System

Abstract

An object of a system according to an embodiment is to improve the efficiency of data cooperation and synchronization work between systems.SOLUTION: A system includes a tag analysis part, a relation specification part, and a relation setting part. The tag analysis unit analyzes a tag or a column name acquired from a cloud tool or a database. The relation identification unit identifies data columns that need to form a relation based on the relevance of the tags and the column names analyzed by the tag analysis unit. The relation setting unit automatically sets a relation of the data columns specified by the relation specifying 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] With conventional technology, data integration and synchronization between systems was often done manually, which was inefficient.

[0005] The system according to the embodiment aims to improve the efficiency of data linkage and synchronization between systems. [Means for solving the problem]

[0006] The system according to the embodiment includes a tag analysis unit, a relation identification unit, and a relation setting unit. The tag analysis unit analyzes tags and column names acquired from a cloud tool or a database. The relation identification unit identifies data columns for which a relation needs to be established based on the associations between the tags and column names analyzed by the tag analysis unit. The relation setting unit automatically sets the relations between the data columns identified by the relation identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of data linkage and synchronization between systems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data linkage system according to an embodiment of the present invention automatically analyzes tags and column names in cloud tools and databases, and automatically identifies data columns that require relationships. This allows the data linkage system to improve the efficiency of data linkage and synchronization between systems.

[0029] A data linkage system according to an embodiment includes a tag analysis unit, a relation identification unit, and a relation setting unit. The tag analysis unit analyzes tags and column names acquired from a cloud tool or a database. For example, the tag analysis unit uses a generation AI to understand the meaning of the tags and column names and find relevance. The tag analysis unit can also analyze tags and column names using natural language processing technology. For example, the generation AI receives tags and column names acquired from a cloud tool or a database as input and analyzes their meaning. The relation identification unit identifies data columns that need to be related based on the relevance of the tags and column names analyzed by the tag analysis unit. For example, the relation identification unit uses the generation AI to identify that "customer ID" and "order ID" are columns that should be related. The relation identification unit can also evaluate the relevance of data columns based on the analysis results. For example, the generation AI receives a list of analyzed tags and column names as input and identifies highly related data columns. The relation setting unit automatically sets the relationship between the data columns identified by the relation identification unit. For example, the relation setting unit uses the generation AI to set relationships between data columns. The relation setting unit can also perform settings to ensure smooth data integration between different systems. For example, the generation AI sets data integration between different databases based on the relationships between the identified data columns. This allows the data integration system according to the embodiment to improve the efficiency of data integration and synchronization work between systems. For example, there is no longer a need to manually set relationships between data columns, significantly reducing the time required for work. Furthermore, because the generation AI automatically sets relationships, the risk of human error is reduced.

[0030] The tag analysis unit can analyze tags and column names by referencing past database structures and relationship history. For example, the generation AI refers to past database structures and relationship history to analyze tags and column names. For example, it learns the patterns of column names and relationships used in the past and applies them to a new database. The tag analysis unit also allows the generation AI to learn from past examples of successful and unsuccessful relationship settings and reflects this in the analysis of tags and column names. For example, it selects the optimal analysis method based on the relationship setting history in past databases. The tag analysis unit also allows the generation AI to analyze past database structures and understand the meaning of tags and column names. For example, it learns the meaning of column names used in past databases and applies them to a new database. In this way, by referring to past database structures and relationship history, the analysis accuracy is improved.

[0031] The tag analysis unit can automatically recognize industry-specific terms and abbreviations and analyze tags and column names. For example, the tag analysis unit allows the generation AI to automatically recognize industry-specific terms and abbreviations and analyze tags and column names. For example, it learns abbreviations used in medical industry databases and reflects them in the analysis. The tag analysis unit also allows the generation AI to learn industry-specific terms and abbreviations and improve the analysis accuracy of tags and column names. For example, it recognizes technical terms used in financial industry databases and reflects them in the analysis. The tag analysis unit also allows the generation AI to automatically recognize industry-specific terms and abbreviations and understand the meaning of tags and column names. For example, it learns abbreviations used in manufacturing industry databases and applies them to a new database. This improves analysis accuracy by recognizing industry-specific terms and abbreviations.

[0032] The tag analysis unit can also include image and audio data in its analysis targets, enabling it to find correlations from multimodal information. For example, when the generation AI analyzes tags and column names, the tag analysis unit also includes image and audio data in its analysis targets. For example, it can find correlations between tags and column names using prototype images and explanatory audio. The tag analysis unit also uses image recognition technology to extract the contents of tags and column names from uploaded image data and reflect this in the analysis. For example, it can analyze product design and sketch images to find correlations between tags and column names. The tag analysis unit also uses audio analysis technology to automatically extract the key points of tags and column names from submitted audio data and reflect this in the analysis. For example, it can convert explanatory audio into text data and find correlations between tags and column names. In this way, including image and audio data in the analysis targets improves the accuracy of the analysis.

[0033] The tag analysis unit can refer to database structures in different industries and fields and apply them to tag and column name analysis. For example, the generation AI refers to database structures in different industries and fields and applies them to tag and column name analysis. For example, it learns the database structures of the medical and financial industries and applies them to a new database. The tag analysis unit also analyzes database structures in different industries and finds correlations between tags and column names. For example, it learns the database structures of the manufacturing and service industries and reflects this in the analysis. The tag analysis unit also refers to database structures in different fields and improves the accuracy of tag and column name analysis. For example, it learns the database structures of the education and entertainment fields and applies them to a new database. In this way, by referencing database structures in different industries and fields, analysis accuracy is improved.

[0034] The relation identification unit can identify relations by analyzing the frequency of data use and access patterns. For example, the generation AI analyzes the frequency of data use and access patterns to identify relations. For example, it associates frequently accessed columns with each other. The relation identification unit also learns the data access patterns and proposes optimal relations. For example, it associates columns that receive concentrated access during specific time periods. The generation AI also analyzes the frequency of data use and identifies relations. For example, it associates columns that are frequently updated with each other. In this way, the optimal relations can be identified by analyzing the frequency of data use and access patterns.

[0035] The relation identification unit can set dynamic relations by taking into account time-series fluctuations in the data. For example, the generation AI analyzes time-series fluctuations in the data and identifies relations. For example, it associates data columns that vary by season. The generation AI also learns time-series fluctuations in the data and sets dynamic relations. For example, it associates data columns that vary in response to specific events. The generation AI also identifies relations by taking into account time-series fluctuations in the data. For example, it associates data columns that vary by time period. In this way, dynamic relations can be set by taking into account time-series fluctuations in the data.

[0036] The relation identification unit can analyze the interdependencies between different databases and set cross-database relations. In the relation identification unit, for example, the generation AI analyzes the interdependencies between different databases and identifies the relations. For example, it analyzes the interdependencies between a customer database and an order database and sets the relations. In addition, the relation identification unit learns the interdependencies between different databases and sets cross-database relations. For example, it analyzes the interdependencies between an inventory database and a sales database and sets the relations. In addition, the relation identification unit analyzes the interdependencies between different databases and identifies the relations. For example, it analyzes the interdependencies between a customer database and a support database and sets the relations. In this way, cross-database relations can be set by analyzing the interdependencies between different databases.

[0037] The relation identification unit can set optimal relations for each region, taking into account the geographic distribution of the data. In the relation identification unit, for example, the generation AI analyzes the geographic distribution of the data and identifies relations. For example, it associates customer data and sales data for each region. The relation identification unit also learns the geographic distribution of the data and sets optimal relations for each region. For example, it associates data columns for urban and rural areas. The relation identification unit also identifies relations, taking into account the geographic distribution of the data. For example, it associates data columns for each country. In this way, it is possible to set optimal relations for each region by taking into account the geographic distribution of the data.

[0038] The relation setting unit can learn from past examples of successful and unsuccessful relationship setting and set the optimal relationship. For example, the generation AI in the relation setting unit learns from past examples of successful and unsuccessful relationship setting and sets the optimal relationship. For example, the generation AI selects the optimal relationship based on the history of relationship setting in a past database. The generation AI also analyzes past examples of successful and unsuccessful relationship setting and sets a new relationship based on that knowledge. For example, it analyzes the factors of success and failure and reflects them in the relationship setting. The generation AI also learns from the history of past relationship setting and sets the optimal relationship. For example, it sets a relationship based on similar cases and maintains data consistency. In this way, the optimal relationship can be set by learning from past examples of successful and unsuccessful relationship setting.

[0039] The relation setting unit can set secure relations by taking into account the security requirements of the data. For example, the generation AI sets secure relations by taking into account the security requirements of the data. For example, the generation AI appropriately separates confidential data from general data and sets relations. The relation setting unit also allows the generation AI to learn the security requirements of the data and set secure relations. For example, the generation AI sets relations by taking into account access control and encryption requirements. The relation setting unit also allows the generation AI to analyze the security requirements of the data and set secure relations. For example, the generation AI sets relations to maintain the confidentiality and integrity of the data. This makes it possible to set secure relations by taking into account the security requirements of the data.

[0040] The relation setting unit can set relations between different data formats. For example, the generation AI sets relations between different data formats in the relation setting unit. For example, it sets a relation that associates structured data with unstructured data. The relation setting unit also allows the generation AI to learn different data formats and set relations. For example, it sets a relation that associates text data with image data. The relation setting unit also allows the generation AI to analyze relations between different data formats and set the optimal relation. For example, it sets a relation that associates audio data with text data. In this way, by setting relations between different data formats, data consistency can be maintained.

[0041] The relation setting unit can set a relation between real-time data and batch data. In the relation setting unit, for example, the generation AI sets a relation between real-time data and batch data. For example, sensor data collected in real time is associated with batch data that is updated periodically. In addition, the relation setting unit allows the generation AI to learn the real-time data and batch data and set a relation. For example, real-time transaction data is associated with batch-processed sales data. In addition, the relation setting unit allows the generation AI to analyze the relation between real-time data and batch data and set an optimal relation. For example, real-time user behavior data is associated with batch-processed marketing data. In this way, by setting a relation between real-time data and batch data, data consistency can be maintained.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data integration system can further include a data quality evaluation unit. The data quality evaluation unit evaluates the consistency, accuracy, and completeness of the data, and checks the quality of the data before data integration. For example, the data quality evaluation unit detects missing values ​​or outliers in the data and makes suggestions for correction. The data quality evaluation unit can also detect duplicate data and suggest merging the duplicate data. Furthermore, the data quality evaluation unit can detect inconsistencies in data formats or units and make suggestions for unification. This allows the quality of the data to be evaluated in advance, improving the accuracy and reliability of data integration.

[0044] The data linkage system can further include a data conversion unit. The data conversion unit converts data in different formats into a unified format. For example, it can convert data in CSV format into JSON format. The data conversion unit can also unify data in different units. For example, it can convert data in meters into feet. The data conversion unit can also unify data in different encodings. For example, it can convert data in UTF-8 encoding into ISO-8859-1 encoding. This makes it possible to unify data in different formats and units, improving the efficiency of data linkage.

[0045] The data linkage system can further include a data security unit. The data security unit encrypts data and controls access to ensure data security. For example, the data security unit encrypts data when it is sent or received to prevent unauthorized access. The data security unit can also set access rights for each user to prevent unauthorized use of data. Furthermore, the data security unit can detect data tampering and maintain data integrity. This ensures data security and enables safe data linkage.

[0046] The data linkage system can further include a data monitoring unit. The data monitoring unit monitors the status of data linkage in real time and detects abnormalities. For example, the data monitoring unit monitors the data transmission and reception speed and error rate, and issues an alert if an abnormality occurs. The data monitoring unit can also monitor data consistency and detect data inconsistencies. Furthermore, the data monitoring unit can monitor data usage and analyze usage frequency and access patterns. This allows the status of data linkage to be grasped in real time and respond quickly.

[0047] The data linkage system may further include a data backup unit. The data backup unit periodically backs up data to prevent data loss. For example, the data backup unit backs up data at a fixed time every day and stores the backup data in a safe location. The data backup unit also has a backup data restoration function, allowing for quick restoration in the event of data loss. Furthermore, the data backup unit can also check the integrity of the backup data to ensure that the backup data is accurate. This prevents data loss and ensures data safety.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The tag analysis unit analyzes tags and column names obtained from cloud tools and databases. For example, the tag analysis unit uses generative AI to understand the meaning of tags and column names and find relationships. The tag analysis unit can also use natural language processing technology to analyze tags and column names. Step 2: The relation identification unit identifies data columns that need to be related based on the associations between tags and column names analyzed by the tag analysis unit. For example, the relation identification unit uses generation AI to identify that "customer ID" and "order ID" are columns that should be associated. The relation identification unit can also evaluate the associations between data columns based on the analysis results. Step 3: The relation setting unit automatically sets the relations between the data columns identified by the relation identification unit. For example, the relation setting unit sets the relations between the data columns using a generation AI. The relation setting unit can also perform settings to ensure smooth data integration between different systems.

[0050] (Example 2) A data linkage system according to an embodiment of the present invention automatically analyzes tags and column names in cloud tools and databases, and automatically identifies data columns that require relationships. This allows the data linkage system to improve the efficiency of data linkage and synchronization between systems.

[0051] A data linkage system according to an embodiment includes a tag analysis unit, a relation identification unit, and a relation setting unit. The tag analysis unit analyzes tags and column names acquired from a cloud tool or a database. For example, the tag analysis unit uses a generation AI to understand the meaning of the tags and column names and find relevance. The tag analysis unit can also analyze tags and column names using natural language processing technology. For example, the generation AI receives tags and column names acquired from a cloud tool or a database as input and analyzes their meaning. The relation identification unit identifies data columns that need to be related based on the relevance of the tags and column names analyzed by the tag analysis unit. For example, the relation identification unit uses the generation AI to identify that "customer ID" and "order ID" are columns that should be related. The relation identification unit can also evaluate the relevance of data columns based on the analysis results. For example, the generation AI receives a list of analyzed tags and column names as input and identifies highly related data columns. The relation setting unit automatically sets the relationship between the data columns identified by the relation identification unit. For example, the relation setting unit uses the generation AI to set relationships between data columns. The relation setting unit can also perform settings to ensure smooth data integration between different systems. For example, the generation AI sets data integration between different databases based on the relationships between the identified data columns. This allows the data integration system according to the embodiment to improve the efficiency of data integration and synchronization work between systems. For example, there is no longer a need to manually set relationships between data columns, significantly reducing the time required for work. Furthermore, because the generation AI automatically sets relationships, the risk of human error is reduced.

[0052] The tag analysis unit can analyze tags and column names by referencing past database structures and relationship history. For example, the generation AI refers to past database structures and relationship history to analyze tags and column names. For example, it learns the patterns of column names and relationships used in the past and applies them to a new database. The tag analysis unit also allows the generation AI to learn from past examples of successful and unsuccessful relationship settings and reflects this in the analysis of tags and column names. For example, it selects the optimal analysis method based on the relationship setting history in past databases. The tag analysis unit also allows the generation AI to analyze past database structures and understand the meaning of tags and column names. For example, it learns the meaning of column names used in past databases and applies them to a new database. In this way, by referring to past database structures and relationship history, the analysis accuracy is improved.

[0053] The tag analysis unit can automatically recognize industry-specific terms and abbreviations and analyze tags and column names. For example, the tag analysis unit allows the generation AI to automatically recognize industry-specific terms and abbreviations and analyze tags and column names. For example, it learns abbreviations used in medical industry databases and reflects them in the analysis. The tag analysis unit also allows the generation AI to learn industry-specific terms and abbreviations and improve the analysis accuracy of tags and column names. For example, it recognizes technical terms used in financial industry databases and reflects them in the analysis. The tag analysis unit also allows the generation AI to automatically recognize industry-specific terms and abbreviations and understand the meaning of tags and column names. For example, it learns abbreviations used in manufacturing industry databases and applies them to a new database. This improves analysis accuracy by recognizing industry-specific terms and abbreviations.

[0054] The tag analysis unit can use the emotion estimation function to analyze the emotion a user has when entering tags and column names and make suggestions to elicit positive emotions. The tag analysis unit, for example, uses the emotion estimation function to analyze the emotion a user has when entering tags and column names. For example, it analyzes the user's facial expressions and voice and makes suggestions to elicit positive emotions. The tag analysis unit also uses the emotion estimation function to analyze the emotion a user has when entering tags and column names in real time and provides an interface for eliciting positive emotions. For example, it presents encouraging messages and success stories. The tag analysis unit also uses the emotion estimation function to analyze the emotion a user has when entering tags and column names and provides advice to elicit positive emotions. For example, it displays appropriate words of encouragement or praise based on the input content. This analyzes the user's emotions and elicits positive emotions, thereby improving the efficiency of input work.

[0055] The tag analysis unit can also include image and audio data in its analysis targets, enabling it to find correlations from multimodal information. For example, when the generation AI analyzes tags and column names, the tag analysis unit also includes image and audio data in its analysis targets. For example, it can find correlations between tags and column names using prototype images and explanatory audio. The tag analysis unit also uses image recognition technology to extract the contents of tags and column names from uploaded image data and reflect this in the analysis. For example, it can analyze product design and sketch images to find correlations between tags and column names. The tag analysis unit also uses audio analysis technology to automatically extract the key points of tags and column names from submitted audio data and reflect this in the analysis. For example, it can convert explanatory audio into text data and find correlations between tags and column names. In this way, including image and audio data in the analysis targets improves the accuracy of the analysis.

[0056] The tag analysis unit can refer to database structures in different industries and fields and apply them to tag and column name analysis. For example, the generation AI refers to database structures in different industries and fields and applies them to tag and column name analysis. For example, it learns the database structures of the medical and financial industries and applies them to a new database. The tag analysis unit also analyzes database structures in different industries and finds correlations between tags and column names. For example, it learns the database structures of the manufacturing and service industries and reflects this in the analysis. The tag analysis unit also refers to database structures in different fields and improves the accuracy of tag and column name analysis. For example, it learns the database structures of the education and entertainment fields and applies them to a new database. In this way, by referencing database structures in different industries and fields, analysis accuracy is improved.

[0057] The tag analysis unit uses the emotion estimation function to analyze the emotion of the user when entering tags and column names in real time, thereby improving the accuracy of the input. For example, the tag analysis unit uses the emotion estimation function to analyze the emotion of the user when entering tags and column names in real time, thereby improving the accuracy of the input. For example, the tag analysis unit analyzes the user's facial expressions and voice to improve the accuracy of the input content. The tag analysis unit also uses the emotion estimation function to analyze the emotion of the user when entering tags and column names in real time, thereby providing an interface for improving the accuracy of the input. For example, the tag analysis unit displays appropriate advice according to the input content. The tag analysis unit also uses the emotion estimation function to analyze the emotion of the user when entering tags and column names in real time, thereby providing feedback for improving the accuracy of the input. For example, the tag analysis unit displays appropriate encouragement or praise according to the input content. In this way, the user's emotion is analyzed in real time, improving the accuracy of the input, and improving work efficiency.

[0058] The relation identification unit can identify relations by analyzing the frequency of data use and access patterns. For example, the generation AI analyzes the frequency of data use and access patterns to identify relations. For example, it associates frequently accessed columns with each other. The relation identification unit also learns the data access patterns and proposes optimal relations. For example, it associates columns that receive concentrated access during specific time periods. The generation AI also analyzes the frequency of data use and identifies relations. For example, it associates columns that are frequently updated with each other. In this way, the optimal relations can be identified by analyzing the frequency of data use and access patterns.

[0059] The relation identification unit can set dynamic relations by taking into account time-series fluctuations in the data. For example, the generation AI analyzes time-series fluctuations in the data and identifies relations. For example, it associates data columns that vary by season. The generation AI also learns time-series fluctuations in the data and sets dynamic relations. For example, it associates data columns that vary in response to specific events. The generation AI also identifies relations by taking into account time-series fluctuations in the data. For example, it associates data columns that vary by time period. In this way, dynamic relations can be set by taking into account time-series fluctuations in the data.

[0060] The relationship identification unit can use the emotion estimation function to analyze the emotion a user has when setting a relationship and propose a relationship that will elicit positive emotions. For example, the relationship identification unit uses the emotion estimation function to analyze the emotion a user has when setting a relationship and propose a relationship that will elicit positive emotions. For example, the relationship identification unit analyzes the user's facial expressions and voice and proposes an appropriate relationship. The relationship identification unit also uses the emotion estimation function to analyze the emotion a user has when setting a relationship in real time and provides an interface for eliciting positive emotions. For example, the relationship identification unit presents encouraging messages and success stories. The relationship identification unit also uses the emotion estimation function to analyze the emotion a user has when setting a relationship and provides advice for eliciting positive emotions. For example, the relationship identification unit displays appropriate encouraging words or compliments based on the input content. In this way, the efficiency of relationship establishment is improved by analyzing the user's emotions and eliciting positive emotions.

[0061] The relation identification unit can analyze the interdependencies between different databases and set cross-database relations. In the relation identification unit, for example, the generation AI analyzes the interdependencies between different databases and identifies the relations. For example, it analyzes the interdependencies between a customer database and an order database and sets the relations. In addition, the relation identification unit learns the interdependencies between different databases and sets cross-database relations. For example, it analyzes the interdependencies between an inventory database and a sales database and sets the relations. In addition, the relation identification unit analyzes the interdependencies between different databases and identifies the relations. For example, it analyzes the interdependencies between a customer database and a support database and sets the relations. In this way, cross-database relations can be set by analyzing the interdependencies between different databases.

[0062] The relation identification unit can set optimal relations for each region, taking into account the geographic distribution of the data. In the relation identification unit, for example, the generation AI analyzes the geographic distribution of the data and identifies relations. For example, it associates customer data and sales data for each region. The relation identification unit also learns the geographic distribution of the data and sets optimal relations for each region. For example, it associates data columns for urban and rural areas. The relation identification unit also identifies relations, taking into account the geographic distribution of the data. For example, it associates data columns for each country. In this way, it is possible to set optimal relations for each region by taking into account the geographic distribution of the data.

[0063] The relationship identification unit can use the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and propose an optimal relationship. For example, the relationship identification unit uses the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and propose an optimal relationship. For example, the relationship identification unit analyzes the user's facial expression or voice and proposes an appropriate relationship. The relationship identification unit also uses the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and provides an interface for proposing an optimal relationship. For example, the relationship identification unit presents encouraging messages and success stories. The relationship identification unit also uses the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and provides advice for proposing an optimal relationship. For example, the relationship identification unit displays appropriate encouraging words or compliments according to the input content. In this way, the efficiency of relationship setting is improved by analyzing the user's emotion in real time and proposing an optimal relationship.

[0064] The relation setting unit can learn from past examples of successful and unsuccessful relationship setting and set the optimal relationship. For example, the generation AI in the relation setting unit learns from past examples of successful and unsuccessful relationship setting and sets the optimal relationship. For example, the generation AI selects the optimal relationship based on the history of relationship setting in a past database. The generation AI also analyzes past examples of successful and unsuccessful relationship setting and sets a new relationship based on that knowledge. For example, it analyzes the factors of success and failure and reflects them in the relationship setting. The generation AI also learns from the history of past relationship setting and sets the optimal relationship. For example, it sets a relationship based on similar cases and maintains data consistency. In this way, the optimal relationship can be set by learning from past examples of successful and unsuccessful relationship setting.

[0065] The relation setting unit can set secure relations by taking into account the security requirements of the data. For example, the generation AI sets secure relations by taking into account the security requirements of the data. For example, the generation AI appropriately separates confidential data from general data and sets relations. The relation setting unit also allows the generation AI to learn the security requirements of the data and set secure relations. For example, the generation AI sets relations by taking into account access control and encryption requirements. The relation setting unit also allows the generation AI to analyze the security requirements of the data and set secure relations. For example, the generation AI sets relations to maintain the confidentiality and integrity of the data. This makes it possible to set secure relations by taking into account the security requirements of the data.

[0066] The relationship setting unit can use the emotion estimation function to analyze the emotion of the user when setting a relationship and propose a relationship that will elicit positive emotions. For example, the relationship setting unit uses the emotion estimation function to analyze the emotion of the user when setting a relationship and propose a relationship that will elicit positive emotions. For example, the relationship setting unit analyzes the user's facial expressions and voice and proposes an appropriate relationship. The relationship setting unit also uses the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and provides an interface for eliciting positive emotions. For example, the relationship setting unit presents encouraging messages and success stories. The relationship setting unit also uses the emotion estimation function to analyze the emotion of the user when setting a relationship and provides advice for eliciting positive emotions. For example, the relationship setting unit displays appropriate encouraging words or compliments based on the input content. This improves the efficiency of relationship setting by analyzing the user's emotions and eliciting positive emotions.

[0067] The relation setting unit can set relations between different data formats. For example, the generation AI sets relations between different data formats in the relation setting unit. For example, it sets a relation that associates structured data with unstructured data. The relation setting unit also allows the generation AI to learn different data formats and set relations. For example, it sets a relation that associates text data with image data. The relation setting unit also allows the generation AI to analyze relations between different data formats and set the optimal relation. For example, it sets a relation that associates audio data with text data. In this way, by setting relations between different data formats, data consistency can be maintained.

[0068] The relation setting unit can set a relation between real-time data and batch data. In the relation setting unit, for example, the generation AI sets a relation between real-time data and batch data. For example, sensor data collected in real time is associated with batch data that is updated periodically. In addition, the relation setting unit allows the generation AI to learn the real-time data and batch data and set a relation. For example, real-time transaction data is associated with batch-processed sales data. In addition, the relation setting unit allows the generation AI to analyze the relation between real-time data and batch data and set an optimal relation. For example, real-time user behavior data is associated with batch-processed marketing data. In this way, by setting a relation between real-time data and batch data, data consistency can be maintained.

[0069] The relationship setting unit can use the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and propose an optimal relationship. For example, the relationship setting unit uses the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and propose an optimal relationship. For example, the relationship setting unit analyzes the user's facial expression and voice and proposes an appropriate relationship. The relationship setting unit also uses the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and provides an interface for proposing an optimal relationship. For example, the relationship setting unit presents encouraging messages and success stories. The relationship setting unit also uses the emotion estimation function to analyze the emotion of the user when setting a relationship in real time and provides advice for proposing an optimal relationship. For example, the relationship setting unit displays appropriate encouraging words or compliments according to the input content. In this way, the efficiency of relationship setting is improved by analyzing the user's emotion in real time and proposing an optimal relationship.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The data integration system can further include a data quality evaluation unit. The data quality evaluation unit evaluates the consistency, accuracy, and completeness of the data, and checks the quality of the data before data integration. For example, the data quality evaluation unit detects missing values ​​or outliers in the data and makes suggestions for correction. The data quality evaluation unit can also detect duplicate data and suggest merging the duplicate data. Furthermore, the data quality evaluation unit can detect inconsistencies in data formats or units and make suggestions for unification. This allows the quality of the data to be evaluated in advance, improving the accuracy and reliability of data integration.

[0072] The data linkage system can further include a data conversion unit. The data conversion unit converts data in different formats into a unified format. For example, it can convert data in CSV format into JSON format. The data conversion unit can also unify data in different units. For example, it can convert data in meters into feet. The data conversion unit can also unify data in different encodings. For example, it can convert data in UTF-8 encoding into ISO-8859-1 encoding. This makes it possible to unify data in different formats and units, improving the efficiency of data linkage.

[0073] The data linkage system can further include a data security unit. The data security unit encrypts data and controls access to ensure data security. For example, the data security unit encrypts data when it is sent or received to prevent unauthorized access. The data security unit can also set access rights for each user to prevent unauthorized use of data. Furthermore, the data security unit can detect data tampering and maintain data integrity. This ensures data security and enables safe data linkage.

[0074] The data linkage system can further include a data monitoring unit. The data monitoring unit monitors the status of data linkage in real time and detects abnormalities. For example, the data monitoring unit monitors the data transmission and reception speed and error rate, and issues an alert if an abnormality occurs. The data monitoring unit can also monitor data consistency and detect data inconsistencies. Furthermore, the data monitoring unit can monitor data usage and analyze usage frequency and access patterns. This allows the status of data linkage to be grasped in real time and respond quickly.

[0075] The data linkage system may further include a data backup unit. The data backup unit periodically backs up data to prevent data loss. For example, the data backup unit backs up data at a fixed time every day and stores the backup data in a safe location. The data backup unit also has a backup data restoration function, allowing for quick restoration in the event of data loss. Furthermore, the data backup unit can also check the integrity of the backup data to ensure that the backup data is accurate. This prevents data loss and ensures data safety.

[0076] The data linkage system can further use an emotion estimation function to analyze a user's stress level and suggest a break at an appropriate time. For example, it can analyze a user's facial expressions and voice and suggest a break if it determines that the stress level is high. It can also use the emotion estimation function to analyze a user's concentration level and suggest refreshing if their concentration drops. It can also use the emotion estimation function to analyze a user's emotions and provide advice to bring out positive emotions. This can reduce the user's stress and improve work efficiency.

[0077] The data linkage system can further use an emotion estimation function to analyze a user's motivation and provide feedback to maintain motivation. For example, it can analyze a user's facial expressions and voice and display an encouraging message if it determines that the user's motivation is declining. It can also use the emotion estimation function to analyze a user's sense of accomplishment and provide feedback to enhance that sense of accomplishment. It can also use the emotion estimation function to analyze a user's emotions and provide advice to elicit positive emotions. This can maintain the user's motivation and improve work efficiency.

[0078] The data linkage system can further use an emotion estimation function to analyze a user's emotions and provide an interface that corresponds to the emotions. For example, the system can analyze the user's facial expressions and voice and change the color scheme or layout according to the emotions. The emotion estimation function can also be used to analyze a user's emotions in real time and provide interactions that correspond to the emotions. The emotion estimation function can also be used to analyze a user's emotions and provide content that corresponds to the emotions. This makes it possible to provide an interface that corresponds to the user's emotions and improve the user experience.

[0079] The data linkage system can further use an emotion estimation function to analyze the user's emotions and suggest task prioritization according to the emotions. For example, by analyzing the user's facial expressions and voice, it can suggest prioritizing easier tasks if the user is highly stressed. It can also use the emotion estimation function to analyze the user's concentration level and suggest prioritizing important tasks if the user is highly concentrated. It can also use the emotion estimation function to analyze the user's emotions and assign tasks according to the emotions. This can suggest task prioritization according to the user's emotions, improving work efficiency.

[0080] The data linkage system can further use an emotion estimation function to analyze a user's emotions and provide feedback according to the emotions. For example, it can analyze a user's facial expressions and voice and provide feedback to elicit positive emotions. It can also use the emotion estimation function to analyze a user's emotions in real time and provide advice according to the emotions. It can also use the emotion estimation function to analyze a user's emotions and provide interactions according to the emotions. This makes it possible to provide feedback according to the user's emotions and improve the user experience.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The tag analysis unit analyzes tags and column names obtained from cloud tools and databases. For example, the tag analysis unit uses generative AI to understand the meaning of tags and column names and find relationships. The tag analysis unit can also use natural language processing technology to analyze tags and column names. Step 2: The relation identification unit identifies data columns that need to be related based on the associations between tags and column names analyzed by the tag analysis unit. For example, the relation identification unit uses generation AI to identify that "customer ID" and "order ID" are columns that should be associated. The relation identification unit can also evaluate the associations between data columns based on the analysis results. Step 3: The relation setting unit automatically sets the relations between the data columns identified by the relation identification unit. For example, the relation setting unit sets the relations between the data columns using a generation AI. The relation setting unit can also perform settings to ensure smooth data integration between different systems.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 tag analysis part that analyzes tags and column names obtained from cloud tools and databases, a relation specifying unit that specifies data columns that need to be related based on the associations between tags and column names analyzed by the tag analysis unit; a relation setting unit that automatically sets the relation of the data column identified by the relation identifying unit. A system characterized by:

2. The tag analysis unit Refer to the history of past database structures and relationships, and analyze tags and column names 2. The system of claim 1.

3. The tag analysis unit Automatically recognize industry-specific terms and abbreviations and parse tags and column names 2. The system of claim 1.

4. The tag analysis unit Analyzes emotions when users enter tags and column names, and makes suggestions to elicit positive emotions.

2. The system of claim 1.

5. The tag analysis unit Analyzing image and audio data to find correlations from multimodal information 2. The system of claim 1.

6. The tag analysis unit Refer to database structures from different industries and fields and apply them to analyzing tags and column names.

2. The system of claim 1.

7. The tag analysis unit Analyze emotions in real time when users enter tags and column names to improve input accuracy 2. The system of claim 1.

8. The relation specifying unit Analyze data usage and access patterns to identify relationships 2. The system of claim 1.

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