system

Abstract

The system according to this embodiment aims to analyze ASI's decision-making process and ensure transparency and accountability. [Solution] The system according to the embodiment comprises an analysis unit, a visualization unit, a provision unit, an explanation generation unit, and an output unit. The analysis unit analyzes the decision-making process of ASI. The visualization unit visualizes the process analyzed by the analysis unit. The provision unit provides the information visualized by the visualization unit. The explanation generation unit generates an explanation based on the information provided by the provision unit. The output unit outputs the explanation generated by the explanation generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that the decision-making process of ASI was black-boxed and transparency and accountability were not ensured.

[0005] The system according to the embodiment aims to analyze the decision-making process of ASI and ensure transparency and accountability.

Means for Solving the Problems

[0007] The system according to this embodiment can analyze the decision-making process of ASI and ensure transparency and accountability. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to the embodiment of the present invention is a system that uses generative AI to ensure transparency and accountability in the decision-making process and data processing of Artificial Superintelligence (ASI). This system addresses the following issues and their solutions: It aims to ensure transparency of information, guarantee accountability, ensure the reliability and integrity of data, realize ethical governance, and optimize human-ASI interaction. For example, a system is constructed in which generative AI analyzes ASI algorithms and data flows and visualizes them as graphs and dashboards. A system is developed in which generative AI automatically generates reports explaining the background and reasons for ASI decisions in natural language and provides them to users and auditing bodies. A system is introduced in which generative AI detects missing data and anomalies and automatically performs data cleaning. A mechanism is introduced in which generative AI monitors ASI behavior based on an ethical checklist and issues alerts if violations are detected. A system is developed in which generative AI learns user behavior patterns and provides individually customized interfaces and guides. As a result, the system can ensure transparency and accountability in the decision-making process and data processing of ASI.

[0029] The system according to this embodiment comprises an analysis unit, a visualization unit, a provision unit, an explanation generation unit, and an output unit. The analysis unit analyzes the decision-making process of ASI. The analysis unit analyzes ASI's algorithms and data flows, for example, using a generative AI. The analysis unit allows the generative AI to analyze the operation of ASI's algorithms and visualize the data flows. The visualization unit visualizes the process analyzed by the analysis unit. The visualization unit visualizes the process analyzed using a generative AI as graphs or dashboards, for example. The visualization unit allows the generative AI to visually display the analysis results and provide them in a format that is easy for users to understand. The provision unit provides the information visualized by the visualization unit. The provision unit provides the information visualized using a generative AI to users or auditing bodies, for example. The provision unit can automatically distribute the information visualized by the generative AI and provide it to relevant parties. The explanation generation unit generates explanations based on the information provided by the provision unit. The explanation generation unit automatically generates reports that explain the background and reasons for ASI's decision-making in natural language, for example, using a generative AI. The explanation generation unit can automatically generate explanations using a generation AI and provide them to users or auditing bodies. The output unit outputs the explanations generated by the explanation generation unit. The output unit can output reports generated using the generation AI to users or auditing bodies, for example. The output unit can automatically output reports generated by the generation AI and provide them to relevant parties. As a result, the system according to this embodiment can ensure transparency and accountability by analyzing, visualizing, providing, generating explanations for ASI's decision-making process and outputting them.

[0030] The analysis unit analyzes ASI's decision-making process. For example, the analysis unit uses generative AI to analyze ASI's algorithms and data flow. Specifically, generative AI analyzes the operation of ASI's algorithms in detail, clarifying the data flow and processing content at each step. For example, generative AI uses deep learning models to analyze how ASI's algorithms process data and make decisions. This allows the analysis unit to gain a detailed understanding of the internal operation of ASI's algorithms and visualize the data flow. Furthermore, the analysis unit can use generative AI to analyze historical data and execution logs to extract patterns and trends in ASI's decision-making process. This allows the analysis unit to increase the transparency of ASI's decision-making process and build a foundation for providing detailed explanations to users and auditing bodies. The analysis unit can also leverage the advanced analytical capabilities provided by generative AI to monitor the operation of ASI's algorithms and data flow in real time, performing anomaly detection and performance optimization. This allows the analysis unit to improve the reliability and efficiency of ASI's decision-making process.

[0031] The visualization unit visualizes the processes analyzed by the analysis unit. For example, the visualization unit visualizes the processes analyzed using generative AI as graphs and dashboards. Specifically, the generative AI visually displays each step of the process and the data flow based on the data provided by the analysis unit. This allows users to intuitively understand ASI's decision-making process. The visualization unit can utilize the advanced graphic capabilities provided by the generative AI to clearly display complex data flows and algorithmic behavior. For example, it can create flowcharts with color-coded process steps and interactive dashboards showing the flow of data. Furthermore, the visualization unit provides customizable views and filtering functions to allow users to quickly obtain the information they need. This allows users to focus on specific processes or data flows and perform detailed analyses. The visualization unit can utilize the real-time update function provided by the generative AI to always display the latest analysis results. This allows users to grasp the latest status of ASI's decision-making process and take quick action.

[0032] The Service Provider provides information visualized by the Visualization Provider. For example, the Service Provider provides users and auditing bodies with information visualized using Generative AI. Specifically, the Generative AI automatically distributes and provides the visualized information to relevant parties. The Service Provider can utilize the automated distribution function provided by the Generative AI to periodically update the visualized information and notify relevant parties. For example, it can provide users and auditing bodies with the latest information via email or notification systems. Furthermore, the Service Provider can utilize the access control function provided by the Generative AI to provide appropriate information to each party. This enables the protection of confidential information and the appropriate sharing of information. The Service Provider utilizes the interactive dashboard function provided by the Generative AI to enable users to quickly obtain the information they need. This allows users to view detailed information about ASI's decision-making process in real time and take appropriate action. The Service Provider can also utilize the advanced data analysis function provided by the Generative AI to create detailed reports based on the visualized information and provide them to relevant parties. This enables the Service Provider to provide users and auditing bodies with information to ensure transparency and accountability regarding ASI's decision-making process.

[0033] The explanation generation unit generates explanations based on information provided by the provider unit. For example, the explanation generation unit automatically generates reports that explain the background and reasons for ASI's decision-making in natural language using generation AI. Specifically, the generation AI explains in detail each step and data flow of ASI's decision-making process based on visualized information provided by the provider unit. The generation AI can explain technical content in an easy-to-understand manner using natural language processing technology. This allows the explanation generation unit to clearly communicate the background and reasons for ASI's decision-making process to users and auditing bodies. Furthermore, the explanation generation unit can utilize the customization functions provided by the generation AI to generate explanations appropriate for each stakeholder. For example, it can generate specialized reports containing technical details or concise explanations that promote general understanding. The explanation generation unit can utilize the automatic update function provided by the generation AI to always provide explanations based on the latest information. This allows users and auditing bodies to obtain accurate information based on the latest situation. The explanation generation unit can also utilize the advanced data analysis functions provided by the generation AI to generate reports that include predictions and suggestions based on historical data and trends. This allows the explanation generation unit to provide users and auditing bodies with a comprehensive explanation of ASI's decision-making process, ensuring transparency and accountability.

[0034] The output unit outputs the explanations generated by the explanation generation unit. For example, the output unit outputs reports generated using the generation AI to users and auditing bodies. Specifically, the generation AI automatically outputs reports provided by the explanation generation unit and provides them to relevant parties. The output unit can utilize the automatic distribution function provided by the generation AI to periodically update reports and notify relevant parties. For example, it can provide users and auditing bodies with the latest reports via email or notification systems. Furthermore, the output unit can utilize the access control function provided by the generation AI to provide appropriate reports to each party. This enables the protection of confidential information and the appropriate sharing of information. The output unit utilizes the interactive dashboard function provided by the generation AI to enable users to quickly obtain the information they need. This allows users to view detailed information about ASI's decision-making process in real time and take appropriate action. The output unit can also utilize the advanced data analysis function provided by the generation AI to create detailed reports based on visualized information and provide them to relevant parties. This allows the output unit to provide users and auditing bodies with information to ensure transparency and accountability regarding ASI's decision-making process.

[0035] The analysis unit can analyze ASI algorithms and data flows. For example, the analysis unit can use generative AI to analyze ASI algorithms and understand their operation. The analysis unit can use generative AI to analyze ASI data flows and visualize the data flow. By analyzing ASI algorithms and data flows, the transparency of the decision-making process can be improved.

[0036] The visualization unit can visualize the analyzed process as graphs and dashboards. For example, the visualization unit can visualize the process analyzed using generative AI as a graph. The visualization unit can visualize the analysis results generated by the generative AI as a dashboard and provide them in a format that is easy for the user to understand. The visualization unit can visually display the analysis results generated by the generative AI and provide them in a format that is easy for the user to understand. This makes the analyzed process easier for the user to understand by visualizing it.

[0037] The service provider can provide visualized information to users and auditing bodies. For example, the service provider can provide visualized information to users using generative AI. The service provider can provide visualized information generated by generative AI to auditing bodies, making it easier for stakeholders to review the information. The service provider can automatically distribute and provide visualized information generated by generative AI to stakeholders. This makes it easier for stakeholders to review the information by providing visualized information.

[0038] The explanation generation unit can automatically generate reports explaining the background and reasons for ASI's decision-making in natural language. For example, the explanation generation unit uses a generation AI to automatically generate reports explaining the background and reasons for ASI's decision-making. The explanation generation unit can generate reports with explanations in natural language using the generation AI and provide them to users and auditing organizations. This allows ASI to fulfill its accountability by explaining the background and reasons for its decision-making.

[0039] The output unit can output the generated reports to users or auditing bodies. For example, the output unit can output reports generated using generation AI to users. The output unit can output reports generated by generation AI to auditing bodies, making it easier for stakeholders to review the information. The output unit can automatically output reports generated by generation AI and provide them to stakeholders. This makes it easier for stakeholders to review the information by outputting the generated reports.

[0040] The analysis unit can detect missing data and outliers and automatically perform data cleaning. For example, the analysis unit uses generative AI to detect missing data. The generative AI then detects outliers, and the analysis unit can automatically perform data cleaning. This ensures the reliability and integrity of the data.

[0041] The visualization unit monitors ASI's actions based on an ethical checklist and can issue alerts if violations are detected. For example, the visualization unit uses generative AI to monitor ASI's actions based on an ethical checklist. The visualization unit can detect violations using the generative AI and issue alerts. The visualization unit can monitor ASI's actions based on an ethical checklist using the generative AI and issue alerts if violations are detected. This enables ethical governance.

[0042] The analysis unit can improve the accuracy of its analysis by referring to past decision history when analyzing the ASI decision-making process. For example, the analysis unit can refer to past decision history using generative AI. The analysis unit can improve the accuracy of its analysis by having the generative AI refer to past decision history. The analysis unit can improve the accuracy of its analysis by having the generative AI refer to past decision history. As a result, the accuracy of the analysis is improved by referring to past decision history.

[0043] The analysis unit can perform analysis while considering the change history of the ASI algorithm. For example, the analysis unit can refer to the change history of the ASI algorithm using a generating AI. The analysis unit can perform analysis based on the algorithm change history of the generating AI. The analysis unit can perform analysis while considering the algorithm change history of the generating AI. As a result, the accuracy of the analysis is improved by considering the algorithm change history.

[0044] The analysis unit can perform analysis while considering the geographical distribution of ASI data flows. For example, the analysis unit can refer to the geographical distribution of ASI data flows using a generative AI. The analysis unit can perform analysis based on the geographical distribution of the generative AI. The analysis unit can perform analysis while considering the geographical distribution of the generative AI. As a result, the accuracy of the analysis is improved by considering the geographical distribution of data flows.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant ASI literature during the analysis process. For example, the analysis unit uses a generating AI to refer to relevant ASI literature. The analysis unit allows the generating AI to perform analysis based on the relevant literature. The analysis unit can improve the accuracy of its analysis by having the generating AI refer to the relevant literature. As a result, the accuracy of the analysis is improved by referring to the relevant literature.

[0046] The visualization unit can adjust the level of detail of the visualization based on the importance of the analyzed process during visualization. For example, the visualization unit uses a generative AI to evaluate the importance of the analyzed process. The visualization unit can adjust the level of detail of the visualization based on the importance determined by the generative AI. The visualization unit can adjust the level of detail of the visualization based on the generative AI's evaluation of the importance of the analyzed process. This allows for more appropriate visualization by adjusting the level of detail of the visualization according to the importance of the process.

[0047] The visualization unit can apply different visualization methods depending on the category of the analyzed process during visualization. For example, the visualization unit classifies the process category using generative AI. The visualization unit can then apply different visualization methods depending on the category, as determined by the generative AI. The visualization unit can then apply different visualization methods as the generative AI classifies the process category. This allows for more appropriate visualization by applying visualization methods according to the process category.

[0048] The visualization unit can determine visualization priorities based on the submission timing of the analyzed processes during visualization. For example, the visualization unit uses a generative AI to evaluate submission timing. The visualization unit can then use the generative AI to determine visualization priorities based on submission timing. This allows for more appropriate visualization by adjusting visualization priorities according to submission timing.

[0049] The visualization unit can adjust the visualization order based on the relationships between the analyzed processes during visualization. For example, the visualization unit uses generative AI to evaluate the relationships between processes. The visualization unit can then adjust the visualization order based on the generative AI's evaluation of the relationships between processes. This allows for more appropriate visualization by adjusting the visualization order according to the relationships between processes.

[0050] The service provider can select the optimal information delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can refer to past usage history using a generation AI. The service provider can use the generation AI to select the optimal information delivery method based on past usage history. The service provider can use the generation AI to refer to past usage history and select the optimal information delivery method. In this way, the optimal information delivery method can be selected by referring to past usage history.

[0051] The service provider can customize information based on the user's current areas of interest at the time of delivery. For example, the service provider can analyze the user's areas of interest using generative AI. The service provider can then use the generative AI to customize information based on the user's current areas of interest. The service provider can use the generative AI to analyze the user's areas of interest and customize the information. This allows for the provision of more relevant information by customizing it based on the user's current areas of interest.

[0052] The service provider can prioritize providing highly relevant information by considering the user's geographical location information at the time of delivery. For example, the service provider can refer to geographical location information using a generation AI. The service provider can provide highly relevant information based on the geographical location information using the generation AI. The service provider can provide highly relevant information by having the generation AI refer to geographical location information. In this way, highly relevant information can be provided by considering geographical location information.

[0053] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can use generative AI to analyze social media activity. The service provider can use generative AI to provide relevant information based on social media activity. The service provider can use generative AI to analyze social media activity and provide relevant information. This allows the service provider to provide highly relevant information by analyzing social media activity.

[0054] The explanation generation unit can adjust the level of detail in the explanation based on the importance of the ASI decision during explanation generation. For example, the explanation generation unit uses a generation AI to evaluate the importance of the decision. The explanation generation unit can adjust the level of detail in the explanation based on the importance of the generation AI. The explanation generation unit can adjust the level of detail in the explanation based on the evaluation of the importance of the decision by the generation AI. This makes it possible to provide a more appropriate explanation by adjusting the level of detail in the explanation according to the importance of the decision.

[0055] The explanation generation unit can apply different explanation algorithms depending on the ASI decision category during explanation generation. For example, the explanation generation unit classifies the decision category using generative AI. The explanation generation unit can then apply different explanation algorithms depending on the category of the generative AI. The explanation generation unit can then apply different explanation algorithms depending on the category of the generative AI. This allows for more appropriate explanations by applying explanation algorithms according to the decision category.

[0056] The explanation generation unit can determine the priority of explanations based on the submission timing of ASI's decision when generating explanations. For example, the explanation generation unit uses a generation AI to evaluate the submission timing. The explanation generation unit can use the generation AI to determine the priority of explanations based on the submission timing. The explanation generation unit can use the generation AI to evaluate the submission timing and determine the priority of explanations. This allows for more appropriate explanations by adjusting the priority of explanations according to the submission timing.

[0057] The explanation generation unit can adjust the order of explanations based on the relevance of ASI's decision-making during explanation generation. For example, the explanation generation unit uses a generative AI to evaluate the relevance of the decision-making. The explanation generation unit can then use the generative AI to adjust the order of explanations based on their relevance. This allows for more appropriate explanations by adjusting the order of explanations according to their relevance.

[0058] The output unit can select the optimal output method by referring to the user's past output history at the time of output. For example, the output unit can refer to past output history using a generation AI. The output unit can use the generation AI to select the optimal output method based on past output history. The output unit can use the generation AI to refer to past output history and select the optimal output method. This allows the optimal output method to be selected by referring to past output history.

[0059] The output unit can customize the output content based on the user's current areas of interest at the time of output. For example, the output unit analyzes the user's areas of interest using a generative AI. The output unit can then use the generative AI to customize the output content based on the user's current areas of interest. This allows for more relevant output by customizing the output content based on the user's current areas of interest.

[0060] The output unit can select the optimal output method by considering the user's geographical location information during output. For example, the output unit uses a generation AI to reference geographical location information. The output unit can use the generation AI to select the optimal output method based on the geographical location information. The output unit can use the generation AI to reference geographical location information and select the optimal output method. This allows for the selection of the optimal output method by considering geographical location information.

[0061] The output unit can analyze the user's social media activity and provide relevant output content at the time of output. For example, the output unit can analyze social media activity using a generative AI. The output unit can provide relevant output content based on the social media activity generated by the generative AI. The output unit can provide relevant output content by analyzing social media activity. This allows for the provision of highly relevant output content through the analysis of social media activity.

[0062] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, the output unit uses a generation AI to reference the device information. The generation AI can then select the optimal display method based on the device information. The output unit can then select the optimal display method by referencing the device information. This allows for the selection of the optimal display method by considering the device information.

[0063] The output unit can provide multilingual output according to the user's language settings at the time of output. The output unit can refer to the language settings, for example, using a generation AI. The output unit can provide multilingual output based on the language settings using the generation AI. The output unit can provide multilingual output by having the generation AI refer to the language settings. This makes it easier for the user to understand by providing multilingual output according to the language settings.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The visualization unit can adjust the level of detail based on the importance of the analyzed process. For example, highly important processes can be displayed in detailed graphs and dashboards to allow users to understand them more deeply. On the other hand, less important processes can be displayed in a simplified form, allowing users to focus their attention on the necessary parts. Furthermore, important information can be visually highlighted by changing the color and font size according to importance. This allows users to efficiently obtain the necessary information by adjusting the level of detail of the visualization according to the importance of the process.

[0066] The explanation generation unit can adjust the level of detail in the explanation based on the importance of the ASI decision during explanation generation. For example, it can generate detailed explanations for high-importance decisions, allowing users to gain a deeper understanding of the background and reasons. On the other hand, it can generate concise explanations for low-importance decisions, saving users time. Furthermore, by changing the format of the explanation according to importance, users can efficiently process the information. In this way, adjusting the level of detail in the explanation according to the importance of the decision makes it possible to provide more appropriate explanations.

[0067] The analysis unit can perform analyses while considering the change history of the ASI algorithm. For example, by analyzing how past algorithm changes have affected the current data flow, more accurate results can be obtained. It is also possible to predict the impact of future algorithm changes based on the change history. Furthermore, by referring to the change history, past problems and areas for improvement can be identified and reflected in the current analysis. In this way, considering the algorithm change history improves the accuracy of the analysis.

[0068] The visualization unit can apply different visualization methods depending on the category of the analyzed process during visualization. For example, business processes can be displayed as flowcharts, while technical processes can be displayed as system diagrams. Furthermore, users can select between simplified and detailed diagrams depending on their level of understanding. Additionally, visual distinction can be made easier by changing colors and icons according to the category. This allows for more appropriate visualization by applying visualization methods according to the process category.

[0069] The information delivery unit can select the most appropriate method of information delivery by referring to the user's past usage history at the time of delivery. For example, a user who previously preferred reading detailed reports can be provided with detailed information. On the other hand, a user who previously preferred concise summaries can be provided with concise information. Furthermore, based on past usage history, it can also suggest new information that the user might be interested in. In this way, the optimal method of information delivery can be selected by referring to past usage history.

[0070] The output unit can select the optimal output method when outputting data, taking into account the user's geographical location. For example, if the user is in a different region, it can prioritize providing information relevant to that region. It can also select an output method that takes into account appropriate language and culture according to geographical characteristics. Furthermore, it can provide real-time information related to the user's current location based on their geographical location. In this way, the optimal output method can be selected by considering geographical location.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The analysis unit analyzes the ASI decision-making process. The analysis unit can use generative AI to analyze and visualize the ASI algorithms and data flow. Step 2: The visualization unit visualizes the process analyzed by the analysis unit. The visualization unit can visualize the process analyzed using the generation AI as graphs and dashboards, providing them in a format that is easy for the user to understand. Step 3: The provisioning unit provides the information visualized by the visualization unit. The provisioning unit can provide the visualized information using generation AI to users and auditing organizations and distribute it automatically. Step 4: The explanation generation unit generates an explanation based on the information provided by the provision unit. The explanation generation unit can use generation AI to automatically generate a report explaining the background and reasons for ASI's decision in natural language, and provide it to users or auditing organizations. Step 5: The output unit outputs the explanation generated by the explanation generation unit. The output unit can automatically output and provide the report generated using the generation AI to the user or auditing body.

[0073] (Example of form 2) The system according to the embodiment of the present invention is a system that uses generative AI to ensure transparency and accountability in the decision-making process and data processing of Artificial Superintelligence (ASI). This system addresses the following issues and their solutions: It aims to ensure transparency of information, guarantee accountability, ensure the reliability and integrity of data, realize ethical governance, and optimize human-ASI interaction. For example, a system is constructed in which generative AI analyzes ASI algorithms and data flows and visualizes them as graphs and dashboards. A system is developed in which generative AI automatically generates reports explaining the background and reasons for ASI decisions in natural language and provides them to users and auditing bodies. A system is introduced in which generative AI detects missing data and anomalies and automatically performs data cleaning. A mechanism is introduced in which generative AI monitors ASI behavior based on an ethical checklist and issues alerts if violations are detected. A system is developed in which generative AI learns user behavior patterns and provides individually customized interfaces and guides. As a result, the system can ensure transparency and accountability in the decision-making process and data processing of ASI.

[0074] The system according to this embodiment comprises an analysis unit, a visualization unit, a provision unit, an explanation generation unit, and an output unit. The analysis unit analyzes the decision-making process of ASI. The analysis unit analyzes ASI's algorithms and data flows, for example, using a generative AI. The analysis unit allows the generative AI to analyze the operation of ASI's algorithms and visualize the data flows. The visualization unit visualizes the process analyzed by the analysis unit. The visualization unit visualizes the process analyzed using a generative AI as graphs or dashboards, for example. The visualization unit allows the generative AI to visually display the analysis results and provide them in a format that is easy for users to understand. The provision unit provides the information visualized by the visualization unit. The provision unit provides the information visualized using a generative AI to users or auditing bodies, for example. The provision unit can automatically distribute the information visualized by the generative AI and provide it to relevant parties. The explanation generation unit generates explanations based on the information provided by the provision unit. The explanation generation unit automatically generates reports that explain the background and reasons for ASI's decision-making in natural language, for example, using a generative AI. The explanation generation unit can automatically generate explanations using a generation AI and provide them to users or auditing bodies. The output unit outputs the explanations generated by the explanation generation unit. The output unit can output reports generated using the generation AI to users or auditing bodies, for example. The output unit can automatically output reports generated by the generation AI and provide them to relevant parties. As a result, the system according to this embodiment can ensure transparency and accountability by analyzing, visualizing, providing, generating explanations for ASI's decision-making process and outputting them.

[0075] The analysis unit analyzes ASI's decision-making process. For example, the analysis unit uses generative AI to analyze ASI's algorithms and data flow. Specifically, generative AI analyzes the operation of ASI's algorithms in detail, clarifying the data flow and processing content at each step. For example, generative AI uses deep learning models to analyze how ASI's algorithms process data and make decisions. This allows the analysis unit to gain a detailed understanding of the internal operation of ASI's algorithms and visualize the data flow. Furthermore, the analysis unit can use generative AI to analyze historical data and execution logs to extract patterns and trends in ASI's decision-making process. This allows the analysis unit to increase the transparency of ASI's decision-making process and build a foundation for providing detailed explanations to users and auditing bodies. The analysis unit can also leverage the advanced analytical capabilities provided by generative AI to monitor the operation of ASI's algorithms and data flow in real time, performing anomaly detection and performance optimization. This allows the analysis unit to improve the reliability and efficiency of ASI's decision-making process.

[0076] The visualization unit visualizes the processes analyzed by the analysis unit. For example, the visualization unit visualizes the processes analyzed using generative AI as graphs and dashboards. Specifically, the generative AI visually displays each step of the process and the data flow based on the data provided by the analysis unit. This allows users to intuitively understand ASI's decision-making process. The visualization unit can utilize the advanced graphic capabilities provided by the generative AI to clearly display complex data flows and algorithmic behavior. For example, it can create flowcharts with color-coded process steps and interactive dashboards showing the flow of data. Furthermore, the visualization unit provides customizable views and filtering functions to allow users to quickly obtain the information they need. This allows users to focus on specific processes or data flows and perform detailed analyses. The visualization unit can utilize the real-time update function provided by the generative AI to always display the latest analysis results. This allows users to grasp the latest status of ASI's decision-making process and take quick action.

[0077] The Service Provider provides information visualized by the Visualization Provider. For example, the Service Provider provides users and auditing bodies with information visualized using Generative AI. Specifically, the Generative AI automatically distributes and provides the visualized information to relevant parties. The Service Provider can utilize the automated distribution function provided by the Generative AI to periodically update the visualized information and notify relevant parties. For example, it can provide users and auditing bodies with the latest information via email or notification systems. Furthermore, the Service Provider can utilize the access control function provided by the Generative AI to provide appropriate information to each party. This enables the protection of confidential information and the appropriate sharing of information. The Service Provider utilizes the interactive dashboard function provided by the Generative AI to enable users to quickly obtain the information they need. This allows users to view detailed information about ASI's decision-making process in real time and take appropriate action. The Service Provider can also utilize the advanced data analysis function provided by the Generative AI to create detailed reports based on the visualized information and provide them to relevant parties. This enables the Service Provider to provide users and auditing bodies with information to ensure transparency and accountability regarding ASI's decision-making process.

[0078] The explanation generation unit generates explanations based on information provided by the provider unit. For example, the explanation generation unit automatically generates reports that explain the background and reasons for ASI's decision-making in natural language using generation AI. Specifically, the generation AI explains in detail each step and data flow of ASI's decision-making process based on visualized information provided by the provider unit. The generation AI can explain technical content in an easy-to-understand manner using natural language processing technology. This allows the explanation generation unit to clearly communicate the background and reasons for ASI's decision-making process to users and auditing bodies. Furthermore, the explanation generation unit can utilize the customization functions provided by the generation AI to generate explanations appropriate for each stakeholder. For example, it can generate specialized reports containing technical details or concise explanations that promote general understanding. The explanation generation unit can utilize the automatic update function provided by the generation AI to always provide explanations based on the latest information. This allows users and auditing bodies to obtain accurate information based on the latest situation. The explanation generation unit can also utilize the advanced data analysis functions provided by the generation AI to generate reports that include predictions and suggestions based on historical data and trends. This allows the explanation generation unit to provide users and auditing bodies with a comprehensive explanation of ASI's decision-making process, ensuring transparency and accountability.

[0079] The output unit outputs the explanations generated by the explanation generation unit. For example, the output unit outputs reports generated using the generation AI to users and auditing bodies. Specifically, the generation AI automatically outputs reports provided by the explanation generation unit and provides them to relevant parties. The output unit can utilize the automatic distribution function provided by the generation AI to periodically update reports and notify relevant parties. For example, it can provide users and auditing bodies with the latest reports via email or notification systems. Furthermore, the output unit can utilize the access control function provided by the generation AI to provide appropriate reports to each party. This enables the protection of confidential information and the appropriate sharing of information. The output unit utilizes the interactive dashboard function provided by the generation AI to enable users to quickly obtain the information they need. This allows users to view detailed information about ASI's decision-making process in real time and take appropriate action. The output unit can also utilize the advanced data analysis function provided by the generation AI to create detailed reports based on visualized information and provide them to relevant parties. This allows the output unit to provide users and auditing bodies with information to ensure transparency and accountability regarding ASI's decision-making process.

[0080] The analysis unit can analyze ASI algorithms and data flows. For example, the analysis unit can use generative AI to analyze ASI algorithms and understand their operation. The analysis unit can use generative AI to analyze ASI data flows and visualize the data flow. By analyzing ASI algorithms and data flows, the transparency of the decision-making process can be improved.

[0081] The visualization unit can visualize the analyzed process as graphs and dashboards. For example, the visualization unit can visualize the process analyzed using generative AI as a graph. The visualization unit can visualize the analysis results generated by the generative AI as a dashboard and provide them in a format that is easy for the user to understand. The visualization unit can visually display the analysis results generated by the generative AI and provide them in a format that is easy for the user to understand. This makes the analyzed process easier for the user to understand by visualizing it.

[0082] The service provider can provide visualized information to users and auditing bodies. For example, the service provider can provide visualized information to users using generative AI. The service provider can provide visualized information generated by generative AI to auditing bodies, making it easier for stakeholders to review the information. The service provider can automatically distribute and provide visualized information generated by generative AI to stakeholders. This makes it easier for stakeholders to review the information by providing visualized information.

[0083] The explanation generation unit can automatically generate reports explaining the background and reasons for ASI's decision-making in natural language. For example, the explanation generation unit uses a generation AI to automatically generate reports explaining the background and reasons for ASI's decision-making. The explanation generation unit can generate reports with explanations in natural language using the generation AI and provide them to users and auditing organizations. This allows ASI to fulfill its accountability by explaining the background and reasons for its decision-making.

[0084] The output unit can output the generated reports to users or auditing bodies. For example, the output unit can output reports generated using generation AI to users. The output unit can output reports generated by generation AI to auditing bodies, making it easier for stakeholders to review the information. The output unit can automatically output reports generated by generation AI and provide them to stakeholders. This makes it easier for stakeholders to review the information by outputting the generated reports.

[0085] The analysis unit can detect missing data and outliers and automatically perform data cleaning. For example, the analysis unit uses generative AI to detect missing data. The generative AI then detects outliers, and the analysis unit can automatically perform data cleaning. This ensures the reliability and integrity of the data.

[0086] The visualization unit monitors ASI's actions based on an ethical checklist and can issue alerts if violations are detected. For example, the visualization unit uses generative AI to monitor ASI's actions based on an ethical checklist. The visualization unit can detect violations using the generative AI and issue alerts. The visualization unit can monitor ASI's actions based on an ethical checklist using the generative AI and issue alerts if violations are detected. This enables ethical governance.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using generative AI. The analysis unit can determine the priority of analysis based on the user emotions estimated by the generative AI. The analysis unit can have the generative AI estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. This allows for more appropriate analysis by adjusting the priority of analysis according to the user's emotions.

[0088] The analysis unit can improve the accuracy of its analysis by referring to past decision history when analyzing the ASI decision-making process. For example, the analysis unit can refer to past decision history using generative AI. The analysis unit can improve the accuracy of its analysis by having the generative AI refer to past decision history. The analysis unit can improve the accuracy of its analysis by having the generative AI refer to past decision history. As a result, the accuracy of the analysis is improved by referring to past decision history.

[0089] The analysis unit can perform analysis while considering the change history of the ASI algorithm. For example, the analysis unit can refer to the change history of the ASI algorithm using a generating AI. The analysis unit can perform analysis based on the algorithm change history of the generating AI. The analysis unit can perform analysis while considering the algorithm change history of the generating AI. As a result, the accuracy of the analysis is improved by considering the algorithm change history.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions using generative AI. The analysis unit can adjust the display method of the analysis results based on the user emotions estimated by the generative AI. The analysis unit can adjust the display method of the analysis results based on the estimated user emotions after the generative AI estimates the user's emotions. This allows for a more appropriate display by adjusting the display method of the analysis results according to the user's emotions.

[0091] The analysis unit can perform analysis while considering the geographical distribution of ASI data flows. For example, the analysis unit can refer to the geographical distribution of ASI data flows using a generative AI. The analysis unit can perform analysis based on the geographical distribution of the generative AI. The analysis unit can perform analysis while considering the geographical distribution of the generative AI. As a result, the accuracy of the analysis is improved by considering the geographical distribution of data flows.

[0092] The analysis unit can improve the accuracy of its analysis by referring to relevant ASI literature during the analysis process. For example, the analysis unit uses a generating AI to refer to relevant ASI literature. The analysis unit allows the generating AI to perform analysis based on the relevant literature. The analysis unit can improve the accuracy of its analysis by having the generating AI refer to the relevant literature. As a result, the accuracy of the analysis is improved by referring to the relevant literature.

[0093] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on those estimated emotions. For example, the visualization unit uses a generative AI to estimate the user's emotions. The visualization unit can then adjust the visualization's presentation based on the user's emotions estimated by the generative AI. This allows for more appropriate visualization by adjusting the visualization's presentation according to the user's emotions.

[0094] The visualization unit can adjust the level of detail of the visualization based on the importance of the analyzed process during visualization. For example, the visualization unit uses a generative AI to evaluate the importance of the analyzed process. The visualization unit can adjust the level of detail of the visualization based on the importance determined by the generative AI. The visualization unit can adjust the level of detail of the visualization based on the generative AI's evaluation of the importance of the analyzed process. This allows for more appropriate visualization by adjusting the level of detail of the visualization according to the importance of the process.

[0095] The visualization unit can apply different visualization methods depending on the category of the analyzed process during visualization. For example, the visualization unit classifies the process category using generative AI. The visualization unit can then apply different visualization methods depending on the category, as determined by the generative AI. The visualization unit can then apply different visualization methods as the generative AI classifies the process category. This allows for more appropriate visualization by applying visualization methods according to the process category.

[0096] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated emotions. For example, the visualization unit uses generative AI to estimate the user's emotions. The visualization unit can adjust the length of the visualization based on the emotions estimated by the generative AI. This allows for more appropriate visualization by adjusting the length of the visualization according to the user's emotions.

[0097] The visualization unit can determine visualization priorities based on the submission timing of the analyzed processes during visualization. For example, the visualization unit uses a generative AI to evaluate submission timing. The visualization unit can then use the generative AI to determine visualization priorities based on submission timing. This allows for more appropriate visualization by adjusting visualization priorities according to submission timing.

[0098] The visualization unit can adjust the visualization order based on the relationships between the analyzed processes during visualization. For example, the visualization unit uses generative AI to evaluate the relationships between processes. The visualization unit can then adjust the visualization order based on the generative AI's evaluation of the relationships between processes. This allows for more appropriate visualization by adjusting the visualization order according to the relationships between processes.

[0099] The information provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, the information provider can estimate the user's emotions using generative AI. The information provider can determine the priority of the information based on the emotions estimated by the generative AI. The information provider can have the generative AI estimate the user's emotions and determine the priority of the information based on the estimated emotions. This makes it possible to provide more appropriate information by adjusting the priority of information according to the user's emotions.

[0100] The service provider can select the optimal information delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can refer to past usage history using a generation AI. The service provider can use the generation AI to select the optimal information delivery method based on past usage history. The service provider can use the generation AI to refer to past usage history and select the optimal information delivery method. In this way, the optimal information delivery method can be selected by referring to past usage history.

[0101] The service provider can customize information based on the user's current areas of interest at the time of delivery. For example, the service provider can analyze the user's areas of interest using generative AI. The service provider can then use the generative AI to customize information based on the user's current areas of interest. The service provider can use the generative AI to analyze the user's areas of interest and customize the information. This allows for the provision of more relevant information by customizing it based on the user's current areas of interest.

[0102] The information provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, the information provider can estimate the user's emotions using generative AI. The information provider can adjust the way the information is displayed based on the emotions estimated by the generative AI. The information provider can use the generative AI to estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. This allows for more appropriate display by adjusting the way the information is displayed according to the user's emotions.

[0103] The service provider can prioritize providing highly relevant information by considering the user's geographical location information at the time of delivery. For example, the service provider can refer to geographical location information using a generation AI. The service provider can provide highly relevant information based on the geographical location information using the generation AI. The service provider can provide highly relevant information by having the generation AI refer to geographical location information. In this way, highly relevant information can be provided by considering geographical location information.

[0104] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can use generative AI to analyze social media activity. The service provider can use generative AI to provide relevant information based on social media activity. The service provider can use generative AI to analyze social media activity and provide relevant information. This allows the service provider to provide highly relevant information by analyzing social media activity.

[0105] The explanation generation unit can estimate the user's emotions and adjust the way the explanation is expressed based on the estimated user emotions. For example, the explanation generation unit estimates the user's emotions using a generation AI. The explanation generation unit can adjust the way the explanation is expressed based on the user's emotions estimated by the generation AI. The explanation generation unit can adjust the way the explanation is expressed based on the estimated user emotions after the generation AI estimates the user's emotions. This makes it possible to provide more appropriate explanations by adjusting the way the explanation is expressed according to the user's emotions.

[0106] The explanation generation unit can adjust the level of detail in the explanation based on the importance of the ASI decision during explanation generation. For example, the explanation generation unit uses a generation AI to evaluate the importance of the decision. The explanation generation unit can adjust the level of detail in the explanation based on the importance of the generation AI. The explanation generation unit can adjust the level of detail in the explanation based on the evaluation of the importance of the decision by the generation AI. This makes it possible to provide a more appropriate explanation by adjusting the level of detail in the explanation according to the importance of the decision.

[0107] The explanation generation unit can apply different explanation algorithms depending on the ASI decision category during explanation generation. For example, the explanation generation unit classifies the decision category using generative AI. The explanation generation unit can then apply different explanation algorithms depending on the category of the generative AI. The explanation generation unit can then apply different explanation algorithms depending on the category of the generative AI. This allows for more appropriate explanations by applying explanation algorithms according to the decision category.

[0108] The explanation generation unit can estimate the user's emotions and adjust the length of the explanation based on the estimated emotions. For example, the explanation generation unit uses a generative AI to estimate the user's emotions. The explanation generation unit can adjust the length of the explanation based on the emotions estimated by the generative AI. This allows for more appropriate explanations by adjusting the length of the explanation according to the user's emotions.

[0109] The explanation generation unit can determine the priority of explanations based on the submission timing of ASI's decision when generating explanations. For example, the explanation generation unit uses a generation AI to evaluate the submission timing. The explanation generation unit can use the generation AI to determine the priority of explanations based on the submission timing. The explanation generation unit can use the generation AI to evaluate the submission timing and determine the priority of explanations. This allows for more appropriate explanations by adjusting the priority of explanations according to the submission timing.

[0110] The explanation generation unit can adjust the order of explanations based on the relevance of ASI's decision-making during explanation generation. For example, the explanation generation unit uses a generative AI to evaluate the relevance of the decision-making. The explanation generation unit can then use the generative AI to adjust the order of explanations based on their relevance. This allows for more appropriate explanations by adjusting the order of explanations according to their relevance.

[0111] The output unit can estimate the user's emotions and adjust the output method based on the estimated user emotions. For example, the output unit estimates the user's emotions using a generative AI. The output unit can adjust the output method based on the user's emotions estimated by the generative AI. The output unit can adjust the output method based on the estimated user emotions, allowing for more appropriate output by adjusting the output method according to the user's emotions.

[0112] The output unit can select the optimal output method by referring to the user's past output history at the time of output. For example, the output unit can refer to past output history using a generation AI. The output unit can use the generation AI to select the optimal output method based on past output history. The output unit can use the generation AI to refer to past output history and select the optimal output method. This allows the optimal output method to be selected by referring to past output history.

[0113] The output unit can customize the output content based on the user's current areas of interest at the time of output. For example, the output unit analyzes the user's areas of interest using a generative AI. The output unit can then use the generative AI to customize the output content based on the user's current areas of interest. This allows for more relevant output by customizing the output content based on the user's current areas of interest.

[0114] The output unit can estimate the user's emotions and determine the output priority based on the estimated user emotions. For example, the output unit estimates the user's emotions using generative AI. The output unit can determine the output priority based on the user's emotions estimated by the generative AI. This allows for more appropriate output by adjusting the output priority according to the user's emotions.

[0115] The output unit can select the optimal output method by considering the user's geographical location information during output. For example, the output unit uses a generation AI to reference geographical location information. The output unit can use the generation AI to select the optimal output method based on the geographical location information. The output unit can use the generation AI to reference geographical location information and select the optimal output method. This allows for the selection of the optimal output method by considering geographical location information.

[0116] The output unit can analyze the user's social media activity and provide relevant output content at the time of output. For example, the output unit can analyze social media activity using a generative AI. The output unit can provide relevant output content based on the social media activity generated by the generative AI. The output unit can provide relevant output content by analyzing social media activity. This allows for the provision of highly relevant output content through the analysis of social media activity.

[0117] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, the output unit uses a generation AI to reference the device information. The generation AI can then select the optimal display method based on the device information. The output unit can then select the optimal display method by referencing the device information. This allows for the selection of the optimal display method by considering the device information.

[0118] The output unit can provide multilingual output according to the user's language settings at the time of output. The output unit can refer to the language settings, for example, using a generation AI. The output unit can provide multilingual output based on the language settings using the generation AI. The output unit can provide multilingual output by having the generation AI refer to the language settings. This makes it easier for the user to understand by providing multilingual output according to the language settings.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The analysis unit can estimate the user's emotions and prioritize analysis based on those emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing high-priority information and provide it quickly. If the user is relaxed, the analysis unit can perform a detailed analysis and provide deeper insights. Furthermore, if the user is excited, the analysis unit can prioritize analyzing new information and interesting data to keep the user engaged. By adjusting the analysis priority according to the user's emotions, more appropriate analysis becomes possible.

[0121] The visualization unit can adjust the level of detail based on the importance of the analyzed process. For example, highly important processes can be displayed in detailed graphs and dashboards to allow users to understand them more deeply. On the other hand, less important processes can be displayed in a simplified form, allowing users to focus their attention on the necessary parts. Furthermore, important information can be visually highlighted by changing the color and font size according to importance. This allows users to efficiently obtain the necessary information by adjusting the level of detail of the visualization according to the importance of the process.

[0122] The information delivery system can estimate the user's emotions and prioritize the information it provides based on those emotions. For example, if a user is feeling anxious, the system can prioritize providing reassuring information. If a user is excited, the system can prioritize providing new discoveries or interesting information. Furthermore, if a user is tired, the system can prioritize providing concise and easy-to-understand information. By adjusting the priority of information according to the user's emotions, more appropriate information can be provided.

[0123] The explanation generation unit can adjust the level of detail in the explanation based on the importance of the ASI decision during explanation generation. For example, it can generate detailed explanations for high-importance decisions, allowing users to gain a deeper understanding of the background and reasons. On the other hand, it can generate concise explanations for low-importance decisions, saving users time. Furthermore, by changing the format of the explanation according to importance, users can efficiently process the information. In this way, adjusting the level of detail in the explanation according to the importance of the decision makes it possible to provide more appropriate explanations.

[0124] The output unit can estimate the user's emotions and adjust the output method based on those emotions. For example, if the user is feeling anxious, the output unit can provide information quickly to alleviate their anxiety. If the user is relaxed, the output unit can provide detailed information to help them understand it more deeply. Furthermore, if the user is excited, the output unit can provide information in a visually appealing format to maintain their interest. By adjusting the output method according to the user's emotions, more appropriate output becomes possible.

[0125] The analysis unit can perform analyses while considering the change history of the ASI algorithm. For example, by analyzing how past algorithm changes have affected the current data flow, more accurate results can be obtained. It is also possible to predict the impact of future algorithm changes based on the change history. Furthermore, by referring to the change history, past problems and areas for improvement can be identified and reflected in the current analysis. In this way, considering the algorithm change history improves the accuracy of the analysis.

[0126] The visualization unit can apply different visualization methods depending on the category of the analyzed process during visualization. For example, business processes can be displayed as flowcharts, while technical processes can be displayed as system diagrams. Furthermore, users can select between simplified and detailed diagrams depending on their level of understanding. Additionally, visual distinction can be made easier by changing colors and icons according to the category. This allows for more appropriate visualization by applying visualization methods according to the process category.

[0127] The information delivery unit can select the most appropriate method of information delivery by referring to the user's past usage history at the time of delivery. For example, a user who previously preferred reading detailed reports can be provided with detailed information. On the other hand, a user who previously preferred concise summaries can be provided with concise information. Furthermore, based on past usage history, it can also suggest new information that the user might be interested in. In this way, the optimal method of information delivery can be selected by referring to past usage history.

[0128] The explanation generation unit can estimate the user's emotions and adjust the way the explanation is presented based on those emotions. For example, if the user is feeling anxious, the explanation generation unit can use reassuring language. If the user is excited, the explanation generation unit can use engaging language. Furthermore, if the user is tired, the explanation generation unit can use concise and easy-to-understand language. By adjusting the way the explanation is presented according to the user's emotions, more appropriate explanations become possible.

[0129] The output unit can select the optimal output method when outputting data, taking into account the user's geographical location. For example, if the user is in a different region, it can prioritize providing information relevant to that region. It can also select an output method that takes into account appropriate language and culture according to geographical characteristics. Furthermore, it can provide real-time information related to the user's current location based on their geographical location. In this way, the optimal output method can be selected by considering geographical location.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The analysis unit analyzes the ASI decision-making process. The analysis unit can use generative AI to analyze and visualize the ASI algorithms and data flow. Step 2: The visualization unit visualizes the process analyzed by the analysis unit. The visualization unit can visualize the process analyzed using the generation AI as graphs and dashboards, providing them in a format that is easy for the user to understand. Step 3: The provisioning unit provides the information visualized by the visualization unit. The provisioning unit can provide the visualized information using generation AI to users and auditing organizations and distribute it automatically. Step 4: The explanation generation unit generates an explanation based on the information provided by the provision unit. The explanation generation unit can use generation AI to automatically generate a report explaining the background and reasons for ASI's decision in natural language, and provide it to users or auditing organizations. Step 5: The output unit outputs the explanation generated by the explanation generation unit. The output unit can automatically output and provide the report generated using the generation AI to the user or auditing body.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the analysis unit, visualization unit, provision unit, explanation generation unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the processor 28 of the data processing unit 12, and analyzes the ASI algorithm and data flow using generation AI. The visualization unit is implemented by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and visualizes the analyzed process as a graph or dashboard. The provision unit is implemented by the communication I / F 44 of the smart device 14 or the communication I / F 26 of the data processing unit 12, and provides the visualized information to the user or auditing body. The explanation generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates an explanation in natural language using generation AI. The output unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and outputs the generated report to the user or auditing body. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the analysis unit, visualization unit, provision unit, explanation generation unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 or the processor 28 of the data processing unit 12, and analyzes the ASI algorithm and data flow using generation AI. The visualization unit is implemented by the display of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and visualizes the analyzed process as a graph or dashboard. The provision unit is implemented by the communication I / F 44 of the smart glasses 214 or the communication I / F 26 of the data processing unit 12, and provides the visualized information to the user or auditing body. The explanation generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates an explanation in natural language using generation AI. The output unit is implemented by the output device of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and outputs the generated report to the user or auditing body. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the analysis unit, visualization unit, provision unit, explanation generation unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 or the processor 28 of the data processing unit 12, and analyzes the ASI algorithm and data flow using generation AI. The visualization unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and visualizes the analyzed process as a graph or dashboard. The provision unit is implemented by the communication I / F 44 of the headset terminal 314 or the communication I / F 26 of the data processing unit 12, and provides the visualized information to the user or auditing body. The explanation generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates an explanation in natural language using generation AI. The output unit is implemented by the output device of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and outputs the generated report to the user or auditing body. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0172] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0175] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the analysis unit, visualization unit, provision unit, explanation generation unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 or the processor 28 of the data processing unit 12, and analyzes the ASI algorithm and data flow using generation AI. The visualization unit is implemented by the display of the robot 414 or the specific processing unit 290 of the data processing unit 12, and visualizes the analyzed process as a graph or dashboard. The provision unit is implemented by the communication I / F 44 of the robot 414 or the communication I / F 26 of the data processing unit 12, and provides the visualized information to the user or auditing body. The explanation generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates an explanation in natural language using generation AI. The output unit is implemented by the output device of the robot 414 or the specific processing unit 290 of the data processing unit 12, and outputs the generated report to the user or auditing body. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0185] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0187] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0188] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0189] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0194] 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.

[0195] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0196] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0197] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0198] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0202] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0203] (Note 1) The analysis unit analyzes the decision-making process of ASI, A visualization unit that visualizes the process analyzed by the aforementioned analysis unit, A providing unit that provides the information visualized by the visualization unit, An explanation generation unit that generates an explanation based on the information provided by the aforementioned provisioning unit, The system includes an output unit that outputs the explanation generated by the explanation generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze ASI algorithms and data flows. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned visualization unit, Visualize the analyzed process as graphs and dashboards. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide visualized information to users and auditing bodies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The explanation generation unit, Automatically generate reports explaining the background and reasons behind ASI's decision-making in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 6) The output unit is, Output the generated report to users or auditing organizations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It detects missing or anomaly data and automatically performs data cleaning. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned visualization unit, ASI's actions are monitored based on an ethical checklist, and alerts are issued if violations are detected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing ASI's decision-making process, we improve the accuracy of the analysis by referring to past decision history. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the change history of the ASI algorithm is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the geographical distribution of ASI data flow will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we refer to relevant ASI literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, During visualization, adjust the level of detail based on the importance of the analyzed process. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, During visualization, different visualization methods are applied depending on the category of the analyzed process. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, It estimates the user's emotions and adjusts the length of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, During visualization, the visualization priority is determined based on when the analyzed process was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, During visualization, adjust the order of visualizations based on the relevance of the analyzed processes. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, the system will select the most suitable method of information delivery by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing information, customize it based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The explanation generation unit, It estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The explanation generation unit, When generating explanations, adjust the level of detail in the explanations based on the importance of ASI's decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 29) The explanation generation unit, When generating explanations, different explanation algorithms are applied depending on the category of ASI decision. The system described in Appendix 1, characterized by the features described herein. (Note 30) The explanation generation unit, It estimates the user's emotions and adjusts the length of the explanation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The explanation generation unit, When generating explanations, prioritize explanations based on when ASI decisions are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The explanation generation unit, During explanation generation, the order of explanations is adjusted based on the relevance of ASI's decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 33) The output unit is, It estimates the user's emotions and adjusts the output method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The output unit is, During output, the system selects the optimal output method by referring to the user's past output history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The output unit is, When outputting, customize the output content based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 36) The output unit is, It estimates the user's emotions and determines the priority of the output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The output unit is, When outputting data, the system selects the optimal output method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The output unit is, When outputting data, the system analyzes the user's social media activity and provides relevant output content. The system described in Appendix 1, characterized by the features described herein. (Note 39) The output unit is, When outputting, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The output unit is, When outputting, it provides multilingual output according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes the decision-making process of ASI, A visualization unit that visualizes the process analyzed by the aforementioned analysis unit, A providing unit that provides the information visualized by the visualization unit, An explanation generation unit that generates an explanation based on the information provided by the aforementioned provisioning unit, The system includes an output unit that outputs the explanation generated by the explanation generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze ASI algorithms and data flows. The system according to feature 1.

3. The aforementioned visualization unit, Visualize the analyzed process as graphs and dashboards. The system according to feature 1.

4. The aforementioned supply unit is, Provide visualized information to users and auditing bodies. The system according to feature 1.

5. The explanation generation unit, Automatically generate reports explaining the background and reasons behind ASI's decision-making in natural language. The system according to feature 1.

6. The output unit is, Output the generated report to users or auditing organizations. The system according to feature 1.

7. The aforementioned analysis unit, It detects missing or anomaly data and automatically performs data cleaning. The system according to feature 1.

8. The aforementioned visualization unit, ASI's actions are monitored based on an ethical checklist, and alerts are issued if violations are detected. The system according to feature 1.

9. The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing ASI's decision-making process, we improve the accuracy of the analysis by referring to past decision history. The system according to feature 1.

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