system

Abstract

The system according to this embodiment aims to automate the creation of SQL statements, data extraction, and the generation of analysis results based on the user's natural language requests. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an extraction unit, an analysis unit, and a creation unit. The reception unit receives requests from users in natural language. The generation unit analyzes the requests received by the reception unit and automatically generates SQL statements. The extraction unit extracts data based on the SQL statements generated by the generation unit. The analysis unit analyzes the data extracted by the extraction unit and grasps the numerical trends. The creation unit automatically creates presentation materials based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, even when a user inputs a request in natural language, the creation of SQL statements, the extraction of data, and the creation of materials for analysis results are not automated, and there is a problem that it takes time and effort.

[0005] The system according to the embodiment aims to automate the creation of SQL statements, the extraction of data, and the creation of materials for analysis results based on the user's natural language request.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an extraction unit, an analysis unit, and a creation unit. The reception unit receives requests from users in natural language. The generation unit analyzes the requests received by the reception unit and automatically generates SQL statements. The extraction unit extracts data based on the SQL statements generated by the generation unit. The analysis unit analyzes the data extracted by the extraction unit and grasps numerical trends. The creation unit automatically creates presentation materials based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the creation of SQL statements, data extraction, and the generation of analysis results based on the user's natural language requests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that automatically generates SQL statements and outputs numerical trends using natural language. This system aims to shorten working hours and reduce the risk of individual dependency. The system starts with a user inputting a request for data extraction or document creation in natural language. Next, the AI ​​analyzes the request and automatically creates an SQL statement to extract the data. Furthermore, based on the extracted data, the AI ​​analyzes numerical trends and automatically creates presentation materials. This mechanism improves the work efficiency of employees involved in data and document creation tasks, and allows anyone to easily extract data and create documents. For example, a user inputs a request such as "Tell me the trend in the number of mobile phone contracts." This request is input to the AI. Next, the AI ​​analyzes the input request and automatically creates an SQL statement. The AI ​​generates an appropriate SQL statement based on the request and extracts the necessary data from the database. For example, in response to the request "Tell me the trend in the number of mobile phone contracts," the AI ​​generates an SQL statement to extract data related to the number of mobile phone contracts. Based on the extracted data, the AI ​​analyzes numerical trends. The AI ​​analyzes the extracted data and grasps the numerical trends. For example, the system analyzes trends in the number of mobile phone subscriptions to understand increases and decreases in subscriptions over a specific period. Furthermore, the AI ​​automatically creates presentation materials based on the analysis results. The AI ​​creates graphs and tables based on the analysis results and places them on the presentation slides. For example, it creates a graph showing trends in the number of mobile phone subscriptions and places it on the presentation slides. This system improves the work efficiency of employees involved in data and document creation. Anyone can easily extract data and create documents, resulting in reduced working hours and a reduction in the risk of individual reliance. In addition, by deploying it as an internal system, the risk to data confidentiality is low, and the use of internal data becomes possible. Furthermore, the risk of damage to the service or the company due to system failures is low, and early release is possible. As a result, the system can reduce working hours and the risk of individual reliance.

[0029] The system according to this embodiment comprises a reception unit, a generation unit, an extraction unit, an analysis unit, and a creation unit. The reception unit receives natural language requests from the user. Natural language requests from the user include, but are not limited to, questions, instructions, and requests. The reception unit receives requests entered by the user in natural language in text format, for example. The reception unit can also receive user requests using voice input. For example, it can use speech recognition technology to convert the user's voice into text and receive it as a request. The generation unit analyzes the requests received by the reception unit and automatically creates SQL statements. The generation unit analyzes user requests using, for example, natural language processing technology and generates appropriate SQL statements. For example, the generation unit generates SQL statements to extract necessary data from a database based on the user's request. The generation unit can also use machine learning algorithms to generate SQL statements that are optimal for the user's request. For example, the generation unit learns the correspondence between past requests and SQL statements and generates appropriate SQL statements for new requests. The extraction unit extracts data based on the SQL statements generated by the generation unit. For example, the extraction unit executes queries to extract the necessary data from a database. For example, the extraction unit sends an SQL statement to the database and retrieves the data returned from the database. The extraction unit can also extract data from multiple databases. For example, the extraction unit executes SQL statements against different databases and retrieves data from each database. The analysis unit analyzes the data extracted by the extraction unit and understands the numerical trends. For example, the analysis unit analyzes the data using statistical methods to understand the numerical trends. For example, the analysis unit calculates the mean and standard deviation of the data to understand the data distribution. The analysis unit can also analyze the data using machine learning algorithms to understand the numerical trends. For example, the analysis unit performs time series analysis of the data to understand the fluctuations in numerical values ​​over a specific period. The creation unit automatically creates presentation materials based on the analysis results obtained by the analysis unit. For example, the creation unit creates graphs and tables based on the analysis results and places them on the presentation slides.For example, the creation unit can create a graph showing the trend in the number of mobile phone contracts and place it on a presentation slide. The creation unit can also create presentation materials using templates. For example, the creation unit can create presentation slides by reflecting the analysis results in a pre-prepared template. In this way, the system according to the embodiment can improve work efficiency by automating SQL creation, data extraction, numerical trend analysis, and presentation material creation based on the user's natural language requests.

[0030] The reception desk receives natural language requests from users. These natural language requests include, but are not limited to, questions, instructions, and requests. The reception desk can, for example, receive requests entered by users in natural language in text format. The reception desk can also receive user requests using voice input. For example, it can use speech recognition technology to convert the user's voice into text and receive it as a request. Specifically, deep learning-based speech recognition models are often used as the speech recognition technology. This model can improve its accuracy in converting voice to text by learning from a large amount of voice data. For example, if a user requests "Tell me this month's sales data" by voice, speech recognition technology is used to convert this voice into the text "Tell me this month's sales data". Furthermore, in order to accurately understand the user's request, the reception desk uses natural language processing technology to analyze the meaning of the text. For example, it performs morphological analysis to divide the text into words and identify the part of speech of each word. Next, it performs dependency structure analysis to clarify the relationships between words. This allows the reception desk to accurately grasp the intent of the user's request.

[0031] The generation unit analyzes requests received by the reception unit and automatically creates SQL statements. For example, the generation unit uses natural language processing technology to analyze user requests and generate appropriate SQL statements. Specifically, in order to analyze user requests, the generation unit first needs to understand the meaning of the text. This is done using natural language processing technology to analyze the meaning of the text. For example, if a user requests "Please tell me this month's sales data," the generation unit analyzes this request and extracts the information "this month's sales data." Next, based on this information, the generation unit generates an SQL statement to extract the necessary data from the database. For example, it generates an SQL statement such as "SELECT * FROM sales WHERE date >= '2023-10-01' AND date <= '2023-10-31'." The generation unit can also use machine learning algorithms to generate the most suitable SQL statements for user requests. For example, the generation unit learns the correspondence between past requests and SQL statements and generates appropriate SQL statements for new requests. This allows the generation unit to respond to user requests quickly and accurately.

[0032] The extraction unit extracts data based on the SQL statements generated by the generation unit. For example, the extraction unit executes queries to extract the necessary data from a database. Specifically, the extraction unit sends the generated SQL statement to the database and retrieves the data returned from the database. For example, if the SQL statement "SELECT * FROM sales WHERE date >= '2023-10-01' AND date <= '2023-10-31'" is generated, the extraction unit sends this SQL statement to the database and retrieves the sales data returned from the database. The extraction unit can also extract data from multiple databases. For example, the extraction unit executes SQL statements against different databases and retrieves data from each database. This allows the extraction unit to extract the necessary data quickly and accurately. Furthermore, the extraction unit can perform data validation and filtering to maintain data integrity. For example, if the extracted data contains missing or outlier values, the extraction unit can maintain data quality by excluding or imputing these values.

[0033] The analysis unit analyzes the data extracted by the extraction unit to understand numerical trends. For example, the analysis unit uses statistical methods to analyze the data and understand numerical trends. Specifically, the analysis unit calculates the mean and standard deviation of the data to understand its distribution. Furthermore, the analysis unit can also use machine learning algorithms to analyze the data and understand numerical trends. For example, the analysis unit performs time-series analysis of the data to understand fluctuations in numerical values ​​over a specific period. This allows the analysis unit to accurately understand data trends and use this information for future predictions and decision-making. In addition, the analysis unit can visualize the data. For example, it can display the data distribution and trends in graphs and charts to facilitate understanding of the data. This allows the analysis unit to visually grasp data trends and support quick and accurate decision-making.

[0034] The creation unit automatically generates presentation materials based on the analysis results obtained by the analysis unit. For example, the creation unit creates graphs and tables based on the analysis results and places them on the presentation slides. Specifically, the creation unit creates a graph showing the trend of sales data based on the analysis results and places it on the presentation slides. The creation unit can also create presentation materials using templates. For example, the creation unit reflects the analysis results into a pre-prepared template and creates presentation slides. This allows the creation unit to create presentation materials quickly and efficiently. Furthermore, the creation unit can automatically adjust the design and layout of the slides. For example, it adjusts the color scheme and font size of the slides to create easy-to-read materials. This allows the creation unit to provide users with clear and effective materials.

[0035] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit can automatically display requests that the user has frequently entered in the past as candidates. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest requests to be used during specific time periods based on the user's past request history. For example, the reception unit can predict similar requests based on requests the user has entered during specific time periods in the past and display them as candidates. In this way, the reception unit can provide the optimal reception method by analyzing the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past request history data into a generating AI and have the generating AI select the optimal reception method.

[0036] The reception unit can filter requests based on the user's current projects and areas of interest. For example, the reception unit prioritizes requests related to the user's current projects. For example, the reception unit filters and accepts relevant requests based on the user's areas of interest. The reception unit also suggests appropriate requests according to the progress of the user's projects. For example, the reception unit understands the progress of the user's current projects and prioritizes receiving requests related to them. This allows for the priority acceptance of highly relevant requests by filtering requests based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0037] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving requests related to that region. For example, the reception unit will suggest the most relevant requests based on the user's current location. The reception unit will also prioritize receiving requests that are highly relevant, taking into account the user's travel history. For example, the reception unit will predict relevant requests based on the user's travel history data and display them as candidates. In this way, by considering the user's geographical location information, highly relevant requests can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant requests.

[0038] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant requests. For example, the reception unit can accept relevant requests by referring to the activities of the user's social media followers and friends. The reception unit can also analyze the user's social media trends and suggest the most suitable requests. For example, the reception unit can predict relevant requests based on social media trend data and display them as candidates. This allows the reception unit to prioritize the acceptance of relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant requests.

[0039] The generation unit can adjust the level of detail of the generated SQL statement based on the importance of the request. For example, the generation unit generates detailed SQL statements for important requests. For example, the generation unit generates concise SQL statements for low-priority requests. The generation unit also generates SQL statements with appropriate levels of detail depending on the importance of the request. For example, the generation unit generates SQL statements that include detailed conditional settings based on the importance of the request. In this way, appropriate SQL statements can be generated by adjusting the level of detail of the SQL statement according to the importance of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request importance data into the generation AI and have the generation AI adjust the level of detail of the SQL statement.

[0040] The generation unit can apply different generation algorithms depending on the category of the request when generating SQL statements. For example, for data analysis requests, the generation unit generates SQL statements specialized for analysis. For example, for data extraction requests, the generation unit generates SQL statements specialized for extraction. Furthermore, for data update requests, the generation unit generates SQL statements specialized for updates. For example, for data update requests, the generation unit generates SQL statements that include update operations. This enables efficient SQL statement generation by applying the appropriate generation algorithm according to the category of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the request into the generation AI and have the generation AI execute the application of the generation algorithm.

[0041] The generation unit can determine the generation priority based on the request submission timing when generating SQL statements. For example, the generation unit will prioritize generating SQL statements for urgent requests. For example, the generation unit will quickly generate SQL statements for requests with an early submission time. The generation unit will also generate SQL statements with an appropriate priority according to the submission time. For example, the generation unit will quickly generate SQL statements for requests with an early submission time. This enables a rapid response by determining the generation priority of SQL statements according to the request submission time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request submission time data into a generation AI and have the generation AI determine the generation priority.

[0042] The generation unit can adjust the generation order of SQL statements based on the relevance of the requests. For example, the generation unit prioritizes generating SQL statements for highly relevant requests. For example, the generation unit postpones generating SQL statements for less relevant requests. The generation unit also generates SQL statements in an appropriate order according to the relevance of the requests. For example, the generation unit prioritizes generating SQL statements for highly relevant requests. This allows for efficient SQL statement generation by adjusting the generation order according to the relevance of the requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request relevance data into a generation AI and have the generation AI adjust the generation order.

[0043] The extraction unit can select the optimal extraction method by referring to past extraction data during data extraction. For example, the extraction unit selects the optimal extraction method based on previously extracted data. For example, the extraction unit analyzes past extraction data and selects an efficient extraction method. The extraction unit also selects the optimal extraction method by referring to past extraction history. For example, the extraction unit selects the optimal extraction method based on past extraction history data. In this way, the optimal data extraction method can be selected by referring to past extraction data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input past extraction data into a generating AI and have the generating AI perform the selection of the optimal extraction method.

[0044] The extraction unit can apply different extraction algorithms depending on the data category during data extraction. For example, the extraction unit applies an extraction algorithm specialized for analysis to data analysis requests. For example, the extraction unit applies an extraction algorithm specialized for extraction to data extraction requests. Furthermore, the extraction unit applies an extraction algorithm specialized for updates to data update requests. For example, the extraction unit applies an extraction algorithm that includes update operations to data update requests. This enables efficient data extraction by applying the appropriate extraction algorithm according to the data category. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data category data into a generating AI and have the generating AI execute the application of the extraction algorithm.

[0045] The extraction unit can perform data extraction while considering the geographical distribution of the data. For example, the extraction unit can prioritize the extraction of data related to a specific region. For example, the extraction unit can extract the optimal data by considering the geographical distribution of the data. The extraction unit can also extract highly relevant data by considering geographical factors. For example, the extraction unit can prioritize the extraction of data related to a specific region. This allows for the extraction of highly relevant data by considering the geographical distribution of the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data into a generating AI and have the generating AI perform the extraction.

[0046] The extraction unit can improve the accuracy of data extraction by referring to relevant literature during the data extraction process. For example, the extraction unit can improve the accuracy of the data to be extracted by referring to relevant literature. For example, the extraction unit can select the optimal data extraction method based on relevant literature. The extraction unit can also improve the accuracy of the extracted data by referring to relevant literature. For example, the extraction unit can improve the accuracy of the data to be extracted by referring to relevant literature. In this way, the accuracy of the extracted data can be improved by referring to relevant literature. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input relevant literature data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0047] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit applies an analysis method specialized for analysis to data analysis requests. For example, the analysis unit applies an analysis method specialized for extraction to data extraction requests. Furthermore, the analysis unit applies an analysis method specialized for updating to data update requests. For example, the analysis unit applies an analysis method that includes update operations to data update requests. This enables efficient data analysis by applying the appropriate analysis method according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data category data into a generating AI and have the generating AI execute the application of analysis methods.

[0048] The analysis unit can determine the priority of analysis based on the data submission timing during the analysis process. For example, the analysis unit will prioritize analysis for urgent requests. For example, the analysis unit will quickly analyze requests with an early submission deadline. The analysis unit will also perform analyses with appropriate priorities according to the submission timing. For example, the analysis unit will quickly analyze requests with an early submission deadline. This enables a rapid response by determining the priority of analysis according to the data submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0049] The analysis unit can perform analysis by referring to relevant market data. For example, the analysis unit can improve the accuracy of the analysis by referring to relevant market data. For example, the analysis unit can select the optimal analysis method based on relevant market data. The analysis unit can also improve the reliability of the analysis results by referring to relevant market data. For example, the analysis unit can improve the accuracy of the analysis by referring to relevant market data. In this way, the accuracy of the analysis can be improved by referring to relevant market data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0050] The creation unit can select the optimal creation method by referring to past document creation data when creating a document. For example, the creation unit selects the optimal creation method based on past document creation data. For example, the creation unit analyzes past document creation data and selects an efficient creation method. The creation unit also selects the optimal creation method by referring to past document creation history. For example, the creation unit selects the optimal creation method based on past document creation history data. In this way, the optimal document creation method can be selected by referring to past document creation data. Some or all of the above processes in the creation unit may be performed using AI, for example, or without using AI. For example, the creation unit can input past document creation data into a generation AI and have the generation AI perform the selection of the optimal creation method.

[0051] The creation unit can apply different creation algorithms depending on the data category when creating documents. For example, the creation unit applies a creation algorithm specialized for analysis to data analysis requests. For example, the creation unit applies a creation algorithm specialized for extraction to data extraction requests. Furthermore, the creation unit applies a creation algorithm specialized for updates to data update requests. For example, the creation unit applies a creation algorithm that includes update operations to data update requests. This enables efficient document creation by applying the appropriate creation algorithm according to the data category. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data category data into a generation AI and have the generation AI execute the application of the creation algorithm.

[0052] The creation unit can create documents while considering the geographical distribution of the data. For example, the creation unit can prioritize reflecting data related to a specific region in the document. For example, the creation unit can create optimal documents by considering the geographical distribution of the data. The creation unit can also create highly relevant documents by considering geographical factors. For example, the creation unit can prioritize reflecting data related to a specific region in the document. This allows for the creation of highly relevant documents by considering the geographical distribution of the data. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input geographical distribution data into a generation AI and have the generation AI perform the document creation.

[0053] The creation unit can improve the accuracy of its creation by referring to relevant literature during the creation process. For example, the creation unit can improve the accuracy of the material by referring to relevant literature. For example, the creation unit can select the optimal method for creating the material based on relevant literature. The creation unit can also improve the accuracy of the created material by referring to relevant literature. For example, the creation unit can improve the accuracy of the material by referring to relevant literature. In this way, the accuracy of the material can be improved by referring to relevant literature. Some or all of the above processes in the creation unit may be performed using AI, for example, or without using AI. For example, the creation unit can input relevant literature data into a generation AI and have the generation AI perform the task of improving the accuracy of material creation.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The reception desk can analyze a user's past request history and select the optimal reception method. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will use during specific time periods based on their past request history. In this way, by analyzing a user's past request history, the system can provide the most suitable reception method.

[0056] The generation unit can adjust the level of detail generated based on the importance of the request when generating SQL statements. For example, it generates detailed SQL statements for important requests and concise SQL statements for low-priority requests. It also generates SQL statements with appropriate level of detail according to the importance of the request. In this way, by adjusting the level of detail of the SQL statement according to the importance of the request, it is possible to generate appropriate SQL statements.

[0057] The extraction unit can select the optimal extraction method by referring to past extraction data during data extraction. For example, it can select the optimal extraction method based on previously extracted data. It can analyze past extraction data to select an efficient extraction method. It can also select the optimal extraction method by referring to past extraction history. In this way, the optimal data extraction method can be selected by referring to past extraction data.

[0058] The analysis unit can apply different analysis methods to each data category during analysis. For example, it applies an analysis method specialized for analysis to data analysis requests, an analysis method specialized for extraction to data extraction requests, and an analysis method specialized for updates to data update requests. This enables efficient data analysis by applying the appropriate analysis method according to the data category.

[0059] The creation department can improve the accuracy of its materials by referring to relevant literature during the creation process. For example, it can improve the accuracy of the materials by referring to relevant literature. Based on the relevant literature, it can select the optimal method for creating the materials. Furthermore, it can improve the accuracy of the created materials by referring to relevant literature. In this way, the accuracy of the materials can be improved by referring to relevant literature.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives natural language requests from users. These requests include questions, instructions, and other requests. The reception desk receives the requests entered by the user in natural language as text. It can also accept user requests using voice input, and uses speech recognition technology to convert the user's voice into text. Step 2: The generation unit analyzes the request received by the reception unit and automatically generates an SQL statement. The generation unit uses natural language processing technology to analyze the user's request and generate an appropriate SQL statement. Furthermore, it can also use machine learning algorithms to learn the correspondence between past requests and SQL statements and generate the optimal SQL statement for new requests. Step 3: The extraction unit extracts data based on the SQL statements generated by the generation unit. The extraction unit executes queries to extract the necessary data from the database and retrieves the data returned from the database. Furthermore, it can also extract data from multiple databases. Step 4: The analysis unit analyzes the data extracted by the extraction unit and grasps the numerical trends. The analysis unit analyzes the data using statistical methods and grasps the numerical trends. Furthermore, it can also analyze the data using machine learning algorithms and grasp the numerical trends. Step 5: The creation unit automatically generates presentation materials based on the analysis results obtained by the analysis unit. The creation unit creates graphs and tables based on the analysis results and places them on the presentation slides. Furthermore, it is also possible to create presentation materials using templates.

[0062] (Example of form 2) The system according to an embodiment of the present invention is a system that automatically generates SQL statements, automatically creates presentations, and outputs numerical trends using natural language. This system aims to reduce working hours and mitigate the risk of individual dependency. The system starts with a user inputting a request for data extraction or document creation in natural language. Next, the AI ​​analyzes the request and automatically creates an SQL statement to extract the data. Furthermore, based on the extracted data, the AI ​​analyzes numerical trends and automatically creates presentation materials. This mechanism improves the work efficiency of employees involved in data and document creation tasks, and allows anyone to easily extract data and create documents. For example, a user inputs a request such as "Tell me the trend in the number of mobile phone contracts." This request is input to the AI. Next, the AI ​​analyzes the input request and automatically creates an SQL statement. The AI ​​generates an appropriate SQL statement based on the request and extracts the necessary data from the database. For example, in response to the request "Tell me the trend in the number of mobile phone contracts," the AI ​​generates an SQL statement to extract data related to the number of mobile phone contracts. Based on the extracted data, the AI ​​analyzes numerical trends. AI analyzes extracted data and identifies numerical trends. For example, it analyzes trends in the number of mobile phone subscriptions to understand increases and decreases in subscriptions over a specific period. Furthermore, the AI ​​automatically creates presentation materials based on the analysis results. The AI ​​creates graphs and tables based on the analysis results and places them on the presentation slides. For example, it creates a graph showing trends in the number of mobile phone subscriptions and places it on the presentation slides. This system improves the work efficiency of employees involved in data and document creation. Anyone can easily extract data and create documents, resulting in reduced working hours and a reduction in the risk of individual reliance. Also, by deploying it as an internal system, the risk to data confidentiality is low, and the use of internal data becomes possible. In addition, the risk of damage to the service or the company due to system failures is low, and early release is possible. As a result, the system can reduce working hours and the risk of individual reliance.

[0063] The system according to this embodiment comprises a reception unit, a generation unit, an extraction unit, an analysis unit, and a creation unit. The reception unit receives natural language requests from the user. Natural language requests from the user include, but are not limited to, questions, instructions, and requests. The reception unit receives requests entered by the user in natural language in text format, for example. The reception unit can also receive user requests using voice input. For example, it can use speech recognition technology to convert the user's voice into text and receive it as a request. The generation unit analyzes the requests received by the reception unit and automatically creates SQL statements. The generation unit analyzes user requests using, for example, natural language processing technology and generates appropriate SQL statements. For example, the generation unit generates SQL statements to extract necessary data from a database based on the user's request. The generation unit can also use machine learning algorithms to generate SQL statements that are optimal for the user's request. For example, the generation unit learns the correspondence between past requests and SQL statements and generates appropriate SQL statements for new requests. The extraction unit extracts data based on the SQL statements generated by the generation unit. For example, the extraction unit executes queries to extract the necessary data from a database. For example, the extraction unit sends an SQL statement to the database and retrieves the data returned from the database. The extraction unit can also extract data from multiple databases. For example, the extraction unit executes SQL statements against different databases and retrieves data from each database. The analysis unit analyzes the data extracted by the extraction unit and understands the numerical trends. For example, the analysis unit analyzes the data using statistical methods to understand the numerical trends. For example, the analysis unit calculates the mean and standard deviation of the data to understand the data distribution. The analysis unit can also analyze the data using machine learning algorithms to understand the numerical trends. For example, the analysis unit performs time series analysis of the data to understand the fluctuations in numerical values ​​over a specific period. The creation unit automatically creates presentation materials based on the analysis results obtained by the analysis unit. For example, the creation unit creates graphs and tables based on the analysis results and places them on the presentation slides.For example, the creation unit can create a graph showing the trend in the number of mobile phone contracts and place it on a presentation slide. The creation unit can also create presentation materials using templates. For example, the creation unit can create presentation slides by reflecting the analysis results in a pre-prepared template. In this way, the system according to the embodiment can improve work efficiency by automating SQL creation, data extraction, numerical trend analysis, and presentation material creation based on the user's natural language requests.

[0064] The reception desk receives natural language requests from users. These natural language requests include, but are not limited to, questions, instructions, and requests. The reception desk can, for example, receive requests entered by users in natural language in text format. The reception desk can also receive user requests using voice input. For example, it can use speech recognition technology to convert the user's voice into text and receive it as a request. Specifically, deep learning-based speech recognition models are often used as the speech recognition technology. This model can improve its accuracy in converting voice to text by learning from a large amount of voice data. For example, if a user requests "Tell me this month's sales data" by voice, speech recognition technology is used to convert this voice into the text "Tell me this month's sales data". Furthermore, in order to accurately understand the user's request, the reception desk uses natural language processing technology to analyze the meaning of the text. For example, it performs morphological analysis to divide the text into words and identify the part of speech of each word. Next, it performs dependency structure analysis to clarify the relationships between words. This allows the reception desk to accurately grasp the intent of the user's request.

[0065] The generation unit analyzes requests received by the reception unit and automatically creates SQL statements. For example, the generation unit uses natural language processing technology to analyze user requests and generate appropriate SQL statements. Specifically, in order to analyze user requests, the generation unit first needs to understand the meaning of the text. This is done using natural language processing technology to analyze the meaning of the text. For example, if a user requests "Please tell me this month's sales data," the generation unit analyzes this request and extracts the information "this month's sales data." Next, based on this information, the generation unit generates an SQL statement to extract the necessary data from the database. For example, it generates an SQL statement such as "SELECT * FROM sales WHERE date >= '2023-10-01' AND date <= '2023-10-31'." The generation unit can also use machine learning algorithms to generate the most suitable SQL statements for user requests. For example, the generation unit learns the correspondence between past requests and SQL statements and generates appropriate SQL statements for new requests. This allows the generation unit to respond to user requests quickly and accurately.

[0066] The extraction unit extracts data based on the SQL statements generated by the generation unit. For example, the extraction unit executes queries to extract the necessary data from a database. Specifically, the extraction unit sends the generated SQL statement to the database and retrieves the data returned from the database. For example, if the SQL statement "SELECT * FROM sales WHERE date >= '2023-10-01' AND date <= '2023-10-31'" is generated, the extraction unit sends this SQL statement to the database and retrieves the sales data returned from the database. The extraction unit can also extract data from multiple databases. For example, the extraction unit executes SQL statements against different databases and retrieves data from each database. This allows the extraction unit to extract the necessary data quickly and accurately. Furthermore, the extraction unit can perform data validation and filtering to maintain data integrity. For example, if the extracted data contains missing or outlier values, the extraction unit can maintain data quality by excluding or imputing these values.

[0067] The analysis unit analyzes the data extracted by the extraction unit to understand numerical trends. For example, the analysis unit uses statistical methods to analyze the data and understand numerical trends. Specifically, the analysis unit calculates the mean and standard deviation of the data to understand its distribution. Furthermore, the analysis unit can also use machine learning algorithms to analyze the data and understand numerical trends. For example, the analysis unit performs time-series analysis of the data to understand fluctuations in numerical values ​​over a specific period. This allows the analysis unit to accurately understand data trends and use this information for future predictions and decision-making. In addition, the analysis unit can visualize the data. For example, it can display the data distribution and trends in graphs and charts to facilitate understanding of the data. This allows the analysis unit to visually grasp data trends and support quick and accurate decision-making.

[0068] The creation unit automatically generates presentation materials based on the analysis results obtained by the analysis unit. For example, the creation unit creates graphs and tables based on the analysis results and places them on the presentation slides. Specifically, the creation unit creates a graph showing the trend of sales data based on the analysis results and places it on the presentation slides. The creation unit can also create presentation materials using templates. For example, the creation unit reflects the analysis results into a pre-prepared template and creates presentation slides. This allows the creation unit to create presentation materials quickly and efficiently. Furthermore, the creation unit can automatically adjust the design and layout of the slides. For example, it adjusts the color scheme and font size of the slides to create easy-to-read materials. This allows the creation unit to provide users with clear and effective materials.

[0069] The reception unit can estimate the user's emotions and adjust how it receives natural language requests based on the estimated emotions. For example, if the user is stressed, the reception unit provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Also, if the user is in a hurry, the reception unit prioritizes voice input to quickly receive natural language requests. For example, if the user is in a hurry, the reception unit uses speech recognition technology to convert the user's voice into text and accept it as a request. This improves user convenience by adjusting how requests are received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the AI ​​perform an estimation of the user's emotions.

[0070] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit can automatically display requests that the user has frequently entered in the past as candidates. For example, the reception unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest requests to be used during specific time periods based on the user's past request history. For example, the reception unit can predict similar requests based on requests the user has entered during specific time periods in the past and display them as candidates. In this way, the reception unit can provide the optimal reception method by analyzing the user's past request history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past request history data into a generating AI and have the generating AI select the optimal reception method.

[0071] The reception unit can filter requests based on the user's current projects and areas of interest. For example, the reception unit prioritizes requests related to the user's current projects. For example, the reception unit filters and accepts relevant requests based on the user's areas of interest. The reception unit also suggests appropriate requests according to the progress of the user's projects. For example, the reception unit understands the progress of the user's current projects and prioritizes receiving requests related to them. This allows for the priority acceptance of highly relevant requests by filtering requests based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0072] The reception desk can estimate the user's emotions and determine the priority of requests to be received based on the estimated emotions. For example, if the user is tense, the reception desk will prioritize important requests. For example, if the user is relaxed, the reception desk will prioritize detailed requests. Also, if the user is in a hurry, the reception desk will prioritize requests that require a quick response. For example, if the user is in a hurry, the reception desk can use speech recognition technology to convert the user's voice into text and receive it as a request. This allows for prioritizing important requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The reception unit can prioritize receiving requests that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit will prioritize receiving requests related to that region. For example, the reception unit will suggest the most relevant requests based on the user's current location. The reception unit will also prioritize receiving requests that are highly relevant, taking into account the user's travel history. For example, the reception unit will predict relevant requests based on the user's travel history data and display them as candidates. In this way, by considering the user's geographical location information, highly relevant requests can be prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant requests.

[0074] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant requests. For example, the reception unit can accept relevant requests by referring to the activities of the user's social media followers and friends. The reception unit can also analyze the user's social media trends and suggest the most suitable requests. For example, the reception unit can predict relevant requests based on social media trend data and display them as candidates. This allows the reception unit to prioritize the acceptance of relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant requests.

[0075] The generation unit can estimate the user's emotions and adjust the SQL statement generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates detailed SQL statements. For example, if the user is in a hurry, the generation unit generates concise SQL statements. Also, if the user is stressed, the generation unit generates simple SQL statements. For example, if the user is stressed, the generation unit generates concise and to-the-point SQL statements. In this way, by adjusting the SQL statement generation method according to the user's emotions, it is possible to generate SQL statements that are suitable for the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI adjust the SQL statement generation method.

[0076] The generation unit can adjust the level of detail of the generated SQL statement based on the importance of the request. For example, the generation unit generates detailed SQL statements for important requests. For example, the generation unit generates concise SQL statements for low-priority requests. The generation unit also generates SQL statements with appropriate levels of detail depending on the importance of the request. For example, the generation unit generates SQL statements that include detailed conditional settings based on the importance of the request. In this way, appropriate SQL statements can be generated by adjusting the level of detail of the SQL statement according to the importance of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request importance data into the generation AI and have the generation AI adjust the level of detail of the SQL statement.

[0077] The generation unit can apply different generation algorithms depending on the category of the request when generating SQL statements. For example, for data analysis requests, the generation unit generates SQL statements specialized for analysis. For example, for data extraction requests, the generation unit generates SQL statements specialized for extraction. Furthermore, for data update requests, the generation unit generates SQL statements specialized for updates. For example, for data update requests, the generation unit generates SQL statements that include update operations. This enables efficient SQL statement generation by applying the appropriate generation algorithm according to the category of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the request into the generation AI and have the generation AI execute the application of the generation algorithm.

[0078] The generation unit can estimate the user's emotions and adjust the length of the SQL statement based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, concise SQL statement. For example, if the user is relaxed, the generation unit generates a longer SQL statement with detailed explanations. Also, if the user is stressed, the generation unit generates a simple and short SQL statement. For example, if the user is stressed, the generation unit generates a concise and concise SQL statement. In this way, by adjusting the length of the SQL statement according to the user's emotions, it is possible to generate an SQL statement that is suitable for the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI adjust the length of the SQL statement.

[0079] The generation unit can determine the generation priority based on the request submission timing when generating SQL statements. For example, the generation unit will prioritize generating SQL statements for urgent requests. For example, the generation unit will quickly generate SQL statements for requests with an early submission time. The generation unit will also generate SQL statements with an appropriate priority according to the submission time. For example, the generation unit will quickly generate SQL statements for requests with an early submission time. This enables a rapid response by determining the generation priority of SQL statements according to the request submission time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request submission time data into a generation AI and have the generation AI determine the generation priority.

[0080] The generation unit can adjust the generation order of SQL statements based on the relevance of the requests. For example, the generation unit prioritizes generating SQL statements for highly relevant requests. For example, the generation unit postpones generating SQL statements for less relevant requests. The generation unit also generates SQL statements in an appropriate order according to the relevance of the requests. For example, the generation unit prioritizes generating SQL statements for highly relevant requests. This allows for efficient SQL statement generation by adjusting the generation order according to the relevance of the requests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request relevance data into a generation AI and have the generation AI adjust the generation order.

[0081] The extraction unit can estimate the user's emotions and adjust the data extraction method based on the estimated emotions. For example, if the user is relaxed, the extraction unit will extract detailed data. For example, if the user is in a hurry, the extraction unit will extract concise data. Also, if the user is stressed, the extraction unit will extract simple data. For example, if the user is stressed, the extraction unit will extract concise and to-the-point data. In this way, by adjusting the data extraction method according to the user's emotions, data suitable for the user's needs can be extracted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can input the user's facial expression data into the generative AI and have the generative AI adjust the data extraction method.

[0082] The extraction unit can select the optimal extraction method by referring to past extraction data during data extraction. For example, the extraction unit selects the optimal extraction method based on previously extracted data. For example, the extraction unit analyzes past extraction data and selects an efficient extraction method. The extraction unit also selects the optimal extraction method by referring to past extraction history. For example, the extraction unit selects the optimal extraction method based on past extraction history data. In this way, the optimal data extraction method can be selected by referring to past extraction data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input past extraction data into a generating AI and have the generating AI perform the selection of the optimal extraction method.

[0083] The extraction unit can apply different extraction algorithms depending on the data category during data extraction. For example, the extraction unit applies an extraction algorithm specialized for analysis to data analysis requests. For example, the extraction unit applies an extraction algorithm specialized for extraction to data extraction requests. Furthermore, the extraction unit applies an extraction algorithm specialized for updates to data update requests. For example, the extraction unit applies an extraction algorithm that includes update operations to data update requests. This enables efficient data extraction by applying the appropriate extraction algorithm according to the data category. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input data category data into a generating AI and have the generating AI execute the application of the extraction algorithm.

[0084] The extraction unit can estimate the user's emotions and determine the priority of data to extract based on the estimated user emotions. For example, if the user is tense, the extraction unit will prioritize extracting important data. For example, if the user is relaxed, the extraction unit will prioritize extracting detailed data. Also, if the user is in a hurry, the extraction unit will prioritize extracting data that requires immediate attention. For example, if the user is in a hurry, the extraction unit will extract concise and to-the-point data. In this way, important data can be prioritized by determining the data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or not using AI. For example, the extraction unit can input user facial expression data into the generative AI and have the generative AI perform the determination of data priority.

[0085] The extraction unit can perform data extraction while considering the geographical distribution of the data. For example, the extraction unit can prioritize the extraction of data related to a specific region. For example, the extraction unit can extract the optimal data by considering the geographical distribution of the data. The extraction unit can also extract highly relevant data by considering geographical factors. For example, the extraction unit can prioritize the extraction of data related to a specific region. This allows for the extraction of highly relevant data by considering the geographical distribution of the data. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can input geographical distribution data into a generating AI and have the generating AI perform the extraction.

[0086] The extraction unit can improve the accuracy of data extraction by referring to relevant literature during the data extraction process. For example, the extraction unit can improve the accuracy of the data to be extracted by referring to relevant literature. For example, the extraction unit can select the optimal data extraction method based on relevant literature. The extraction unit can also improve the accuracy of the extracted data by referring to relevant literature. For example, the extraction unit can improve the accuracy of the data to be extracted by referring to relevant literature. In this way, the accuracy of the extracted data can be improved by referring to relevant literature. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without using AI. For example, the extraction unit can input relevant literature data into a generating AI and have the generating AI perform the extraction accuracy improvement.

[0087] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. For example, if the user is in a hurry, the analysis unit performs a concise analysis. Also, if the user is stressed, the analysis unit performs a simple analysis. For example, if the user is stressed, the analysis unit performs a concise and to-the-point analysis. By adjusting the analysis method according to the user's emotions, it becomes possible to perform an analysis that is suitable for the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the adjustment of the analysis method.

[0088] The analysis unit can apply different analysis methods to each data category during analysis. For example, the analysis unit applies an analysis method specialized for analysis to data analysis requests. For example, the analysis unit applies an analysis method specialized for extraction to data extraction requests. Furthermore, the analysis unit applies an analysis method specialized for updating to data update requests. For example, the analysis unit applies an analysis method that includes update operations to data update requests. This enables efficient data analysis by applying the appropriate analysis method according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data category data into a generating AI and have the generating AI execute the application of analysis methods.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit provides a concise and to-the-point display method. For example, if the user is in a hurry, the analysis unit provides a concise and to-the-point display method. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the adjustment of the display method.

[0090] The analysis unit can determine the priority of analysis based on the data submission timing during the analysis process. For example, the analysis unit will prioritize analysis for urgent requests. For example, the analysis unit will quickly analyze requests with an early submission deadline. The analysis unit will also perform analyses with appropriate priorities according to the submission timing. For example, the analysis unit will quickly analyze requests with an early submission deadline. This enables a rapid response by determining the priority of analysis according to the data submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data submission timing data into a generating AI and have the generating AI determine the analysis priority.

[0091] The analysis unit can perform analysis by referring to relevant market data. For example, the analysis unit can improve the accuracy of the analysis by referring to relevant market data. For example, the analysis unit can select the optimal analysis method based on relevant market data. The analysis unit can also improve the reliability of the analysis results by referring to relevant market data. For example, the analysis unit can improve the accuracy of the analysis by referring to relevant market data. In this way, the accuracy of the analysis can be improved by referring to relevant market data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0092] The creation unit can estimate the user's emotions and adjust the method of creating the document based on the estimated emotions. For example, if the user is relaxed, the creation unit will create a detailed document. For example, if the user is in a hurry, the creation unit will create a concise document. Also, if the user is stressed, the creation unit will create a simple document. For example, if the user is stressed, the creation unit will create a concise and to-the-point document. In this way, by adjusting the method of creating the document according to the user's emotions, it is possible to create a document that is suitable for the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input user facial expression data into the generative AI and have the generative AI perform the adjustment of the document creation method.

[0093] The creation unit can select the optimal creation method by referring to past document creation data when creating a document. For example, the creation unit selects the optimal creation method based on past document creation data. For example, the creation unit analyzes past document creation data and selects an efficient creation method. The creation unit also selects the optimal creation method by referring to past document creation history. For example, the creation unit selects the optimal creation method based on past document creation history data. In this way, the optimal document creation method can be selected by referring to past document creation data. Some or all of the above processes in the creation unit may be performed using AI, for example, or without using AI. For example, the creation unit can input past document creation data into a generation AI and have the generation AI perform the selection of the optimal creation method.

[0094] The creation unit can apply different creation algorithms depending on the data category when creating documents. For example, the creation unit applies a creation algorithm specialized for analysis to data analysis requests. For example, the creation unit applies a creation algorithm specialized for extraction to data extraction requests. Furthermore, the creation unit applies a creation algorithm specialized for updates to data update requests. For example, the creation unit applies a creation algorithm that includes update operations to data update requests. This enables efficient document creation by applying the appropriate creation algorithm according to the data category. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input data category data into a generation AI and have the generation AI execute the application of the creation algorithm.

[0095] The creation unit can estimate the user's emotions and determine the priority of document creation based on the estimated emotions. For example, if the user is nervous, the creation unit will prioritize creating important documents. For example, if the user is relaxed, the creation unit will prioritize creating detailed documents. Also, if the user is in a hurry, the creation unit will prioritize creating documents that require a quick response. For example, if the user is in a hurry, the creation unit will create concise and to-the-point documents. In this way, by determining the priority of document creation according to the user's emotions, important documents can be created first. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, or not using AI. For example, the creation unit can input user facial expression data into the generative AI and have the generative AI determine the priority of document creation.

[0096] The creation unit can create documents while considering the geographical distribution of the data. For example, the creation unit can prioritize reflecting data related to a specific region in the document. For example, the creation unit can create optimal documents by considering the geographical distribution of the data. The creation unit can also create highly relevant documents by considering geographical factors. For example, the creation unit can prioritize reflecting data related to a specific region in the document. This allows for the creation of highly relevant documents by considering the geographical distribution of the data. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input geographical distribution data into a generation AI and have the generation AI perform the document creation.

[0097] The creation unit can improve the accuracy of its creation by referring to relevant literature during the creation process. For example, the creation unit can improve the accuracy of the material by referring to relevant literature. For example, the creation unit can select the optimal method for creating the material based on relevant literature. The creation unit can also improve the accuracy of the created material by referring to relevant literature. For example, the creation unit can improve the accuracy of the material by referring to relevant literature. In this way, the accuracy of the material can be improved by referring to relevant literature. Some or all of the above processes in the creation unit may be performed using AI, for example, or without using AI. For example, the creation unit can input relevant literature data into a generation AI and have the generation AI perform the task of improving the accuracy of material creation.

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

[0099] The reception desk can estimate the user's emotions and adjust the interface colors and design based on those estimates. For example, if the user is stressed, it can provide a calming color scheme to create a relaxed atmosphere. If the user is relaxed, it can provide a bright and vibrant color scheme to boost their motivation. Furthermore, if the user is in a hurry, it can provide a simple and intuitive design to support quick operation. This allows for an improved user experience by adjusting the interface according to the user's emotions.

[0100] The generation unit can estimate the user's emotions and adjust the content of error messages in SQL statement generation based on those emotions. For example, if the user is stressed, the error message will be simple and easy to understand, clearly stating the solution. If the user is relaxed, a detailed error message will be provided, carefully explaining the cause of the problem and how to solve it. If the user is in a hurry, the error message will be concise to allow for a quick response. In this way, by adjusting error messages according to the user's emotions, it is possible to reduce user stress and support efficient problem solving.

[0101] The extraction unit can estimate the user's emotions during data extraction and adjust the format of the extracted data based on the estimated emotions. For example, if the user is relaxed, it provides a format with detailed data. If the user is in a hurry, it provides a concise and to-the-point format. If the user is stressed, it provides a format with visually easy-to-understand graphs and charts. By adjusting the data format according to the user's emotions, it can help the user understand the data and support efficient data utilization.

[0102] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is relaxed, a detailed analysis result is sent via email. If the user is in a hurry, a concise analysis result is sent via push notification. Also, if the user is stressed, the analysis result is displayed in a visually easy-to-understand format. In this way, the notification method of the analysis results can be adjusted according to the user's emotions, thereby improving user convenience.

[0103] The creation function can estimate the user's emotions and adjust the template used for document creation based on those emotions. For example, if the user is relaxed, it provides a detailed template. If the user is in a hurry, it provides a concise template. If the user is stressed, it provides a visually easy-to-understand template. By adjusting the template according to the user's emotions, the system can improve the user's work efficiency.

[0104] The reception desk can analyze a user's past request history and select the optimal reception method. For example, it can automatically display requests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest requests that the user will use during specific time periods based on their past request history. In this way, by analyzing a user's past request history, the system can provide the most suitable reception method.

[0105] The generation unit can adjust the level of detail generated based on the importance of the request when generating SQL statements. For example, it generates detailed SQL statements for important requests and concise SQL statements for low-priority requests. It also generates SQL statements with appropriate level of detail according to the importance of the request. In this way, by adjusting the level of detail of the SQL statement according to the importance of the request, it is possible to generate appropriate SQL statements.

[0106] The extraction unit can select the optimal extraction method by referring to past extraction data during data extraction. For example, it can select the optimal extraction method based on previously extracted data. It can analyze past extraction data to select an efficient extraction method. It can also select the optimal extraction method by referring to past extraction history. In this way, the optimal data extraction method can be selected by referring to past extraction data.

[0107] The analysis unit can apply different analysis methods to each data category during analysis. For example, it applies an analysis method specialized for analysis to data analysis requests, an analysis method specialized for extraction to data extraction requests, and an analysis method specialized for updates to data update requests. This enables efficient data analysis by applying the appropriate analysis method according to the data category.

[0108] The creation department can improve the accuracy of its materials by referring to relevant literature during the creation process. For example, it can improve the accuracy of the materials by referring to relevant literature. Based on the relevant literature, it can select the optimal method for creating the materials. Furthermore, it can improve the accuracy of the created materials by referring to relevant literature. In this way, the accuracy of the materials can be improved by referring to relevant literature.

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

[0110] Step 1: The reception desk receives natural language requests from users. These requests include questions, instructions, and other requests. The reception desk receives the requests entered by the user in natural language as text. It can also accept user requests using voice input, and uses speech recognition technology to convert the user's voice into text. Step 2: The generation unit analyzes the request received by the reception unit and automatically generates an SQL statement. The generation unit uses natural language processing technology to analyze the user's request and generate an appropriate SQL statement. Furthermore, it can also use machine learning algorithms to learn the correspondence between past requests and SQL statements and generate the optimal SQL statement for new requests. Step 3: The extraction unit extracts data based on the SQL statements generated by the generation unit. The extraction unit executes queries to extract the necessary data from the database and retrieves the data returned from the database. Furthermore, it can also extract data from multiple databases. Step 4: The analysis unit analyzes the data extracted by the extraction unit and grasps the numerical trends. The analysis unit analyzes the data using statistical methods and grasps the numerical trends. Furthermore, it can also analyze the data using machine learning algorithms and grasp the numerical trends. Step 5: The creation unit automatically generates presentation materials based on the analysis results obtained by the analysis unit. The creation unit creates graphs and tables based on the analysis results and places them on the presentation slides. Furthermore, it is also possible to create presentation materials using templates.

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

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

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

[0114] Each of the multiple elements described above, including the reception unit, generation unit, extraction unit, analysis unit, and creation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives requests from the user in natural language. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates SQL statements using natural language processing technology. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts data from the database 24 based on the generated SQL statements. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the extracted data to grasp numerical trends. The creation unit is implemented by the control unit 46A of the smart device 14 and automatically creates presentation materials based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0130] Each of the multiple elements described above, including the reception unit, generation unit, extraction unit, analysis unit, and creation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's natural language requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates SQL statements using natural language processing technology. The extraction unit is implemented by the specific processing unit 290 of the data processing unit 12 and extracts data from the database 24 based on the generated SQL statements. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the extracted data to grasp numerical trends. The creation unit is implemented by the control unit 46A of the smart glasses 214 and automatically creates presentation materials based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0146] Each of the multiple elements described above, including the reception unit, generation unit, extraction unit, analysis unit, and creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's natural language requests. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates SQL statements using natural language processing technology. The extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and extracts data from the database 24 based on the generated SQL statements. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the extracted data to grasp numerical trends. The creation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically creates presentation materials based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0163] Each of the multiple elements described above, including the reception unit, generation unit, extraction unit, analysis unit, and creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives requests from the user in natural language. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates SQL statements using natural language processing technology. The extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and extracts data from the database 24 based on the generated SQL statements. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the extracted data to grasp numerical trends. The creation unit is implemented by, for example, the control unit 46A of the robot 414 and automatically creates presentation materials based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0182] (Note 1) A reception desk that receives natural language requests from users, A generation unit analyzes the request received by the aforementioned reception unit and automatically creates an SQL statement, An extraction unit that extracts data based on the SQL statement generated by the generation unit, An analysis unit analyzes the data extracted by the extraction unit and grasps the numerical trends, The system includes a creation unit that automatically creates presentation materials based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts how natural language requests are received based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past request history and select the optimal request processing method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When a request is received, it is filtered based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts how SQL statements are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating SQL statements, adjust the level of detail based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating SQL statements, different generation algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's sentiment and adjusts the length of the SQL statement based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating SQL statements, the generation priority is determined based on when the request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating SQL statements, the generation order is adjusted based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The extraction unit is We estimate the user's emotions and adjust the data extraction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The extraction unit is During data extraction, the optimal extraction method is selected by referring to past extraction data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The extraction unit is When extracting data, different extraction algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The extraction unit is It estimates the user's emotions and determines the priority of data to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The extraction unit is When extracting data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The extraction unit is When extracting data, we refer to relevant literature to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, different analytical methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, relevant market data will be referenced. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creation unit, We estimate the user's emotions and adjust the method of creating materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creation unit, When creating documents, refer to past document creation data to select the most suitable creation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creation unit, When creating documents, different creation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned creation unit, The system estimates user emotions and prioritizes document creation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned creation unit, When creating documents, take into account the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned creation unit, When creating documents, refer to relevant literature to improve the accuracy of the creation process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A reception desk that receives natural language requests from users, A generation unit analyzes the request received by the aforementioned reception unit and automatically creates an SQL statement, An extraction unit that extracts data based on the SQL statement generated by the generation unit, An analysis unit analyzes the data extracted by the extraction unit and grasps the numerical trends, The system includes a creation unit that automatically creates presentation materials based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts how natural language requests are received based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past request history and select the optimal request processing method. The system according to feature 1.

4. The aforementioned reception unit is When a request is received, it is filtered based on the user's current projects and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to accept based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts how SQL statements are generated based on those estimated emotions. The system according to feature 1.

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