System

Abstract

An object of a system according to an embodiment is to enable a company administrator to appropriately manage and check a process of determination made by a AI.SOLUTION: A system according to an embodiment includes a determination process analysis unit, a result provision unit, and a visualization unit. The determination process analysis unit analyzes a determination process in which another AI is made. A result providing part provides the result of the determination process analyzed by the determination process analysis part to the enterprise manager. The visualization unit visualizes the determination process.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that it was difficult for corporate managers to properly manage and check the decision-making process made by AI.

[0005] The system of the embodiment aims to enable corporate administrators to properly manage and check the decision-making process made by AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a decision-making process analysis unit, a result provision unit, and a visualization unit. The decision-making process analysis unit analyzes the decision-making process made by other AIs. The result provision unit provides the results of the decision-making process analyzed by the decision-making process analysis unit to the company administrator. The visualization unit visualizes the decision-making process. [Effects of the Invention]

[0007] The system according to the embodiment enables corporate administrators to properly manage and check the decision-making process made by AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​monitoring system according to an embodiment of the present invention is a system that allows a company administrator to manage and check whether the decisions made by the AI ​​are correct and whether the decision-making process was appropriate. As a result, the AI ​​monitoring system can analyze the AI's decision-making process and provide the results to the company administrator.

[0029] An AI monitoring system according to an embodiment includes a decision-making process analysis unit, a result providing unit, and a visualization unit. The decision-making process analysis unit analyzes the decision-making process made by other AIs. For example, the decision-making process analysis unit analyzes in detail the process to determine what data the AI ​​used to make a decision when processing a customer complaint. The decision-making process analysis unit evaluates whether the decision was appropriate based on data related to the decision made by the AI ​​and information related to the decision-making process. The result providing unit provides the results of the decision-making process analyzed by the decision-making process analysis unit to a company administrator. For example, the result providing unit evaluates the decision as "appropriate" based on the analysis results and generates a report that explains the reason and basis in detail. The result providing unit also includes an interface for providing the analysis results to the company administrator. The visualization unit visualizes the decision-making process. For example, the visualization unit visually displays the process using a flowchart or graph, showing which data the AI ​​analyzed and how it made its decision. The visualization unit also displays each step of the decision-making process in detail, allowing the company administrator to intuitively understand it. As a result, the AI ​​monitoring system according to the embodiment analyzes the AI's decision-making process, provides the results, and visualizes them, allowing corporate administrators to check the appropriateness of the AI's decisions. For example, corporate administrators can check whether the AI ​​is appropriately handling customer complaints and make improvements as necessary. In addition, visualizing the AI's decision-making process based on the analysis results makes it easier for corporate administrators to intuitively understand the AI's decision-making process.

[0030] The decision-making process analysis unit can track the details of data conversion and filtering at each step of the decision-making process and detect data fluctuations. The decision-making process analysis unit, for example, tracks the details of data conversion and filtering performed at each step of the decision-making process. For example, it analyzes noise removal and feature extraction techniques at the data preprocessing stage to detect subtle data fluctuations. The decision-making process analysis unit also collects and analyzes logs for tracking the details of data conversion and filtering. For example, it records data fluctuations that occur during data conversion and evaluates the impact of those fluctuations on the decision. This makes it possible to track the details of data conversion and filtering at each step of the decision-making process and detect subtle data fluctuations.

[0031] The decision-making process analysis unit can consider the influence of the external environment on the decision-making process and perform a more precise analysis. The decision-making process analysis unit, for example, considers the influence of the external environment on the decision-making process. For example, it analyzes seasonal fluctuations and market fluctuations and evaluates the impact on the decision-making process. The decision-making process analysis unit also collects and analyzes data to consider the impact of the external environment. For example, it collects weather data and market data and evaluates the impact on the decision-making process. This allows the influence of the external environment on the decision-making process to be considered and a more precise analysis to be performed.

[0032] The decision-making process analysis unit analyzes not only AI but also human decision-making processes, and can perform comparative analysis of AI and human decision-making. The decision-making process analysis unit, for example, compares and analyzes the decision-making processes of AI and humans. For example, it compares decisions made by AI and humans using the same data set and analyzes the differences in decisions. The decision-making process analysis unit also collects and analyzes data for comparative analysis of AI and human decision-making processes. For example, it collects and compares data on decisions made by AI and decisions made by humans. This makes it possible to compare and analyze the decision-making processes of AI and humans and clarify the differences in decisions.

[0033] The decision-making process analysis unit can simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences. The decision-making process analysis unit, for example, simultaneously analyzes AI decision-making processes in different industries and fields. For example, it compares AI decision-making processes in the financial industry and the medical industry, revealing similarities and differences. The decision-making process analysis unit also collects and analyzes data to analyze AI decision-making processes in different industries and fields. For example, it collects data from the financial industry and the medical industry and analyzes the similarities and differences. This makes it possible to simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences.

[0034] When providing the analysis results, the result providing unit can add a detailed explanation for each step of the decision-making process, thereby increasing transparency. For example, the result providing unit adds a detailed explanation for each step of the decision-making process when providing the analysis results. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is explained in detail. The result providing unit also has an interface for providing the analysis results. For example, it has a dashboard and report generation function for providing the analysis results to a company administrator. This allows for adding a detailed explanation for each step of the decision-making process when providing the analysis results, thereby increasing transparency.

[0035] When providing the analysis results, the result providing unit can perform a comparative analysis with past similar judgments and evaluate the consistency of the judgment. For example, when providing the analysis results, the result providing unit performs a comparative analysis with past similar judgments. For example, it compares past judgment results with current judgment results and evaluates consistency. The result providing unit also collects and analyzes data for performing the comparative analysis with past similar judgments. For example, it collects past judgment data and compares it with current judgment data. In this way, when providing the analysis results, it can perform a comparative analysis with past similar judgments and evaluate the consistency of the judgment.

[0036] The result providing unit enables the analysis results to be provided in different formats, allowing customization according to the user's needs. The result providing unit, for example, enables the analysis results to be provided in different formats. For example, the analysis results may be provided in the form of a text report, audio commentary, video presentation, or the like. The result providing unit also includes an interface for customization according to the user's needs. For example, the result providing unit includes a user setting and personalization function for customization according to the user's needs. This enables the analysis results to be provided in different formats, allowing customization according to the user's needs.

[0037] When providing the analysis results, the result providing unit can compare them with benchmark data of other companies or industries and perform a relative evaluation. For example, when providing the analysis results, the result providing unit compares them with benchmark data of other companies or industries. For example, it compares them with data of other companies in the same industry and performs a relative evaluation. The result providing unit also has an interface for collecting and analyzing benchmark data. For example, it collects industry standard data or competitor data and compares it with the analysis results. As a result, when providing the analysis results, it can compare them with benchmark data of other companies or industries and perform a relative evaluation.

[0038] The visualization unit, when visualizing a decision-making process, can display the interdependencies between each step in detail, allowing for an intuitive understanding of the complex decision-making process. The visualization unit, for example, displays the interdependencies between each step in detail when visualizing the decision-making process. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is displayed in a flowchart. The visualization unit also has an interface for displaying the interdependencies. For example, the interdependencies between each step are visually displayed using a network diagram or a flowchart. This allows the interdependencies between each step in detail when visualizing the decision-making process, allowing for an intuitive understanding of the complex decision-making process.

[0039] The visualization unit can compare the current decision-making process with past decision-making processes in visualization and clearly indicate changes and areas for improvement. The visualization unit, for example, compares the current decision-making process with past decision-making processes in visualization. For example, the visualization unit displays the past decision-making process and the current decision-making process side by side to clearly indicate changes and areas for improvement. The visualization unit also collects and analyzes data for comparison with past decision-making processes. For example, the visualization unit collects past decision-making data and compares it with current decision-making data. This makes it possible to compare the current decision-making process with past decision-making processes in visualization and clearly indicate changes and areas for improvement.

[0040] The visualization unit can use different visualization techniques to display the process from multiple perspectives when visualizing the decision-making process. The visualization unit, for example, uses 3D graphs when visualizing the decision-making process. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is displayed in a 3D graph. The visualization unit also has an interface for using different visualization techniques. For example, each step is displayed from multiple perspectives using an interactive map or a 3D graph. This makes it possible to use different visualization techniques to display the process from multiple perspectives when visualizing the decision-making process.

[0041] The visualization unit can link with other AI systems and data sources to realize integrated visualization when visualizing the decision-making process. The visualization unit, for example, links with other AI systems and data sources to realize integrated visualization. For example, the visualization unit integrates the decision-making processes of multiple AI systems and displays them in a flowchart. The visualization unit also has an interface for linking with other AI systems and data sources. For example, an API can be used to link data with other systems to realize integrated visualization. This makes it possible to link with other AI systems and data sources to realize integrated visualization when visualizing the decision-making process.

[0042] When an anomaly is detected, the decision process analysis unit can classify the type and cause of the anomaly in detail and propose specific countermeasures. For example, when an anomaly is detected, the decision process analysis unit classifies the type and cause of the anomaly in detail. For example, it classifies into data anomalies, algorithm anomalies, system anomalies, etc., and proposes specific countermeasures. The decision process analysis unit also collects and analyzes data for classifying the type and cause of the anomaly. For example, it collects log data and identifies the type and cause of the anomaly. As a result, when an anomaly is detected, it can classify the type and cause of the anomaly in detail and propose specific countermeasures.

[0043] The decision process analysis unit can refer to past abnormality data when detecting an anomaly and propose measures to prevent the anomaly from recurring. The decision process analysis unit, for example, refers to past abnormality data when detecting an anomaly. For example, it analyzes past abnormality data and proposes measures to prevent the anomaly from recurring. The decision process analysis unit also collects and analyzes data for referencing past abnormality data. For example, it collects past abnormality data and proposes measures to prevent the anomaly from recurring. In this way, it can refer to past abnormality data when detecting an anomaly and propose measures to prevent the anomaly from recurring.

[0044] The decision-making process analysis unit expands the scope of anomaly detection to include not only AI but also human decision-making processes, enabling early detection of anomalies. The decision-making process analysis unit expands the scope of anomaly detection to include not only AI but also human decision-making processes. For example, it can simultaneously monitor AI and human decision-making processes to enable early detection of anomalies. The decision-making process analysis unit also collects and analyzes data to expand the scope of anomaly detection. For example, it can collect AI and human decision-making data to enable early detection of anomalies. This allows the scope of anomaly detection to be expanded to include not only AI but also human decision-making processes, enabling early detection of anomalies.

[0045] The decision process analysis unit can refer to data from different industries and fields to identify commonalities and differences in anomalies. The decision process analysis unit, for example, refers to data from different industries and fields to identify commonalities and differences in anomalies. For example, it compares data from the financial industry and the medical industry to analyze commonalities and differences in anomalies. The decision process analysis unit also collects and analyzes data to identify commonalities and differences in anomalies. For example, it collects data from the financial industry and the medical industry to analyze commonalities and differences in anomalies. This makes it possible to refer to data from different industries and fields to identify commonalities and differences in anomalies.

[0046] The decision-making process analysis unit can incorporate the opinions of external experts during continuous learning and improve the decision-making process. The decision-making process analysis unit, for example, incorporates the opinions of external experts during continuous learning. For example, the decision-making process is improved based on the opinions of the experts. The decision-making process analysis unit also includes an interface for collecting and analyzing the opinions of external experts. For example, the opinions of experts are collected and reflected in improvements to the decision-making process. This allows the opinion of external experts to be incorporated during continuous learning and improve the decision-making process.

[0047] The decision-making process analysis unit expands the scope of continuous learning to include not only AI but also human decision-making processes, making it possible to make comprehensive improvements. The decision-making process analysis unit expands the scope of continuous learning to include not only AI but also human decision-making processes. For example, it can simultaneously learn about AI and human decision-making processes and make comprehensive improvements. The decision-making process analysis unit also collects and analyzes data for learning about AI and human decision-making processes. For example, it can collect AI and human decision-making data and make comprehensive improvements. This allows it to expand the scope of continuous learning to include not only AI but also human decision-making processes, making it possible to make comprehensive improvements.

[0048] The decision-making process analysis unit can refer to data from different industries and fields during continuous learning and identify common areas for improvement. The decision-making process analysis unit, for example, refers to data from different industries and fields during continuous learning. For example, data from the financial industry and the medical industry can be compared to identify common areas for improvement. The decision-making process analysis unit also has an interface for collecting and analyzing data from different industries and fields. For example, data from the financial industry and the medical industry can be collected and analyzed to identify common areas for improvement. This makes it possible to refer to data from different industries and fields during continuous learning and identify common areas for improvement.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] When analyzing an AI's decision-making process, the decision-making process analysis unit can compare the performance of different algorithms and models and select the optimal model. For example, the decision-making process analysis unit compares the performance of a machine learning model and a deep learning model to evaluate which one makes more accurate decisions. The decision-making process analysis unit also collects and analyzes data to compare the performance of different algorithms. For example, it collects and compares and analyzes the decision results using different algorithms. This makes it possible to compare the performance of different algorithms and models and select the optimal model.

[0051] When analyzing the decision-making process of an AI, the decision-making process analysis unit can evaluate the reliability of the data used in the decision and ensure data quality. For example, the decision-making process analysis unit analyzes the source and collection method of the data and evaluates its reliability. The decision-making process analysis unit also sets standards for evaluating the reliability of the data and ensures data quality. For example, it evaluates the consistency and accuracy of the data and eliminates unreliable data. This makes it possible to evaluate the reliability of the data used in the decision and ensure data quality.

[0052] When analyzing the AI's decision-making process, the decision-making process analysis unit can detect potential biases that may affect decisions and minimize the impact of those biases. For example, the decision-making process analysis unit analyzes biases contained in a dataset and evaluates the impact of those biases. The decision-making process analysis unit also uses an algorithm for detecting bias to analyze the impact of bias at each step of the decision-making process. For example, it can detect biases based on specific attributes and propose measures to minimize their impact. This makes it possible to detect potential biases that may affect decisions and minimize their impact.

[0053] The decision-making process analysis unit analyzes not only AI but also human decision-making processes, and can perform comparative analysis of AI and human decision-making. For example, it can compare decisions made by AI and humans using the same data set and analyze the differences in decisions. The decision-making process analysis unit also collects and analyzes data for comparative analysis of AI and human decision-making processes. For example, it can collect and compare data on decisions made by AI and humans. This makes it possible to compare and analyze the decision-making processes of AI and humans and clarify the differences in decisions.

[0054] The decision-making process analysis unit can simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences. For example, it can compare AI decision-making processes in the financial and medical industries, revealing similarities and differences. The decision-making process analysis unit also collects and analyzes data to analyze AI decision-making processes in different industries and fields. For example, it can collect data from the financial and medical industries and analyze the similarities and differences. This allows it to simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences.

[0055] When providing the analysis results, the result providing unit can add a detailed explanation for each step of the decision-making process to increase transparency. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is explained in detail. The result providing unit also has an interface for providing the analysis results. For example, it has a dashboard and report generation function for providing the analysis results to company administrators. This allows the result providing unit to add a detailed explanation for each step of the decision-making process to increase transparency when providing the analysis results.

[0056] When providing the analysis results, the result providing unit can perform a comparative analysis with similar past judgments and evaluate the consistency of the judgment. For example, the result providing unit compares the past judgment results with the current judgment results and evaluates the consistency. The result providing unit also collects and analyzes data for the comparative analysis with similar past judgments. For example, the result providing unit collects past judgment data and compares it with the current judgment data. In this way, when providing the analysis results, the result providing unit can perform a comparative analysis with similar past judgments and evaluate the consistency of the judgment.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The decision-making process analysis unit analyzes the decision-making process made by other AIs. For example, it analyzes the process in detail to determine what data the AI ​​used to make a decision when processing a customer complaint. The decision-making process analysis unit also evaluates whether the decision was appropriate based on data related to the decision made by the AI ​​and information about that process. Step 2: The result provider provides the results of the decision-making process analyzed by the decision-making process analysis unit to the company administrator. For example, based on the analysis results, it may evaluate the decision as "appropriate" and generate a report that explains the reasons and grounds in detail. The result provider also has an interface for providing the analysis results to the company administrator. Step 3: The visualization part visualizes the decision-making process. For example, it visually displays the process using flowcharts and graphs to show which data the AI ​​analyzed and how it made its decision. It also displays each step of the decision-making process in detail, allowing corporate managers to intuitively understand it.

[0059] (Example 2) The AI ​​monitoring system according to an embodiment of the present invention is a system that allows a company administrator to manage and check whether the decisions made by the AI ​​are correct and whether the decision-making process was appropriate. As a result, the AI ​​monitoring system can analyze the AI's decision-making process and provide the results to the company administrator.

[0060] An AI monitoring system according to an embodiment includes a decision-making process analysis unit, a result providing unit, and a visualization unit. The decision-making process analysis unit analyzes the decision-making process made by other AIs. For example, the decision-making process analysis unit analyzes in detail the process to determine what data the AI ​​used to make a decision when processing a customer complaint. The decision-making process analysis unit evaluates whether the decision was appropriate based on data related to the decision made by the AI ​​and information related to the decision-making process. The result providing unit provides the results of the decision-making process analyzed by the decision-making process analysis unit to a company administrator. For example, the result providing unit evaluates the decision as "appropriate" based on the analysis results and generates a report that explains the reason and basis in detail. The result providing unit also includes an interface for providing the analysis results to the company administrator. The visualization unit visualizes the decision-making process. For example, the visualization unit visually displays the process using a flowchart or graph, showing which data the AI ​​analyzed and how it made its decision. The visualization unit also displays each step of the decision-making process in detail, allowing the company administrator to intuitively understand it. As a result, the AI ​​monitoring system according to the embodiment analyzes the AI's decision-making process, provides the results, and visualizes them, allowing corporate administrators to check the appropriateness of the AI's decisions. For example, corporate administrators can check whether the AI ​​is appropriately handling customer complaints and make improvements as necessary. In addition, visualizing the AI's decision-making process based on the analysis results makes it easier for corporate administrators to intuitively understand the AI's decision-making process.

[0061] The decision-making process analysis unit can track the details of data conversion and filtering at each step of the decision-making process and detect data fluctuations. The decision-making process analysis unit, for example, tracks the details of data conversion and filtering performed at each step of the decision-making process. For example, it analyzes noise removal and feature extraction techniques at the data preprocessing stage to detect subtle data fluctuations. The decision-making process analysis unit also collects and analyzes logs for tracking the details of data conversion and filtering. For example, it records data fluctuations that occur during data conversion and evaluates the impact of those fluctuations on the decision. This makes it possible to track the details of data conversion and filtering at each step of the decision-making process and detect subtle data fluctuations.

[0062] The decision-making process analysis unit can consider the influence of the external environment on the decision-making process and perform a more precise analysis. The decision-making process analysis unit, for example, considers the influence of the external environment on the decision-making process. For example, it analyzes seasonal fluctuations and market fluctuations and evaluates the impact on the decision-making process. The decision-making process analysis unit also collects and analyzes data to consider the impact of the external environment. For example, it collects weather data and market data and evaluates the impact on the decision-making process. This allows the influence of the external environment on the decision-making process to be considered and a more precise analysis to be performed.

[0063] The decision-making process analysis unit uses the emotion estimation function to analyze changes in the user's emotions during the decision-making process, and can evaluate the impact of emotional influences on the decision. The decision-making process analysis unit, for example, uses the emotion estimation function to analyze changes in the user's emotions during the decision-making process. For example, it analyzes the user's facial expressions and voice and evaluates the impact of changes in emotions on the decision. The decision-making process analysis unit also uses the emotion estimation function to collect and analyze data for analyzing changes in emotions. For example, it collects facial expression data and voice data of the user and analyzes changes in emotions. This makes it possible to analyze changes in the user's emotions during the decision-making process using the emotion estimation function and evaluate the impact of emotional influences on the decision.

[0064] The decision-making process analysis unit analyzes not only AI but also human decision-making processes, and can perform comparative analysis of AI and human decision-making. The decision-making process analysis unit, for example, compares and analyzes the decision-making processes of AI and humans. For example, it compares decisions made by AI and humans using the same data set and analyzes the differences in decisions. The decision-making process analysis unit also collects and analyzes data for comparative analysis of AI and human decision-making processes. For example, it collects and compares data on decisions made by AI and decisions made by humans. This makes it possible to compare and analyze the decision-making processes of AI and humans and clarify the differences in decisions.

[0065] The decision-making process analysis unit can simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences. The decision-making process analysis unit, for example, simultaneously analyzes AI decision-making processes in different industries and fields. For example, it compares AI decision-making processes in the financial industry and the medical industry, revealing similarities and differences. The decision-making process analysis unit also collects and analyzes data to analyze AI decision-making processes in different industries and fields. For example, it collects data from the financial industry and the medical industry and analyzes the similarities and differences. This makes it possible to simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences.

[0066] The decision-making process analysis unit can use the emotion estimation function to monitor the user's emotions during the decision-making process in real time and provide feedback based on the emotions. The decision-making process analysis unit, for example, uses the emotion estimation function to monitor the user's emotions during the decision-making process in real time. For example, it analyzes the user's facial expressions and voice and provides feedback based on the emotions. The decision-making process analysis unit also uses the emotion estimation function to collect and analyze data for monitoring emotions. For example, it collects the user's facial expression data and voice data and analyzes changes in emotions in real time. This allows the emotion estimation function to monitor the user's emotions during the decision-making process in real time and provide feedback based on the emotions.

[0067] When providing the analysis results, the result providing unit can add a detailed explanation for each step of the decision-making process, thereby increasing transparency. For example, the result providing unit adds a detailed explanation for each step of the decision-making process when providing the analysis results. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is explained in detail. The result providing unit also has an interface for providing the analysis results. For example, it has a dashboard and report generation function for providing the analysis results to a company administrator. This allows for adding a detailed explanation for each step of the decision-making process when providing the analysis results, thereby increasing transparency.

[0068] When providing the analysis results, the result providing unit can perform a comparative analysis with past similar judgments and evaluate the consistency of the judgment. For example, when providing the analysis results, the result providing unit performs a comparative analysis with past similar judgments. For example, it compares past judgment results with current judgment results and evaluates consistency. The result providing unit also collects and analyzes data for performing the comparative analysis with past similar judgments. For example, it collects past judgment data and compares it with current judgment data. In this way, when providing the analysis results, it can perform a comparative analysis with past similar judgments and evaluate the consistency of the judgment.

[0069] The result providing unit can use the emotion estimation function to collect the corporate administrator's emotional reactions to the analysis results and evaluate the degree of understanding and satisfaction of the results. The result providing unit, for example, uses the emotion estimation function to collect the corporate administrator's emotional reactions to the analysis results. For example, it analyzes the administrator's facial expressions and voice and evaluates the administrator's understanding and satisfaction of the results. The result providing unit also uses the emotion estimation function to collect and analyze data for collecting the emotional reactions. For example, it collects the administrator's facial expression data and voice data and analyzes the emotional reactions. In this way, the emotion estimation function can be used to collect the corporate administrator's emotional reactions to the analysis results and evaluate the degree of understanding and satisfaction of the results.

[0070] The result providing unit enables the analysis results to be provided in different formats, allowing customization according to the user's needs. The result providing unit, for example, enables the analysis results to be provided in different formats. For example, the analysis results may be provided in the form of a text report, audio commentary, video presentation, or the like. The result providing unit also includes an interface for customization according to the user's needs. For example, the result providing unit includes a user setting and personalization function for customization according to the user's needs. This enables the analysis results to be provided in different formats, allowing customization according to the user's needs.

[0071] When providing the analysis results, the result providing unit can compare them with benchmark data of other companies or industries and perform a relative evaluation. For example, when providing the analysis results, the result providing unit compares them with benchmark data of other companies or industries. For example, it compares them with data of other companies in the same industry and performs a relative evaluation. The result providing unit also has an interface for collecting and analyzing benchmark data. For example, it collects industry standard data or competitor data and compares it with the analysis results. As a result, when providing the analysis results, it can compare them with benchmark data of other companies or industries and perform a relative evaluation.

[0072] The visualization unit, when visualizing a decision-making process, can display the interdependencies between each step in detail, allowing for an intuitive understanding of the complex decision-making process. The visualization unit, for example, displays the interdependencies between each step in detail when visualizing the decision-making process. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is displayed in a flowchart. The visualization unit also has an interface for displaying the interdependencies. For example, the interdependencies between each step are visually displayed using a network diagram or a flowchart. This allows the interdependencies between each step in detail when visualizing the decision-making process, allowing for an intuitive understanding of the complex decision-making process.

[0073] The visualization unit can compare the current decision-making process with past decision-making processes in visualization and clearly indicate changes and areas for improvement. The visualization unit, for example, compares the current decision-making process with past decision-making processes in visualization. For example, the visualization unit displays the past decision-making process and the current decision-making process side by side to clearly indicate changes and areas for improvement. The visualization unit also collects and analyzes data for comparison with past decision-making processes. For example, the visualization unit collects past decision-making data and compares it with current decision-making data. This makes it possible to compare the current decision-making process with past decision-making processes in visualization and clearly indicate changes and areas for improvement.

[0074] The visualization unit uses the emotion estimation function to visualize the user's emotional changes during the decision-making process, allowing the user to visually understand the emotional impact. The visualization unit, for example, uses the emotion estimation function to visualize the user's emotional changes during the decision-making process. For example, the visualization unit analyzes the user's facial expressions and voice and displays the emotional changes in a graph. The visualization unit also collects and analyzes data for visualizing the emotional changes. For example, the visualization unit collects the user's facial expression data and voice data and analyzes the emotional changes. In this way, the emotion estimation function can be used to visualize the user's emotional changes during the decision-making process, allowing the user to visually understand the emotional impact.

[0075] The visualization unit can use different visualization techniques to display the process from multiple perspectives when visualizing the decision-making process. The visualization unit, for example, uses 3D graphs when visualizing the decision-making process. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is displayed in a 3D graph. The visualization unit also has an interface for using different visualization techniques. For example, each step is displayed from multiple perspectives using an interactive map or a 3D graph. This makes it possible to use different visualization techniques to display the process from multiple perspectives when visualizing the decision-making process.

[0076] The visualization unit can link with other AI systems and data sources to realize integrated visualization when visualizing the decision-making process. The visualization unit, for example, links with other AI systems and data sources to realize integrated visualization. For example, the visualization unit integrates the decision-making processes of multiple AI systems and displays them in a flowchart. The visualization unit also has an interface for linking with other AI systems and data sources. For example, an API can be used to link data with other systems to realize integrated visualization. This makes it possible to link with other AI systems and data sources to realize integrated visualization when visualizing the decision-making process.

[0077] The visualization unit can use the emotion estimation function to visualize the user's emotions during the decision-making process in real time and provide feedback based on the emotions. The visualization unit, for example, uses the emotion estimation function to visualize the user's emotions during the decision-making process in real time. For example, the visualization unit analyzes the user's facial expressions and voice and displays changes in emotions in a graph. The visualization unit also collects and analyzes data for visualizing emotions. For example, the visualization unit collects the user's facial expression data and voice data and analyzes changes in emotions in real time. This makes it possible to visualize the user's emotions during the decision-making process in real time using the emotion estimation function and provide feedback based on the emotions.

[0078] When an anomaly is detected, the decision process analysis unit can classify the type and cause of the anomaly in detail and propose specific countermeasures. For example, when an anomaly is detected, the decision process analysis unit classifies the type and cause of the anomaly in detail. For example, it classifies into data anomalies, algorithm anomalies, system anomalies, etc., and proposes specific countermeasures. The decision process analysis unit also collects and analyzes data for classifying the type and cause of the anomaly. For example, it collects log data and identifies the type and cause of the anomaly. As a result, when an anomaly is detected, it can classify the type and cause of the anomaly in detail and propose specific countermeasures.

[0079] The decision process analysis unit can refer to past abnormality data when detecting an anomaly and propose measures to prevent the anomaly from recurring. The decision process analysis unit, for example, refers to past abnormality data when detecting an anomaly. For example, it analyzes past abnormality data and proposes measures to prevent the anomaly from recurring. The decision process analysis unit also collects and analyzes data for referencing past abnormality data. For example, it collects past abnormality data and proposes measures to prevent the anomaly from recurring. In this way, it can refer to past abnormality data when detecting an anomaly and propose measures to prevent the anomaly from recurring.

[0080] The decision-making process analysis unit can use the emotion estimation function to analyze the user's emotional reaction when an anomaly is detected and propose measures to reduce emotional stress. The decision-making process analysis unit, for example, uses the emotion estimation function to analyze the user's emotional reaction when an anomaly is detected. For example, it analyzes the user's facial expression and voice and proposes measures to reduce emotional stress. The decision-making process analysis unit also collects and analyzes data for analyzing the emotional reaction. For example, it collects the user's facial expression data and voice data and analyzes the emotional reaction. This makes it possible to use the emotion estimation function to analyze the user's emotional reaction when an anomaly is detected and propose measures to reduce emotional stress.

[0081] The decision-making process analysis unit expands the scope of anomaly detection to include not only AI but also human decision-making processes, enabling early detection of anomalies. The decision-making process analysis unit expands the scope of anomaly detection to include not only AI but also human decision-making processes. For example, it can simultaneously monitor AI and human decision-making processes to enable early detection of anomalies. The decision-making process analysis unit also collects and analyzes data to expand the scope of anomaly detection. For example, it can collect AI and human decision-making data to enable early detection of anomalies. This allows the scope of anomaly detection to be expanded to include not only AI but also human decision-making processes, enabling early detection of anomalies.

[0082] The decision process analysis unit can refer to data from different industries and fields to identify commonalities and differences in anomalies. The decision process analysis unit, for example, refers to data from different industries and fields to identify commonalities and differences in anomalies. For example, it compares data from the financial industry and the medical industry to analyze commonalities and differences in anomalies. The decision process analysis unit also collects and analyzes data to identify commonalities and differences in anomalies. For example, it collects data from the financial industry and the medical industry to analyze commonalities and differences in anomalies. This makes it possible to refer to data from different industries and fields to identify commonalities and differences in anomalies.

[0083] The decision process analysis unit can use the emotion estimation function to monitor the user's emotion in real time when an anomaly is detected and provide an alert based on the emotion. The decision process analysis unit, for example, uses the emotion estimation function to monitor the user's emotion in real time when an anomaly is detected. For example, it analyzes the user's facial expression and voice and provides an alert based on the emotion. The decision process analysis unit also collects and analyzes data for monitoring emotions. For example, it collects the user's facial expression data and voice data and analyzes changes in emotion in real time. This makes it possible to use the emotion estimation function to monitor the user's emotion in real time when an anomaly is detected and provide an alert based on the emotion.

[0084] The decision-making process analysis unit can incorporate the opinions of external experts during continuous learning and improve the decision-making process. The decision-making process analysis unit, for example, incorporates the opinions of external experts during continuous learning. For example, the decision-making process is improved based on the opinions of the experts. The decision-making process analysis unit also includes an interface for collecting and analyzing the opinions of external experts. For example, the opinions of experts are collected and reflected in improvements to the decision-making process. This allows the opinion of external experts to be incorporated during continuous learning and improve the decision-making process.

[0085] The decision-making process analysis unit uses the emotion estimation function to collect the user's emotional reactions during the learning process and can make improvement suggestions based on the emotions. The decision-making process analysis unit, for example, uses the emotion estimation function to collect the user's emotional reactions during the learning process. For example, it analyzes the user's facial expressions and voice and makes improvement suggestions based on the emotions. The decision-making process analysis unit also collects data for collecting and analyzing the emotional reactions. For example, it collects the user's facial expression data and voice data and analyzes the emotional reactions. In this way, it is possible to collect the user's emotional reactions during the learning process using the emotion estimation function and make improvement suggestions based on the emotions.

[0086] The decision-making process analysis unit expands the scope of continuous learning to include not only AI but also human decision-making processes, making it possible to make comprehensive improvements. The decision-making process analysis unit expands the scope of continuous learning to include not only AI but also human decision-making processes. For example, it can simultaneously learn about AI and human decision-making processes and make comprehensive improvements. The decision-making process analysis unit also collects and analyzes data for learning about AI and human decision-making processes. For example, it can collect AI and human decision-making data and make comprehensive improvements. This allows it to expand the scope of continuous learning to include not only AI but also human decision-making processes, making it possible to make comprehensive improvements.

[0087] The decision-making process analysis unit can refer to data from different industries and fields during continuous learning and identify common areas for improvement. The decision-making process analysis unit, for example, refers to data from different industries and fields during continuous learning. For example, data from the financial industry and the medical industry can be compared to identify common areas for improvement. The decision-making process analysis unit also has an interface for collecting and analyzing data from different industries and fields. For example, data from the financial industry and the medical industry can be collected and analyzed to identify common areas for improvement. This makes it possible to refer to data from different industries and fields during continuous learning and identify common areas for improvement.

[0088] The decision-making process analysis unit can use the emotion estimation function to monitor the user's emotions during the learning process in real time and provide feedback based on the emotions. The decision-making process analysis unit, for example, uses the emotion estimation function to monitor the user's emotions during the learning process in real time. For example, it analyzes the user's facial expressions and voice and provides feedback based on the emotions. The decision-making process analysis unit also collects and analyzes data for monitoring emotions. For example, it collects the user's facial expression data and voice data and analyzes changes in emotions in real time. This makes it possible to use the emotion estimation function to monitor the user's emotions during the learning process in real time and provide feedback based on the emotions.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When analyzing an AI's decision-making process, the decision-making process analysis unit can compare the performance of different algorithms and models and select the optimal model. For example, the decision-making process analysis unit compares the performance of a machine learning model and a deep learning model to evaluate which one makes more accurate decisions. The decision-making process analysis unit also collects and analyzes data to compare the performance of different algorithms. For example, it collects and compares and analyzes the decision results using different algorithms. This makes it possible to compare the performance of different algorithms and models and select the optimal model.

[0091] When analyzing the decision-making process of an AI, the decision-making process analysis unit can evaluate the reliability of the data used in the decision and ensure data quality. For example, the decision-making process analysis unit analyzes the source and collection method of the data and evaluates its reliability. The decision-making process analysis unit also sets standards for evaluating the reliability of the data and ensures data quality. For example, it evaluates the consistency and accuracy of the data and eliminates unreliable data. This makes it possible to evaluate the reliability of the data used in the decision and ensure data quality.

[0092] When analyzing the AI's decision-making process, the decision-making process analysis unit can detect potential biases that may affect decisions and minimize the impact of those biases. For example, the decision-making process analysis unit analyzes biases contained in a dataset and evaluates the impact of those biases. The decision-making process analysis unit also uses an algorithm for detecting bias to analyze the impact of bias at each step of the decision-making process. For example, it can detect biases based on specific attributes and propose measures to minimize their impact. This makes it possible to detect potential biases that may affect decisions and minimize their impact.

[0093] The decision-making process analysis unit uses the emotion estimation function to analyze changes in the user's emotions during the decision-making process and can evaluate the impact of emotional influences on the decision. For example, it analyzes the user's facial expressions and voice and evaluates the impact of changes in emotions on the decision. The decision-making process analysis unit also uses the emotion estimation function to collect and analyze data for analyzing changes in emotions. For example, it collects facial expression data and voice data of the user and analyzes changes in emotions. This allows the emotion estimation function to analyze changes in the user's emotions during the decision-making process and evaluate the impact of emotional influences on the decision.

[0094] The decision-making process analysis unit analyzes not only AI but also human decision-making processes, and can perform comparative analysis of AI and human decision-making. For example, it can compare decisions made by AI and humans using the same data set and analyze the differences in decisions. The decision-making process analysis unit also collects and analyzes data for comparative analysis of AI and human decision-making processes. For example, it can collect and compare data on decisions made by AI and humans. This makes it possible to compare and analyze the decision-making processes of AI and humans and clarify the differences in decisions.

[0095] The decision-making process analysis unit can simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences. For example, it can compare AI decision-making processes in the financial and medical industries, revealing similarities and differences. The decision-making process analysis unit also collects and analyzes data to analyze AI decision-making processes in different industries and fields. For example, it can collect data from the financial and medical industries and analyze the similarities and differences. This allows it to simultaneously analyze AI decision-making processes in different industries and fields, revealing similarities and differences.

[0096] The decision-making process analysis unit can use the emotion estimation function to monitor the user's emotions during the decision-making process in real time and provide feedback based on the emotions. For example, it can analyze the user's facial expressions and voice and provide feedback based on the emotions. The decision-making process analysis unit also uses the emotion estimation function to collect and analyze data for monitoring emotions. For example, it can collect the user's facial expression data and voice data and analyze changes in emotions in real time. This allows the emotion estimation function to monitor the user's emotions during the decision-making process in real time and provide feedback based on the emotions.

[0097] When providing the analysis results, the result providing unit can add a detailed explanation for each step of the decision-making process to increase transparency. For example, each step, such as data preprocessing, feature extraction, model application, and result interpretation, is explained in detail. The result providing unit also has an interface for providing the analysis results. For example, it has a dashboard and report generation function for providing the analysis results to company administrators. This allows the result providing unit to add a detailed explanation for each step of the decision-making process to increase transparency when providing the analysis results.

[0098] When providing the analysis results, the result providing unit can perform a comparative analysis with similar past judgments and evaluate the consistency of the judgment. For example, the result providing unit compares the past judgment results with the current judgment results and evaluates the consistency. The result providing unit also collects and analyzes data for the comparative analysis with similar past judgments. For example, the result providing unit collects past judgment data and compares it with the current judgment data. In this way, when providing the analysis results, the result providing unit can perform a comparative analysis with similar past judgments and evaluate the consistency of the judgment.

[0099] The result providing unit can use the emotion estimation function to collect the emotional reactions of the company administrator to the analysis results and evaluate the degree of understanding and satisfaction of the results. For example, it can analyze the facial expressions and voice of the administrator and evaluate the degree of understanding and satisfaction of the results. The result providing unit also uses the emotion estimation function to collect and analyze data for collecting emotional reactions. For example, it can collect facial expression data and voice data of the administrator and analyze the emotional reactions. In this way, it can use the emotion estimation function to collect the emotional reactions of the company administrator to the analysis results and evaluate the degree of understanding and satisfaction of the results.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The decision-making process analysis unit analyzes the decision-making process made by other AIs. For example, it analyzes the process in detail to determine what data the AI ​​used to make a decision when processing a customer complaint. The decision-making process analysis unit also evaluates whether the decision was appropriate based on data related to the decision made by the AI ​​and information about that process. Step 2: The result provider provides the results of the decision-making process analyzed by the decision-making process analysis unit to the company administrator. For example, based on the analysis results, it may evaluate the decision as "appropriate" and generate a report that explains the reasons and grounds in detail. The result provider also has an interface for providing the analysis results to the company administrator. Step 3: The visualization part visualizes the decision-making process. For example, it visually displays the process using flowcharts and graphs to show which data the AI ​​analyzed and how it made its decision. It also displays each step of the decision-making process in detail, allowing corporate managers to intuitively understand it.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with surveillance AI, The monitoring AI is A decision-making process analysis unit that analyzes the decision-making processes made by other AIs, a result providing unit that provides a result of the decision-making process analyzed by the decision-making process analysis unit to a company administrator; a visualization unit that visualizes the judgment process. A system characterized by:

2. The decision process analysis unit Analyzing the user's emotional changes during the decision-making process and evaluating the impact of emotional influences on the decision-making process.

2. The system of claim 1.

3. The result providing unit When providing analysis results, provide detailed explanations for each step of the decision-making process to enhance transparency.

2. The system of claim 1.

4. The visualization unit In the visualization of the decision-making process, the interdependencies between the steps of the process are displayed in detail, allowing the complex decision-making process to be intuitively understood.

2. The system of claim 1.

5. The decision process analysis unit Analyzing users' emotional reactions when anomalies are detected and proposing measures to reduce emotional stress 2. The system of claim 1.

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