System

Abstract

An object of a system according to an embodiment is to efficiently grasp and manage a flow of large-scale and complicated data.SOLUTION: A system according to an embodiment includes a data collection unit, a data classification unit, a data anonymization unit, and a data flow visualization unit. The data collection unit collects data. The data classification unit classifies the data collected by the data collection unit. The data anonymizing section anonymizes the data classified by the data classifying section. The data flow visualization unit visualizes a flow of the data anonymized by the data anonymization 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 have had the problem of making it difficult to efficiently grasp and manage large-scale, complex data flows.

[0005] The system according to the embodiment aims to efficiently grasp and manage large-scale and complex data flows. [Means for solving the problem]

[0006] A system according to an embodiment includes a data collection unit, a data classification unit, a data anonymization unit, and a data flow visualization unit. The data collection unit collects data. The data classification unit classifies the data collected by the data collection unit. The data anonymization unit anonymizes the data classified by the data classification unit. The data flow visualization unit visualizes the flow of data anonymized by the data anonymization unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp and manage large-scale and complex data flows. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data management system according to an embodiment of the present invention is a system for efficiently grasping and managing the flow of large-scale, complex data handled by a company. As a result, the data management system can efficiently grasp and manage the flow of large-scale, complex data handled by a company.

[0029] A data management system according to an embodiment includes a data collection unit, a data classification unit, a data anonymization unit, and a data flow visualization unit. The data collection unit collects data. For example, it may collect environmental data using a sensor. It may also acquire data from an external database via an API. It may also collect data by manual input. The data classification unit classifies the collected data. For example, it may classify the data into categories using a machine learning algorithm. It may also organize data by tagging. It may also analyze and classify text data using natural language processing technology. The data anonymization unit anonymizes the classified data. For example, it may hide personal information using data masking technology. It may also convert data using pseudo-anonymization technology. It may also anonymize data using complete anonymization technology. The data flow visualization unit visualizes the flow of anonymized data. For example, it may display the data flow using a flowchart. It may also visually represent the data flow using graphs. It may also monitor the data flow in real time using a dashboard. This allows the data management system according to an embodiment to efficiently grasp and manage the large-scale and complex data flows handled by companies.

[0030] The data collection unit can evaluate the reliability of the data as it is collected and automatically filter out unreliable data. The data collection unit can, for example, use generative AI to evaluate the reliability of each data point as it is collected. For example, it can calculate a reliability score based on the source of the data and the collection method, and automatically filter out unreliable data. The data collection unit can also evaluate the consistency of the data and filter out unreliable data. For example, it can check the consistency of the data and filter out contradictory data. The data collection unit can also evaluate the completeness of the data and filter out unreliable data. For example, it can check for missing data and filter out missing data. This makes it possible to improve data quality by automatically filtering out unreliable data.

[0031] The data collection unit can detect duplicate data in real time as data is collected and automatically eliminate the duplicate data. The data collection unit, for example, uses generative AI to detect duplicate data in real time as data is collected. For example, if the same customer information is collected multiple times, the duplicates are detected and merged into one. The data collection unit can also detect duplicate data using a hash function. For example, it calculates a hash value for the data and detects data with the same hash value as duplicates. The data collection unit can also detect duplicate data using a database query. For example, it searches for identical records in a database and detects duplicates. This enables efficient data management by automatically eliminating duplicate data.

[0032] The data classification unit can automatically classify and organize at least one unstructured data, including voice data and image data. The data classification unit, for example, automatically analyzes voice data using a generative AI, converts it into text data, and classifies and organizes it. For example, it analyzes customer voice feedback and saves it as text data. The data classification unit can also automatically analyze image data using a generative AI and classify it into categories. For example, it analyzes product images and organizes them by category. The data classification unit can also analyze text data using a generative AI to classify and organize unstructured data. For example, it analyzes customer reviews and classifies them into positive and negative reviews. This makes data management easier by automatically classifying and organizing unstructured data.

[0033] The data anonymization unit can automatically select the optimal anonymization method depending on the intended use of the data. For example, the data anonymization unit automatically selects the optimal anonymization method depending on the intended use of the data using generative AI. For example, different anonymization methods may be applied to marketing data and medical data. The data anonymization unit can also select an anonymization method depending on the type of data. For example, data masking may be applied to data containing personal information, and pseudo-anonymization may be applied to data containing confidential information. The data anonymization unit can also select an anonymization method depending on legal requirements. For example, an anonymization method that complies with GDPR may be applied. This makes it possible to strengthen privacy protection by automatically selecting the optimal anonymization method depending on the intended use of the data.

[0034] The data flow visualization unit can color-code the importance of data flows when visualizing data flows. For example, the data flow visualization unit uses generative AI to color-code the importance of data flows when visualizing data flows. For example, data flows with high importance are displayed in red, and data flows with low importance are displayed in blue. The data flow visualization unit can also evaluate the importance of data flows and color-code them. For example, it can calculate a data importance score and set a color according to the score. The data flow visualization unit can also update the importance of data flows in real time and adjust the color coding. For example, it can change the color according to changes in the data flow. This makes data management easier by color-coding the importance of data flows.

[0035] The data flow visualization unit can automatically detect abnormal data flows and issue alerts when monitoring data flows. For example, the data flow visualization unit can automatically detect abnormal data flows and issue alerts when monitoring data flows using generative AI. For example, it can detect data flows that differ from normal and issue alerts. The data flow visualization unit can also detect abnormal data flows using an anomaly detection algorithm. For example, it can analyze data flow patterns and detect anomalies. The data flow visualization unit can also detect abnormal data flows using threshold settings. For example, it can issue an alert if the data flow exceeds a set threshold. This allows for automatic detection of abnormal data flows and issuing an alert, enabling rapid response.

[0036] The data flow visualization unit can perform visualization using a 3D model when visualizing data flow. The data flow visualization unit, for example, uses generative AI to perform visualization using a 3D model when visualizing data flow. For example, the data flow can be displayed in a 3D graph, allowing the user to intuitively understand it. The data flow visualization unit can also visually represent the data flow using a 3D model. For example, the data flow can be displayed in 3D space, visualizing the relationships between the data. The data flow visualization unit can also update the data flow in real time using the 3D model. For example, the 3D model can be updated according to changes in the data flow. As a result, the data flow can be intuitively understood by visualization using the 3D model.

[0037] The data flow visualization unit can integrate data flows from different departments or regions to visualize the overall data flow. For example, the data flow visualization unit can use generative AI to integrate data flows from different departments or regions to visualize the overall data flow. For example, it can display the data flows of each department in a single view. The data flow visualization unit can also integrate data flows from different departments or regions using data integration tools. For example, it can perform data mapping to integrate data. The data flow visualization unit can also perform data conversion to integrate data in different formats. For example, it can convert data from different databases into a unified format. This makes overall data management easier by integrating data flows from different departments or regions.

[0038] The data consistency check unit can analyze data correlations and automatically detect data with low correlations. The data consistency check unit can, for example, use generative AI to analyze data correlations and automatically detect data with low correlations. For example, the data consistency check unit can analyze data correlations using correlation coefficients. For example, the data consistency check unit can calculate the correlation coefficient of data and detect data with low correlations. The data consistency check unit can also analyze data correlations using regression analysis. For example, the data consistency check unit can create a regression model of data and detect data with low correlations. In this way, data consistency can be ensured by analyzing data correlations and automatically detecting data with low correlations.

[0039] In anomaly detection, the data consistency check unit can learn past data patterns and detect anomalous patterns in real time. For example, the data consistency check unit uses generative AI to learn past data patterns and detect anomalous patterns in real time in anomaly detection. For example, it learns past sales data and detects anomalous sales patterns. The data consistency check unit can also learn past data patterns using a machine learning algorithm. For example, it can build a model using a training dataset and detect anomalous patterns. The data consistency check unit can also detect anomalous patterns using real-time data processing technology. For example, it can analyze data in real time and detect anomalies. This allows it to learn past data patterns and detect anomalous patterns in real time, enabling early detection of anomalies.

[0040] The data integrity check unit can integrate data from different data sources and perform consistency checks. For example, the data integrity check unit can integrate data from different data sources using generative AI and perform consistency checks. For example, the data integrity check unit can integrate a customer database and a sales database and check their consistency. The data integrity check unit can also integrate data using an ETL tool. For example, the data integrity check unit can automate the process of extracting, converting, and loading data. The data integrity check unit can also integrate data using a data warehouse. For example, the data integrity check unit can collect data from different data sources and centrally manage it. The data integrity check unit can also integrate data using API integration. For example, the data integrity check unit can link data between different systems and check its consistency. This makes it possible to ensure data consistency by integrating data from different data sources and performing consistency checks.

[0041] The data management unit can optimize data storage locations and propose storage placement according to access frequency. The data management unit can, for example, use generative AI to optimize data storage locations and propose storage placement according to access frequency. For example, frequently accessed data can be placed in high-speed storage. The data management unit can also propose storage placement according to data size. For example, large volumes of data can be placed in low-cost storage. The data management unit can also propose optimal placement according to storage costs. For example, it selects cost-effective storage and places data there. In this way, by optimizing data storage locations and proposing storage placement according to access frequency, data management costs can be reduced.

[0042] The data management unit can automate data backup schedules and propose cost-effective backup methods. For example, the data management unit can use generative AI to automate data backup schedules and propose cost-effective backup methods. For example, it can prioritize backing up important data. The data management unit can also automate backup schedules using a scheduling tool. For example, it can automatically perform periodic backups. The data management unit can also propose cost-effective backup methods using cloud storage. For example, it can perform differential backups and incremental backups. In this way, by automating data backup schedules and proposing cost-effective backup methods, it is possible to reduce data management costs.

[0043] The data management department can propose hybrid storage that combines cloud storage and on-premise storage. For example, the data management department can use generative AI to propose hybrid storage that combines cloud storage and on-premise storage. For example, frequently accessed data can be placed in cloud storage and important data can be placed in on-premise storage. The data management department can also propose a data distribution method. For example, some of the data can be stored in cloud storage and the rest in on-premise storage. The data management department can also optimize the hybrid storage configuration. For example, it can adjust the storage placement based on the data access pattern. This makes it possible to reduce data management costs by combining cloud storage and on-premise storage.

[0044] The data management unit can apply data compression technology to reduce storage capacity. The data management unit can, for example, use generative AI to apply data compression technology to reduce storage capacity. For example, it can compress uncompressed data to reduce storage capacity. The data management unit can also apply lossless compression technology. For example, it can compress data while maintaining its quality. The data management unit can also apply lossy compression technology. For example, it can compress data by deleting part of it. The data management unit can also select a compression algorithm and apply the optimal compression method. For example, it can select a compression algorithm depending on the type of data. In this way, by applying data compression technology, it is possible to reduce storage capacity and cut data management costs.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The data collection unit can monitor fluctuations in environmental data in real time during data collection and detect abnormal fluctuations. For example, a temperature sensor can be used to detect sudden temperature fluctuations and issue an alert. The data collection unit can also detect abnormal humidity fluctuations using a humidity sensor. For example, an alert can be issued if humidity rises suddenly. The data collection unit can also detect abnormal air pressure fluctuations using a barometric pressure sensor. For example, an alert can be issued if air pressure drops suddenly. This allows abnormal fluctuations in environmental data to be monitored in real time and enables rapid response.

[0047] The data collection unit can automatically record the origin of data when collecting data and evaluate the reliability of the data. For example, if the data is collected from a trusted source, it can assign high reliability. On the other hand, if the data is collected from an unknown source, it can assign low reliability. The data collection unit can also record the method by which the data is collected and evaluate the reliability. For example, if the data is collected from an automatic sensor, it can assign high reliability. On the other hand, if the data is entered manually, it can assign low reliability. This makes it possible to evaluate reliability based on the origin and collection method of the data and improve data quality.

[0048] The data classifier can improve classification accuracy by combining different algorithms in data classification. For example, it can combine machine learning algorithms and rule-based algorithms. The data classifier can also integrate and classify data from different data sources. For example, it can integrate and classify text data and image data. The data classifier can also incorporate classification results into a feedback loop to continuously improve classification accuracy. For example, it can adjust algorithms based on user feedback. This allows the accuracy of data classification to be improved by combining different algorithms and data sources.

[0049] When anonymizing data, the data anonymization unit can provide customizable anonymization templates according to the intended use of the data. For example, a template for medical data and a template for marketing data can be prepared. The data anonymization unit can also provide a function that allows the user to create their own anonymization template. For example, a template can be created to anonymize specific fields. The data anonymization unit can also preview the results of applying the template so that the user can check them. For example, the data after anonymization can be displayed and checked by the user. This makes it easier to anonymize data by providing customizable anonymization templates according to the intended use of the data.

[0050] The data flow visualization unit can provide a user-customizable dashboard in data flow visualization. For example, the user can select data flow items to display. The data flow visualization unit can also provide a user-customizable widget. For example, a widget that highlights a specific data flow can be added. The data flow visualization unit can also provide a user-customizable alert setting. For example, an alert can be set based on a specific condition. This makes data flow visualization more flexible by providing a user-customizable dashboard.

[0051] The data consistency check unit can automatically convert different data formats to ensure consistency during data consistency checks. For example, it can convert CSV format data into JSON format. The data consistency check unit can also record the data conversion history to make it traceable. For example, it can record which data was converted and how. The data consistency check unit can also verify the results of data conversion and confirm consistency. For example, it can check whether the converted data is in the correct format. This makes data management easier by automatically converting different data formats and ensuring consistency.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data collection unit collects data. For example, it collects environmental data using sensors. It can also obtain data from an external database via an API. It can also collect data by manual input. Step 2: The data classification unit classifies the collected data. For example, it uses machine learning algorithms to classify the data into categories. It can also tag and organize the data. It can also analyze and classify text data using natural language processing techniques. Step 3: The data anonymization unit anonymizes the classified data. For example, it uses data masking technology to hide personal information. It can also convert data using pseudo-anonymization technology. It can also anonymize data using complete anonymization technology. Step 4: The data flow visualization unit visualizes the flow of anonymized data. For example, it can display the data flow using a flowchart. It can also visually represent the data flow using graphs. Furthermore, it can monitor the data flow in real time using a dashboard.

[0054] (Example 2) A data management system according to an embodiment of the present invention is a system for efficiently grasping and managing the flow of large-scale, complex data handled by a company. As a result, the data management system can efficiently grasp and manage the flow of large-scale, complex data handled by a company.

[0055] A data management system according to an embodiment includes a data collection unit, a data classification unit, a data anonymization unit, and a data flow visualization unit. The data collection unit collects data. For example, it may collect environmental data using a sensor. It may also acquire data from an external database via an API. It may also collect data by manual input. The data classification unit classifies the collected data. For example, it may classify the data into categories using a machine learning algorithm. It may also organize data by tagging. It may also analyze and classify text data using natural language processing technology. The data anonymization unit anonymizes the classified data. For example, it may hide personal information using data masking technology. It may also convert data using pseudo-anonymization technology. It may also anonymize data using complete anonymization technology. The data flow visualization unit visualizes the flow of anonymized data. For example, it may display the data flow using a flowchart. It may also visually represent the data flow using graphs. It may also monitor the data flow in real time using a dashboard. This allows the data management system according to an embodiment to efficiently grasp and manage the large-scale and complex data flows handled by companies.

[0056] The data collection unit can evaluate the reliability of the data as it is collected and automatically filter out unreliable data. The data collection unit can, for example, use generative AI to evaluate the reliability of each data point as it is collected. For example, it can calculate a reliability score based on the source of the data and the collection method, and automatically filter out unreliable data. The data collection unit can also evaluate the consistency of the data and filter out unreliable data. For example, it can check the consistency of the data and filter out contradictory data. The data collection unit can also evaluate the completeness of the data and filter out unreliable data. For example, it can check for missing data and filter out missing data. This makes it possible to improve data quality by automatically filtering out unreliable data.

[0057] The data collection unit can detect duplicate data in real time as data is collected and automatically eliminate the duplicate data. The data collection unit, for example, uses generative AI to detect duplicate data in real time as data is collected. For example, if the same customer information is collected multiple times, the duplicates are detected and merged into one. The data collection unit can also detect duplicate data using a hash function. For example, it calculates a hash value for the data and detects data with the same hash value as duplicates. The data collection unit can also detect duplicate data using a database query. For example, it searches for identical records in a database and detects duplicates. This enables efficient data management by automatically eliminating duplicate data.

[0058] The data classification unit can automatically classify and organize at least one unstructured data, including voice data and image data. The data classification unit, for example, automatically analyzes voice data using a generative AI, converts it into text data, and classifies and organizes it. For example, it analyzes customer voice feedback and saves it as text data. The data classification unit can also automatically analyze image data using a generative AI and classify it into categories. For example, it analyzes product images and organizes them by category. The data classification unit can also analyze text data using a generative AI to classify and organize unstructured data. For example, it analyzes customer reviews and classifies them into positive and negative reviews. This makes data management easier by automatically classifying and organizing unstructured data.

[0059] The data anonymization unit can automatically select the optimal anonymization method depending on the intended use of the data. For example, the data anonymization unit automatically selects the optimal anonymization method depending on the intended use of the data using generative AI. For example, different anonymization methods may be applied to marketing data and medical data. The data anonymization unit can also select an anonymization method depending on the type of data. For example, data masking may be applied to data containing personal information, and pseudo-anonymization may be applied to data containing confidential information. The data anonymization unit can also select an anonymization method depending on legal requirements. For example, an anonymization method that complies with GDPR may be applied. This makes it possible to strengthen privacy protection by automatically selecting the optimal anonymization method depending on the intended use of the data.

[0060] The data flow visualization unit can color-code the importance of data flows when visualizing data flows. For example, the data flow visualization unit uses generative AI to color-code the importance of data flows when visualizing data flows. For example, data flows with high importance are displayed in red, and data flows with low importance are displayed in blue. The data flow visualization unit can also evaluate the importance of data flows and color-code them. For example, it can calculate a data importance score and set a color according to the score. The data flow visualization unit can also update the importance of data flows in real time and adjust the color coding. For example, it can change the color according to changes in the data flow. This makes data management easier by color-coding the importance of data flows.

[0061] The data flow visualization unit can automatically detect abnormal data flows and issue alerts when monitoring data flows. For example, the data flow visualization unit can automatically detect abnormal data flows and issue alerts when monitoring data flows using generative AI. For example, it can detect data flows that differ from normal and issue alerts. The data flow visualization unit can also detect abnormal data flows using an anomaly detection algorithm. For example, it can analyze data flow patterns and detect anomalies. The data flow visualization unit can also detect abnormal data flows using threshold settings. For example, it can issue an alert if the data flow exceeds a set threshold. This allows for automatic detection of abnormal data flows and issuing an alert, enabling rapid response.

[0062] The data flow visualization unit can perform visualization using a 3D model when visualizing data flow. The data flow visualization unit, for example, uses generative AI to perform visualization using a 3D model when visualizing data flow. For example, the data flow can be displayed in a 3D graph, allowing the user to intuitively understand it. The data flow visualization unit can also visually represent the data flow using a 3D model. For example, the data flow can be displayed in 3D space, visualizing the relationships between the data. The data flow visualization unit can also update the data flow in real time using the 3D model. For example, the 3D model can be updated according to changes in the data flow. As a result, the data flow can be intuitively understood by visualization using the 3D model.

[0063] The data flow visualization unit can integrate data flows from different departments or regions to visualize the overall data flow. For example, the data flow visualization unit can use generative AI to integrate data flows from different departments or regions to visualize the overall data flow. For example, it can display the data flows of each department in a single view. The data flow visualization unit can also integrate data flows from different departments or regions using data integration tools. For example, it can perform data mapping to integrate data. The data flow visualization unit can also perform data conversion to integrate data in different formats. For example, it can convert data from different databases into a unified format. This makes overall data management easier by integrating data flows from different departments or regions.

[0064] The data consistency check unit can analyze data correlations and automatically detect data with low correlations. The data consistency check unit can, for example, use generative AI to analyze data correlations and automatically detect data with low correlations. For example, the data consistency check unit can analyze data correlations using correlation coefficients. For example, the data consistency check unit can calculate the correlation coefficient of data and detect data with low correlations. The data consistency check unit can also analyze data correlations using regression analysis. For example, the data consistency check unit can create a regression model of data and detect data with low correlations. In this way, data consistency can be ensured by analyzing data correlations and automatically detecting data with low correlations.

[0065] In anomaly detection, the data consistency check unit can learn past data patterns and detect anomalous patterns in real time. For example, the data consistency check unit uses generative AI to learn past data patterns and detect anomalous patterns in real time in anomaly detection. For example, it learns past sales data and detects anomalous sales patterns. The data consistency check unit can also learn past data patterns using a machine learning algorithm. For example, it can build a model using a training dataset and detect anomalous patterns. The data consistency check unit can also detect anomalous patterns using real-time data processing technology. For example, it can analyze data in real time and detect anomalies. This allows it to learn past data patterns and detect anomalous patterns in real time, enabling early detection of anomalies.

[0066] The data integrity check unit can integrate data from different data sources and perform consistency checks. For example, the data integrity check unit can integrate data from different data sources using generative AI and perform consistency checks. For example, the data integrity check unit can integrate a customer database and a sales database and check their consistency. The data integrity check unit can also integrate data using an ETL tool. For example, the data integrity check unit can automate the process of extracting, converting, and loading data. The data integrity check unit can also integrate data using a data warehouse. For example, the data integrity check unit can collect data from different data sources and centrally manage it. The data integrity check unit can also integrate data using API integration. For example, the data integrity check unit can link data between different systems and check its consistency. This makes it possible to ensure data consistency by integrating data from different data sources and performing consistency checks.

[0067] The data management unit can optimize data storage locations and propose storage placement according to access frequency. The data management unit can, for example, use generative AI to optimize data storage locations and propose storage placement according to access frequency. For example, frequently accessed data can be placed in high-speed storage. The data management unit can also propose storage placement according to data size. For example, large volumes of data can be placed in low-cost storage. The data management unit can also propose optimal placement according to storage costs. For example, it selects cost-effective storage and places data there. In this way, by optimizing data storage locations and proposing storage placement according to access frequency, data management costs can be reduced.

[0068] The data management unit can automate data backup schedules and propose cost-effective backup methods. For example, the data management unit can use generative AI to automate data backup schedules and propose cost-effective backup methods. For example, it can prioritize backing up important data. The data management unit can also automate backup schedules using a scheduling tool. For example, it can automatically perform periodic backups. The data management unit can also propose cost-effective backup methods using cloud storage. For example, it can perform differential backups and incremental backups. In this way, by automating data backup schedules and proposing cost-effective backup methods, it is possible to reduce data management costs.

[0069] The data management department can propose hybrid storage that combines cloud storage and on-premise storage. For example, the data management department can use generative AI to propose hybrid storage that combines cloud storage and on-premise storage. For example, frequently accessed data can be placed in cloud storage and important data can be placed in on-premise storage. The data management department can also propose a data distribution method. For example, some of the data can be stored in cloud storage and the rest in on-premise storage. The data management department can also optimize the hybrid storage configuration. For example, it can adjust the storage placement based on the data access pattern. This makes it possible to reduce data management costs by combining cloud storage and on-premise storage.

[0070] The data management unit can apply data compression technology to reduce storage capacity. The data management unit can, for example, use generative AI to apply data compression technology to reduce storage capacity. For example, it can compress uncompressed data to reduce storage capacity. The data management unit can also apply lossless compression technology. For example, it can compress data while maintaining its quality. The data management unit can also apply lossy compression technology. For example, it can compress data by deleting part of it. The data management unit can also select a compression algorithm and apply the optimal compression method. For example, it can select a compression algorithm depending on the type of data. In this way, by applying data compression technology, it is possible to reduce storage capacity and cut data management costs.

[0071] The data management unit can use the emotion estimation function to monitor user emotions in real time when reducing data management costs and dynamically adjust the cost reduction method. For example, the data management unit can use the emotion estimation function to monitor user emotions in real time when reducing data management costs and dynamically adjust the cost reduction method. For example, if the user feels anxious, the cost reduction method can be changed. The data management unit can also analyze user emotions using an emotion analysis algorithm. For example, it can use sensor data to monitor user emotions in real time. The data management unit can also collect feedback through a user interface and adjust the cost reduction method. For example, it can change settings based on user feedback. The data management unit can also monitor user emotions using real-time data processing technology and dynamically adjust the cost reduction method. For example, it can analyze data in real time and change settings according to the user's emotions. In this way, by monitoring user emotions in real time and dynamically adjusting the cost reduction method, it is possible to reduce costs while increasing user satisfaction.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The data collection unit can monitor fluctuations in environmental data in real time during data collection and detect abnormal fluctuations. For example, a temperature sensor can be used to detect sudden temperature fluctuations and issue an alert. The data collection unit can also detect abnormal humidity fluctuations using a humidity sensor. For example, an alert can be issued if humidity rises suddenly. The data collection unit can also detect abnormal air pressure fluctuations using a barometric pressure sensor. For example, an alert can be issued if air pressure drops suddenly. This allows abnormal fluctuations in environmental data to be monitored in real time and enables rapid response.

[0074] The data classification unit can estimate the user's emotions and adjust the data classification method based on the estimated user's emotions. For example, if the user is feeling stressed, the data classification can be simplified. On the other hand, if the user is relaxed, detailed classification can be performed. The data classification unit can also change the way data is displayed according to the user's emotions. For example, if the user is feeling anxious, the data can be displayed in a visually easy-to-understand manner. On the other hand, if the user is interested, detailed data can be displayed. This makes it possible to classify and display data according to the user's emotions.

[0075] When anonymizing data, the data anonymization unit can estimate the emotions of the user of the data and adjust the level of anonymization based on the estimated emotions. For example, if the user is anxious about privacy, stricter anonymization can be performed. On the other hand, if the user is comfortable with privacy, the level of anonymization can be relaxed. The data anonymization unit can also select an anonymization method according to the user's emotions. For example, if the user is sensitive about privacy, data masking can be applied. On the other hand, if the user is tolerant of privacy, pseudo-anonymization can be applied. This makes it possible to anonymize data according to the user's emotions.

[0076] The data flow visualization unit can estimate the user's emotions when visualizing data flows and adjust the visualization method based on the estimated emotions. For example, if the user is feeling stressed, the data flow can be displayed in a simplified form. On the other hand, if the user is relaxed, a detailed data flow can be displayed. The data flow visualization unit can also change the color coding method according to the user's emotions. For example, if the user is feeling anxious, calm colors can be used. On the other hand, if the user is interested, vivid colors can be used. This makes it possible to visualize data flows according to the user's emotions.

[0077] The data consistency check unit can estimate the user's emotions during data consistency checks and adjust the check method based on the estimated emotions. For example, if the user is feeling stressed, the frequency of checks can be reduced. Also, if the user is relaxed, a more detailed check can be performed. The data consistency check unit can also change the alert method according to the user's emotions. For example, if the user is feeling anxious, the alert can be made less frequent. Also, if the user is interested, a more detailed alert can be issued. This makes it possible to check the consistency of data according to the user's emotions.

[0078] The data collection unit can automatically record the origin of data when collecting data and evaluate the reliability of the data. For example, if the data is collected from a trusted source, it can assign high reliability. On the other hand, if the data is collected from an unknown source, it can assign low reliability. The data collection unit can also record the method by which the data is collected and evaluate the reliability. For example, if the data is collected from an automatic sensor, it can assign high reliability. On the other hand, if the data is entered manually, it can assign low reliability. This makes it possible to evaluate reliability based on the origin and collection method of the data and improve data quality.

[0079] The data classifier can improve classification accuracy by combining different algorithms in data classification. For example, it can combine machine learning algorithms and rule-based algorithms. The data classifier can also integrate and classify data from different data sources. For example, it can integrate and classify text data and image data. The data classifier can also incorporate classification results into a feedback loop to continuously improve classification accuracy. For example, it can adjust algorithms based on user feedback. This allows the accuracy of data classification to be improved by combining different algorithms and data sources.

[0080] When anonymizing data, the data anonymization unit can provide customizable anonymization templates according to the intended use of the data. For example, a template for medical data and a template for marketing data can be prepared. The data anonymization unit can also provide a function that allows the user to create their own anonymization template. For example, a template can be created to anonymize specific fields. The data anonymization unit can also preview the results of applying the template so that the user can check them. For example, the data after anonymization can be displayed and checked by the user. This makes it easier to anonymize data by providing customizable anonymization templates according to the intended use of the data.

[0081] The data flow visualization unit can provide a user-customizable dashboard in data flow visualization. For example, the user can select data flow items to display. The data flow visualization unit can also provide a user-customizable widget. For example, a widget that highlights a specific data flow can be added. The data flow visualization unit can also provide a user-customizable alert setting. For example, an alert can be set based on a specific condition. This makes data flow visualization more flexible by providing a user-customizable dashboard.

[0082] The data consistency check unit can automatically convert different data formats to ensure consistency during data consistency checks. For example, it can convert CSV format data into JSON format. The data consistency check unit can also record the data conversion history to make it traceable. For example, it can record which data was converted and how. The data consistency check unit can also verify the results of data conversion and confirm consistency. For example, it can check whether the converted data is in the correct format. This makes data management easier by automatically converting different data formats and ensuring consistency.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data collection unit collects data. For example, it collects environmental data using sensors. It can also obtain data from an external database via an API. It can also collect data by manual input. Step 2: The data classification unit classifies the collected data. For example, it uses machine learning algorithms to classify the data into categories. It can also tag and organize the data. It can also analyze and classify text data using natural language processing techniques. Step 3: The data anonymization unit anonymizes the classified data. For example, it uses data masking technology to hide personal information. It can also convert data using pseudo-anonymization technology. It can also anonymize data using complete anonymization technology. Step 4: The data flow visualization unit visualizes the flow of anonymized data. For example, it can display the data flow using a flowchart. It can also visually represent the data flow using graphs. Furthermore, it can monitor the data flow in real time using a dashboard.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 collection unit that collects data; a data classification unit that classifies the data collected by the data collection unit; a data anonymization unit that anonymizes the data classified by the data classification unit; a data flow visualization unit that visualizes the flow of data anonymized by the data anonymization unit. A system characterized by:

2. The data collection unit Evaluate data reliability during data collection and automatically filter out unreliable data 2. The system of claim 1.

3. The data classification unit Automatically classify and organize unstructured data of at least one of audio data and image data.

2. The system of claim 1.

4. The data anonymization unit Automatically selects the optimal anonymization method depending on the purpose of data use 2. The system of claim 1.

5. The data flow visualization unit In data flow visualization, the importance of data flow is displayed by color coding.

2. The system of claim 1.

6. The data integrity check section Analyze data correlations and automatically detect data with low correlation 2. The system of claim 1.

7. The data management department Optimize data storage locations and suggest storage placement based on access frequency 2. The system of claim 1.

8. The data management department To monitor user sentiment in real time when reducing data management costs and dynamically adjust the cost reduction method.

2. The system of claim 1.

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