System

Abstract

An object of a system according to an embodiment is to optimize data utilization of an entire organization by sharing and integrating data and enabling data governance.SOLUTION: A system according to an embodiment includes a data standardization unit, a data integration unit, a data governance unit, a visualization unit, an analysis unit, and a decision-making support unit. The data-standardizing unit uses the generated AI to standardize the date. The data integration unit integrates the pieces of data standardized by the data standardization unit. The data governance unit performs data governance based on the data integrated by the data integration unit. The visualization unit visualizes the data managed by the data governance unit. The analysis unit analyzes the data visualized by the visualization unit. The decision-making support unit supports decision making based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not allow for information sharing between organizations or standardization of data, leaving room for improvement in the accuracy and security of data management.

[0005] The system according to the embodiment aims to optimize data utilization throughout the organization by standardizing and integrating data and implementing data governance. [Means for solving the problem]

[0006] The system according to the embodiment includes a data standardization unit, a data integration unit, a data governance unit, a visualization unit, an analysis unit, and a decision support unit. The data standardization unit standardizes data using a generative AI. The data integration unit integrates the data standardized by the data standardization unit. The data governance unit performs data governance based on the data integrated by the data integration unit. The visualization unit visualizes the data managed by the data governance unit. The analysis unit analyzes the data visualized by the visualization unit. The decision support unit supports decision-making based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize data utilization throughout the organization by standardizing and integrating data and implementing effective data governance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The data utilization system according to the embodiment of the present invention is a system that uses generative AI to standardize, integrate, govern, visualize, analyze, and support decision-making of data, thereby improving the efficiency of data management throughout the company and its accuracy and security.

[0029] The data utilization system according to the embodiment includes a data standardization unit, a data integration unit, a data governance unit, a visualization unit, an analysis unit, and a decision-making support unit. The data standardization unit standardizes data using a generation AI. For example, the generation AI analyzes data provided by each department and converts it into a unified format. The generation AI can also normalize the data to ensure data consistency. The data integration unit integrates the data standardized by the data standardization unit. For example, the generation AI aggregates the standardized data into a single database that all employees can access. The generation AI can also check the consistency of the data to ensure data accuracy. The data governance unit performs data governance based on the data integrated by the data integration unit. For example, the generation AI sets data access permissions and restricts data viewing and editing according to specific departments or positions. The generation AI can also perform data quality control to maintain data accuracy. The visualization unit visualizes the data managed by the data governance unit. For example, the generation AI converts sales data into graphs and charts to display them visually. The generative AI can also analyze data trends and patterns and provide them to users. The analysis unit analyzes the data visualized by the visualization unit. For example, the generative AI performs statistical analysis to identify data trends. The generative AI can also apply machine learning models to make data predictions. The decision-making support unit supports decision-making based on the data analyzed by the analysis unit. For example, the generative AI can analyze past data and propose optimal strategies. The generative AI can also perform scenario analysis to evaluate the risks of decision-making. This allows the data utilization system to consistently standardize, integrate, govern, visualize, analyze, and support decisions about data. For example, data consistency and accuracy are improved, enabling faster and more accurate decision-making. Data visualization also makes it easier for users to intuitively understand the data. Furthermore, data governance promotes appropriate use of data.

[0030] The Data Standardization Department can calculate a reliability score for each piece of data and prioritize the integration of highly reliable data. For example, the Data Standardization Department has the generation AI calculate a reliability score for data collected from each department and prioritize the integration of highly reliable data. For example, it evaluates reliability based on the source of the data and how often it is updated. The generation AI also calculates a reliability score for the data and filters out low-reliability data. For example, it detects missing or inconsistent data and excludes low-reliability data. The data integration process is also optimized based on the reliability score. For example, it prioritizes the integration of highly reliable data and postpones the integration of low-reliability data. In this way, the accuracy of the data is improved by prioritizing the integration of highly reliable data.

[0031] The data standardization unit can add information about the source and creator of the data as metadata to ensure data traceability. In the data standardization unit, for example, the generation AI adds information about the source and creator of the data as metadata to ensure data traceability. For example, the creation date and time of the data and update history are recorded. The metadata is also used to make the source and creator of the data traceable. For example, the source information is referenced to evaluate the reliability and accuracy of the data. The generation AI also automatically generates metadata to ensure data traceability. For example, the data change history and access history are recorded to make it traceable. In this way, data traceability is ensured by adding information about the source and creator of the data.

[0032] The Data Standardization Department can standardize data from different industries and fields, promoting cross-industry data utilization. For example, the Data Standardization Department uses generative AI to standardize data from different industries and promote cross-industry data utilization. For example, medical data and financial data can be integrated to create new business models. Furthermore, by standardizing data from different fields, new insights can be gained. For example, data from the manufacturing and service industries can be integrated to achieve efficient operations. Furthermore, generative AI can analyze and standardize data from different industries and fields. For example, data from the energy and transportation industries can be integrated to support sustainable urban planning. In this way, new business opportunities can be created by standardizing data from different industries and fields.

[0033] The data standardization department can introduce a system that checks data consistency in real time and makes corrections immediately. For example, the data standardization department introduces a system whereby generation AI checks data consistency in real time and makes corrections immediately. For example, it detects data inconsistencies and automatically corrects them. In addition, data consistency is checked in real time during the data standardization process. For example, duplicate or missing data is detected and corrected immediately. In addition, a system is built that checks data consistency in real time and makes corrections. For example, it checks consistency when data is updated and issues an alert if an inconsistency occurs. This ensures data consistency in real time and makes corrections immediately, improving data accuracy.

[0034] The data integration unit can automatically analyze data correlations and build optimal data models. In the data integration unit, for example, the generation AI automatically analyzes data correlations and builds optimal data models. For example, it analyzes the correlation between sales data and customer data and creates an integrated data model. It also optimizes data models by analyzing data correlations. For example, it builds an efficient inventory management model based on the correlation between inventory data and sales data. It also analyzes data correlations and automatically generates data models. For example, it analyzes the correlation between customer behavior data and marketing data and creates an effective marketing model. This analyzes data correlations and builds optimal data models, improving data consistency and accuracy.

[0035] The Data Integration Department can develop algorithms that automatically detect and resolve data duplication and inconsistencies between different data sources. For example, the Data Integration Department develops algorithms that allow the Generation AI to automatically detect and resolve data duplication and inconsistencies between different data sources. For example, integrating duplicate data for the same customer. The Data Integration Department also uses algorithms that detect data duplication and inconsistencies to ensure the consistency of the data model. For example, resolving inconsistencies in data provided by different departments. The Generation AI also develops algorithms that automatically correct data duplication and inconsistencies. For example, integrating duplicate data from different data sources to build a consistent data model. This automatically detects and resolves data duplication and inconsistencies, improving data consistency and accuracy.

[0036] The Data Integration Department aggregates data models across different companies, promoting data utilization across the industry. The Data Integration Department, for example, uses generative AI to aggregate data models across different companies, promoting data utilization across the industry. For example, it integrates data from companies in the same industry to understand industry-wide trends. It also creates new business opportunities by standardizing data from different companies and aggregating data models. For example, it performs market analysis based on data from competitors. It also uses generative AI to analyze data from different companies and build a common data model. For example, it integrates data from the entire supply chain to support efficient operations. In this way, it aggregates data models across different companies, promoting data utilization across the industry.

[0037] The data integration unit performs version control of the data model, making it easier to compare it with past data models. In the data integration unit, for example, the generation AI performs version control of the data model, making it easier to compare it with past data models. For example, it records the change history of the data model and performs comparative analysis. Furthermore, by performing version control of the data model, it makes the differences from past data models clear. For example, it visualizes changes based on the update history of the data model. Furthermore, the generation AI automates version control of the data model and supports comparison with past data models. For example, it displays different versions of the data model side by side and analyzes the differences. In this way, by performing version control of the data model, it becomes easier to compare it with past data models.

[0038] The data governance department can automatically generate data governance rules and customize them to meet the needs of each department. For example, the data governance department's generation AI automatically generates data governance rules and customizes them to meet the needs of each department. For example, it sets data usage policies for each department and grants appropriate access rights. It also analyzes the data usage needs of each department and customizes data governance rules based on that. For example, it sets different data access rights for the sales department and the engineering department. It also applies the data governance rules automatically generated by the generation AI to each department and customizes them to meet their needs. For example, it monitors data usage status for each department and adjusts the rules. In this way, the appropriate use of data is promoted by automatically generating data governance rules and customizing them to meet the needs of each department.

[0039] The Data Governance Department can introduce a mechanism to track data usage history and detect unauthorized use. For example, the Data Governance Department introduces a mechanism in which generation AI tracks data usage history and detects unauthorized use. For example, it detects abnormal data access patterns and issues alerts. It also develops algorithms that analyze data usage history and detect signs of unauthorized use. For example, it detects access from unusual times or locations. It also builds a mechanism in which generation AI monitors data usage history in real time and immediately detects unauthorized use. For example, it detects abnormal data operations and restricts access. This improves data security by tracking data usage history and detecting unauthorized use.

[0040] The Data Governance Department can compare data governance rules from different industries and regions and introduce best practices. For example, the Data Governance Department uses generative AI to analyze data governance rules from different industries and regions and introduce best practices. For example, it compares governance rules from the financial and healthcare industries and adopts the most appropriate rules. It also compares data governance rules from different regions and introduces the most appropriate rules. For example, it compares Europe's GDPR and the US CCPA and sets appropriate governance rules. It also uses generative AI to analyze data governance rules from different industries and regions and introduce common best practices. For example, it sets the most appropriate rules from the perspective of security and privacy protection. In this way, by comparing data governance rules from different industries and regions and introducing best practices, the quality of data governance is improved.

[0041] The data governance department can build a system that updates data governance rules in real time and responds to the latest security risks. The data governance department, for example, builds a system in which generative AI updates data governance rules in real time and responds to the latest security risks. For example, it updates the rules immediately when a new threat emerges. It also analyzes security risks in real time and updates data governance rules based on that. For example, it sets rules to respond to the latest attack methods. It also automatically updates data governance rules to respond to the latest security risks. For example, it immediately modifies rules when a new vulnerability is discovered. In this way, data security is improved by updating data governance rules in real time and responding to the latest security risks.

[0042] The visualization unit can perform customization according to the user's visual preferences. In the visualization unit, for example, the generation AI analyzes the user's visual preferences and customizes the data visualization based on them. For example, it automatically applies the colors and layout the user prefers. The data visualization is also customized according to the user's visual preferences. For example, it displays graphs and charts according to the theme and style selected by the user. The generation AI also learns the user's visual preferences and optimizes the data visualization based on them. For example, it suggests the optimal display format based on settings selected by the user in the past. This makes data visualization more effective by customizing according to the user's visual preferences.

[0043] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0044] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0045] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0046] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0047] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0048] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0049] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0050] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0051] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0052] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0053] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0054] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0055] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0056] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0057] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0058] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0059] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0060] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0061] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0062] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0063] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0064] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0065] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0066] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0067] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0068] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0069] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0070] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The data standardization unit can automatically translate and standardize data written in different languages. For example, the generation AI can analyze data provided in different languages, such as English, French, and Chinese, and convert it into a unified language. The generation AI can also take into account the nuances and context of the language to perform accurate translations. Furthermore, the generation AI can check the consistency of the translated data and make corrections as necessary. This standardization of data written in different languages ​​will promote international data utilization.

[0073] The data integration unit performs time-series analysis of data and can predict future trends from past data. For example, the generation AI performs time-series analysis of sales data to predict future sales trends. The generation AI can also perform time-series analysis of inventory data to forecast demand. Furthermore, the generation AI can perform time-series analysis of customer behavior data to support the optimization of marketing strategies. This makes it possible to predict future trends and make strategic decisions by performing time-series analysis of data.

[0074] The Data Governance Department manages the data lifecycle and can centrally manage the process from data generation to disposal. For example, the Generation AI adds metadata when data is generated and tracks the data lifecycle. The Generation AI can also monitor data usage and archive or discard data at the appropriate time. Furthermore, the Generation AI can strengthen data security measures based on data lifecycle management. In this way, data lifecycle management ensures the appropriate use and security of data.

[0075] The visualization unit can provide an interface to reduce the visual burden on the user when visualizing data. For example, the generation AI can automatically select a color palette that takes into consideration users with color vision deficiencies when visualizing data. The generation AI can also adjust the font size and layout to reduce the visual burden when visualizing data. Furthermore, the generation AI can provide an interface to reduce the visual burden on the user, making data visualization more intuitive. This reduces the visual burden on the user, making data visualization more effective.

[0076] The decision support unit can present multiple decision-making options based on different scenarios and compare the risks and benefits. For example, the generation AI can propose multiple sales strategies that take market fluctuations into account and compare the risks and benefits of each. The generation AI can also simulate different economic situations and propose optimal investment strategies. Furthermore, the generation AI can predict project progress and propose optimal resource allocation based on different scenarios. This allows the presentation of multiple decision-making options based on different scenarios to compare the risks and benefits and make optimal decisions.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The Data Standardization Department standardizes data using the Generation AI. For example, the Generation AI analyzes data provided by each department and converts it into a unified format. The Generation AI can also normalize the data to ensure data consistency. Step 2: The Data Integration Department integrates the data that has been standardized by the Data Standardization Department. For example, the Generation AI aggregates the standardized data into a single database that all employees can access. The Generation AI can also check the consistency of the data to ensure its accuracy. Step 3: The Data Governance Department performs data governance based on the data integrated by the Data Integration Department. For example, the Generation AI sets data access permissions and restricts data viewing and editing according to specific departments or positions. The Generation AI can also perform data quality control to maintain data accuracy. Step 4: The visualization department visualizes the data managed by the data governance department. For example, the generation AI converts sales data into graphs and charts and displays them visually. The generation AI can also analyze trends and patterns in the data and provide them to users. Step 5: The analysis unit analyzes the data visualized by the visualization unit. For example, the generation AI performs statistical analysis to identify trends in the data. The generation AI can also apply machine learning models to make predictions on the data. Step 6: The decision support unit supports decision-making based on the data analyzed by the analysis unit. For example, the generation AI analyzes past data and proposes optimal strategies. The generation AI can also perform scenario analysis and evaluate the risks of decision-making.

[0079] (Example 2) The data utilization system according to the embodiment of the present invention is a system that uses generative AI to standardize, integrate, govern, visualize, analyze, and support decision-making of data, thereby improving the efficiency of data management throughout the company and its accuracy and security.

[0080] The data utilization system according to the embodiment includes a data standardization unit, a data integration unit, a data governance unit, a visualization unit, an analysis unit, and a decision-making support unit. The data standardization unit standardizes data using a generation AI. For example, the generation AI analyzes data provided by each department and converts it into a unified format. The generation AI can also normalize the data to ensure data consistency. The data integration unit integrates the data standardized by the data standardization unit. For example, the generation AI aggregates the standardized data into a single database that all employees can access. The generation AI can also check the consistency of the data to ensure data accuracy. The data governance unit performs data governance based on the data integrated by the data integration unit. For example, the generation AI sets data access permissions and restricts data viewing and editing according to specific departments or positions. The generation AI can also perform data quality control to maintain data accuracy. The visualization unit visualizes the data managed by the data governance unit. For example, the generation AI converts sales data into graphs and charts to display them visually. The generative AI can also analyze data trends and patterns and provide them to users. The analysis unit analyzes the data visualized by the visualization unit. For example, the generative AI performs statistical analysis to identify data trends. The generative AI can also apply machine learning models to make data predictions. The decision-making support unit supports decision-making based on the data analyzed by the analysis unit. For example, the generative AI can analyze past data and propose optimal strategies. The generative AI can also perform scenario analysis to evaluate the risks of decision-making. This allows the data utilization system to consistently standardize, integrate, govern, visualize, analyze, and support decisions about data. For example, data consistency and accuracy are improved, enabling faster and more accurate decision-making. Data visualization also makes it easier for users to intuitively understand the data. Furthermore, data governance promotes appropriate use of data.

[0081] The Data Standardization Department can calculate a reliability score for each piece of data and prioritize the integration of highly reliable data. For example, the Data Standardization Department has the generation AI calculate a reliability score for data collected from each department and prioritize the integration of highly reliable data. For example, it evaluates reliability based on the source of the data and how often it is updated. The generation AI also calculates a reliability score for the data and filters out low-reliability data. For example, it detects missing or inconsistent data and excludes low-reliability data. The data integration process is also optimized based on the reliability score. For example, it prioritizes the integration of highly reliable data and postpones the integration of low-reliability data. In this way, the accuracy of the data is improved by prioritizing the integration of highly reliable data.

[0082] The data standardization unit can add information about the source and creator of the data as metadata to ensure data traceability. In the data standardization unit, for example, the generation AI adds information about the source and creator of the data as metadata to ensure data traceability. For example, the creation date and time of the data and update history are recorded. The metadata is also used to make the source and creator of the data traceable. For example, the source information is referenced to evaluate the reliability and accuracy of the data. The generation AI also automatically generates metadata to ensure data traceability. For example, the data change history and access history are recorded to make it traceable. In this way, data traceability is ensured by adding information about the source and creator of the data.

[0083] The data commonization unit can use the emotion estimation function to analyze the user's emotions regarding data commonization and build a feedback loop to elicit positive emotions. The data commonization unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the data commonization process in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a feedback loop to elicit positive emotions based on the user's emotion data. For example, if the user's emotions are negative, it displays an encouraging message. It also uses the emotion estimation data to improve the data commonization process. For example, it identifies areas for improvement in the process based on the user's emotional response and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and eliciting positive emotions, acceptance of the data commonization process is improved.

[0084] The Data Standardization Department can standardize data from different industries and fields, promoting cross-industry data utilization. For example, the Data Standardization Department uses generative AI to standardize data from different industries and promote cross-industry data utilization. For example, medical data and financial data can be integrated to create new business models. Furthermore, by standardizing data from different fields, new insights can be gained. For example, data from the manufacturing and service industries can be integrated to achieve efficient operations. Furthermore, generative AI can analyze and standardize data from different industries and fields. For example, data from the energy and transportation industries can be integrated to support sustainable urban planning. In this way, new business opportunities can be created by standardizing data from different industries and fields.

[0085] The data standardization department can introduce a system that checks data consistency in real time and makes corrections immediately. For example, the data standardization department introduces a system whereby generation AI checks data consistency in real time and makes corrections immediately. For example, it detects data inconsistencies and automatically corrects them. In addition, data consistency is checked in real time during the data standardization process. For example, duplicate or missing data is detected and corrected immediately. In addition, a system is built that checks data consistency in real time and makes corrections. For example, it checks consistency when data is updated and issues an alert if an inconsistency occurs. This ensures data consistency in real time and makes corrections immediately, improving data accuracy.

[0086] The data commonization unit can use the emotion estimation function to monitor the user's stress level during the data commonization process and provide an interface for reducing stress. The data commonization unit, for example, uses the emotion estimation function to monitor the user's stress level during the data commonization process in real time. For example, it analyzes the user's facial expressions and voice to calculate a stress score. It also provides an interface for reducing stress based on the user's stress level. For example, it displays designs and messages that help the user relax. It also uses the emotion estimation data to improve the data commonization process. For example, if the user's stress level is high, it makes suggestions to simplify the process. In this way, by monitoring the user's stress level and providing an interface for reducing stress, acceptance of the data commonization process is improved.

[0087] The data integration unit can automatically analyze data correlations and build optimal data models. In the data integration unit, for example, the generation AI automatically analyzes data correlations and builds optimal data models. For example, it analyzes the correlation between sales data and customer data and creates an integrated data model. It also optimizes data models by analyzing data correlations. For example, it builds an efficient inventory management model based on the correlation between inventory data and sales data. It also analyzes data correlations and automatically generates data models. For example, it analyzes the correlation between customer behavior data and marketing data and creates an effective marketing model. This analyzes data correlations and builds optimal data models, improving data consistency and accuracy.

[0088] The Data Integration Department can develop algorithms that automatically detect and resolve data duplication and inconsistencies between different data sources. For example, the Data Integration Department develops algorithms that allow the Generation AI to automatically detect and resolve data duplication and inconsistencies between different data sources. For example, integrating duplicate data for the same customer. The Data Integration Department also uses algorithms that detect data duplication and inconsistencies to ensure the consistency of the data model. For example, resolving inconsistencies in data provided by different departments. The Generation AI also develops algorithms that automatically correct data duplication and inconsistencies. For example, integrating duplicate data from different data sources to build a consistent data model. This automatically detects and resolves data duplication and inconsistencies, improving data consistency and accuracy.

[0089] The data integration unit can use the emotion estimation function to analyze the user's emotions regarding the data model aggregation and propose a data model that is easy for the user to accept. The data integration unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the data model aggregation in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also proposes a data model that is easy for the user to accept based on the user's emotion data. For example, it preferentially proposes data models that have a positive user emotion. It also uses the emotion estimation data to improve the data model aggregation process. For example, it identifies areas for improvement in the data model based on the user's emotional response and makes proposals that are easy for the user to accept. In this way, by analyzing the user's emotions and proposing a data model that is easy for the user to accept, acceptance of the data model is improved.

[0090] The Data Integration Department aggregates data models across different companies, promoting data utilization across the industry. The Data Integration Department, for example, uses generative AI to aggregate data models across different companies, promoting data utilization across the industry. For example, it integrates data from companies in the same industry to understand industry-wide trends. It also creates new business opportunities by standardizing data from different companies and aggregating data models. For example, it performs market analysis based on data from competitors. It also uses generative AI to analyze data from different companies and build a common data model. For example, it integrates data from the entire supply chain to support efficient operations. In this way, it aggregates data models across different companies, promoting data utilization across the industry.

[0091] The data integration unit performs version control of the data model, making it easier to compare it with past data models. In the data integration unit, for example, the generation AI performs version control of the data model, making it easier to compare it with past data models. For example, it records the change history of the data model and performs comparative analysis. Furthermore, by performing version control of the data model, it makes the differences from past data models clear. For example, it visualizes changes based on the update history of the data model. Furthermore, the generation AI automates version control of the data model and supports comparison with past data models. For example, it displays different versions of the data model side by side and analyzes the differences. In this way, by performing version control of the data model, it becomes easier to compare it with past data models.

[0092] The data integration unit can use the emotion estimation function to monitor user satisfaction in the data model aggregation process in real time and make improvements to improve satisfaction. The data integration unit, for example, uses the emotion estimation function to monitor user satisfaction in the data model aggregation process in real time. For example, it analyzes the user's facial expressions and voice to calculate a satisfaction score. It also improves the data model aggregation process based on the user satisfaction data. For example, if satisfaction is low, it identifies areas for improvement in the process and takes measures. It also uses the emotion estimation data to build a feedback loop to improve user satisfaction. For example, it prioritizes the adoption of processes that result in high user satisfaction. In this way, it is possible to monitor user satisfaction in real time and make improvements to improve satisfaction, thereby improving data model acceptance.

[0093] The data governance department can automatically generate data governance rules and customize them to meet the needs of each department. For example, the data governance department's generation AI automatically generates data governance rules and customizes them to meet the needs of each department. For example, it sets data usage policies for each department and grants appropriate access rights. It also analyzes the data usage needs of each department and customizes data governance rules based on that. For example, it sets different data access rights for the sales department and the engineering department. It also applies the data governance rules automatically generated by the generation AI to each department and customizes them to meet their needs. For example, it monitors data usage status for each department and adjusts the rules. In this way, the appropriate use of data is promoted by automatically generating data governance rules and customizing them to meet the needs of each department.

[0094] The Data Governance Department can introduce a mechanism to track data usage history and detect unauthorized use. For example, the Data Governance Department introduces a mechanism in which generation AI tracks data usage history and detects unauthorized use. For example, it detects abnormal data access patterns and issues alerts. It also develops algorithms that analyze data usage history and detect signs of unauthorized use. For example, it detects access from unusual times or locations. It also builds a mechanism in which generation AI monitors data usage history in real time and immediately detects unauthorized use. For example, it detects abnormal data operations and restricts access. This improves data security by tracking data usage history and detecting unauthorized use.

[0095] The data governance unit can use the emotion estimation function to analyze user emotions toward data governance and propose governance rules to elicit positive emotions. The data governance unit, for example, uses the emotion estimation function to analyze user emotions toward data governance in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also proposes governance rules to elicit positive emotions based on the user's emotion data. For example, it preferentially adopts rules that elicit positive emotions from the user. It also uses the emotion estimation data to improve the data governance rules. For example, it identifies areas for improvement in the rules based on the user's emotional reactions and takes measures to elicit positive emotions. In this way, by analyzing user emotions and proposing governance rules to elicit positive emotions, acceptance of data governance is improved.

[0096] The Data Governance Department can compare data governance rules from different industries and regions and introduce best practices. For example, the Data Governance Department uses generative AI to analyze data governance rules from different industries and regions and introduce best practices. For example, it compares governance rules from the financial and healthcare industries and adopts the most appropriate rules. It also compares data governance rules from different regions and introduces the most appropriate rules. For example, it compares Europe's GDPR and the US CCPA and sets appropriate governance rules. It also uses generative AI to analyze data governance rules from different industries and regions and introduce common best practices. For example, it sets the most appropriate rules from the perspective of security and privacy protection. In this way, by comparing data governance rules from different industries and regions and introducing best practices, the quality of data governance is improved.

[0097] The data governance department can build a system that updates data governance rules in real time and responds to the latest security risks. The data governance department, for example, builds a system in which generative AI updates data governance rules in real time and responds to the latest security risks. For example, it updates the rules immediately when a new threat emerges. It also analyzes security risks in real time and updates data governance rules based on that. For example, it sets rules to respond to the latest attack methods. It also automatically updates data governance rules to respond to the latest security risks. For example, it immediately modifies rules when a new vulnerability is discovered. In this way, data security is improved by updating data governance rules in real time and responding to the latest security risks.

[0098] The data governance unit can use the emotion estimation function to monitor user reactions to data governance rules and make improvements to improve the acceptability of the rules. The data governance unit, for example, uses the emotion estimation function to monitor user reactions to data governance rules in real time. For example, it analyzes the user's facial expressions and voice to calculate an emotion score. Furthermore, it makes improvements to improve the acceptability of the rules based on the user's emotion data. For example, it modifies rules for which the user's emotion is negative. Furthermore, it uses the emotion estimation data to improve the data governance rules. For example, it identifies areas for improvement in the rules based on the user's emotional reactions and proposes rules that are more acceptable. In this way, by monitoring user reactions and making improvements to improve the acceptability of the rules, the quality of data governance is improved.

[0099] The visualization unit can perform customization according to the user's visual preferences. In the visualization unit, for example, the generation AI analyzes the user's visual preferences and customizes the data visualization based on them. For example, it automatically applies the colors and layout the user prefers. The data visualization is also customized according to the user's visual preferences. For example, it displays graphs and charts according to the theme and style selected by the user. The generation AI also learns the user's visual preferences and optimizes the data visualization based on them. For example, it suggests the optimal display format based on settings selected by the user in the past. This makes data visualization more effective by customizing according to the user's visual preferences.

[0100] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0101] The visualization unit can use the emotion estimation function to analyze the user's emotions regarding the data visualization and propose a visual design that elicits positive emotions. The visualization unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the data visualization in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also proposes a visual design that elicits positive emotions based on the user's emotion data. For example, it preferentially adopts designs that elicit positive emotions from the user. It also uses the emotion estimation data to improve the data visualization process. For example, it identifies areas for improvement in the visual design based on the user's emotional response and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and proposing a visual design that elicits positive emotions, the data visualization becomes more effective.

[0102] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0103] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0104] The visualization unit can use the emotion estimation function to monitor user emotions in the data visualization process in real time and make improvements to improve the user experience. The visualization unit, for example, uses the emotion estimation function to monitor user emotions in the data visualization process in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The visualization unit also improves the data visualization process based on the user's emotion data. For example, if the user's emotion is negative, it modifies the visualization design. The emotion estimation data is also used to build a feedback loop to improve the user experience. For example, it identifies areas for improvement in the visualization process based on the user's emotional response and takes measures. In this way, by monitoring user emotions in real time and making improvements to improve the user experience, the data visualization becomes more effective.

[0105] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0106] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0107] The decision support unit can use the emotion estimation function to analyze the user's emotions regarding decision-making and operations and provide support to elicit positive emotions. The decision support unit, for example, uses the emotion estimation function to analyze the user's emotions regarding decision-making and operations in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also provides support to elicit positive emotions based on the user's emotion data. For example, if the user's emotions are positive, it makes suggestions to support decision-making. It also uses the emotion estimation data to improve support for decision-making and operations. For example, it identifies areas for improvement in support based on the user's emotional reactions and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and providing support to elicit positive emotions, the quality of decision-making and operations is improved.

[0108] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0109] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0110] The decision support unit can use the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time and make improvements to reduce the user's stress. The decision support unit, for example, uses the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also makes improvements to reduce stress based on the user's emotion data. For example, if the user's emotion is negative, it makes suggestions to simplify the process. It also uses the emotion estimation data to improve the decision-making and operation processes. For example, it identifies areas to improve the process based on the user's emotional response and takes measures to reduce stress. In this way, by monitoring the user's emotions in real time and making improvements to reduce the user's stress, the quality of decision-making and operations is improved.

[0111] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0112] The visualization unit can use the emotion estimation function to analyze the user's emotions regarding the data visualization and propose a visual design that elicits positive emotions. The visualization unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the data visualization in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also proposes a visual design that elicits positive emotions based on the user's emotion data. For example, it preferentially adopts designs that elicit positive emotions from the user. It also uses the emotion estimation data to improve the data visualization process. For example, it identifies areas for improvement in the visual design based on the user's emotional response and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and proposing a visual design that elicits positive emotions, the data visualization becomes more effective.

[0113] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0114] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0115] The visualization unit can use the emotion estimation function to monitor user emotions in the data visualization process in real time and make improvements to improve the user experience. The visualization unit, for example, uses the emotion estimation function to monitor user emotions in the data visualization process in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The visualization unit also improves the data visualization process based on the user's emotion data. For example, if the user's emotion is negative, it modifies the visualization design. The emotion estimation data is also used to build a feedback loop to improve the user experience. For example, it identifies areas for improvement in the visualization process based on the user's emotional response and takes measures. In this way, by monitoring user emotions in real time and making improvements to improve the user experience, the data visualization becomes more effective.

[0116] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0117] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0118] The decision support unit can use the emotion estimation function to analyze the user's emotions regarding decision-making and operations and provide support to elicit positive emotions. The decision support unit, for example, uses the emotion estimation function to analyze the user's emotions regarding decision-making and operations in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also provides support to elicit positive emotions based on the user's emotion data. For example, if the user's emotions are positive, it makes suggestions to support decision-making. It also uses the emotion estimation data to improve support for decision-making and operations. For example, it identifies areas for improvement in support based on the user's emotional reactions and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and providing support to elicit positive emotions, the quality of decision-making and operations is improved.

[0119] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0120] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0121] The decision support unit can use the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time and make improvements to reduce the user's stress. The decision support unit, for example, uses the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also makes improvements to reduce stress based on the user's emotion data. For example, if the user's emotion is negative, it makes suggestions to simplify the process. It also uses the emotion estimation data to improve the decision-making and operation processes. For example, it identifies areas to improve the process based on the user's emotional response and takes measures to reduce stress. In this way, by monitoring the user's emotions in real time and making improvements to reduce the user's stress, the quality of decision-making and operations is improved.

[0122] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0123] The visualization unit can use the emotion estimation function to analyze the user's emotions regarding the data visualization and propose a visual design that elicits positive emotions. The visualization unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the data visualization in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also proposes a visual design that elicits positive emotions based on the user's emotion data. For example, it preferentially adopts designs that elicit positive emotions from the user. It also uses the emotion estimation data to improve the data visualization process. For example, it identifies areas for improvement in the visual design based on the user's emotional response and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and proposing a visual design that elicits positive emotions, the data visualization becomes more effective.

[0124] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0125] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0126] The visualization unit can use the emotion estimation function to monitor user emotions in the data visualization process in real time and make improvements to improve the user experience. The visualization unit, for example, uses the emotion estimation function to monitor user emotions in the data visualization process in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The visualization unit also improves the data visualization process based on the user's emotion data. For example, if the user's emotion is negative, it modifies the visualization design. The emotion estimation data is also used to build a feedback loop to improve the user experience. For example, it identifies areas for improvement in the visualization process based on the user's emotional response and takes measures. In this way, by monitoring user emotions in real time and making improvements to improve the user experience, the data visualization becomes more effective.

[0127] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0128] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0129] The decision support unit can use the emotion estimation function to analyze the user's emotions regarding decision-making and operations and provide support to elicit positive emotions. The decision support unit, for example, uses the emotion estimation function to analyze the user's emotions regarding decision-making and operations in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also provides support to elicit positive emotions based on the user's emotion data. For example, if the user's emotions are positive, it makes suggestions to support decision-making. It also uses the emotion estimation data to improve support for decision-making and operations. For example, it identifies areas for improvement in support based on the user's emotional reactions and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and providing support to elicit positive emotions, the quality of decision-making and operations is improved.

[0130] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0131] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0132] The decision support unit can use the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time and make improvements to reduce the user's stress. The decision support unit, for example, uses the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also makes improvements to reduce stress based on the user's emotion data. For example, if the user's emotion is negative, it makes suggestions to simplify the process. It also uses the emotion estimation data to improve the decision-making and operation processes. For example, it identifies areas to improve the process based on the user's emotional response and takes measures to reduce stress. In this way, by monitoring the user's emotions in real time and making improvements to reduce the user's stress, the quality of decision-making and operations is improved.

[0133] The visualization unit can introduce a mechanism that automatically detects abnormal values ​​in the data and issues an alert. For example, the visualization unit introduces a mechanism where the generation AI automatically detects abnormal values ​​in the data and issues an alert. For example, an alert is displayed if there is an abnormal fluctuation in sales data. In addition, an algorithm is developed to detect abnormal values ​​in the data, and the generation AI automatically issues an alert. For example, a notification is sent if an abnormal value is detected in inventory data. In addition, a mechanism is built where the generation AI monitors abnormal values ​​in the data in real time and immediately issues an alert when an abnormality occurs. For example, it detects abnormal transactions and issues an alert. In this way, by automatically detecting abnormal values ​​in the data and issuing an alert, early detection of anomalies becomes possible.

[0134] The visualization unit can use the emotion estimation function to analyze the user's emotions regarding the data visualization and propose a visual design that elicits positive emotions. The visualization unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the data visualization in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also proposes a visual design that elicits positive emotions based on the user's emotion data. For example, it preferentially adopts designs that elicit positive emotions from the user. It also uses the emotion estimation data to improve the data visualization process. For example, it identifies areas for improvement in the visual design based on the user's emotional response and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and proposing a visual design that elicits positive emotions, the data visualization becomes more effective.

[0135] The visualization unit can visualize data optimized for different devices. For example, the generation AI in the visualization unit visualizes data optimized for different devices. For example, it automatically generates compact graphs and charts for smartphones. It also selects the optimal display format for each device and visualizes the data. For example, it provides interactive graphs and charts for tablets. The generation AI also analyzes the characteristics of the device and optimizes the data visualization based on that. For example, it automatically generates a detailed data display for PCs. This improves user convenience by visualizing data optimized for different devices.

[0136] The visualization unit can add interactive elements to make it easier for users to manipulate data. For example, the generation AI adds interactive elements to data visualization to make it easier for users to manipulate data. For example, it adds zoom and filtering functions to graphs and charts. Interactive elements can also be used to allow users to intuitively manipulate data. For example, it can provide a function to move data points by drag and drop. The generation AI can also develop interactive data visualization tools to make it easier for users to manipulate data. For example, it can add a function to display detailed information by clicking. In this way, adding interactive elements makes it easier for users to manipulate data.

[0137] The visualization unit can use the emotion estimation function to monitor user emotions in the data visualization process in real time and make improvements to improve the user experience. The visualization unit, for example, uses the emotion estimation function to monitor user emotions in the data visualization process in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The visualization unit also improves the data visualization process based on the user's emotion data. For example, if the user's emotion is negative, it modifies the visualization design. The emotion estimation data is also used to build a feedback loop to improve the user experience. For example, it identifies areas for improvement in the visualization process based on the user's emotional response and takes measures. In this way, by monitoring user emotions in real time and making improvements to improve the user experience, the data visualization becomes more effective.

[0138] The decision support unit can propose optimal options based on past successes and failures. In the decision support unit, for example, the generative AI analyzes past successes and failures to propose optimal options when making decisions. For example, it proposes optimal sales strategies based on past data. The generative AI also supports decision-making based on past cases. For example, it analyzes the factors that led to success and failures in past projects to propose optimal project plans. The generative AI also learns from past successes and failures to propose optimal options when making decisions. For example, it proposes optimal campaign strategies based on data from past marketing campaigns. This improves the quality of decision-making by proposing optimal options based on past successes and failures.

[0139] The decision support unit can monitor the progress of work in real time and propose necessary actions. In the decision support unit, for example, the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the progress of a project and proposes countermeasures if a delay occurs. It also monitors the progress of work in real time and the generation AI proposes necessary actions. For example, it analyzes the inventory management status and proposes replenishment if a shortage occurs. It also builds a system in which the generation AI monitors the progress of work in real time and proposes necessary actions. For example, it analyzes the operating status of a manufacturing line and proposes an efficient production plan. This allows the progress of work to be monitored in real time and proposes necessary actions, improving work efficiency.

[0140] The decision support unit can use the emotion estimation function to analyze the user's emotions regarding decision-making and operations and provide support to elicit positive emotions. The decision support unit, for example, uses the emotion estimation function to analyze the user's emotions regarding decision-making and operations in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also provides support to elicit positive emotions based on the user's emotion data. For example, if the user's emotions are positive, it makes suggestions to support decision-making. It also uses the emotion estimation data to improve support for decision-making and operations. For example, it identifies areas for improvement in support based on the user's emotional reactions and takes measures to elicit positive emotions. In this way, by analyzing the user's emotions and providing support to elicit positive emotions, the quality of decision-making and operations is improved.

[0141] The decision support unit can compare decision-making processes across different industries and fields and introduce best practices. For example, the decision support unit uses generative AI to analyze decision-making processes across different industries and fields and introduce best practices. For example, it compares decision-making processes between the manufacturing and service industries and adopts the optimal process. It also compares decision-making processes across different industries and uses generative AI to suggest best practices. For example, it analyzes decision-making processes between the financial and medical industries and adopts the optimal process. It also analyzes decision-making processes across different fields and introduces common best practices. For example, it compares decision-making processes between the technical and marketing fields and adopts the optimal process. In this way, by comparing decision-making processes across different industries and fields and introducing best practices, the quality of decision-making is improved.

[0142] The decision-making support unit can automatically set task priorities and support efficient business execution. In the decision-making support unit, for example, the generation AI automatically sets task priorities in support of operations. For example, it determines task priorities based on the importance and urgency of the work. The generation AI also automatically sets task priorities and supports efficient business execution. For example, it analyzes the progress of a project and suggests tasks that should be addressed as a priority. The generation AI also sets task priorities in real time, building a system that supports efficient business execution. For example, it analyzes resource usage and suggests optimal task allocation. This automatically sets task priorities and supports efficient business execution, thereby improving business efficiency.

[0143] The decision support unit can use the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time and make improvements to reduce the user's stress. The decision support unit, for example, uses the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also makes improvements to reduce stress based on the user's emotion data. For example, if the user's emotion is negative, it makes suggestions to simplify the process. It also uses the emotion estimation data to improve the decision-making and operation processes. For example, it identifies areas to improve the process based on the user's emotional response and takes measures to reduce stress. In this way, by monitoring the user's emotions in real time and making improvements to reduce the user's stress, the quality of decision-making and operations is improved.

[0144] The decision support unit can use the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time and make improvements to reduce the user's stress. The decision support unit, for example, uses the emotion estimation function to monitor the user's emotions in decision-making and operation processes in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also makes improvements to reduce stress based on the user's emotion data. For example, if the user's emotion is negative, it makes suggestions to simplify the process. It also uses the emotion estimation data to improve the decision-making and operation processes. For example, it identifies areas to improve the process based on the user's emotional response and takes measures to reduce stress. In this way, by monitoring the user's emotions in real time and making improvements to reduce the user's stress, the quality of decision-making and operations is improved.

[0145] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0146] The data standardization unit can automatically translate and standardize data written in different languages. For example, the generation AI can analyze data provided in different languages, such as English, French, and Chinese, and convert it into a unified language. The generation AI can also take into account the nuances and context of the language to perform accurate translations. Furthermore, the generation AI can check the consistency of the translated data and make corrections as necessary. This standardization of data written in different languages ​​will promote international data utilization.

[0147] The data integration unit performs time-series analysis of data and can predict future trends from past data. For example, the generation AI performs time-series analysis of sales data to predict future sales trends. The generation AI can also perform time-series analysis of inventory data to forecast demand. Furthermore, the generation AI can perform time-series analysis of customer behavior data to support the optimization of marketing strategies. This makes it possible to predict future trends and make strategic decisions by performing time-series analysis of data.

[0148] The Data Governance Department manages the data lifecycle and can centrally manage the process from data generation to disposal. For example, the Generation AI adds metadata when data is generated and tracks the data lifecycle. The Generation AI can also monitor data usage and archive or discard data at the appropriate time. Furthermore, the Generation AI can strengthen data security measures based on data lifecycle management. In this way, data lifecycle management ensures the appropriate use and security of data.

[0149] The visualization unit can provide an interface to reduce the visual burden on the user when visualizing data. For example, the generation AI can automatically select a color palette that takes into consideration users with color vision deficiencies when visualizing data. The generation AI can also adjust the font size and layout to reduce the visual burden when visualizing data. Furthermore, the generation AI can provide an interface to reduce the visual burden on the user, making data visualization more intuitive. This reduces the visual burden on the user, making data visualization more effective.

[0150] The decision support unit can present multiple decision-making options based on different scenarios and compare the risks and benefits. For example, the generation AI can propose multiple sales strategies that take market fluctuations into account and compare the risks and benefits of each. The generation AI can also simulate different economic situations and propose optimal investment strategies. Furthermore, the generation AI can predict project progress and propose optimal resource allocation based on different scenarios. This allows the presentation of multiple decision-making options based on different scenarios to compare the risks and benefits and make optimal decisions.

[0151] The data standardization unit can use the emotion estimation function to provide feedback to increase the user's motivation in the data standardization process. For example, when a user performs data standardization work, the emotion estimation function analyzes the user's emotions in real time and displays a message to increase motivation. The emotion estimation function can also identify areas for improvement in the standardization process based on the user's emotion data and take measures to increase the user's motivation. Furthermore, the emotion estimation function can analyze the user's emotions and create a feedback loop to elicit positive emotions. This increases the user's motivation and improves the efficiency of the data standardization process.

[0152] The data integration unit can use the emotion estimation function to provide an interface for improving user satisfaction in the data integration process. For example, the emotion estimation function can analyze user emotions in real time and provide an interface for improving satisfaction. The emotion estimation function can also identify areas for improvement in the data integration process based on the user's emotion data and take measures to improve satisfaction. Furthermore, the emotion estimation function can analyze the user's emotions and provide an interface for eliciting positive emotions. This improves user satisfaction and thereby increases acceptance of the data integration process.

[0153] The data governance department can use the emotion estimation function to analyze user emotions regarding data governance rules and propose governance rules to elicit positive emotions. For example, the emotion estimation function can analyze user emotions in real time and propose governance rules to elicit positive emotions. The emotion estimation function can also identify areas for improvement in governance rules based on user emotion data and take measures to elicit positive emotions. Furthermore, the emotion estimation function can analyze user emotions and build a feedback loop to elicit positive emotions. As a result, by analyzing user emotions and proposing governance rules to elicit positive emotions, acceptance of data governance improves.

[0154] The visualization unit can use the emotion estimation function to analyze a user's emotions regarding data visualization and propose a visual design that elicits positive emotions. For example, the emotion estimation function can analyze a user's emotions in real time and propose a visual design that elicits positive emotions. The emotion estimation function can also identify areas for improvement in the visual design based on the user's emotion data and take measures to elicit positive emotions. Furthermore, the emotion estimation function can analyze the user's emotions and build a feedback loop to elicit positive emotions. This makes data visualization more effective by analyzing the user's emotions and proposing a visual design that elicits positive emotions.

[0155] The decision-making support unit can use the emotion estimation function to analyze the user's emotions regarding decision-making and operations and provide support for eliciting positive emotions. For example, the emotion estimation function can analyze the user's emotions in real time and provide support for eliciting positive emotions. The emotion estimation function can also identify areas for improvement in support for decision-making and operations based on the user's emotion data and take measures to elicit positive emotions. Furthermore, the emotion estimation function can analyze the user's emotions and build a feedback loop for eliciting positive emotions. As a result, the quality of decision-making and operations can be improved by analyzing the user's emotions and providing support for eliciting positive emotions.

[0156] The processing flow of the second embodiment will be briefly explained below.

[0157] Step 1: The Data Standardization Department standardizes data using the Generation AI. For example, the Generation AI analyzes data provided by each department and converts it into a unified format. The Generation AI can also normalize the data to ensure data consistency. Step 2: The Data Integration Department integrates the data that has been standardized by the Data Standardization Department. For example, the Generation AI aggregates the standardized data into a single database that all employees can access. The Generation AI can also check the consistency of the data to ensure its accuracy. Step 3: The Data Governance Department performs data governance based on the data integrated by the Data Integration Department. For example, the Generation AI sets data access permissions and restricts data viewing and editing according to specific departments or positions. The Generation AI can also perform data quality control to maintain data accuracy. Step 4: The visualization department visualizes the data managed by the data governance department. For example, the generation AI converts sales data into graphs and charts and displays them visually. The generation AI can also analyze trends and patterns in the data and provide them to users. Step 5: The analysis unit analyzes the data visualized by the visualization unit. For example, the generation AI performs statistical analysis to identify trends in the data. The generation AI can also apply machine learning models to make predictions on the data. Step 6: The decision support unit supports decision-making based on the data analyzed by the analysis unit. For example, the generation AI analyzes past data and proposes optimal strategies. The generation AI can also perform scenario analysis and evaluate the risks of decision-making.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0186] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0202] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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."

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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]

[0225] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data sharing unit that uses generation AI to share data; a data integration unit that integrates the data that has been standardized by the data standardization unit; a data governance unit that performs data governance based on the data integrated by the data integration unit; a visualization unit that visualizes the data managed by the data governance unit; an analysis unit that analyzes the data visualized by the visualization unit; a decision support unit that supports decision making based on the data analyzed by the analysis unit. A system characterized by:

2. The data sharing unit Calculate a reliability score for each piece of data and prioritize integration of highly reliable data 2. The system of claim 1.

3. The data sharing unit Adding metadata about the origin and creator of the data ensures data traceability.

2. The system of claim 1.

4. The data sharing unit Analyze user sentiment toward data sharing and build a feedback loop to elicit positive sentiment.

2. The system of claim 1.

5. The data sharing unit Sharing data from different industries and fields to promote cross-industry data utilization 2. The system of claim 1.

6. The data sharing unit Implement a system to check the consistency of the data in real time and make corrections immediately.

2. The system of claim 1.

Images