system

Abstract

The system according to this embodiment aims to efficiently build a new version of the database in a short period of time. [Solution] The system according to the embodiment comprises an input unit, an analysis unit, a construction unit, and a release unit. The input unit inputs data such as documents, source code, and DB configuration parameters necessary for DB preparation. The analysis unit analyzes the information input by the input unit and generates an optimal DB construction plan. The construction unit automatically constructs a new version of the DB based on the DB construction plan generated by the analysis unit. The release unit releases the new version of the DB constructed by the construction unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a lot of time and labor to construct a new version of a database, and it is difficult to construct it efficiently.

[0005] The system according to the embodiment aims to efficiently construct a new version of a database in a short period of time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, an analysis unit, a construction unit, and a release unit. The input unit receives data such as documents, source code, and DB configuration parameters necessary for DB preparation. The analysis unit analyzes the information entered by the input unit and generates an optimal DB construction plan. The construction unit automatically constructs a new version of the DB based on the DB construction plan generated by the analysis unit. The release unit releases the new version of the DB constructed by the construction unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently build new versions of the database in a short period of time. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The database construction system according to an embodiment of the present invention is a system that utilizes generative AI to build a new version of a database in a short period of time. This database construction system takes data such as documents, source code, and DB configuration parameters necessary for DB preparation as input, and the requirements for the database to be built (OS environment, user information, data size, performance, etc.) as input. The generative AI analyzes this information and provides a new version of the DB service in a short period of time (e.g., within one month). This mechanism enables the rapid provision of new versions (new functions), improving operational efficiency and providing high-quality DB services. Furthermore, by enabling the construction of databases in various environments, even those currently limited to specified environments, it is envisioned that the system will be provided to various systems within the company. In the future, it is expected to contribute to increased company revenue through external sales. For example, the database construction system inputs data such as documents, source code, and DB configuration parameters necessary for DB preparation to the generative AI. This includes, for example, PostgreSQL configuration files, scripts, and performance test results. Next, the database construction system inputs the requirements for the database to be built. For example, the user inputs include the OS environment (Linux, Windows, etc.), user information (number of users, access frequency, etc.), data size (from several GB to several TB), and performance requirements (response time, throughput, etc.). The generating AI analyzes the input information and generates an optimal DB construction plan. This includes, for example, optimal parameter settings, script generation, and automated performance test execution. Based on the generated DB construction plan, the database construction system automatically builds a new version of the DB. For example, the generating AI automatically executes scripts, configures the DB, and performs performance tests. The newly built DB version becomes ready for release in a short period of time. For example, the new version of the DB service can be provided within one month. This mechanism allows the database construction system to quickly provide new versions of the DB, resulting in improved operational efficiency and the provision of high-quality DB services.Furthermore, its ability to adapt to various environments allows it to be provided to internal and external systems, and is expected to increase revenue through external sales in the future. As a result, the database construction system can build and release new versions of the database in a short period of time.

[0029] The database construction system according to the embodiment comprises an input unit, an analysis unit, a construction unit, and a release unit. The input unit inputs data such as documents, source code, and DB configuration parameters necessary for DB preparation. For example, the input unit can input data such as PostgreSQL configuration files, scripts, and performance test results. The input unit can input data such as schema definitions, index settings, and user permission settings. The analysis unit analyzes the information input by the input unit and generates an optimal DB construction plan. For example, the analysis unit can analyze the input information and generate optimal parameter settings and scripts. For example, the analysis unit can generate memory settings, cache settings, and index creation scripts. The construction unit automatically constructs a new version of the DB based on the DB construction plan generated by the analysis unit. For example, the construction unit can configure the DB and perform performance tests based on the generated DB construction plan. For example, the construction unit can execute scripts, apply configuration files, and perform tests. The release unit releases the new version of the DB constructed by the construction unit. For example, the release unit can release the constructed new version of the DB in a short period of time. The release section can handle things like version control, deployment procedures, and user notification methods. As a result, the database construction system according to the embodiment can build and release new versions of the DB in a short period of time.

[0030] The input section takes data such as documents, source code, and database configuration parameters necessary for database preparation. Specifically, the input section can take data such as PostgreSQL configuration files, scripts, and performance test results. This includes, for example, PostgreSQL configuration files such as postgresql.conf and pg_hba.conf. These files describe detailed settings related to database performance and security, and the input section accurately reads these files and extracts the necessary information. Scripts include table creation scripts, data insertion scripts, and index creation scripts. These scripts define the database structure and data placement, and the input section parses these scripts to obtain the information necessary for database construction. Furthermore, performance test results include data such as query execution time, resource utilization, and throughput. This data is important for evaluating database performance, and the input section provides information to generate an optimal database construction plan based on this data. The input section can take data such as schema definitions, index settings, and user permission settings. Schema definitions include table structure, column data types, and constraints, while index settings include index types, target columns, and index creation methods. User permission settings include configuring the permissions and roles of users who can access the database, thereby ensuring the security of the database. The input section plays the role of efficiently inputting this diverse data and providing the basic information for database construction.

[0031] The analysis unit analyzes the information entered by the input unit and generates an optimal database construction plan. Specifically, the analysis unit can analyze the input information and generate optimal parameter settings and scripts. For example, the analysis unit analyzes the PostgreSQL configuration file and derives optimal parameters such as memory settings, cache settings, and connection settings. This includes memory-related parameters such as shared_buffers, work_mem, and maintenance_work_mem, and cache-related parameters such as effective_cache_size and wal_buffers. The analysis unit also analyzes the input script and generates optimal index creation scripts and table creation scripts. This includes index types such as B-tree indexes, hash indexes, and GIN indexes, index creation methods, and selection of target columns. Furthermore, the analysis unit analyzes the results of performance tests and derives settings to optimize database performance. This includes analyzing query execution plans, identifying bottlenecks, and optimizing resource allocation. Based on this information, the analysis unit generates an optimal database construction plan and provides it to the construction unit. The analysis unit can perform these analyses using AI. For example, machine learning algorithms are used to predict optimal parameter settings based on past data and performance test results. Furthermore, natural language processing techniques are used to extract necessary information from documents and configuration files for analysis. This allows the analysis unit to efficiently and accurately analyze information and generate an optimal database construction plan.

[0032] The construction department automatically builds a new version of the database based on the database construction plan generated by the analysis department. Specifically, the construction department can configure the database and conduct performance tests based on the generated database construction plan. For example, the construction department applies configuration files generated by the analysis department to configure the database's memory, cache, and connection settings. The construction department also executes scripts generated by the analysis department to create tables, indexes, and insert data. This includes table creation scripts, index creation scripts, and data insertion scripts. Furthermore, the construction department conducts performance tests to evaluate the database's performance. This includes evaluation items such as query execution time, resource utilization, and throughput. Based on these evaluation results, the construction department adjusts settings and modifies scripts as needed to achieve an optimal database construction. The construction department can automate these tasks. For example, automating script execution, configuration file application, and test execution improves efficiency and accuracy. The construction department can also build the database using cloud or virtual environments. This enables flexible resource utilization and improved scalability. Through these functions, the development team can quickly and accurately build new versions of the database, improving the overall system performance and reliability.

[0033] The Release Department releases new versions of the database built by the Development Department. Specifically, the Release Department can release new versions of the built database in a short period of time. The Release Department can handle things like version control, deployment procedures, and user notification methods. For version control, they use version control systems such as Git and SVN to manage the change history and release notes of new versions of the database. For deployment procedures, they use CI / CD tools to achieve an automated deployment process. This includes tools such as Jenkins, GitLab CI, and CircleCI. This allows the Release Department to deploy new versions of the database quickly and reliably. For user notification methods, they provide release information to users using email, chat tools, and notification systems. This includes release notes, explanations of changes, and usage guides. The Release Department properly manages this information and provides it to users in an easy-to-understand manner to promote the use of new versions of the database. Furthermore, the Release Department can collect feedback after the release and incorporate it into the next release. This includes opinions, requests, and bug reports from users. Based on this feedback, the Release Department aims to improve the quality and functionality of the database. This allows the release department to quickly and reliably release new versions of the database, thereby improving user satisfaction.

[0034] The input section can accept data such as PostgreSQL configuration files, scripts, and performance test results. For example, the input section can accept PostgreSQL configuration files (postgresql.conf, pg_hba.conf, etc.). For example, the input section can accept performance test result reports. For example, the input section can accept test scripts. By inputting data such as PostgreSQL configuration files, scripts, and performance test results, the database is ready for construction. Some or all of the above processing in the input section may be performed using AI, for example, or not. For example, the input section can input data such as PostgreSQL configuration files, scripts, and performance test results into the AI, which can then analyze this data.

[0035] The analysis unit can analyze the input information and generate optimal parameter settings and scripts. For example, the analysis unit can analyze the input information and generate optimal memory settings. For example, the analysis unit can analyze the input information and generate optimal cache settings. For example, the analysis unit can analyze the input information and generate optimal index creation scripts. This improves the efficiency of database construction by analyzing the input information and generating optimal parameter settings and scripts. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the input information into a generation AI, and the generation AI can generate optimal parameter settings and scripts.

[0036] The construction unit can configure the database and perform performance tests based on the generated database construction plan. For example, the construction unit can configure the database based on the generated database construction plan. For example, the construction unit can perform performance tests based on the generated database construction plan. The construction unit can, for example, execute scripts, apply configuration files, and perform tests. This improves the quality of the database by configuring it and performing performance tests based on the generated database construction plan. Some or all of the above processes in the construction unit may be performed using a generation AI, or without a generation AI. For example, the construction unit can input the generated database construction plan into a generation AI, which can then configure the database and perform performance tests.

[0037] The release unit can release a new version of the constructed database in a short period of time. For example, the release unit can release a new version of the constructed database within a few hours. For example, the release unit can release a new version of the constructed database within a day. The release unit can automate the steps of the release process and perform releases quickly. This enables rapid service delivery by releasing a new version of the constructed database in a short period of time. Some or all of the above processes in the release unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the release unit can input the new version of the constructed database into a generation AI, and the generation AI can automate the release process.

[0038] The analysis unit can generate database construction plans that accommodate various OS environments, data sizes, and performance requirements. For example, the analysis unit can generate a database construction plan for a Windows environment. For example, the analysis unit can generate a database construction plan for a Linux environment. For example, the analysis unit can generate a database construction plan that accommodates data sizes ranging from several GB to several TB. For example, the analysis unit can generate a database construction plan that accommodates performance requirements such as response time and throughput. This enables flexible database construction by generating database construction plans that accommodate various OS environments, data sizes, and performance requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input database construction plans that accommodate various OS environments, data sizes, and performance requirements into a generation AI, and the generation AI can generate the optimal plan.

[0039] The input unit can evaluate the reliability of the input data and automatically filter out unreliable data. For example, the input unit can verify the source of the data and exclude data from unreliable sources. For example, the input unit can check the consistency of the data and filter out inconsistent data. For example, the input unit can evaluate the frequency of data updates and automatically exclude outdated data. This improves data quality by filtering out unreliable data. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can use AI to evaluate the reliability of the data and automatically filter out unreliable data.

[0040] The input unit can automatically convert the format of input data and unify data in different formats. For example, the input unit can automatically convert CSV data to JSON format. For example, the input unit can automatically convert XML data to SQL format. For example, the input unit can convert data from an Excel file into a format that can be directly imported into a database. This ensures data consistency by unifying data in different formats. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data in different formats into AI, and the AI ​​can automatically convert the data format.

[0041] The input unit can automatically select the most suitable data by referring to the user's past input history when acquiring input data. For example, the input unit can prioritize acquiring data that the user has frequently entered in the past. For example, the input unit can automatically select highly relevant data from the user's past input history. For example, the input unit can analyze the user's past input patterns and acquire the most suitable data. This allows the system to select the most suitable data by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history into AI, and the AI ​​can automatically select the most suitable data.

[0042] The input unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring input data. For example, if the user is in a specific region, the input unit can prioritize the acquisition of data related to that region. For example, the input unit can prioritize the acquisition of data for locations close to the user's current location. For example, the input unit can acquire highly relevant data by considering the user's travel history. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without using AI. For example, the input unit can input the user's geographical location information into the AI, and the AI ​​can prioritize the acquisition of highly relevant data.

[0043] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the input data during the analysis. For example, the analysis unit can improve accuracy by analyzing the correlations between data. For example, the analysis unit can perform the analysis by considering the dependencies between data. For example, the analysis unit can improve the accuracy of the analysis by checking the consistency of data. As a result, the accuracy of the analysis is improved by considering the interrelationships of the input data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the interrelationships of the input data into a generative AI, and the generative AI can analyze the correlations and dependencies to improve accuracy.

[0044] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can adjust the algorithm parameters based on past analysis results. For example, the analysis unit can select the optimal algorithm from past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. This makes it possible to optimize the analysis algorithm by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past analysis results into a generative AI, which can then adjust the algorithm parameters and select the optimal algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the input data during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can automatically adjust the priority of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the input data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the submission date of the input data into the generating AI, and the generating AI can determine the priority of analysis.

[0046] The analysis unit can improve the accuracy of the analysis by referring to relevant literature for the input data during the analysis. For example, the analysis unit can improve the accuracy of the analysis based on relevant literature. For example, the analysis unit can reflect knowledge obtained from relevant literature into the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input relevant literature into a generative AI, and the generative AI can improve the accuracy of the analysis.

[0047] The construction unit can automatically verify each step of the generated DB construction plan during construction and detect errors in advance. For example, the construction unit can perform pre-verification and detect errors before executing each step. For example, the construction unit can simulate each step of the construction plan and identify errors in advance. For example, the construction unit can verify the results and detect errors after executing each step. In this way, by verifying each step in advance, the occurrence of errors can be prevented. Some or all of the above processing in the construction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the construction unit can input the generated DB construction plan into a generation AI, which can verify each step and detect errors in advance.

[0048] The construction unit can automatically adjust settings to accommodate different environments during construction. For example, the construction unit can automatically adjust configuration files according to the OS environment. For example, the construction unit can automatically adjust parameters according to the hardware configuration. For example, the construction unit can automatically adjust settings according to the network environment. This enables flexible database construction by automatically adjusting settings to accommodate different environments. Some or all of the above-described processes in the construction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the construction unit can input settings to accommodate different environments into a generation AI, and the generation AI can automatically adjust them.

[0049] The construction unit can select the optimal construction procedure by referring to the user's past construction history during construction. For example, the construction unit can prioritize selecting construction procedures that the user has successfully performed in the past. For example, the construction unit can automatically select the optimal procedure from the user's past construction history. For example, the construction unit can analyze the user's past construction patterns and propose the optimal procedure. This allows the optimal construction procedure to be selected by referring to the user's past construction history. Some or all of the above processing in the construction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the construction unit can input the user's past construction history into a generative AI, and the generative AI can select the optimal construction procedure.

[0050] The construction unit can select the optimal server during construction by considering the user's geographical location information. For example, the construction unit can select the server closest to the user's current location. For example, the construction unit can automatically select the optimal server based on the user's geographical location information. For example, the construction unit can select the optimal server by considering the user's travel history. In this way, the optimal server can be selected by considering the user's geographical location information. Some or all of the above processing in the construction unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the construction unit can input the user's geographical location information into a generation AI, and the generation AI can select the optimal server.

[0051] The release unit can automatically perform final pre-release verification at the time of release and detect errors in advance. For example, the release unit can automatically verify all settings before release and detect errors. For example, the release unit can perform simulations before release and identify errors in advance. For example, the release unit can automatically verify operation in a test environment before release and detect errors. In this way, by automatically performing final pre-release verification, the occurrence of errors can be prevented. Some or all of the above processes in the release unit may be performed using, for example, a generation AI, or without a generation AI. For example, the release unit can input the final pre-release verification into a generation AI, and the generation AI can detect errors in advance.

[0052] The release unit can automatically adjust settings to accommodate different environments during release. For example, the release unit can automatically adjust configuration files according to the OS environment. For example, the release unit can automatically adjust parameters according to the hardware configuration. For example, the release unit can automatically adjust settings according to the network environment. This enables flexible releases by automatically adjusting settings to accommodate different environments. Some or all of the above processing in the release unit may be performed using, for example, a generation AI, or without a generation AI. For example, the release unit can input settings to accommodate different environments into a generation AI, and the generation AI can automatically adjust them.

[0053] The release unit can select the optimal release procedure by referring to the user's past release history at the time of release. For example, the release unit can prioritize the selection of release procedures that have been successful for the user in the past. For example, the release unit can automatically select the optimal procedure from the user's past release history. For example, the release unit can analyze the user's past release patterns and propose the optimal procedure. This allows the optimal release procedure to be selected by referring to the user's past release history. Some or all of the above processing in the release unit may be performed using, for example, a generative AI, or without a generative AI. For example, the release unit can input the user's past release history into a generative AI, and the generative AI can select the optimal release procedure.

[0054] The release unit can select the optimal release method at the time of release, taking into account the user's geographical location information. For example, the release unit can select the server closest to the user's current location. For example, the release unit can automatically select the optimal release method based on the user's geographical location information. For example, the release unit can select the optimal release method by taking into account the user's travel history. In this way, the optimal release method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the release unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the release unit can input the user's geographical location information into a generation AI, and the generation AI can select the optimal release method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The database construction system can also include a security monitoring unit. This unit can monitor the security status of the database in real time during construction and after release, and issue alerts if an anomaly is detected. For example, the security monitoring unit can detect unauthorized access to the database and notify the administrator. It can also monitor database configuration changes and unauthorized data manipulation, and automatically take countermeasures if an anomaly occurs. Furthermore, the security monitoring unit can periodically generate security reports and provide them to the administrator. This enhances the database's security, allowing for safer use.

[0057] The database construction system can also include a backup management unit. This unit can automatically schedule and regularly perform database backups. For example, it can take a full database backup every night. Furthermore, it can check the integrity of the backup data and notify the administrator if any problems occur. Additionally, the backup management unit can store backup data in cloud storage, enabling rapid recovery in the event of a disaster. This ensures that database data is securely protected and can handle any unforeseen problems.

[0058] The database construction system can also include a user feedback collection unit. This unit can collect feedback from database users and use it to improve the system. For example, it can conduct regular surveys to understand user satisfaction and areas for improvement. It can also provide a platform where users can submit suggestions and opinions about the system. Furthermore, it can analyze the collected feedback and formulate system improvement plans. This enables system improvements that meet user needs, leading to increased user satisfaction.

[0059] The database construction system can also include a performance optimization unit. This unit can monitor database performance in real time and suggest optimizations. For example, it can monitor query execution times and identify queries experiencing delays. It can also suggest index optimizations and cache settings to improve database performance. Furthermore, the performance optimization unit can periodically generate performance reports and provide them to administrators. This ensures that database performance is always kept optimal, enabling efficient operation.

[0060] The database construction system can also be equipped with an energy efficiency management unit. This unit can monitor energy consumption in database operations and propose efficient energy use. For example, it can monitor server operating status and reduce energy consumption by stopping unnecessary resources. It can also optimize database load balancing to improve energy efficiency. Furthermore, it can periodically generate and provide energy consumption reports to administrators. This reduces database operating costs and enables environmentally friendly operation.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The input section is where you enter data necessary for database preparation, such as documents, source code, and database configuration parameters. For example, you can enter data such as PostgreSQL configuration files and scripts, performance test results, schema definitions, index settings, and user permission settings. Step 2: The analysis unit analyzes the information entered by the input unit and generates an optimal database construction plan. For example, it can analyze the entered information and generate optimal parameter settings, scripts, memory settings, cache settings, index creation scripts, etc. Step 3: The construction unit automatically builds a new version of the database based on the database construction plan generated by the analysis unit. For example, based on the generated database construction plan, it can configure the database, perform performance tests, execute scripts, apply configuration files, and run tests. Step 4: The release team releases the new version of the database built by the construction team. For example, they can release a new version of the built database in a short period of time and manage version control, deployment procedures, and user notification methods.

[0063] (Example of form 2) The database construction system according to an embodiment of the present invention is a system that utilizes generative AI to build a new version of a database in a short period of time. This database construction system takes data such as documents, source code, and DB configuration parameters necessary for DB preparation as input, and the requirements for the database to be built (OS environment, user information, data size, performance, etc.) as input. The generative AI analyzes this information and provides a new version of the DB service in a short period of time (e.g., within one month). This mechanism enables the rapid provision of new versions (new functions), improving operational efficiency and providing high-quality DB services. Furthermore, by enabling the construction of databases in various environments, even those currently limited to specified environments, it is envisioned that the system will be provided to various systems within the company. In the future, it is expected to contribute to increased company revenue through external sales. For example, the database construction system inputs data such as documents, source code, and DB configuration parameters necessary for DB preparation to the generative AI. This includes, for example, PostgreSQL configuration files, scripts, and performance test results. Next, the database construction system inputs the requirements for the database to be built. For example, the user inputs include the OS environment (Linux, Windows, etc.), user information (number of users, access frequency, etc.), data size (from several GB to several TB), and performance requirements (response time, throughput, etc.). The generating AI analyzes the input information and generates an optimal DB construction plan. This includes, for example, optimal parameter settings, script generation, and automated performance test execution. Based on the generated DB construction plan, the database construction system automatically builds a new version of the DB. For example, the generating AI automatically executes scripts, configures the DB, and performs performance tests. The newly built DB version becomes ready for release in a short period of time. For example, the new version of the DB service can be provided within one month. This mechanism allows the database construction system to quickly provide new versions of the DB, resulting in improved operational efficiency and the provision of high-quality DB services.Furthermore, its ability to adapt to various environments allows it to be provided to internal and external systems, and is expected to increase revenue through external sales in the future. As a result, the database construction system can build and release new versions of the database in a short period of time.

[0064] The database construction system according to the embodiment comprises an input unit, an analysis unit, a construction unit, and a release unit. The input unit inputs data such as documents, source code, and DB configuration parameters necessary for DB preparation. For example, the input unit can input data such as PostgreSQL configuration files, scripts, and performance test results. The input unit can input data such as schema definitions, index settings, and user permission settings. The analysis unit analyzes the information input by the input unit and generates an optimal DB construction plan. For example, the analysis unit can analyze the input information and generate optimal parameter settings and scripts. For example, the analysis unit can generate memory settings, cache settings, and index creation scripts. The construction unit automatically constructs a new version of the DB based on the DB construction plan generated by the analysis unit. For example, the construction unit can configure the DB and perform performance tests based on the generated DB construction plan. For example, the construction unit can execute scripts, apply configuration files, and perform tests. The release unit releases the new version of the DB constructed by the construction unit. For example, the release unit can release the constructed new version of the DB in a short period of time. The release section can handle things like version control, deployment procedures, and user notification methods. As a result, the database construction system according to the embodiment can build and release new versions of the DB in a short period of time.

[0065] The input section takes data such as documents, source code, and database configuration parameters necessary for database preparation. Specifically, the input section can take data such as PostgreSQL configuration files, scripts, and performance test results. This includes, for example, PostgreSQL configuration files such as postgresql.conf and pg_hba.conf. These files describe detailed settings related to database performance and security, and the input section accurately reads these files and extracts the necessary information. Scripts include table creation scripts, data insertion scripts, and index creation scripts. These scripts define the database structure and data placement, and the input section parses these scripts to obtain the information necessary for database construction. Furthermore, performance test results include data such as query execution time, resource utilization, and throughput. This data is important for evaluating database performance, and the input section provides information to generate an optimal database construction plan based on this data. The input section can take data such as schema definitions, index settings, and user permission settings. Schema definitions include table structure, column data types, and constraints, while index settings include index types, target columns, and index creation methods. User permission settings include configuring the permissions and roles of users who can access the database, thereby ensuring the security of the database. The input section plays the role of efficiently inputting this diverse data and providing the basic information for database construction.

[0066] The analysis unit analyzes the information entered by the input unit and generates an optimal database construction plan. Specifically, the analysis unit can analyze the input information and generate optimal parameter settings and scripts. For example, the analysis unit analyzes the PostgreSQL configuration file and derives optimal parameters such as memory settings, cache settings, and connection settings. This includes memory-related parameters such as shared_buffers, work_mem, and maintenance_work_mem, and cache-related parameters such as effective_cache_size and wal_buffers. The analysis unit also analyzes the input script and generates optimal index creation scripts and table creation scripts. This includes index types such as B-tree indexes, hash indexes, and GIN indexes, index creation methods, and selection of target columns. Furthermore, the analysis unit analyzes the results of performance tests and derives settings to optimize database performance. This includes analyzing query execution plans, identifying bottlenecks, and optimizing resource allocation. Based on this information, the analysis unit generates an optimal database construction plan and provides it to the construction unit. The analysis unit can perform these analyses using AI. For example, machine learning algorithms are used to predict optimal parameter settings based on past data and performance test results. Furthermore, natural language processing techniques are used to extract necessary information from documents and configuration files for analysis. This allows the analysis unit to efficiently and accurately analyze information and generate an optimal database construction plan.

[0067] The construction department automatically builds a new version of the database based on the database construction plan generated by the analysis department. Specifically, the construction department can configure the database and conduct performance tests based on the generated database construction plan. For example, the construction department applies configuration files generated by the analysis department to configure the database's memory, cache, and connection settings. The construction department also executes scripts generated by the analysis department to create tables, indexes, and insert data. This includes table creation scripts, index creation scripts, and data insertion scripts. Furthermore, the construction department conducts performance tests to evaluate the database's performance. This includes evaluation items such as query execution time, resource utilization, and throughput. Based on these evaluation results, the construction department adjusts settings and modifies scripts as needed to achieve an optimal database construction. The construction department can automate these tasks. For example, automating script execution, configuration file application, and test execution improves efficiency and accuracy. The construction department can also build the database using cloud or virtual environments. This enables flexible resource utilization and improved scalability. Through these functions, the development team can quickly and accurately build new versions of the database, improving the overall system performance and reliability.

[0068] The Release Department releases new versions of the database built by the Development Department. Specifically, the Release Department can release new versions of the built database in a short period of time. The Release Department can handle things like version control, deployment procedures, and user notification methods. For version control, they use version control systems such as Git and SVN to manage the change history and release notes of new versions of the database. For deployment procedures, they use CI / CD tools to achieve an automated deployment process. This includes tools such as Jenkins, GitLab CI, and CircleCI. This allows the Release Department to deploy new versions of the database quickly and reliably. For user notification methods, they provide release information to users using email, chat tools, and notification systems. This includes release notes, explanations of changes, and usage guides. The Release Department properly manages this information and provides it to users in an easy-to-understand manner to promote the use of new versions of the database. Furthermore, the Release Department can collect feedback after the release and incorporate it into the next release. This includes opinions, requests, and bug reports from users. Based on this feedback, the Release Department aims to improve the quality and functionality of the database. This allows the release department to quickly and reliably release new versions of the database, thereby improving user satisfaction.

[0069] The input section can accept data such as PostgreSQL configuration files, scripts, and performance test results. For example, the input section can accept PostgreSQL configuration files (postgresql.conf, pg_hba.conf, etc.). For example, the input section can accept performance test result reports. For example, the input section can accept test scripts. By inputting data such as PostgreSQL configuration files, scripts, and performance test results, the database is ready for construction. Some or all of the above processing in the input section may be performed using AI, for example, or not. For example, the input section can input data such as PostgreSQL configuration files, scripts, and performance test results into the AI, which can then analyze this data.

[0070] The analysis unit can analyze the input information and generate optimal parameter settings and scripts. For example, the analysis unit can analyze the input information and generate optimal memory settings. For example, the analysis unit can analyze the input information and generate optimal cache settings. For example, the analysis unit can analyze the input information and generate optimal index creation scripts. This improves the efficiency of database construction by analyzing the input information and generating optimal parameter settings and scripts. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input the input information into a generation AI, and the generation AI can generate optimal parameter settings and scripts.

[0071] The construction unit can configure the database and perform performance tests based on the generated database construction plan. For example, the construction unit can configure the database based on the generated database construction plan. For example, the construction unit can perform performance tests based on the generated database construction plan. The construction unit can, for example, execute scripts, apply configuration files, and perform tests. This improves the quality of the database by configuring it and performing performance tests based on the generated database construction plan. Some or all of the above processes in the construction unit may be performed using a generation AI, or without a generation AI. For example, the construction unit can input the generated database construction plan into a generation AI, which can then configure the database and perform performance tests.

[0072] The release unit can release a new version of the constructed database in a short period of time. For example, the release unit can release a new version of the constructed database within a few hours. For example, the release unit can release a new version of the constructed database within a day. The release unit can automate the steps of the release process and perform releases quickly. This enables rapid service delivery by releasing a new version of the constructed database in a short period of time. Some or all of the above processes in the release unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the release unit can input the new version of the constructed database into a generation AI, and the generation AI can automate the release process.

[0073] The analysis unit can generate database construction plans that accommodate various OS environments, data sizes, and performance requirements. For example, the analysis unit can generate a database construction plan for a Windows environment. For example, the analysis unit can generate a database construction plan for a Linux environment. For example, the analysis unit can generate a database construction plan that accommodates data sizes ranging from several GB to several TB. For example, the analysis unit can generate a database construction plan that accommodates performance requirements such as response time and throughput. This enables flexible database construction by generating database construction plans that accommodate various OS environments, data sizes, and performance requirements. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input database construction plans that accommodate various OS environments, data sizes, and performance requirements into a generation AI, and the generation AI can generate the optimal plan.

[0074] The input unit can estimate the user's emotions and prioritize input data based on the estimated emotions. For example, if the user is stressed, the input unit can adjust to prioritize inputting important data. For example, if the user is relaxed, the input unit can adjust to inputting all data equally. For example, if the user is in a hurry, the input unit can adjust to prioritize inputting only the most important data. This allows for more appropriate data input by prioritizing input data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of the input data.

[0075] The input unit can evaluate the reliability of the input data and automatically filter out unreliable data. For example, the input unit can verify the source of the data and exclude data from unreliable sources. For example, the input unit can check the consistency of the data and filter out inconsistent data. For example, the input unit can evaluate the frequency of data updates and automatically exclude outdated data. This improves data quality by filtering out unreliable data. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can use AI to evaluate the reliability of the data and automatically filter out unreliable data.

[0076] The input unit can automatically convert the format of input data and unify data in different formats. For example, the input unit can automatically convert CSV data to JSON format. For example, the input unit can automatically convert XML data to SQL format. For example, the input unit can convert data from an Excel file into a format that can be directly imported into a database. This ensures data consistency by unifying data in different formats. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input data in different formats into AI, and the AI ​​can automatically convert the data format.

[0077] The input unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated emotions. For example, if the user is relaxed, the input unit can acquire data immediately. For example, if the user is stressed, the input unit can postpone data acquisition. For example, if the user is in a hurry, the input unit can prioritize acquiring the most important data. This allows for data acquisition at a more appropriate time by adjusting the timing of data acquisition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not. For example, the input unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of data acquisition.

[0078] The input unit can automatically select the most suitable data by referring to the user's past input history when acquiring input data. For example, the input unit can prioritize acquiring data that the user has frequently entered in the past. For example, the input unit can automatically select highly relevant data from the user's past input history. For example, the input unit can analyze the user's past input patterns and acquire the most suitable data. This allows the system to select the most suitable data by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history into AI, and the AI ​​can automatically select the most suitable data.

[0079] The input unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring input data. For example, if the user is in a specific region, the input unit can prioritize the acquisition of data related to that region. For example, the input unit can prioritize the acquisition of data for locations close to the user's current location. For example, the input unit can acquire highly relevant data by considering the user's travel history. In this way, highly relevant data can be prioritized by considering the user's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without using AI. For example, the input unit can input the user's geographical location information into the AI, and the AI ​​can prioritize the acquisition of highly relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can use an algorithm that performs a detailed analysis. For example, if the user is in a hurry, the analysis unit can use an algorithm that performs a rapid analysis. For example, if the user is stressed, the analysis unit can use an algorithm that performs a simplified analysis. By adjusting the analysis algorithm based on the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the analysis algorithm.

[0081] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of the input data during the analysis. For example, the analysis unit can improve accuracy by analyzing the correlations between data. For example, the analysis unit can perform the analysis by considering the dependencies between data. For example, the analysis unit can improve the accuracy of the analysis by checking the consistency of data. As a result, the accuracy of the analysis is improved by considering the interrelationships of the input data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the interrelationships of the input data into a generative AI, and the generative AI can analyze the correlations and dependencies to improve accuracy.

[0082] The analysis unit can optimize the analysis algorithm by referring to past analysis results during the analysis. For example, the analysis unit can adjust the algorithm parameters based on past analysis results. For example, the analysis unit can select the optimal algorithm from past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. This makes it possible to optimize the analysis algorithm by referring to past analysis results. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past analysis results into a generative AI, which can then adjust the algorithm parameters and select the optimal algorithm.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the display method of the analysis results.

[0084] The analysis unit can determine the priority of analysis based on the submission date of the input data during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can postpone the analysis of older data. For example, the analysis unit can automatically adjust the priority of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the input data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the submission date of the input data into the generating AI, and the generating AI can determine the priority of analysis.

[0085] The analysis unit can improve the accuracy of the analysis by referring to relevant literature for the input data during the analysis. For example, the analysis unit can improve the accuracy of the analysis based on relevant literature. For example, the analysis unit can reflect knowledge obtained from relevant literature into the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input relevant literature into a generative AI, and the generative AI can improve the accuracy of the analysis.

[0086] The construction unit can estimate the user's emotions and adjust the construction procedure based on the estimated emotions. For example, if the user is relaxed, the construction unit can provide a detailed construction procedure. For example, if the user is in a hurry, the construction unit can provide a simplified construction procedure. For example, if the user is stressed, the construction unit can provide a simple construction procedure. This allows for more appropriate construction by adjusting the construction procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the construction unit may be performed using a generative AI, or not using a generative AI. For example, the construction unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the construction procedure.

[0087] The construction unit can automatically verify each step of the generated DB construction plan during construction and detect errors in advance. For example, the construction unit can perform pre-verification and detect errors before executing each step. For example, the construction unit can simulate each step of the construction plan and identify errors in advance. For example, the construction unit can verify the results and detect errors after executing each step. In this way, by verifying each step in advance, the occurrence of errors can be prevented. Some or all of the above processing in the construction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the construction unit can input the generated DB construction plan into a generation AI, which can verify each step and detect errors in advance.

[0088] The construction unit can automatically adjust settings to accommodate different environments during construction. For example, the construction unit can automatically adjust configuration files according to the OS environment. For example, the construction unit can automatically adjust parameters according to the hardware configuration. For example, the construction unit can automatically adjust settings according to the network environment. This enables flexible database construction by automatically adjusting settings to accommodate different environments. Some or all of the above-described processes in the construction unit may be performed using, for example, a generation AI, or without a generation AI. For example, the construction unit can input settings to accommodate different environments into a generation AI, and the generation AI can automatically adjust them.

[0089] The construction unit can estimate the user's emotions and notify the user of the construction progress in real time based on the estimated user emotions. For example, if the user is nervous, the construction unit can notify the user of the progress in detail. For example, if the user is relaxed, the construction unit can notify the user of the progress concisely. For example, if the user is in a hurry, the construction unit can notify the user of the progress quickly. This enhances the user's sense of security by notifying them of the construction progress in real time based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the construction unit may be performed using a generative AI, or not using a generative AI. For example, the construction unit can input user emotion data into a generative AI, which can estimate the emotions and notify the user of the construction progress in real time.

[0090] The construction unit can select the optimal construction procedure by referring to the user's past construction history during construction. For example, the construction unit can prioritize selecting construction procedures that the user has successfully performed in the past. For example, the construction unit can automatically select the optimal procedure from the user's past construction history. For example, the construction unit can analyze the user's past construction patterns and propose the optimal procedure. This allows the optimal construction procedure to be selected by referring to the user's past construction history. Some or all of the above processing in the construction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the construction unit can input the user's past construction history into a generative AI, and the generative AI can select the optimal construction procedure.

[0091] The construction unit can select the optimal server during construction by considering the user's geographical location information. For example, the construction unit can select the server closest to the user's current location. For example, the construction unit can automatically select the optimal server based on the user's geographical location information. For example, the construction unit can select the optimal server by considering the user's travel history. In this way, the optimal server can be selected by considering the user's geographical location information. Some or all of the above processing in the construction unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the construction unit can input the user's geographical location information into a generation AI, and the generation AI can select the optimal server.

[0092] The release unit can estimate the user's emotions and adjust the timing of the release based on the estimated emotions. For example, if the user is relaxed, the release unit can release immediately. For example, if the user is stressed, the release unit can postpone the release. For example, if the user is in a hurry, the release unit can release quickly. By adjusting the timing of the release based on the user's emotions, it becomes possible to release at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the release unit may be performed using a generative AI, or not using a generative AI. For example, the release unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the timing of the release.

[0093] The release unit can automatically perform final pre-release verification at the time of release and detect errors in advance. For example, the release unit can automatically verify all settings before release and detect errors. For example, the release unit can perform simulations before release and identify errors in advance. For example, the release unit can automatically verify operation in a test environment before release and detect errors. In this way, by automatically performing final pre-release verification, the occurrence of errors can be prevented. Some or all of the above processes in the release unit may be performed using, for example, a generation AI, or without a generation AI. For example, the release unit can input the final pre-release verification into a generation AI, and the generation AI can detect errors in advance.

[0094] The release unit can automatically adjust settings to accommodate different environments during release. For example, the release unit can automatically adjust configuration files according to the OS environment. For example, the release unit can automatically adjust parameters according to the hardware configuration. For example, the release unit can automatically adjust settings according to the network environment. This enables flexible releases by automatically adjusting settings to accommodate different environments. Some or all of the above processing in the release unit may be performed using, for example, a generation AI, or without a generation AI. For example, the release unit can input settings to accommodate different environments into a generation AI, and the generation AI can automatically adjust them.

[0095] The release unit can estimate the user's emotions and adjust the release notification method based on the estimated emotions. For example, if the user is nervous, the release unit can provide a detailed notification. If the user is relaxed, the release unit can provide a concise notification. If the user is in a hurry, the release unit can provide a quick notification. By adjusting the release notification method based on the user's emotions, more appropriate notifications can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the release unit may be performed using a generative AI, or not using a generative AI. For example, the release unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the release notification method.

[0096] The release unit can select the optimal release procedure by referring to the user's past release history at the time of release. For example, the release unit can prioritize the selection of release procedures that have been successful for the user in the past. For example, the release unit can automatically select the optimal procedure from the user's past release history. For example, the release unit can analyze the user's past release patterns and propose the optimal procedure. This allows the optimal release procedure to be selected by referring to the user's past release history. Some or all of the above processing in the release unit may be performed using, for example, a generative AI, or without a generative AI. For example, the release unit can input the user's past release history into a generative AI, and the generative AI can select the optimal release procedure.

[0097] The release unit can select the optimal release method at the time of release, taking into account the user's geographical location information. For example, the release unit can select the server closest to the user's current location. For example, the release unit can automatically select the optimal release method based on the user's geographical location information. For example, the release unit can select the optimal release method by taking into account the user's travel history. In this way, the optimal release method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the release unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the release unit can input the user's geographical location information into a generation AI, and the generation AI can select the optimal release method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The database construction system can also include a security monitoring unit. This unit can monitor the security status of the database in real time during construction and after release, and issue alerts if an anomaly is detected. For example, the security monitoring unit can detect unauthorized access to the database and notify the administrator. It can also monitor database configuration changes and unauthorized data manipulation, and automatically take countermeasures if an anomaly occurs. Furthermore, the security monitoring unit can periodically generate security reports and provide them to the administrator. This enhances the database's security, allowing for safer use.

[0100] The database construction system can also include a backup management unit. This unit can automatically schedule and regularly perform database backups. For example, it can take a full database backup every night. Furthermore, it can check the integrity of the backup data and notify the administrator if any problems occur. Additionally, the backup management unit can store backup data in cloud storage, enabling rapid recovery in the event of a disaster. This ensures that database data is securely protected and can handle any unforeseen problems.

[0101] The database construction system can also include a user feedback collection unit. This unit can collect feedback from database users and use it to improve the system. For example, it can conduct regular surveys to understand user satisfaction and areas for improvement. It can also provide a platform where users can submit suggestions and opinions about the system. Furthermore, it can analyze the collected feedback and formulate system improvement plans. This enables system improvements that meet user needs, leading to increased user satisfaction.

[0102] The database construction system can also include a performance optimization unit. This unit can monitor database performance in real time and suggest optimizations. For example, it can monitor query execution times and identify queries experiencing delays. It can also suggest index optimizations and cache settings to improve database performance. Furthermore, the performance optimization unit can periodically generate performance reports and provide them to administrators. This ensures that database performance is always kept optimal, enabling efficient operation.

[0103] The database construction system can also be equipped with an energy efficiency management unit. This unit can monitor energy consumption in database operations and propose efficient energy use. For example, it can monitor server operating status and reduce energy consumption by stopping unnecessary resources. It can also optimize database load balancing to improve energy efficiency. Furthermore, it can periodically generate and provide energy consumption reports to administrators. This reduces database operating costs and enables environmentally friendly operation.

[0104] A database building system can estimate a user's emotions and provide customized help based on those emotions. For example, if a user is confused, it can provide help with detailed instructions. If a user is in a hurry, it can provide a concise and quick solution. Furthermore, if a user is relaxed, it can provide additional learning resources and relevant information. This ensures appropriate support is provided according to the user's emotions, improving the user experience. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The database construction system can estimate the user's emotions and dynamically change the interface design based on the estimated emotions. For example, if the user is stressed, a simple and intuitive interface can be provided. If the user is relaxed, detailed information and customization options can be provided. Furthermore, if the user is in a hurry, an interface that allows quick access to important functions can be provided. This provides an optimal interface tailored to the user's emotions, improving usability. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The database building system can estimate the user's emotions and adjust the frequency of notifications based on those emotions. For example, if a user is stressed, the notification frequency can be reduced, and only important notifications can be sent. If the user is relaxed, the normal notification frequency can be maintained. Furthermore, if the user is in a hurry, important notifications can be prioritized. This provides appropriate notifications tailored to the user's emotions, reducing the user's burden. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The database building system can estimate the user's emotions and customize troubleshooting steps based on those emotions. For example, if the user is confused, it can provide detailed explanations and step-by-step guidance. If the user is in a hurry, it can provide concise and quick solutions. Furthermore, if the user is relaxed, it can provide additional background information and relevant troubleshooting hints. This ensures that appropriate support is provided according to the user's emotions, facilitating smooth problem resolution. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] A database building system can estimate a user's emotions and dynamically adjust its performance based on those emotions. For example, if a user is stressed, system resources can be prioritized to reduce response times. If the user is relaxed, normal resource allocation can be maintained. Furthermore, if the user is in a hurry, resources can be concentrated on important tasks for faster processing. This provides optimal performance tailored to the user's emotions, improving the user experience. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The input section is where you enter data necessary for database preparation, such as documents, source code, and database configuration parameters. For example, you can enter data such as PostgreSQL configuration files and scripts, performance test results, schema definitions, index settings, and user permission settings. Step 2: The analysis unit analyzes the information entered by the input unit and generates an optimal database construction plan. For example, it can analyze the entered information and generate optimal parameter settings, scripts, memory settings, cache settings, index creation scripts, etc. Step 3: The construction unit automatically builds a new version of the database based on the database construction plan generated by the analysis unit. For example, based on the generated database construction plan, it can configure the database, perform performance tests, execute scripts, apply configuration files, and run tests. Step 4: The release team releases the new version of the database built by the construction team. For example, they can release a new version of the built database in a short period of time and manage version control, deployment procedures, and user notification methods.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the input unit, analysis unit, construction unit, and release unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart device 14 and inputs data such as documents, source code, and DB setting parameters necessary for DB preparation. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate an optimal DB construction plan. The construction unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically constructs a new version of the DB based on the generated DB construction plan. The release unit is implemented by the control unit 46A of the smart device 14 and releases the newly constructed version of the DB. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the input unit, analysis unit, construction unit, and release unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart glasses 214 and inputs data such as documents, source code, and DB setting parameters necessary for DB preparation. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate an optimal DB construction plan. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically constructs a new version of the DB based on the generated DB construction plan. The release unit is implemented by, for example, the control unit 46A of the smart glasses 214 and releases the newly constructed version of the DB. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the input unit, analysis unit, construction unit, and release unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the headset terminal 314 and inputs data such as documents, source code, and DB setting parameters necessary for DB preparation. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate an optimal DB construction plan. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically constructs a new version of the DB based on the generated DB construction plan. The release unit is implemented by, for example, the control unit 46A of the headset terminal 314 and releases the newly constructed DB. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the input unit, analysis unit, construction unit, and release unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the robot 414 and inputs data such as documents, source code, and DB setting parameters necessary for DB preparation. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input information to generate an optimal DB construction plan. The construction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically constructs a new version of the DB based on the generated DB construction plan. The release unit is implemented by, for example, the control unit 46A of the robot 414 and releases the newly constructed version of the DB. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes multiple computers, including computer 22.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An input section for entering data such as documents, source code, and DB configuration parameters necessary for preparing the database. An analysis unit analyzes the information input by the aforementioned input unit and generates an optimal DB construction plan, A construction unit that automatically constructs a new version of the database based on the database construction plan generated by the aforementioned analysis unit, The system comprises a release unit that releases a new version of the DB constructed by the aforementioned construction unit. A system characterized by the following features. (Note 2) The aforementioned input unit is Enter data such as PostgreSQL configuration files, scripts, and performance test results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes the input information and generates optimal parameter settings and scripts. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned construction unit is Based on the generated DB construction plan, configure the DB and perform performance tests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned release section is Release a new version of the built database in a short period of time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Generates database construction plans that accommodate various OS environments, data sizes, and performance requirements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is Evaluate the reliability of input data and automatically filter out unreliable data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is Automatically converts the format of input data and unifies data in different formats. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is The system estimates the user's emotions and adjusts the timing of input data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is When acquiring input data, the system automatically selects the most suitable data by referring to the user's past input history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is When acquiring input data, the system prioritizes the acquisition of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, consider the interrelationships between input data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the input data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature for the input data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned construction unit is We estimate the user's emotions and adjust the build process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned construction unit is During the build process, each step of the generated DB build plan is automatically validated to detect errors in advance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned construction unit is During setup, the system automatically adjusts settings to accommodate different environments. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned construction unit is It estimates user sentiment and provides real-time notifications of the development progress based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned construction unit is During the build process, the system selects the optimal build procedure by referring to the user's past build history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned construction unit is During the setup process, the optimal server is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned release section is We estimate user sentiment and adjust the release timing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned release section is During release, the system automatically performs final pre-release validation to detect errors in advance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned release section is During release, settings are automatically adjusted to accommodate different environments. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned release section is We estimate user sentiment and adjust how releases are notified based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned release section is During a release, the system selects the optimal release procedure by referring to the user's past release history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned release section is When releasing, the optimal release method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering data such as documents, source code, and DB configuration parameters necessary for preparing the database. An analysis unit analyzes the information input by the aforementioned input unit and generates an optimal DB construction plan, A construction unit that automatically constructs a new version of the database based on the database construction plan generated by the aforementioned analysis unit, The system comprises a release unit that releases a new version of the DB constructed by the aforementioned construction unit. A system characterized by the following features.

2. The aforementioned input unit is Enter data such as PostgreSQL configuration files, scripts, and performance test results. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the input information and generates optimal parameter settings and scripts. The system according to feature 1.

4. The aforementioned construction unit is Based on the generated DB construction plan, configure the DB and perform performance tests. The system according to feature 1.

5. The aforementioned release section is Release a new version of the built database in a short period of time. The system according to feature 1.

6. The aforementioned analysis unit, Generates database construction plans that accommodate various OS environments, data sizes, and performance requirements. The system according to feature 1.

7. The aforementioned input unit is It estimates the user's emotions and prioritizes input data based on the estimated user emotions. The system according to feature 1.

8. The aforementioned input unit is Evaluate the reliability of input data and automatically filter out unreliable data. The system according to feature 1.

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