user to encourage a prompt response. Some or all of the above-mentioned processing in the countermeasure unit may be performed, for example, using AI or may be performed without using AI. For example, the countermeasure unit can input the detection of abnormal behavior or malicious behavior into a generation AI and have the generation AI execute a warning notification.

[0067] The countermeasure unit can remove malicious programs. For example, if a malicious program is detected, the countermeasure unit deletes it. For example, the countermeasure unit can detect a malicious program that the AI ​​is attempting to install and delete that program. The countermeasure unit can also detect malicious programs that have already been installed and delete them. The countermeasure unit can also monitor the AI's operating environment and detect abnormal behavior to prevent the installation of malicious programs. This makes it possible to ensure the security of the system by deleting a malicious program if it is detected. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input the detection of a malicious program to the generation AI and have the generation AI delete the program.

[0068] The coordination unit can instantly share information between the monitoring unit and the countermeasure unit. For example, the coordination unit transmits abnormality information detected by the monitoring unit to the countermeasure unit in real time. For example, the coordination unit can instantly communicate information about abnormal behavior or malicious actions detected by the monitoring unit to the countermeasure unit. The coordination unit can also transmit information about countermeasures implemented by the countermeasure unit to the monitoring unit in real time. For example, the coordination unit can instantly communicate information about AI operation suspension or warning notifications implemented by the countermeasure unit to the monitoring unit. The coordination unit can also synchronize all information shared between the monitoring unit and the countermeasure unit in real time. This allows information to be shared between the monitoring unit and the countermeasure unit in real time, enabling prompt and effective countermeasures to be taken. Some or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI or without AI. For example, the coordination unit can input information shared between the monitoring unit and the countermeasure unit to the generation AI and have the generation AI synchronize the information.

[0069] The monitoring unit can predict the user's emotions and adjust the monitoring intensity based on the predicted user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit can increase the monitoring intensity and perform more detailed monitoring. Furthermore, if the user is relaxed, the monitoring unit can lower the monitoring intensity and perform the minimum necessary monitoring. Furthermore, if the user is in a hurry, the monitoring unit can set the monitoring intensity to a medium level and quickly detect abnormalities. This allows for more appropriate monitoring by adjusting the monitoring intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and have the generation AI adjust the monitoring intensity.

[0070] During monitoring, the monitoring unit can improve the accuracy of anomaly detection by referring to the AI's past operation history. The monitoring unit, for example, improves the accuracy of anomaly detection by referring to the AI's past operation history. For example, the monitoring unit can detect similar patterns based on the timing of past anomalies. The monitoring unit can also identify time periods when anomalies are likely to occur from the past operation history and focus monitoring on those time periods. The monitoring unit can also learn past abnormal operation patterns and detect new abnormal operations early. In this way, by referring to the past operation history, the accuracy of anomaly detection can be improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the AI's past operation history into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0071] The monitoring unit learns the AI's behavior patterns during monitoring and can quickly detect abnormal behavior. The monitoring unit, for example, learns the AI's normal behavior patterns and detects behavior that differs from them as abnormal. For example, the monitoring unit can monitor changes in the AI's behavior patterns in real time and detect abnormalities early. The monitoring unit can also continuously learn the AI's behavior patterns and improve the accuracy of abnormality detection. In this way, by learning the AI's behavior patterns, abnormal behavior can be detected early. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the AI's behavior patterns to the generation AI and cause the generation AI to detect abnormal behavior.

[0072] During monitoring, the monitoring unit can analyze the content of the AI's product and detect anomalies based on specific keywords or phrases. The monitoring unit, for example, monitors whether specific keywords are included in text generated by the AI. For example, the monitoring unit can analyze phrases in the text generated by the AI ​​and detect anomalous content. The monitoring unit can also analyze the context of the text generated by the AI ​​and detect anomalous content. In this way, by analyzing the content of the AI's product, anomalies can be detected based on specific keywords or phrases. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input text data generated by the AI ​​into the generation AI and cause the generation AI to detect anomalous content.

[0073] The monitoring unit can predict the user's emotions and adjust the display method of the monitoring results based on the predicted user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the display method of the monitoring results based on the estimated user emotions. For example, if the user is feeling anxious, the monitoring unit can display detailed monitoring results to provide a sense of security. Furthermore, if the user is relaxed, the monitoring unit can display concise monitoring results to reduce stress. Furthermore, if the user is in a hurry, the monitoring unit can display monitoring results that focus on the main points to provide information quickly. This enables more appropriate information to be provided by adjusting the display method of the monitoring results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI. For example, the monitoring unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the monitoring results.

[0074] During monitoring, the monitoring unit can detect anomalies based on the operating environment of the AI. For example, the monitoring unit monitors the state of the hardware on which the AI ​​is running and detects anomalies. For example, the monitoring unit can monitor the version of the software on which the AI ​​is running and detect anomalies. The monitoring unit can also monitor the entire environment in which the AI ​​is running and detect anomalies. This allows for improved accuracy in anomaly detection by taking the operating environment of the AI ​​into consideration. Some or all of the above-mentioned processing in the monitoring unit may be performed using, or without, an AI. For example, the monitoring unit can input operating environment data of the AI ​​to the generation AI and cause the generation AI to perform anomaly detection.

[0075] During monitoring, the monitoring unit monitors the AI's operating speed or resource usage and can identify abnormal resource consumption. The monitoring unit, for example, monitors the AI's CPU usage and detects abnormal resource consumption. For example, the monitoring unit can monitor the AI's memory usage and detect abnormal resource consumption. The monitoring unit can also monitor the AI's disk I / O and detect abnormal resource consumption. In this way, by monitoring the AI's operating speed and resource usage, abnormal resource consumption can be detected early. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the AI's resource usage data to the generation AI and cause the generation AI to detect abnormal resource consumption.

[0076] During monitoring, the monitoring unit can identify anomalies by referring to external data sources related to the operation of the AI. The monitoring unit, for example, monitors the status of external data sources accessed by the AI ​​and detects anomalies. For example, the monitoring unit can monitor the responses of APIs used by the AI ​​and detect anomalies. The monitoring unit can also monitor the status of external services on which the AI ​​depends and detect anomalies. By referring to external data sources, the accuracy of anomaly detection can be improved. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, for example, or may be performed without using AI. For example, the monitoring unit can input information from external data sources to the generation AI and cause the generation AI to perform anomaly detection.

[0077] The countermeasure unit can predict the user's emotions and prioritize countermeasures based on the predicted user emotions. The countermeasure unit, for example, estimates the user's emotions and prioritizes countermeasures based on the estimated user emotions. For example, the countermeasure unit can take countermeasures most quickly if the user is feeling anxious. Furthermore, the countermeasure unit can set the priority of countermeasures to normal if the user is relaxed. Furthermore, the countermeasure unit can prioritize important countermeasures if the user is in a hurry. This allows more appropriate countermeasures to be taken by prioritizing countermeasures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the countermeasure unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the countermeasure unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of countermeasures.

[0078] When taking countermeasures, the countermeasure unit can select the optimal countermeasure by referring to the AI's past abnormal operation history. The countermeasure unit selects the optimal countermeasure, for example, based on countermeasures that were effective in the past. For example, the countermeasure unit can select a countermeasure for a similar abnormality from the past abnormal operation history. The countermeasure unit can also analyze the past countermeasure history and select the most effective countermeasure. In this way, the optimal countermeasure can be selected by referring to the past abnormal operation history. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, or without, AI, for example. For example, the countermeasure unit can input the past abnormal operation history into the generation AI and have the generation AI select the optimal countermeasure.

[0079] When taking countermeasures, the countermeasure unit can analyze the operating environment of the AI ​​and select the optimal countermeasure. For example, the countermeasure unit can analyze the state of the hardware on which the AI ​​is running and select the optimal countermeasure. For example, the countermeasure unit can analyze the version of the software on which the AI ​​is running and select the optimal countermeasure. The countermeasure unit can also analyze the entire environment in which the AI ​​is running and select the optimal countermeasure. In this way, the optimal countermeasure can be selected by analyzing the operating environment of the AI. Some or all of the above-mentioned processing in the countermeasure unit may be performed, for example, using AI, or may be performed without using AI. For example, the countermeasure unit can input the operating environment data of the AI ​​to the generation AI and have the generation AI select the optimal countermeasure.

[0080] The countermeasure unit can improve the countermeasure method by reflecting user feedback when taking countermeasures. The countermeasure unit improves the countermeasure method, for example, based on user feedback. For example, the countermeasure unit can adjust the priority of countermeasures by reflecting user opinions. The countermeasure unit can also collect user feedback and use it to improve the countermeasure method. In this way, the countermeasure method can be continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the countermeasure unit may be performed using AI, for example, or may be performed without using AI. For example, the countermeasure unit can input user feedback data into a generation AI and have the generation AI improve the countermeasure method.

[0081] The countermeasure unit can predict the user's emotions and adjust the timing of countermeasure execution based on the predicted user emotions. The countermeasure unit, for example, estimates the user's emotions and adjusts the timing of countermeasure execution based on the estimated user emotions. For example, the countermeasure unit can quickly execute countermeasures when the user is feeling anxious. Furthermore, the countermeasure unit can execute countermeasures at a normal timing when the user is relaxed. Furthermore, the countermeasure unit can prioritize the execution of important countermeasures when the user is in a hurry. This allows the countermeasure execution timing to be adjusted according to the user's emotions, thereby enabling the countermeasure to be executed at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the countermeasure unit may be performed using an AI, for example, or without an AI. For example, the countermeasure unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of countermeasure execution.

[0082] When taking countermeasures, the countermeasure unit can select the optimal countermeasure by referring to external data sources related to the behavior of the AI. The countermeasure unit, for example, refers to the status of external data sources accessed by the AI ​​and selects the optimal countermeasure. For example, the countermeasure unit can select the optimal countermeasure by referring to the response of an API used by the AI. The countermeasure unit can also select the optimal countermeasure by referring to the status of external services on which the AI ​​depends. In this way, the optimal countermeasure can be selected by referring to external data sources. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, or without, AI, for example. For example, the countermeasure unit can input information from external data sources into the generation AI and have the generation AI select the optimal countermeasure.

[0083] When taking countermeasures, the countermeasure unit can execute the countermeasure taking into consideration the AI's operating speed and resource usage. The countermeasure unit, for example, takes into consideration the AI's CPU usage rate to execute the optimal countermeasure. For example, the countermeasure unit can take into consideration the AI's memory usage to execute the optimal countermeasure. The countermeasure unit can also take into consideration the AI's disk I / O to execute the optimal countermeasure. This makes it possible to execute the optimal countermeasure by taking into consideration the AI's operating speed and resource usage. Some or all of the above-mentioned processing in the countermeasure unit may be performed using an AI, for example, or may be performed without using an AI. For example, the countermeasure unit can input the AI's resource usage data into the generation AI and have the generation AI execute the optimal countermeasure.

[0084] When taking countermeasures, the countermeasure unit can execute the countermeasures based on the operating environment of the AI. The countermeasure unit, for example, takes into account the state of the hardware on which the AI ​​is running and executes the optimal countermeasure. For example, the countermeasure unit can take into account the version of the software on which the AI ​​is running and execute the optimal countermeasure. The countermeasure unit can also execute the optimal countermeasure by taking into account the entire environment in which the AI ​​is running. In this way, the optimal countermeasure can be executed by taking into account the operating environment of the AI. Some or all of the above-mentioned processing in the countermeasure unit may be performed using, or without, an AI. For example, the countermeasure unit can input operating environment data of the AI ​​into the generation AI and cause the generation AI to execute the optimal countermeasure.

[0085] The collaboration unit can predict a user's emotions and adjust the information sharing method based on the predicted user emotions. For example, the collaboration unit can estimate a user's emotions and adjust the information sharing method based on the estimated user emotions. For example, if a user feels anxious, the collaboration unit can share detailed information to provide a sense of security. If a user is relaxed, the collaboration unit can share concise information to reduce stress. If a user is in a hurry, the collaboration unit can share information that focuses on the main points and respond quickly. This enables more appropriate information to be provided by adjusting the information sharing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collaboration unit may be performed using an AI, for example, or without an AI. For example, the collaboration unit can input user emotion data into the generation AI and cause the generation AI to adjust the information sharing method.

[0086] During collaboration, the collaboration unit can improve the accuracy of information sharing by referring to the past collaboration history between the monitoring unit and the countermeasure unit. The collaboration unit, for example, selects the optimal information sharing method based on the past collaboration history. For example, the collaboration unit can optimize the timing of information sharing from the past collaboration history. The collaboration unit can also analyze the past collaboration history and improve the accuracy of information sharing. In this way, the accuracy of information sharing can be improved by referring to the past collaboration history. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input past collaboration history data into the generation AI and have the generation AI improve the accuracy of information sharing.

[0087] During collaboration, the coordination unit can encrypt information between the monitoring unit and the countermeasure unit to share the information securely. The coordination unit, for example, encrypts and transmits information from the monitoring unit to the countermeasure unit. For example, the coordination unit can encrypt and transmit feedback information from the countermeasure unit to the monitoring unit. The coordination unit can also encrypt and transmit all information shared between the monitoring unit and the countermeasure unit. This allows for secure information sharing by encrypting the information. Some or all of the above-mentioned processing in the coordination unit may be performed using AI, for example, or may be performed without using AI. For example, the coordination unit can input encryption of the information to the generation AI and have the generation AI encrypt and transmit the information.

[0088] During collaboration, the collaboration unit can instantly synchronize information between the monitoring unit and the countermeasure unit. For example, the collaboration unit transmits abnormality information detected by the monitoring unit to the countermeasure unit in real time. For example, the collaboration unit can transmit information on countermeasures taken by the countermeasure unit to the monitoring unit in real time. The collaboration unit can also synchronize all information between the monitoring unit and the countermeasure unit in real time. This allows for quick and effective countermeasures to be taken by synchronizing information in real time. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the collaboration unit can input information synchronization to the generation AI and have the generation AI perform real-time synchronization of information.

[0089] The collaboration unit can predict a user's emotions and adjust the frequency of information sharing based on the predicted user emotions. The collaboration unit, for example, estimates a user's emotions and adjusts the frequency of information sharing based on the estimated user emotions. For example, if a user feels anxious, the collaboration unit can share information frequently to provide a sense of security. Furthermore, if a user is relaxed, the collaboration unit can share the minimum amount of information necessary to reduce stress. Furthermore, if a user is in a hurry, the collaboration unit can prioritize sharing important information and respond quickly. This enables more appropriate information provision by adjusting the frequency of information sharing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collaboration unit may be performed using, for example, an AI. For example, the collaboration unit can input user emotion data into the generation AI and cause the generation AI to adjust the frequency of information sharing.

[0090] During collaboration, the coordination unit can compress information between the monitoring unit and the countermeasure unit to efficiently share information. For example, the coordination unit compresses and transmits information from the monitoring unit to the countermeasure unit, thereby reducing communication volume. For example, the coordination unit can compress and transmit feedback information from the countermeasure unit to the monitoring unit, thereby improving communication speed. The coordination unit can also compress and transmit all information shared between the monitoring unit and the countermeasure unit, thereby achieving efficient information sharing. In this way, information can be shared efficiently by compressing the information. Some or all of the above-mentioned processing in the coordination unit may be performed, for example, using AI, or may be performed without using AI. For example, the coordination unit can input information compression to the generation AI and have the generation AI compress and transmit the information.

[0091] During collaboration, the collaboration unit can filter information between the monitoring unit and the countermeasure unit to share only necessary information. For example, the collaboration unit transmits only important information from among the anomaly information detected by the monitoring unit to the countermeasure unit. For example, the collaboration unit can transmit only necessary information from among the countermeasure information implemented by the countermeasure unit to the monitoring unit. The collaboration unit can also filter all information between the monitoring unit and the countermeasure unit to share only necessary information. This enables efficient information sharing by sharing only necessary information. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input information filtering to the generation AI and have the generation AI select and share the necessary information.

[0092] During collaboration, the collaboration unit can share information between the monitoring unit and the countermeasure unit in multiple languages. For example, the collaboration unit can transmit information from the monitoring unit to the countermeasure unit in multiple languages, enabling international responses. For example, the collaboration unit can transmit feedback information from the countermeasure unit to the monitoring unit in multiple languages, enabling global collaboration. The collaboration unit can also transmit all information shared between the monitoring unit and the countermeasure unit in multiple languages, enabling efficient information sharing. This enables international responses through multilingual support. Some or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the multilingual translation of the information into a generation AI and have the generation AI translate and share the information. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, countermeasure unit, and collaboration unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit monitors the operation and output of the AI ​​using the camera 42 and sensors of the smart device 14, and detects abnormal operation or malicious behavior using the control unit 46A. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and takes measures in response to a detected abnormality, such as temporarily suspending the operation of the AI, issuing a warning to the user, or deleting malicious programs. The collaboration unit shares information in real time via, for example, the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing device 12, and takes measures quickly and effectively. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, countermeasure unit, and collaboration unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit monitors the operation and output of the AI ​​using the camera 42 and sensors of the smart glasses 214, and detects abnormal operation or malicious behavior using the control unit 46A. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and takes measures in response to a detected abnormality, such as temporarily suspending the operation of the AI, issuing a warning to the user, or deleting malicious programs. The collaboration unit shares information in real time via, for example, the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing device 12, and takes measures quickly and effectively. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, countermeasure unit, and collaboration unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the monitoring unit monitors the operation and output of the AI ​​using the camera 42 and sensors of the headset terminal 314, and detects abnormal operation or malicious behavior using the control unit 46A. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and takes measures in response to a detected abnormality, such as temporarily suspending the operation of the AI, issuing a warning to the user, or deleting malicious programs. The collaboration unit shares information in real time via, for example, the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing device 12, and takes measures quickly and effectively. === Hard Collateral 1-4 === Each of the multiple elements, including the monitoring unit, countermeasure unit, and collaboration unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit monitors the AI's behavior and output using the camera 42 and sensors of the robot 414, and detects abnormal behavior or malicious actions using the control unit 46A. The countermeasure unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and takes measures in response to a detected abnormality, such as temporarily suspending the AI's behavior, issuing a warning to the user, or deleting malicious programs. The collaboration unit shares information in real time via, for example, the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing device 12, and takes measures quickly and effectively.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When monitoring AI behavior, the monitoring unit can also evaluate the reliability of the data generated by the AI. For example, the monitoring unit can check the source of the data generated by the AI ​​and filter out unreliable data. The monitoring unit can also check the consistency of the data generated by the AI ​​and issue a warning if there are any inconsistencies. Furthermore, the monitoring unit can evaluate the accuracy of the data generated by the AI ​​and suggest corrections if there are large errors. This helps ensure the reliability of the data generated by the AI ​​and provides more accurate information.

[0095] The countermeasures department can also temporarily restrict the AI's operations if abnormal or malicious behavior is detected. For example, the countermeasures department can temporarily prohibit the AI ​​from using certain functions. The countermeasures department can also limit the databases that the AI ​​can access, preventing access to important data. Furthermore, the countermeasures department can restrict the AI's communication with external parties to prevent information leaks. In this way, damage can be minimized by restricting the AI's operations if abnormal or malicious behavior is detected.

[0096] The coordination unit can also set the priority of information when sharing information between the monitoring unit and the countermeasures unit. For example, the coordination unit can prioritize sending information with a high level of urgency to the countermeasures unit. The coordination unit can also prioritize sending information with a high level of importance to the monitoring unit to encourage a quick response. Furthermore, the coordination unit can adjust the frequency of information sharing depending on the importance of the information. In this way, by setting the priority of information, quick and effective countermeasures can be taken.

[0097] When monitoring the behavior of the AI, the monitoring unit can also perform emotional analysis of the data generated by the AI. For example, the monitoring unit can analyze the emotions in the text data generated by the AI ​​and issue a warning if it contains negative emotions. The monitoring unit can also analyze the emotions in the voice data generated by the AI ​​and take measures if it contains emotions such as anger or sadness. Furthermore, the monitoring unit can analyze the emotions in the image data generated by the AI ​​and suggest corrections if it contains inappropriate content. In this way, by analyzing the emotions in the data generated by the AI, more appropriate measures can be taken.

[0098] The countermeasures department can also provide a detailed report to the user when abnormal or malicious behavior is detected. For example, the countermeasures department can analyze the cause of the abnormal or malicious behavior and report it to the user. The countermeasures department can also record the implementation status of the countermeasures in detail and report it to the user. Furthermore, the countermeasures department can formulate a future countermeasure plan and propose it to the user. In this way, by providing the user with a detailed report, transparency can be ensured and reliability can be improved.

[0099] When monitoring the behavior of AI, the monitoring unit can also evaluate the ethics of the data generated by AI. For example, the monitoring unit can check whether the text data generated by AI contains discriminatory content. The monitoring unit can also evaluate whether the image data generated by AI contains inappropriate content. Furthermore, the monitoring unit can check whether the audio data generated by AI contains violent content. In this way, by evaluating the ethics of the data generated by AI, it is possible to provide socially acceptable content.

[0100] When abnormal behavior or malicious behavior is detected, the countermeasure unit can take measures taking into account the user's emotions. For example, if the user feels anxious, the countermeasure unit can take measures quickly to provide a sense of security. Also, if the user feels relaxed, the countermeasure unit can take normal measures. Furthermore, if the user is in a hurry, the countermeasure unit can prioritize taking important measures. In this way, taking measures taking into account the user's emotions enables more appropriate responses.

[0101] The coordination department can also evaluate the reliability of information when sharing it between the monitoring department and the countermeasures department. For example, the coordination department can check the source of information sent from the monitoring department and filter out unreliable information. The coordination department can also check the consistency of information sent from the countermeasures department and issue a warning if there are any inconsistencies. Furthermore, the coordination department can evaluate the accuracy of the information and suggest corrections if there are large errors. This allows for more accurate information sharing by evaluating the reliability of the information.

[0102] When monitoring the behavior of the AI, the monitoring unit can predict the emotions in the data generated by the AI ​​and adjust the intensity of monitoring based on the predicted emotions. For example, the monitoring unit can analyze the emotions in the text data generated by the AI ​​and increase the intensity of monitoring if it contains negative emotions. The monitoring unit can also analyze the emotions in the voice data generated by the AI ​​and decrease the intensity of monitoring if it contains positive emotions. Furthermore, the monitoring unit can analyze the emotions in the image data generated by the AI ​​and set the intensity of monitoring to medium if it contains neutral emotions. This enables more appropriate monitoring by predicting the emotions in the data generated by the AI.

[0103] When abnormal behavior or malicious behavior is detected, the countermeasure unit can predict the user's emotions and adjust the content of the countermeasure based on the predicted emotions. For example, if the user feels anxious, the countermeasure unit can take detailed countermeasures to provide a sense of security. Also, if the user feels relaxed, the countermeasure unit can take simple countermeasures. Furthermore, if the user is in a hurry, the countermeasure unit can take quick countermeasures. In this way, by predicting the user's emotions and adjusting the content of the countermeasures, more appropriate responses are possible.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The monitoring unit monitors the AI's behavior or products. For example, the monitoring unit monitors the answers and actions generated by the AI ​​in real time and detects abnormal or malicious behavior. The monitoring unit can use sensors and log data to monitor the AI's behavior and products. For example, the monitoring unit can analyze text data generated by the AI ​​and detect abnormalities based on specific keywords or phrases. The monitoring unit can also learn the AI's behavior patterns and detect abnormal behavior early. Step 2: The countermeasures department takes measures against abnormal or malicious behavior detected by the monitoring department. For example, the countermeasures department may temporarily stop the AI's operation, issue a warning to the user, or delete malicious programs. To control the AI's behavior, the countermeasures department can take into account the AI's operating environment (hardware, software) when implementing countermeasures. Step 3: The coordination unit shares information between the monitoring unit and the countermeasures unit. For example, the coordination unit sends information about abnormalities detected by the monitoring unit to the countermeasures unit in real time, and also sends information about countermeasures taken by the countermeasures unit to the monitoring unit in real time. The coordination unit can also encrypt information to share it securely.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 monitoring unit that monitors the operation or production of the AI; a countermeasure unit that takes measures against abnormal behavior or malicious behavior detected by the monitoring unit; a coordination unit that shares information between the monitoring unit and the countermeasure unit; A system characterized by:

2. The monitoring unit Monitor AI-generated answers or actions in real time to detect anomalous or malicious behavior 2. The system of claim 1.

3. The countermeasure unit Temporarily suspending AI operations in response to detected abnormal or malicious behavior 2. The system of claim 1.

4. The countermeasure unit Notify the user of the warning 2. The system of claim 1.

5. The countermeasure unit Remove malicious programs 2. The system of claim 1.

6. The linking unit is Immediately share information between the monitoring department and the countermeasures department 2. The system of claim 1.

7. The monitoring unit Predicting user emotions and adjusting monitoring intensity based on the predicted user emotions 2. The system of claim 1.

8. The monitoring unit During monitoring, the AI's past operation history is referenced to improve the accuracy of anomaly detection.

2. The system of claim 1.

9. The monitoring unit During monitoring, the AI ​​learns behavioral patterns and quickly detects abnormal behavior.

2. The system of claim 1.

10. The monitoring unit During monitoring, the content of AI artifacts is analyzed to detect anomalies based on specific keywords or phrases.

2. The system of claim 1.

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