system

Abstract

An object of the system according to the embodiment is to automatically generate an SQL sentence based on a rule or a manner for each company or department from ambiguous Japanese.SOLUTION: A system includes an ambiguous Japanese analysis part, a rule application part, and an SQL generation part. The ambiguous Japanese analysis unit analyzes ambiguous Japanese. A rule application part applies a rule and a style for each company or department on the basis of the ambiguous Japanese analyzed by the ambiguous Japanese analysis part. The SQL generation unit generates an SQL statement based on the rule and the manner applied by the rule application unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that it was difficult for beginners to create SQL statements based on the rules and practices of each company or department.

[0005] The system according to the embodiment aims to automatically generate SQL statements from ambiguous Japanese based on the rules and practices of each company or department. [Means for solving the problem]

[0006] The system according to the embodiment includes an ambiguous Japanese analysis unit, a rule application unit, and an SQL generation unit. The ambiguous Japanese analysis unit analyzes ambiguous Japanese. The rule application unit applies company or department rules and practices based on the ambiguous Japanese analyzed by the ambiguous Japanese analysis unit. The SQL generation unit generates SQL statements based on the rules and practices applied by the rule application unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate SQL statements from ambiguous Japanese based on the rules and practices of each company or department. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The SQL automatic generation system according to the embodiment of the present invention is a system that automatically generates SQL from ambiguous Japanese and takes into consideration the rules and practices of each company or department. This allows even SQL novices and beginners to easily generate SQL statements to be used in the workplace.

[0029] The SQL automatic generation system according to the embodiment includes an ambiguous Japanese analysis unit, a rule application unit, and an SQL generation unit. The ambiguous Japanese analysis unit analyzes ambiguous Japanese. For example, it recognizes ambiguous expressions, synonyms, and similar words and extracts information for generating appropriate SQL statements. The ambiguous Japanese analysis unit can also refer to a user's past input history to perform analysis optimized for each individual user. For example, it learns previously entered ambiguous Japanese instructions and generated SQL statements and uses them for future analyses. The rule application unit applies company or department rules and practices based on the ambiguous Japanese analyzed by the ambiguous Japanese analysis unit. For example, these include naming conventions for specific table names and column names, and generation of SQL statements based on a specific database structure. The rule application unit can also learn the past history of SQL statement usage and automatically apply optimal rules. For example, it automatically references specific industry standards and regulations to generate appropriate SQL statements. The SQL generation unit generates SQL statements based on the rules and practices applied by the rule application unit. For example, the generation AI generates SQL statements using a text generation AI (e.g., LLM). The generation AI can also optimize SQL statements using multimodal generation AI. The generation AI can also automatically correct the generated SQL statements and convert them into efficient queries. For example, it can remove redundant parts or convert them into efficient queries. This allows the automatic SQL generation system according to the embodiment to automatically generate SQL statements from ambiguous Japanese, taking into account the rules and practices of each company or department. For example, it can quickly and accurately generate SQL statements required for daily tasks, such as aggregating sales data or extracting customer data.

[0030] The ambiguous Japanese analysis unit can refer to a user's past input history and generate SQL statements optimized for each individual user. For example, the generation AI stores a user's past input history in a database and references that history when analyzing ambiguous Japanese instructions. For example, if a user previously received the instruction "Aggregate and display sales data by month," the unit generates the optimal SQL statement based on that history. The ambiguous Japanese analysis unit also learns ambiguous Japanese instructions previously entered by the user and the SQL statements generated, and uses this information for future instruction analysis. For example, if the same user again enters "Aggregate and display sales data by month," the unit generates more accurate SQL statements based on the past history. The ambiguous Japanese analysis unit also analyzes a user's past input history and identifies frequently used patterns and phrases. For example, the generation AI learns phrases such as "sales data" and "aggregate by month" and generates the optimal SQL statement based on that. This allows the generation AI to generate SQL statements optimized for each individual user by referring to the user's past input history.

[0031] The ambiguous Japanese analysis unit automatically recognizes synonyms and thesaurus, enabling the generation of more accurate SQL statements. For example, the ambiguous Japanese analysis unit automatically recognizes ambiguous Japanese instructions when the generation AI refers to a database of synonyms and thesaurus. For example, if the instruction is "Aggregate and display sales data by month," the generation AI recognizes synonyms such as "sales," "revenue," and "profit" and generates an SQL statement. The ambiguous Japanese analysis unit automatically replaces ambiguous Japanese instructions entered by the user with synonyms and thesaurus to generate the optimal SQL statement. For example, the generation AI recognizes the instruction "Aggregate and display sales data by month" as "Aggregate and display revenue data by month" and generates an SQL statement. The ambiguous Japanese analysis unit also uses the generation AI's synonym and thesaurus recognition function to analyze ambiguous Japanese instructions and generate more accurate SQL statements. For example, if the instruction is "Aggregate and display sales data by month," the generation AI recognizes synonyms such as "sales," "revenue," and "profit" and generates an SQL statement. This allows for the automatic recognition of synonyms and similar words, resulting in the generation of more accurate SQL statements.

[0032] The ambiguous Japanese analysis unit can accept voice input and generate SQL statements from the voice. In the ambiguous Japanese analysis unit, for example, a generation AI accepts voice input, analyzes ambiguous Japanese instructions, and generates SQL statements. For example, if a user gives a voice command such as "Aggregate sales data by month and display it," the voice is analyzed and an SQL statement is generated. In addition, the ambiguous Japanese analysis unit uses voice recognition technology to allow a generation AI to accept voice input, analyze ambiguous Japanese instructions, and generate an SQL statement. For example, if a user gives a voice command such as "Aggregate sales data by month and display it," the voice is analyzed and an SQL statement is generated. In addition, the ambiguous Japanese analysis unit builds a system in which a generation AI accepts voice input, analyzes ambiguous Japanese instructions, and generates an SQL statement. For example, if a user gives a voice command such as "Aggregate sales data by month and display it," the voice is analyzed and an SQL statement is generated. In this way, voice input can be accepted and an SQL statement can be generated from the voice.

[0033] The ambiguous Japanese analysis unit can analyze images and charts and generate SQL statements based on them. For example, in the ambiguous Japanese analysis unit, a generation AI analyzes images and charts and generates SQL statements based on ambiguous Japanese instructions. For example, if a user instructs the system to "aggregate and display sales data by month" and uploads a sales data graph, the system analyzes the image and generates SQL statements. The ambiguous Japanese analysis unit also uses image recognition technology to enable a generation AI to analyze images and charts and generate SQL statements based on ambiguous Japanese instructions. For example, if a user instructs the system to "aggregate and display sales data by month" and uploads a sales data graph, the system analyzes the image and generates SQL statements. The ambiguous Japanese analysis unit also builds a system in which a generation AI analyzes images and charts and generates SQL statements based on ambiguous Japanese instructions. For example, if a user instructs the system to "aggregate and display sales data by month" and uploads a sales data graph, the system analyzes the image and generates SQL statements. This allows the system to analyze images and charts and generate SQL statements based on them.

[0034] The rule application unit can automatically refer to specific industry standards and regulations and generate appropriate SQL statements. For example, the rule application unit uses a generation AI to automatically refer to specific industry standards and regulations and generate SQL statements taking into account the rules and practices of each company or department. For example, it generates SQL statements based on financial industry regulations. The rule application unit also stores industry standards and regulations in a database, and the generation AI refers to them to generate SQL statements that take into account the rules and practices of each company or department. For example, it generates SQL statements based on medical industry regulations. The rule application unit also builds a system in which the generation AI automatically refers to specific industry standards and regulations and generates optimal SQL statements taking into account the rules and practices of each company or department. For example, it generates SQL statements based on manufacturing industry regulations. This makes it possible to automatically refer to specific industry standards and regulations and generate appropriate SQL statements.

[0035] The rule application unit can refer to rules from different industries and fields and generate crossover SQL statements. For example, the rule application unit allows the generation AI to refer to rules from different industries and fields and generate crossover SQL statements taking into account the rules and practices of each company or department. For example, it generates an SQL statement that combines the rules of the financial industry and the medical industry. The rule application unit also stores rules from different industries and fields in a database, and the generation AI refers to them to generate crossover SQL statements taking into account the rules and practices of each company or department. For example, it generates an SQL statement that combines the rules of the manufacturing industry and the service industry. The rule application unit also builds a system in which the generation AI refers to rules from different industries and fields and generates crossover SQL statements taking into account the rules and practices of each company or department. For example, it generates an SQL statement that combines the rules of the education industry and the entertainment industry. This makes it possible to generate crossover SQL statements by referring to rules from different industries and fields.

[0036] The rule application unit provides an interface that allows the user to customize rules, making it possible to generate flexible SQL statements. For example, the rule application unit provides an interface that allows the generation AI to customize rules to the user, generating flexible SQL statements that take into account the rules and practices of each company or department. For example, it provides an interface that allows the user to set specific naming conventions and database structures. The rule application unit also uses an interface that allows the user to customize rules to build a system in which the generation AI generates flexible SQL statements that take into account the rules and practices of each company or department. For example, it provides an interface that allows the user to set specific naming conventions for table names and column names. The rule application unit also provides an interface that allows the generation AI to customize rules to the user, generating flexible SQL statements that take into account the rules and practices of each company or department. For example, it provides an interface that allows the user to set a specific database structure. This provides an interface that allows the user to customize rules, making it possible to generate flexible SQL statements.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The SQL automatic generation system can further include a backup unit that automatically creates a backup of the database based on user input. For example, when a user inputs, "Aggregate and display sales data by month," the backup unit automatically creates a backup of the database. The backup unit can also create backups of the database periodically. For example, it can create a backup automatically every night at midnight. The backup unit can also create a backup manually by the user. For example, if a user inputs, "Create a backup now," the backup will be created based on that instruction. This ensures the safety of the data and makes it possible to prepare for the unlikely event of data loss.

[0039] The SQL automatic generation system can also be equipped with a visualization unit that automatically visualizes data based on user input. For example, when a user inputs, "Aggregate and display sales data by month," the visualization unit displays the data as a graph or chart. The visualization unit can also visualize data in a format specified by the user. For example, the data can be displayed in a format that suits the user's preferences, such as a bar graph or pie chart. The visualization unit can also output the results of data visualization as a report. For example, it can generate a report in PDF format and provide it to the user. This can deepen understanding of the data and support decision-making.

[0040] The SQL automatic generation system can also be equipped with a cleaning unit that automatically cleans data based on user input. For example, if a user inputs, "Summarize and display sales data by month," the cleaning unit will detect missing or outliers in the data and take appropriate action. The cleaning unit can also detect duplicate data and delete it. For example, if there is multiple data for the same customer, the duplicates will be deleted. The cleaning unit can also standardize data formats. For example, it can standardize date formats and perform other processes to maintain data consistency. This improves data quality and enables accurate analysis.

[0041] An automatic SQL generation system can also be equipped with a security unit that automatically strengthens data security based on user input. For example, when a user inputs "Display monthly sales data," the security unit sets access permissions for that data. The security unit can also encrypt data. For example, it can encrypt highly confidential data to protect it from unauthorized access. The security unit can also record and monitor data access logs. For example, it can record who accessed which data and when, and issue an alert if unauthorized access occurs. This strengthens data security and prevents information leaks.

[0042] The SQL automatic generation system can also include an integration unit that automatically integrates data based on user input. For example, when a user inputs, "Summarize and display sales data by month," the integration unit collects and integrates data from multiple data sources. The integration unit can also unify data in different formats. For example, it can unify and process data in CSV format and data in Excel format. The integration unit can also perform data mapping. For example, it can map tables from different databases to generate consistent data. This allows multiple data sources to be integrated and comprehensive data analysis to be performed.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The ambiguous Japanese analysis unit analyzes ambiguous Japanese. For example, it recognizes ambiguous expressions, synonyms, and similar words, and extracts information to generate appropriate SQL statements. It can also refer to the user's past input history to perform analysis optimized for each individual user. It learns from previously entered ambiguous Japanese instructions and generated SQL statements, and uses this information for future analyses. Step 2: The rule application unit applies company or department rules and practices based on the ambiguous Japanese parsed by the ambiguous Japanese parser. For example, this includes naming conventions for specific table and column names, and generating SQL statements based on specific database structures. It can also learn from the history of past SQL statement usage and automatically apply the most appropriate rules. It automatically references specific industry standards and regulations to generate appropriate SQL statements. Step 3: The SQL generation unit generates SQL statements based on the rules and methods applied by the rule application unit. For example, the generation AI generates SQL statements using text generation AI (e.g., LLM). Multimodal generation AI can also be used to optimize SQL statements. The generated SQL statements can be automatically corrected and converted into efficient queries. For example, redundant parts can be removed or converted into efficient queries.

[0045] (Example 2) The SQL automatic generation system according to the embodiment of the present invention is a system that automatically generates SQL from ambiguous Japanese and takes into consideration the rules and practices of each company or department. This allows even SQL novices and beginners to easily generate SQL statements to be used in the workplace.

[0046] The SQL automatic generation system according to the embodiment includes an ambiguous Japanese analysis unit, a rule application unit, and an SQL generation unit. The ambiguous Japanese analysis unit analyzes ambiguous Japanese. For example, it recognizes ambiguous expressions, synonyms, and similar words and extracts information for generating appropriate SQL statements. The ambiguous Japanese analysis unit can also refer to a user's past input history to perform analysis optimized for each individual user. For example, it learns previously entered ambiguous Japanese instructions and generated SQL statements and uses them for future analyses. The rule application unit applies company or department rules and practices based on the ambiguous Japanese analyzed by the ambiguous Japanese analysis unit. For example, these include naming conventions for specific table names and column names, and generation of SQL statements based on a specific database structure. The rule application unit can also learn the past history of SQL statement usage and automatically apply optimal rules. For example, it automatically references specific industry standards and regulations to generate appropriate SQL statements. The SQL generation unit generates SQL statements based on the rules and practices applied by the rule application unit. For example, the generation AI generates SQL statements using a text generation AI (e.g., LLM). The generation AI can also optimize SQL statements using multimodal generation AI. The generation AI can also automatically correct the generated SQL statements and convert them into efficient queries. For example, it can remove redundant parts or convert them into efficient queries. This allows the automatic SQL generation system according to the embodiment to automatically generate SQL statements from ambiguous Japanese, taking into account the rules and practices of each company or department. For example, it can quickly and accurately generate SQL statements required for daily tasks, such as aggregating sales data or extracting customer data.

[0047] The ambiguous Japanese analysis unit can refer to a user's past input history and generate SQL statements optimized for each individual user. For example, the generation AI stores a user's past input history in a database and references that history when analyzing ambiguous Japanese instructions. For example, if a user previously received the instruction "Aggregate and display sales data by month," the unit generates the optimal SQL statement based on that history. The ambiguous Japanese analysis unit also learns ambiguous Japanese instructions previously entered by the user and the SQL statements generated, and uses this information for future instruction analysis. For example, if the same user again enters "Aggregate and display sales data by month," the unit generates more accurate SQL statements based on the past history. The ambiguous Japanese analysis unit also analyzes a user's past input history and identifies frequently used patterns and phrases. For example, the generation AI learns phrases such as "sales data" and "aggregate by month" and generates the optimal SQL statement based on that. This allows the generation AI to generate SQL statements optimized for each individual user by referring to the user's past input history.

[0048] The ambiguous Japanese analysis unit automatically recognizes synonyms and thesaurus, enabling the generation of more accurate SQL statements. For example, the ambiguous Japanese analysis unit automatically recognizes ambiguous Japanese instructions when the generation AI refers to a database of synonyms and thesaurus. For example, if the instruction is "Aggregate and display sales data by month," the generation AI recognizes synonyms such as "sales," "revenue," and "profit" and generates an SQL statement. The ambiguous Japanese analysis unit automatically replaces ambiguous Japanese instructions entered by the user with synonyms and thesaurus to generate the optimal SQL statement. For example, the generation AI recognizes the instruction "Aggregate and display sales data by month" as "Aggregate and display revenue data by month" and generates an SQL statement. The ambiguous Japanese analysis unit also uses the generation AI's synonym and thesaurus recognition function to analyze ambiguous Japanese instructions and generate more accurate SQL statements. For example, if the instruction is "Aggregate and display sales data by month," the generation AI recognizes synonyms such as "sales," "revenue," and "profit" and generates an SQL statement. This allows for the automatic recognition of synonyms and similar words, resulting in the generation of more accurate SQL statements.

[0049] The ambiguous Japanese parser uses the emotion estimation function to analyze the user's emotions when typing, and can generate simpler SQL statements if the user is feeling stressed. For example, the generation AI of the ambiguous Japanese parser analyzes the user's emotions when typing, and generates simpler SQL statements if the user is feeling stressed. For example, if the user is feeling stressed when typing "Aggregate sales data by month and display," a simple SQL statement is generated. The ambiguous Japanese parser also uses the emotion estimation function to analyze the user's emotions when typing in real time, and generates simpler SQL statements if the user is feeling stressed. For example, if the user is feeling stressed when typing "Aggregate sales data by month and display," a simple SQL statement is generated. The ambiguous Japanese parser also uses the emotion estimation function to analyze the user's emotions when typing, and generates simpler SQL statements if the user is feeling stressed. For example, if the user is feeling stressed when typing "Aggregate sales data by month and display," a simple SQL statement is generated. This allows the generation AI to analyze the user's emotions and generate simpler SQL statements if the user is feeling stressed.

[0050] The ambiguous Japanese analysis unit can accept voice input and generate SQL statements from the voice. In the ambiguous Japanese analysis unit, for example, a generation AI accepts voice input, analyzes ambiguous Japanese instructions, and generates SQL statements. For example, if a user gives a voice command such as "Aggregate sales data by month and display it," the voice is analyzed and an SQL statement is generated. In addition, the ambiguous Japanese analysis unit uses voice recognition technology to allow a generation AI to accept voice input, analyze ambiguous Japanese instructions, and generate an SQL statement. For example, if a user gives a voice command such as "Aggregate sales data by month and display it," the voice is analyzed and an SQL statement is generated. In addition, the ambiguous Japanese analysis unit builds a system in which a generation AI accepts voice input, analyzes ambiguous Japanese instructions, and generates an SQL statement. For example, if a user gives a voice command such as "Aggregate sales data by month and display it," the voice is analyzed and an SQL statement is generated. In this way, voice input can be accepted and an SQL statement can be generated from the voice.

[0051] The ambiguous Japanese analysis unit can analyze images and charts and generate SQL statements based on them. For example, in the ambiguous Japanese analysis unit, a generation AI analyzes images and charts and generates SQL statements based on ambiguous Japanese instructions. For example, if a user instructs the system to "aggregate and display sales data by month" and uploads a sales data graph, the system analyzes the image and generates SQL statements. The ambiguous Japanese analysis unit also uses image recognition technology to enable a generation AI to analyze images and charts and generate SQL statements based on ambiguous Japanese instructions. For example, if a user instructs the system to "aggregate and display sales data by month" and uploads a sales data graph, the system analyzes the image and generates SQL statements. The ambiguous Japanese analysis unit also builds a system in which a generation AI analyzes images and charts and generates SQL statements based on ambiguous Japanese instructions. For example, if a user instructs the system to "aggregate and display sales data by month" and uploads a sales data graph, the system analyzes the image and generates SQL statements. This allows the system to analyze images and charts and generate SQL statements based on them.

[0052] The ambiguous Japanese analysis unit can use the emotion estimation function to analyze emotions in response to ambiguous Japanese instructions entered by a user, and provide feedback to elicit positive emotions. For example, the ambiguous Japanese analysis unit uses the emotion estimation function of a generation AI to analyze emotions in response to ambiguous Japanese instructions entered by a user, and provide feedback to elicit positive emotions. For example, positive feedback is provided when a user enters, "Aggregate the sales data by month and display it." Furthermore, the ambiguous Japanese analysis unit uses the emotion estimation function to build a system that analyzes emotions in response to ambiguous Japanese instructions entered by a user, and provides feedback to elicit positive emotions. For example, positive feedback is provided when a user enters, "Aggregate the sales data by month and display it." Furthermore, the ambiguous Japanese analysis unit uses the emotion estimation function of a generation AI to analyze emotions in response to ambiguous Japanese instructions entered by a user, and provides feedback to elicit positive emotions. For example, positive feedback is provided when a user enters, "Aggregate the sales data by month and display it." In this way, it is possible to analyze the user's emotions and provide feedback to elicit positive emotions.

[0053] The rule application unit can automatically refer to specific industry standards and regulations and generate appropriate SQL statements. For example, the rule application unit uses a generation AI to automatically refer to specific industry standards and regulations and generate SQL statements taking into account the rules and practices of each company or department. For example, it generates SQL statements based on financial industry regulations. The rule application unit also stores industry standards and regulations in a database, and the generation AI refers to them to generate SQL statements that take into account the rules and practices of each company or department. For example, it generates SQL statements based on medical industry regulations. The rule application unit also builds a system in which the generation AI automatically refers to specific industry standards and regulations and generates optimal SQL statements taking into account the rules and practices of each company or department. For example, it generates SQL statements based on manufacturing industry regulations. This makes it possible to automatically refer to specific industry standards and regulations and generate appropriate SQL statements.

[0054] The rule application unit can use the emotion estimation function to analyze the stress a user feels toward rules and manners, and generate SQL statements for reducing stress. In the rule application unit, for example, the generation AI uses the emotion estimation function to analyze the stress a user feels toward rules and manners, and generate SQL statements for reducing stress. For example, if a user feels stressed about a specific rule, a simple SQL statement taking that rule into consideration is generated. The rule application unit also uses the emotion estimation function to build a system that analyzes the stress a user feels toward rules and manners in real time, and generates SQL statements for reducing stress. For example, if a user feels stressed about a specific rule, a simple SQL statement taking that rule into consideration is generated. In the rule application unit, the generation AI uses the emotion estimation function to analyze the stress a user feels toward rules and manners, and generate SQL statements for reducing stress. For example, if a user feels stressed about a specific rule, a simple SQL statement taking that rule into consideration is generated. In this way, the stress a user feels toward rules and manners can be analyzed, and SQL statements for reducing stress can be generated.

[0055] The rule application unit can refer to rules from different industries and fields and generate crossover SQL statements. For example, the rule application unit allows the generation AI to refer to rules from different industries and fields and generate crossover SQL statements taking into account the rules and practices of each company or department. For example, it generates an SQL statement that combines the rules of the financial industry and the medical industry. The rule application unit also stores rules from different industries and fields in a database, and the generation AI refers to them to generate crossover SQL statements taking into account the rules and practices of each company or department. For example, it generates an SQL statement that combines the rules of the manufacturing industry and the service industry. The rule application unit also builds a system in which the generation AI refers to rules from different industries and fields and generates crossover SQL statements taking into account the rules and practices of each company or department. For example, it generates an SQL statement that combines the rules of the education industry and the entertainment industry. This makes it possible to generate crossover SQL statements by referring to rules from different industries and fields.

[0056] The rule application unit provides an interface that allows the user to customize rules, making it possible to generate flexible SQL statements. For example, the rule application unit provides an interface that allows the generation AI to customize rules to the user, generating flexible SQL statements that take into account the rules and practices of each company or department. For example, it provides an interface that allows the user to set specific naming conventions and database structures. The rule application unit also uses an interface that allows the user to customize rules to build a system in which the generation AI generates flexible SQL statements that take into account the rules and practices of each company or department. For example, it provides an interface that allows the user to set specific naming conventions for table names and column names. The rule application unit also provides an interface that allows the generation AI to customize rules to the user, generating flexible SQL statements that take into account the rules and practices of each company or department. For example, it provides an interface that allows the user to set a specific database structure. This provides an interface that allows the user to customize rules, making it possible to generate flexible SQL statements.

[0057] The rule application unit can use the emotion estimation function to analyze the emotions a user feels toward rules and etiquette, and propose rules for eliciting positive emotions. For example, the generation AI in the rule application unit uses the emotion estimation function to analyze the emotions a user feels toward rules and etiquette, and propose rules for eliciting positive emotions. For example, if a user has positive emotions toward a particular rule, the rule application unit preferentially proposes that rule. The rule application unit also uses the emotion estimation function to build a system that analyzes the emotions a user feels toward rules and etiquette in real time, and proposes rules for eliciting positive emotions. For example, if a user has positive emotions toward a particular rule, the rule application unit preferentially proposes that rule. The generation AI in the rule application unit uses the emotion estimation function to analyze the emotions a user feels toward rules and etiquette, and proposes rules for eliciting positive emotions. For example, if a user has positive emotions toward a particular rule, the rule application unit preferentially proposes that rule. This makes it possible to analyze the emotions a user feels toward rules and etiquette, and propose rules for eliciting positive emotions.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The SQL automatic generation system can further include a backup unit that automatically creates a backup of the database based on user input. For example, when a user inputs, "Aggregate and display sales data by month," the backup unit automatically creates a backup of the database. The backup unit can also create backups of the database periodically. For example, it can create a backup automatically every night at midnight. The backup unit can also create a backup manually by the user. For example, if a user inputs, "Create a backup now," the backup will be created based on that instruction. This ensures the safety of the data and makes it possible to prepare for the unlikely event of data loss.

[0060] The SQL automatic generation system can also be equipped with a visualization unit that automatically visualizes data based on user input. For example, when a user inputs, "Aggregate and display sales data by month," the visualization unit displays the data as a graph or chart. The visualization unit can also visualize data in a format specified by the user. For example, the data can be displayed in a format that suits the user's preferences, such as a bar graph or pie chart. The visualization unit can also output the results of data visualization as a report. For example, it can generate a report in PDF format and provide it to the user. This can deepen understanding of the data and support decision-making.

[0061] The SQL automatic generation system can also be equipped with a cleaning unit that automatically cleans data based on user input. For example, if a user inputs, "Summarize and display sales data by month," the cleaning unit will detect missing or outliers in the data and take appropriate action. The cleaning unit can also detect duplicate data and delete it. For example, if there is multiple data for the same customer, the duplicates will be deleted. The cleaning unit can also standardize data formats. For example, it can standardize date formats and perform other processes to maintain data consistency. This improves data quality and enables accurate analysis.

[0062] An automatic SQL generation system can also be equipped with a security unit that automatically strengthens data security based on user input. For example, when a user inputs "Display monthly sales data," the security unit sets access permissions for that data. The security unit can also encrypt data. For example, it can encrypt highly confidential data to protect it from unauthorized access. The security unit can also record and monitor data access logs. For example, it can record who accessed which data and when, and issue an alert if unauthorized access occurs. This strengthens data security and prevents information leaks.

[0063] The SQL automatic generation system can also include an integration unit that automatically integrates data based on user input. For example, when a user inputs, "Summarize and display sales data by month," the integration unit collects and integrates data from multiple data sources. The integration unit can also unify data in different formats. For example, it can unify and process data in CSV format and data in Excel format. The integration unit can also perform data mapping. For example, it can map tables from different databases to generate consistent data. This allows multiple data sources to be integrated and comprehensive data analysis to be performed.

[0064] The SQL automatic generation system can further include an assistance unit that estimates the user's emotions and provides appropriate assistance to the user based on the estimated emotions. For example, when a user inputs "Display sales data aggregated by month," the assistance unit can display a simple operation guide if the user is feeling stressed. The assistance unit can also provide detailed help if the user is confused. For example, it can display a tutorial on how to write a specific SQL statement. The assistance unit can also suggest more advanced functions if the user has positive emotions. For example, it can suggest data visualization and analysis functions. This makes it possible to provide appropriate assistance according to the user's emotions and improve the user experience.

[0065] The SQL automatic generation system can further include a relaxation unit that estimates the user's emotions and provides the user with a relaxing effect based on the estimated emotions. For example, when the user inputs "Display sales data aggregated by month," the relaxation unit plays relaxing music if the user is feeling stressed. The relaxation unit can also display relaxing images or videos if the user is tired. For example, it can display natural scenery or soothing images. The relaxation unit can also suggest simple stretches or breathing techniques to help the user relax. For example, it can display a message encouraging the user to take a short break. This provides a relaxing effect according to the user's emotions and improves work efficiency.

[0066] The SQL automatic generation system can further include a motivation unit that estimates the user's emotions and provides motivation to the user based on the estimated emotions. For example, when a user inputs "Summarize and display sales data by month," the motivation unit displays an encouraging message if the user is tired. If the user is losing motivation, the motivation unit can also suggest goal setting to increase motivation. For example, it can set a short-term goal and support the user in achieving it. If the user is feeling positive, the motivation unit can also suggest further challenges. For example, it can encourage the user to try a new data analysis method. This makes it possible to provide motivation according to the user's emotions and improve work efficiency.

[0067] The SQL automatic generation system can further include a feedback unit that estimates the user's emotions and provides appropriate feedback to the user based on the estimated emotions. For example, when a user inputs "Summarize and display sales data by month," the feedback unit displays an encouraging message if the user is feeling stressed. The feedback unit can also provide specific advice if the user is confused, for example, by pointing out areas for improvement in the SQL statement. The feedback unit can also suggest further improvements if the user has positive emotions, for example, by suggesting methods for optimizing the data. This makes it possible to provide appropriate feedback according to the user's emotions and improve the user experience.

[0068] The SQL automatic generation system can further include a reminder unit that estimates the user's emotions and provides the user with appropriate reminders based on the estimated emotions. For example, when a user inputs "Display sales data by month," the reminder unit displays a message urging the user to take a break if the user is feeling stressed. The reminder unit can also provide a reminder to check the progress of work if the user is tired. For example, it can display a message periodically checking the progress. The reminder unit can also suggest the next task if the user is feeling positive. For example, it can remind the user of the next task to be tackled. This allows the system to provide appropriate reminders according to the user's emotions and improve work efficiency.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The ambiguous Japanese analysis unit analyzes ambiguous Japanese. For example, it recognizes ambiguous expressions, synonyms, and similar words, and extracts information to generate appropriate SQL statements. It can also refer to the user's past input history to perform analysis optimized for each individual user. It learns from previously entered ambiguous Japanese instructions and generated SQL statements, and uses this information for future analyses. Step 2: The rule application unit applies company or department rules and practices based on the ambiguous Japanese parsed by the ambiguous Japanese parser. For example, this includes naming conventions for specific table and column names, and generating SQL statements based on specific database structures. It can also learn from the history of past SQL statement usage and automatically apply the most appropriate rules. It automatically references specific industry standards and regulations to generate appropriate SQL statements. Step 3: The SQL generation unit generates SQL statements based on the rules and methods applied by the rule application unit. For example, the generation AI generates SQL statements using text generation AI (e.g., LLM). Multimodal generation AI can also be used to optimize SQL statements. The generated SQL statements can be automatically corrected and converted into efficient queries. For example, redundant parts can be removed or converted into efficient queries.

[0071] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0077] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0078] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0079] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0081] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0082] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0086] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0112] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0117] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0119] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0121] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0122] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0123] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0124] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0125] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0126] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0127] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0128] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0129] 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.

[0130] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0131] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0132] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0133] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0134] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0135] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0136] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An ambiguous Japanese analysis unit that analyzes ambiguous Japanese, a rule application unit that applies rules and practices for each company or department based on the ambiguous Japanese analyzed by the ambiguous Japanese analysis unit; an SQL generation unit that generates an SQL statement based on the rules and practices applied by the rule application unit; A system characterized by:

2. The ambiguous Japanese analysis unit Refer to the user's past input history and generate the SQL statement optimized for each individual user.

2. The system of claim 1.

3. The ambiguous Japanese analysis unit Automatically recognizes synonyms and similar words to generate more accurate SQL statements 2. The system of claim 1.

4. The ambiguous Japanese analysis unit Analyzes the user's emotions when typing, and generates a simpler SQL statement if the user is feeling stressed.

2. The system of claim 1.

5. The ambiguous Japanese analysis unit Accepts voice input and generates the SQL statement from the voice 2. The system of claim 1.

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