System

Abstract

An object of the system according to the embodiment is to improve accuracy and efficiency of data processing.SOLUTION: A system according to an embodiment includes an input unit, an analysis unit, a processing unit, and an output unit. The input unit inputs data. The analysis unit analyzes the data input by the input unit and proposes an appropriate processing method. The processing unit processes the data based on the processing method proposed by the analysis unit. The output unit outputs the data processed by the processing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to achieve both accuracy and efficiency in data processing, which resulted in a heavy workload for data scientists.

[0005] The system according to the embodiment aims to improve the accuracy and efficiency of data processing. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a processing unit, and an output unit. The input unit inputs data. The analysis unit analyzes the data input by the input unit and proposes an appropriate processing method. The processing unit processes the data based on the processing method proposed by the analysis unit. The output unit outputs the data processed by the processing unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the accuracy and efficiency of data processing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data processing system according to an embodiment of the present invention utilizes a generative AI to process data quickly and accurately. In this system, a data scientist inputs the data they want to process, and the generative AI analyzes the data, proposes the optimal processing method, processes the data, and outputs the results. For example, a data scientist inputs data such as a CSV file or Excel file. The generative AI then analyzes the input data and proposes the optimal processing method. The generative AI learns the history and patterns of past data processing and selects the optimal processing method based on this. Finally, the generative AI processes the data based on the selected processing method and outputs the results. For example, the generative AI outputs the processed data, such as data with missing values ​​filled or data with outliers corrected. This mechanism allows data scientists to significantly reduce data processing time and consistently obtain highly accurate analytical data. For example, tasks that would normally take several hours to process manually can be completed in just a few minutes using generative AI. Furthermore, because the generative AI is constantly learning the latest data processing techniques, it can always provide the optimal processing method. This allows data scientists to significantly reduce data processing time and consistently obtain highly accurate analytical data. For example, tasks that would take several hours using traditional manual data processing can be completed in just a few minutes using generative AI. In addition, because generative AI is constantly learning the latest data processing techniques, it can always provide the most optimal processing method.

[0029] A data processing system according to an embodiment includes an input unit, an analysis unit, a processing unit, and an output unit. The input unit inputs data that a data scientist wants to process. The input unit selects an appropriate input method depending on the format and content of the data. For example, the input unit can input data in various formats, such as CSV files and Excel files. The analysis unit uses a generation AI to analyze the data input by the input unit and propose an optimal processing method. The generation AI learns past data processing history and patterns and selects the optimal processing method based on this. For example, the analysis unit detects missing values ​​in the data and proposes a completion method. The analysis unit can also detect outliers in the data and propose a correction method. The processing unit uses the generation AI to process the data based on the processing method proposed by the analysis unit. For example, the processing unit complements the data based on the proposed completion method. The processing unit can also correct the data based on the proposed correction method. The output unit uses the generation AI to output the data processed by the processing unit. For example, the output unit outputs processed data, such as data with missing values ​​complemented or data with outliers corrected. As a result, the data processing system according to the embodiment can consistently perform processes from data input to analysis, processing, and output. For example, the output unit displays the processed data through a web application or a mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the data scientist. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI.

[0030] The analysis unit can detect missing values ​​in the data and propose a method of imputation. For example, the analysis unit detects missing values ​​in the data and proposes a method of imputation. For example, the analysis unit uses an algorithm to detect which portions of the data are considered missing. The analysis unit can also propose a specific method for imputing the missing values. For example, the analysis unit proposes methods such as mean value imputation, regression imputation, and imputation using machine learning. This automatically detects missing values ​​in the data and proposes a method of imputation, thereby improving the accuracy of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to detect missing values ​​and propose a method of imputation.

[0031] The processing unit can interpolate data based on the proposed interpolation method. The processing unit, for example, interpolates data based on the proposed interpolation method. For example, the processing unit interpolates data using mean value interpolation. The processing unit can also interpolate data using regression interpolation. The processing unit can also interpolate data using machine learning. In this way, data consistency is maintained by interpolating data based on the proposed interpolation method. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can cause the generation AI to execute the interpolation method.

[0032] The analysis unit can detect outliers in the data and propose a correction method. The analysis unit, for example, detects outliers in the data and proposes a correction method. For example, the analysis unit uses an algorithm that detects statistical outliers. The analysis unit can also detect outliers using machine learning. The analysis unit can also propose a specific method for correcting the outliers. For example, the analysis unit proposes methods such as recollecting data or applying a correction algorithm. This automatically detects outliers in the data and proposes a correction method, thereby improving the reliability of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to detect outliers and propose a correction method.

[0033] The processing unit can correct the data based on the proposed correction method. The processing unit corrects the data based on the proposed correction method, for example. For example, the processing unit recollects the data. The processing unit can also correct the data by applying a correction algorithm. The processing unit can also correct the data using machine learning. In this way, the accuracy of the data is maintained by correcting the data based on the proposed correction method. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can cause the generation AI to execute the correction method.

[0034] The output unit can output the processed data. The output unit outputs, for example, the processed data. For example, the output unit outputs processed data such as data with missing values ​​filled in or data with outliers corrected. The output unit can display the processed data through a web application or a mobile application. The output unit can also print the results using a printer if feedback on paper is desired. The output unit can also send the results directly to a data scientist via email. This makes it easier to use the data by outputting the processed data. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can cause a generation AI to output the processed data.

[0035] The input unit can automatically select an appropriate input method depending on the format and content of the data. The input unit automatically selects an appropriate input method depending on, for example, the format and content of the data. For example, when a CSV file is input, the input unit causes the generation AI to automatically select an input method compatible with the CSV format. Furthermore, when an Excel file is input, the input unit can also automatically select an input method compatible with the Excel format. Furthermore, when text data is input, the input unit can also automatically select an input method compatible with the text format. This improves the efficiency of data input by selecting the optimal input method depending on the format and content of the data. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the format and content of the data to the generation AI and cause the generation AI to select the optimal input method.

[0036] The input unit can check the consistency of data and automatically correct errors when data is input. The input unit, for example, checks the consistency of data and automatically corrects errors when data is input. For example, if the input data contains missing values, the generation AI automatically proposes a method of completion and corrects the missing values. The input unit can also automatically detect and correct abnormal values ​​when the input data contains abnormal values. The input unit can also automatically correct the format when the input data contains format errors. This improves data quality by checking the consistency of data and automatically correcting errors. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can cause the generation AI to check the consistency of data and correct errors.

[0037] The input unit can optimize the input method by referring to the user's past input history when inputting data. For example, the input unit optimizes the input method by referring to the user's past input history when inputting data. For example, the input unit automatically selects an input method that the user has frequently used in the past using the generation AI. The input unit can also have the generation AI suggest an optimal input method based on the user's past input history. The input unit can also analyze the user's past input patterns and have the generation AI select an optimal input method. This improves the efficiency of data input by optimizing the input method by referring to the user's past input history. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's past input history into the generation AI and have the generation AI select an optimal input method.

[0038] The input unit can prioritize inputting highly relevant data based on the user's geographical location information when inputting data. For example, the input unit prioritizes inputting highly relevant data based on the user's geographical location information when inputting data. For example, when the user is in a specific area, the input unit prioritizes inputting data related to that area. Furthermore, when the user is moving, the input unit can prioritize inputting data related to the user's current location. Furthermore, when the user is in a specific location, the input unit can prioritize inputting data related to that location. This improves the efficiency of data input by prioritized input of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant data.

[0039] The input unit can analyze the user's social media activity and input related data when inputting data. For example, the input unit can analyze the user's social media activity and input related data when inputting data. For example, the input unit can input related data based on data shared by the user on social media. The input unit can also analyze the content of the user's social media posts and input related data. The input unit can also input related data with reference to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and inputting related data, the accuracy of data input is improved. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI and cause the generation AI to select related data.

[0040] The input unit can customize the input method by reflecting the user's past feedback when inputting data. For example, the input unit customizes the input method by reflecting the user's past feedback when inputting data. For example, the input unit customizes the input method by having the generation AI customize the input method based on feedback previously provided by the user. The input unit can also analyze the user's past feedback and have the generation AI suggest an optimal input method. The input unit can also optimize the input method by referring to the user's past feedback. This improves the efficiency of data input by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's past feedback to the generation AI and have the generation AI customize the input method.

[0041] The analysis unit, when detecting missing values ​​in data and proposing a completion method, can adjust the level of detail of the proposal based on the importance of the data. For example, when detecting missing values ​​in data and proposing a completion method, the analysis unit adjusts the level of detail of the proposal based on the importance of the data. For example, the analysis unit causes the generation AI to propose a detailed completion method for missing values ​​of data with high importance. The analysis unit can also cause the generation AI to propose a simple completion method for missing values ​​of data with low importance. The analysis unit can also cause the generation AI to adjust the level of detail of the completion method according to the importance of the data. This enables efficient data completion by adjusting the level of detail of the completion method based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the completion method.

[0042] The analysis unit can apply different analysis algorithms depending on the data category when detecting outliers in the data and proposing correction methods. For example, the analysis unit can apply different analysis algorithms depending on the data category when detecting outliers in the data and proposing correction methods. For example, the analysis unit can have the generation AI use statistical methods to propose correction methods for outliers in numerical data. The analysis unit can also have the generation AI use natural language processing to propose correction methods for outliers in text data. The analysis unit can also have the generation AI use image analysis methods to propose correction methods for outliers in image data. In this way, applying different analysis algorithms depending on the data category improves the accuracy of correcting outliers. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to detect outliers and propose correction methods.

[0043] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to past analysis results, for example. For example, the analysis unit causes the generation AI to select the optimal analysis method based on past analysis results. The analysis unit can also cause the generation AI to improve the accuracy of the analysis by referring to past analysis results. The analysis unit can also learn past analysis results and cause the generation AI to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] When analyzing data, the analysis unit can determine the analysis priority based on the time of data submission. When analyzing data, the analysis unit can determine the analysis priority based on the time of data submission, for example. For example, the analysis unit prioritizes analysis of data with an approaching deadline. The analysis unit can also prioritize analysis of data with an early submission date. The analysis unit can also have the generation AI determine the analysis priority based on the submission date. This enables efficient data analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data when analyzing the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data when analyzing the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also allow the generation AI to adjust the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise when analyzing data. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise when analyzing data. For example, the analysis unit can provide analysis results in which the generation AI uses a lot of technical terminology to a user with high level of expertise. The analysis unit can also provide analysis results in which the generation AI avoids technical terminology to a user with low level of expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it easier to understand the analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0047] The processing unit can adjust the level of detail of the completion based on the importance of the data when completing data based on the data completion method. For example, when completing data based on the data completion method, the processing unit adjusts the level of detail of the completion based on the importance of the data. For example, the processing unit causes the generation AI to apply a detailed completion method to data with high importance. The processing unit can also cause the generation AI to apply a simplified completion method to data with low importance. The processing unit can also cause the generation AI to adjust the level of detail of the completion based on the importance of the data. This enables efficient data completion by adjusting the level of detail of the completion based on the importance of the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the completion.

[0048] The processing unit can apply different processing algorithms depending on the data category when correcting data based on the data correction method. For example, when correcting data based on the data correction method, the processing unit applies different processing algorithms depending on the data category. For example, the processing unit causes the generation AI to use a statistical method to correct numerical data. The processing unit can also cause the generation AI to use natural language processing to correct text data. The processing unit can also cause the generation AI to use an image analysis method to correct image data. In this way, by applying different processing algorithms depending on the data category, the accuracy of data correction is improved. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the data category to the generation AI and cause the generation AI to execute the correction method.

[0049] The processing unit can improve the accuracy of processing by referring to past processing results when processing data. For example, the processing unit improves the accuracy of processing by referring to past processing results when processing data. For example, the processing unit causes the generation AI to select the optimal processing method based on past processing results. The processing unit can also cause the generation AI to improve the accuracy of processing by referring to past processing results. The processing unit can also learn past processing results and cause the generation AI to improve the accuracy of processing. In this way, the accuracy of processing is improved by referring to past processing results. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input past processing results into the generation AI and cause the generation AI to improve the accuracy of processing.

[0050] The processing unit can process data based on the geographical distribution of the data when processing the data. For example, the processing unit processes data based on the geographical distribution of the data when processing the data. For example, the processing unit prioritizes processing of data related to a specific region. The processing unit can also integrate and process geographically dispersed data. The processing unit can also allow the generation AI to select the optimal processing method based on the geographical distribution. This enables efficient data processing by processing based on the geographical distribution of the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the geographical distribution of the data into the generation AI and have the generation AI select the processing method.

[0051] The processing unit can improve the accuracy of processing by referring to literature related to the data when processing the data. For example, the processing unit improves the accuracy of processing by referring to literature related to the data when processing the data. For example, the processing unit causes the generation AI to select the optimal processing method based on the related literature. The processing unit can also cause the generation AI to improve the accuracy of processing by referring to the related literature. The processing unit can also learn the related literature and cause the generation AI to improve the accuracy of processing. In this way, the accuracy of processing is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of processing.

[0052] The processing unit can process data based on the market value of the data when processing the data. For example, the processing unit processes data based on the market value of the data when processing the data. For example, the processing unit prioritizes processing of data with high market value. The processing unit can also postpone processing of data with low market value. The processing unit can also select the optimal processing method for the generation AI based on the market value. This enables efficient data processing by processing based on the market value of the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the market value of the data into the generation AI and have the generation AI select the processing method.

[0053] When outputting processed data, the output unit can adjust the level of detail of the output based on the importance of the data. For example, when outputting processed data, the output unit adjusts the level of detail of the output based on the importance of the data. For example, in the output unit, the generation AI provides detailed output for data with high importance. In addition, in the output unit, the generation AI can also provide simplified output for data with low importance. In addition, in the output unit, the generation AI can adjust the level of detail of the output according to the importance of the data. As a result, efficient data output is possible by adjusting the level of detail of the output based on the importance of the data. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the output.

[0054] When outputting processed data, the output unit can apply different output formats depending on the category of the data. For example, when outputting processed data, the output unit applies different output formats depending on the category of the data. For example, the output unit causes the generation AI to output numerical data in a graph format. The output unit can also cause the generation AI to output text data in a report format. The output unit can also cause the generation AI to output image data in a slideshow format. This enables efficient data output by applying different output formats depending on the category of the data. Some or all of the above-mentioned processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the data category to the generation AI and cause the generation AI to apply the output format.

[0055] When outputting processed data, the output unit can improve the accuracy of the output by referring to the user's past output results. For example, when outputting processed data, the output unit improves the accuracy of the output by referring to the user's past output results. For example, the output unit causes the generation AI to select an optimal output format based on the past output results. The output unit can also cause the generation AI to improve the accuracy of the output by referring to the past output results. The output unit can also learn the past output results and cause the generation AI to improve the accuracy of the output. In this way, the accuracy of the output is improved by referring to the user's past output results. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input past output results to the generation AI and cause the generation AI to improve the accuracy of the output.

[0056] When outputting processed data, the output unit can determine the output priority based on the time of data submission. For example, when outputting processed data, the output unit determines the output priority based on the time of data submission. For example, the output unit prioritizes output of data with an approaching deadline. The output unit can also prioritize output of data with an early submission time. The output unit can also have the generation AI determine the output priority based on the submission time. This enables efficient data output by determining the output priority based on the time of data submission. Some or all of the above-described processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the time of data submission to the generation AI and have the generation AI determine the output priority.

[0057] When outputting processed data, the output unit can adjust the output order based on the relevance of the data. For example, when outputting processed data, the output unit adjusts the output order based on the relevance of the data. For example, the output unit prioritizes output of highly relevant data. The output unit can also postpone output of less relevant data. The output unit can also allow the generation AI to adjust the output order based on the relevance of the data. This enables efficient data output by adjusting the output order based on the relevance of the data. Some or all of the above-mentioned processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the relevance of the data to the generation AI and have the generation AI adjust the output order.

[0058] When outputting the processed data, the output unit can adjust the use of technical terminology in the output according to the user's level of expertise. For example, when outputting the processed data, the output unit adjusts the use of technical terminology in the output according to the user's level of expertise. For example, the output unit may provide an output in which the generation AI uses a lot of technical terminology for a user with high level of expertise. The output unit may also provide an output in which the generation AI avoids technical terminology for a user with low level of expertise. The output unit may also adjust the use of technical terminology in the output according to the user's level of expertise. This makes it easier to understand the output result by adjusting the use of technical terminology in the output according to the user's level of expertise. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The data processing system can further include an encryption unit to enhance data security. The encryption unit automatically encrypts data when it is input, and can maintain the confidentiality of the data even during processing in the analysis unit and processing unit. For example, data input in the input unit is encrypted, and remains encrypted even when it is analyzed in the analysis unit or processed in the processing unit. Furthermore, when it is output from the output unit, the data can be decrypted as needed. This enhances data security and reduces the risk of confidential information leaks. Furthermore, the encryption unit can automatically select the data encryption algorithm and apply the optimal encryption method. For example, encryption algorithms such as AES and RSA can be used.

[0061] The analysis unit may include a reliability evaluation unit that evaluates the reliability of the data when analyzing the data. The reliability evaluation unit evaluates the source of the data, the collection method, the consistency of the data, etc., and calculates a reliability score for the data. For example, the reliability evaluation unit evaluates whether the source of the data is reliable and assigns a reliability score. It can also evaluate whether the data collection method is appropriate and adjust the reliability score. Furthermore, it can check the consistency of the data and assign a high reliability score to data with few outliers and missing values. This allows the analysis unit to prioritize analysis of highly reliable data and improve the accuracy of the analysis results.

[0062] The processing unit can be equipped with a version management unit that manages data versions when data is processed. The version management unit records the data processing history and manages each version of the data. For example, when the processing unit complements data, it records the data before and after the complement in the version management unit. Also, when data is modified, it can record the data before and after the modification in the version management unit. Furthermore, the version management unit can refer to past versions of data and revert to a specific version if necessary. This makes the data processing history clear and makes it easier to track the data change history.

[0063] The analysis unit may include a visualization unit that visualizes data during data analysis. The visualization unit displays the analysis results in a visual format, such as a graph or chart, allowing the user to intuitively understand the analysis results. For example, when the analysis unit detects missing values ​​in the data, it displays the distribution of the missing values ​​in a heat map. It can also display the results of abnormal value detection in a box plot. It can also display the correlation of data in a scatter plot, allowing the user to visually grasp the trends in the data. This allows the user to easily understand the analysis results and intuitively grasp the characteristics of the data.

[0064] The processing unit may include a quality evaluation unit that evaluates the quality of the data when processing the data. The quality evaluation unit evaluates the quality of the processed data and calculates a quality score. For example, the quality evaluation unit evaluates the consistency and accuracy of the data and assigns a quality score. It can also check the data for missing values ​​or abnormal values ​​and adjust the quality score. It can also evaluate whether the data processing method is appropriate and assign a quality score. This allows the processing unit to provide high-quality data and improve the reliability of the data.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The input section is where data scientists input the data they want to process. The appropriate input method is selected depending on the format and content of the data. For example, the input section can input data in various formats, such as CSV files and Excel files. Step 2: The analysis unit uses the generation AI to analyze the data entered by the input unit and propose the optimal processing method. The generation AI learns the history and patterns of past data processing, and selects the optimal processing method based on this. For example, the analysis unit detects missing values ​​in the data and proposes methods to fill them in. The analysis unit can also detect abnormal values ​​in the data and propose methods to correct them. Step 3: The processing unit uses the generating AI to process the data based on the processing method proposed by the analysis unit. For example, the processing unit complements the data based on the proposed complementation method. The processing unit can also correct the data based on the proposed correction method. Step 4: The output unit uses the generation AI to output the data processed by the processing unit. For example, the output unit outputs processed data, such as data with missing values ​​filled in or data with outliers corrected. This allows the data processing system according to the embodiment to consistently perform processes from data input to analysis, processing, and output. For example, the output unit displays the processed data through a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides rapid feedback by sending the results directly to the data scientist. Some or all of the above-described processing in the output unit may be performed, for example, using AI, or may be performed without using AI.

[0067] (Example 2) A data processing system according to an embodiment of the present invention utilizes a generative AI to process data quickly and accurately. In this system, a data scientist inputs the data they want to process, and the generative AI analyzes the data, proposes the optimal processing method, processes the data, and outputs the results. For example, a data scientist inputs data such as a CSV file or Excel file. The generative AI then analyzes the input data and proposes the optimal processing method. The generative AI learns the history and patterns of past data processing and selects the optimal processing method based on this. Finally, the generative AI processes the data based on the selected processing method and outputs the results. For example, the generative AI outputs the processed data, such as data with missing values ​​filled or data with outliers corrected. This mechanism allows data scientists to significantly reduce data processing time and consistently obtain highly accurate analytical data. For example, tasks that would normally take several hours to process manually can be completed in just a few minutes using generative AI. Furthermore, because the generative AI is constantly learning the latest data processing techniques, it can always provide the optimal processing method. This allows data scientists to significantly reduce data processing time and consistently obtain highly accurate analytical data. For example, tasks that would take several hours using traditional manual data processing can be completed in just a few minutes using generative AI. In addition, because generative AI is constantly learning the latest data processing techniques, it can always provide the most optimal processing method.

[0068] A data processing system according to an embodiment includes an input unit, an analysis unit, a processing unit, and an output unit. The input unit inputs data that a data scientist wants to process. The input unit selects an appropriate input method depending on the format and content of the data. For example, the input unit can input data in various formats, such as CSV files and Excel files. The analysis unit uses a generation AI to analyze the data input by the input unit and propose an optimal processing method. The generation AI learns past data processing history and patterns and selects the optimal processing method based on this. For example, the analysis unit detects missing values ​​in the data and proposes a completion method. The analysis unit can also detect outliers in the data and propose a correction method. The processing unit uses the generation AI to process the data based on the processing method proposed by the analysis unit. For example, the processing unit complements the data based on the proposed completion method. The processing unit can also correct the data based on the proposed correction method. The output unit uses the generation AI to output the data processed by the processing unit. For example, the output unit outputs processed data, such as data with missing values ​​complemented or data with outliers corrected. As a result, the data processing system according to the embodiment can consistently perform processes from data input to analysis, processing, and output. For example, the output unit displays the processed data through a web application or a mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the data scientist. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI.

[0069] The analysis unit can detect missing values ​​in the data and propose a method of imputation. For example, the analysis unit detects missing values ​​in the data and proposes a method of imputation. For example, the analysis unit uses an algorithm to detect which portions of the data are considered missing. The analysis unit can also propose a specific method for imputing the missing values. For example, the analysis unit proposes methods such as mean value imputation, regression imputation, and imputation using machine learning. This automatically detects missing values ​​in the data and proposes a method of imputation, thereby improving the accuracy of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to detect missing values ​​and propose a method of imputation.

[0070] The processing unit can interpolate data based on the proposed interpolation method. The processing unit, for example, interpolates data based on the proposed interpolation method. For example, the processing unit interpolates data using mean value interpolation. The processing unit can also interpolate data using regression interpolation. The processing unit can also interpolate data using machine learning. In this way, data consistency is maintained by interpolating data based on the proposed interpolation method. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can cause the generation AI to execute the interpolation method.

[0071] The analysis unit can detect outliers in the data and propose a correction method. The analysis unit, for example, detects outliers in the data and proposes a correction method. For example, the analysis unit uses an algorithm that detects statistical outliers. The analysis unit can also detect outliers using machine learning. The analysis unit can also propose a specific method for correcting the outliers. For example, the analysis unit proposes methods such as recollecting data or applying a correction algorithm. This automatically detects outliers in the data and proposes a correction method, thereby improving the reliability of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can cause a generation AI to detect outliers and propose a correction method.

[0072] The processing unit can correct the data based on the proposed correction method. The processing unit corrects the data based on the proposed correction method, for example. For example, the processing unit recollects the data. The processing unit can also correct the data by applying a correction algorithm. The processing unit can also correct the data using machine learning. In this way, the accuracy of the data is maintained by correcting the data based on the proposed correction method. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can cause the generation AI to execute the correction method.

[0073] The output unit can output the processed data. The output unit outputs, for example, the processed data. For example, the output unit outputs processed data such as data with missing values ​​filled in or data with outliers corrected. The output unit can display the processed data through a web application or a mobile application. The output unit can also print the results using a printer if feedback on paper is desired. The output unit can also send the results directly to a data scientist via email. This makes it easier to use the data by outputting the processed data. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can cause a generation AI to output the processed data.

[0074] The input unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. The input unit, for example, estimates the user's emotions and adjusts the timing of data input based on the estimated user emotions. For example, if the user is feeling stressed, the input unit causes the generation AI to delay the input timing and wait until the user is relaxed. Furthermore, if the user is relaxed, the input unit can cause the generation AI to speed up the input timing to promote rapid data input. Furthermore, if the user is in a hurry, the input unit can cause the generation AI to immediately start data input and perform rapid processing. This reduces the burden on the user by adjusting the timing of data input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the input unit may be performed using, for example, an AI. For example, the input unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0075] The input unit can automatically select an appropriate input method depending on the format and content of the data. The input unit automatically selects an appropriate input method depending on, for example, the format and content of the data. For example, when a CSV file is input, the input unit causes the generation AI to automatically select an input method compatible with the CSV format. Furthermore, when an Excel file is input, the input unit can also automatically select an input method compatible with the Excel format. Furthermore, when text data is input, the input unit can also automatically select an input method compatible with the text format. This improves the efficiency of data input by selecting the optimal input method depending on the format and content of the data. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the format and content of the data to the generation AI and cause the generation AI to select the optimal input method.

[0076] The input unit can check the consistency of data and automatically correct errors when data is input. The input unit, for example, checks the consistency of data and automatically corrects errors when data is input. For example, if the input data contains missing values, the generation AI automatically proposes a method of completion and corrects the missing values. The input unit can also automatically detect and correct abnormal values ​​when the input data contains abnormal values. The input unit can also automatically correct the format when the input data contains format errors. This improves data quality by checking the consistency of data and automatically correcting errors. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can cause the generation AI to check the consistency of data and correct errors.

[0077] The input unit can optimize the input method by referring to the user's past input history when inputting data. For example, the input unit optimizes the input method by referring to the user's past input history when inputting data. For example, the input unit automatically selects an input method that the user has frequently used in the past using the generation AI. The input unit can also have the generation AI suggest an optimal input method based on the user's past input history. The input unit can also analyze the user's past input patterns and have the generation AI select an optimal input method. This improves the efficiency of data input by optimizing the input method by referring to the user's past input history. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's past input history into the generation AI and have the generation AI select an optimal input method.

[0078] The input unit can estimate the user's emotions and prioritize the input data based on the estimated user emotions. The input unit, for example, estimates the user's emotions and prioritizes the input data based on the estimated user emotions. For example, when the user is feeling stressed, the input unit causes the generation AI to prioritize input of less important data. Furthermore, when the user is relaxed, the input unit can also cause the generation AI to prioritize input of more important data. Furthermore, when the user is in a hurry, the input unit can also cause the generation AI to prioritize input of the most important data. This reduces the burden on the user by prioritizing the input data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the input unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0079] The input unit can prioritize inputting highly relevant data based on the user's geographical location information when inputting data. For example, the input unit prioritizes inputting highly relevant data based on the user's geographical location information when inputting data. For example, when the user is in a specific area, the input unit prioritizes inputting data related to that area. Furthermore, when the user is moving, the input unit can prioritize inputting data related to the user's current location. Furthermore, when the user is in a specific location, the input unit can prioritize inputting data related to that location. This improves the efficiency of data input by prioritized input of highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant data.

[0080] The input unit can analyze the user's social media activity and input related data when inputting data. For example, the input unit can analyze the user's social media activity and input related data when inputting data. For example, the input unit can input related data based on data shared by the user on social media. The input unit can also analyze the content of the user's social media posts and input related data. The input unit can also input related data with reference to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity and inputting related data, the accuracy of data input is improved. Some or all of the above-mentioned processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input the user's social media activity data to the generation AI and cause the generation AI to select related data.

[0081] The input unit can customize the input method by reflecting the user's past feedback when inputting data. For example, the input unit customizes the input method by reflecting the user's past feedback when inputting data. For example, the input unit customizes the input method by having the generation AI customize the input method based on feedback previously provided by the user. The input unit can also analyze the user's past feedback and have the generation AI suggest an optimal input method. The input unit can also optimize the input method by referring to the user's past feedback. This improves the efficiency of data input by customizing the input method by reflecting the user's past feedback. Some or all of the above-described processing in the input unit may be performed using AI, for example, or may be performed without using AI. For example, the input unit can input the user's past feedback to the generation AI and have the generation AI customize the input method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit causes the generation AI to provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can cause the generation AI to provide a concise analysis result. This makes the analysis result easier to understand by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0083] The analysis unit, when detecting missing values ​​in data and proposing a completion method, can adjust the level of detail of the proposal based on the importance of the data. For example, when detecting missing values ​​in data and proposing a completion method, the analysis unit adjusts the level of detail of the proposal based on the importance of the data. For example, the analysis unit causes the generation AI to propose a detailed completion method for missing values ​​of data with high importance. The analysis unit can also cause the generation AI to propose a simple completion method for missing values ​​of data with low importance. The analysis unit can also cause the generation AI to adjust the level of detail of the completion method according to the importance of the data. This enables efficient data completion by adjusting the level of detail of the completion method based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the completion method.

[0084] The analysis unit can apply different analysis algorithms depending on the data category when detecting outliers in the data and proposing correction methods. For example, the analysis unit can apply different analysis algorithms depending on the data category when detecting outliers in the data and proposing correction methods. For example, the analysis unit can have the generation AI use statistical methods to propose correction methods for outliers in numerical data. The analysis unit can also have the generation AI use natural language processing to propose correction methods for outliers in text data. The analysis unit can also have the generation AI use image analysis methods to propose correction methods for outliers in image data. In this way, applying different analysis algorithms depending on the data category improves the accuracy of correcting outliers. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the generation AI and cause the generation AI to detect outliers and propose correction methods.

[0085] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to past analysis results. When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to past analysis results, for example. For example, the analysis unit causes the generation AI to select the optimal analysis method based on past analysis results. The analysis unit can also cause the generation AI to improve the accuracy of the analysis by referring to past analysis results. The analysis unit can also learn past analysis results and cause the generation AI to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can cause the generation AI to provide a short, concise analysis result. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can cause the generation AI to provide a visually stimulating analysis result. This makes it easier to understand the analysis result by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0087] When analyzing data, the analysis unit can determine the analysis priority based on the time of data submission. When analyzing data, the analysis unit can determine the analysis priority based on the time of data submission, for example. For example, the analysis unit prioritizes analysis of data with an approaching deadline. The analysis unit can also prioritize analysis of data with an early submission date. The analysis unit can also have the generation AI determine the analysis priority based on the submission date. This enables efficient data analysis by determining the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI determine the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data when analyzing the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data when analyzing the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also allow the generation AI to adjust the order of analysis based on the relevance of the data. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0089] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise when analyzing data. For example, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise when analyzing data. For example, the analysis unit can provide analysis results in which the generation AI uses a lot of technical terminology to a user with high level of expertise. The analysis unit can also provide analysis results in which the generation AI avoids technical terminology to a user with low level of expertise. The analysis unit can also adjust the use of technical terminology in the analysis according to the user's level of expertise. This makes it easier to understand the analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0090] The processing unit can estimate the user's emotions and adjust the data processing method based on the estimated user emotions. For example, the processing unit estimates the user's emotions and adjusts the data processing method based on the estimated user emotions. For example, if the user is nervous, the processing unit causes the generation AI to select a simple data processing method. Furthermore, if the user is relaxed, the processing unit can cause the generation AI to select a detailed data processing method. Furthermore, if the user is in a hurry, the processing unit can cause the generation AI to select a quick data processing method. This reduces the burden on the user by adjusting the data processing method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the processing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0091] The processing unit can adjust the level of detail of the completion based on the importance of the data when completing data based on the data completion method. For example, when completing data based on the data completion method, the processing unit adjusts the level of detail of the completion based on the importance of the data. For example, the processing unit causes the generation AI to apply a detailed completion method to data with high importance. The processing unit can also cause the generation AI to apply a simplified completion method to data with low importance. The processing unit can also cause the generation AI to adjust the level of detail of the completion based on the importance of the data. This enables efficient data completion by adjusting the level of detail of the completion based on the importance of the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the completion.

[0092] The processing unit can apply different processing algorithms depending on the data category when correcting data based on the data correction method. For example, when correcting data based on the data correction method, the processing unit applies different processing algorithms depending on the data category. For example, the processing unit causes the generation AI to use a statistical method to correct numerical data. The processing unit can also cause the generation AI to use natural language processing to correct text data. The processing unit can also cause the generation AI to use an image analysis method to correct image data. In this way, by applying different processing algorithms depending on the data category, the accuracy of data correction is improved. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the data category to the generation AI and cause the generation AI to execute the correction method.

[0093] The processing unit can improve the accuracy of processing by referring to past processing results when processing data. For example, the processing unit improves the accuracy of processing by referring to past processing results when processing data. For example, the processing unit causes the generation AI to select the optimal processing method based on past processing results. The processing unit can also cause the generation AI to improve the accuracy of processing by referring to past processing results. The processing unit can also learn past processing results and cause the generation AI to improve the accuracy of processing. In this way, the accuracy of processing is improved by referring to past processing results. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input past processing results into the generation AI and cause the generation AI to improve the accuracy of processing.

[0094] The processing unit can estimate the user's emotions and determine the priority of data processing based on the estimated user emotions. The processing unit, for example, estimates the user's emotions and determines the priority of data processing based on the estimated user emotions. For example, if the user is feeling stressed, the processing unit causes the generation AI to prioritize processing less important data. Furthermore, if the user is relaxed, the processing unit can also cause the generation AI to prioritize processing more important data. Furthermore, if the user is in a hurry, the processing unit can also cause the generation AI to prioritize processing the most important data. This reduces the burden on the user by determining the priority of data processing based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the processing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0095] The processing unit can process data based on the geographical distribution of the data when processing the data. For example, the processing unit processes data based on the geographical distribution of the data when processing the data. For example, the processing unit prioritizes processing of data related to a specific region. The processing unit can also integrate and process geographically dispersed data. The processing unit can also allow the generation AI to select the optimal processing method based on the geographical distribution. This enables efficient data processing by processing based on the geographical distribution of the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the geographical distribution of the data into the generation AI and have the generation AI select the processing method.

[0096] The processing unit can improve the accuracy of processing by referring to literature related to the data when processing the data. For example, the processing unit improves the accuracy of processing by referring to literature related to the data when processing the data. For example, the processing unit causes the generation AI to select the optimal processing method based on the related literature. The processing unit can also cause the generation AI to improve the accuracy of processing by referring to the related literature. The processing unit can also learn the related literature and cause the generation AI to improve the accuracy of processing. In this way, the accuracy of processing is improved by referring to literature related to the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input related literature into the generation AI and cause the generation AI to improve the accuracy of processing.

[0097] The processing unit can process data based on the market value of the data when processing the data. For example, the processing unit processes data based on the market value of the data when processing the data. For example, the processing unit prioritizes processing of data with high market value. The processing unit can also postpone processing of data with low market value. The processing unit can also select the optimal processing method for the generation AI based on the market value. This enables efficient data processing by processing based on the market value of the data. Some or all of the above-mentioned processing in the processing unit may be performed using AI, for example, or may be performed without using AI. For example, the processing unit can input the market value of the data into the generation AI and have the generation AI select the processing method.

[0098] The output unit can estimate the user's emotion and adjust the display method of the output data based on the estimated user emotion. For example, the output unit estimates the user's emotion and adjusts the display method of the output data based on the estimated user emotion. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. If the user is relaxed, the output unit can provide a display method including detailed information. If the user is in a hurry, the output unit can provide a display method that focuses on the main points. This reduces the burden on the user by adjusting the display method of the output data according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the output unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the output unit can input the user's emotion data to the generation AI and have the generation AI perform emotion estimation.

[0099] When outputting processed data, the output unit can adjust the level of detail of the output based on the importance of the data. For example, when outputting processed data, the output unit adjusts the level of detail of the output based on the importance of the data. For example, in the output unit, the generation AI provides detailed output for data with high importance. In addition, in the output unit, the generation AI can also provide simplified output for data with low importance. In addition, in the output unit, the generation AI can adjust the level of detail of the output according to the importance of the data. As a result, efficient data output is possible by adjusting the level of detail of the output based on the importance of the data. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the output.

[0100] When outputting processed data, the output unit can apply different output formats depending on the category of the data. For example, when outputting processed data, the output unit applies different output formats depending on the category of the data. For example, the output unit causes the generation AI to output numerical data in a graph format. The output unit can also cause the generation AI to output text data in a report format. The output unit can also cause the generation AI to output image data in a slideshow format. This enables efficient data output by applying different output formats depending on the category of the data. Some or all of the above-mentioned processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the data category to the generation AI and cause the generation AI to apply the output format.

[0101] When outputting processed data, the output unit can improve the accuracy of the output by referring to the user's past output results. For example, when outputting processed data, the output unit improves the accuracy of the output by referring to the user's past output results. For example, the output unit causes the generation AI to select an optimal output format based on the past output results. The output unit can also cause the generation AI to improve the accuracy of the output by referring to the past output results. The output unit can also learn the past output results and cause the generation AI to improve the accuracy of the output. In this way, the accuracy of the output is improved by referring to the user's past output results. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input past output results to the generation AI and cause the generation AI to improve the accuracy of the output.

[0102] The output unit can estimate the user's emotions and prioritize the output data based on the estimated user emotions. The output unit, for example, estimates the user's emotions and prioritizes the output data based on the estimated user emotions. For example, when the user is stressed, the output unit causes the generation AI to prioritize output of less important data. Furthermore, when the user is relaxed, the output unit can also cause the generation AI to prioritize output of more important data. Furthermore, when the user is in a hurry, the output unit can also prioritize output of the most important data. This reduces the burden on the user by prioritizing the output data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the output unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the output unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0103] When outputting processed data, the output unit can determine the output priority based on the time of data submission. For example, when outputting processed data, the output unit determines the output priority based on the time of data submission. For example, the output unit prioritizes output of data with an approaching deadline. The output unit can also prioritize output of data with an early submission time. The output unit can also have the generation AI determine the output priority based on the submission time. This enables efficient data output by determining the output priority based on the time of data submission. Some or all of the above-described processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the time of data submission to the generation AI and have the generation AI determine the output priority.

[0104] When outputting processed data, the output unit can adjust the output order based on the relevance of the data. For example, when outputting processed data, the output unit adjusts the output order based on the relevance of the data. For example, the output unit prioritizes output of highly relevant data. The output unit can also postpone output of less relevant data. The output unit can also allow the generation AI to adjust the output order based on the relevance of the data. This enables efficient data output by adjusting the output order based on the relevance of the data. Some or all of the above-mentioned processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the relevance of the data to the generation AI and have the generation AI adjust the output order.

[0105] When outputting the processed data, the output unit can adjust the use of technical terminology in the output according to the user's level of expertise. For example, when outputting the processed data, the output unit adjusts the use of technical terminology in the output according to the user's level of expertise. For example, the output unit may provide an output in which the generation AI uses a lot of technical terminology for a user with high level of expertise. The output unit may also provide an output in which the generation AI avoids technical terminology for a user with low level of expertise. The output unit may also adjust the use of technical terminology in the output according to the user's level of expertise. This makes it easier to understand the output result by adjusting the use of technical terminology in the output according to the user's level of expertise. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit may input the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, processing unit, and output unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the reception device 38 of the smart device 14, through which a data scientist inputs data such as CSV files or Excel files. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the input data using a generative AI and proposes an optimal processing method. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which processes the data based on the proposed processing method. The output unit is realized, for example, by the output device 40 of the smart device 14, which outputs the processed data. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, processing unit, and output unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the smart glasses 214, through which a data scientist inputs data by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the input data using a generative AI and proposes an optimal processing method. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which processes the data based on the proposed processing method. The output unit is realized, for example, by the speaker 240 of the smart glasses 214, which outputs the processed data by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, processing unit, and output unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the headset-type terminal 314, and a data scientist inputs data by voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generative AI and proposes an optimal processing method. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and processes the data based on the proposed processing method. The output unit is realized, for example, by the display 343 of the headset-type terminal 314, and displays the processed data. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, processing unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the microphone 238 of the robot 414, through which a data scientist inputs data by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which analyzes the input data using a generative AI and proposes an optimal processing method. The processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, which processes the data based on the proposed processing method. The output unit is realized, for example, by the speaker 240 of the robot 414, which outputs the processed data by voice.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The data processing system can further include an encryption unit to enhance data security. The encryption unit automatically encrypts data when it is input, and can maintain the confidentiality of the data even during processing in the analysis unit and processing unit. For example, data input in the input unit is encrypted, and remains encrypted even when it is analyzed in the analysis unit or processed in the processing unit. Furthermore, when it is output from the output unit, the data can be decrypted as needed. This enhances data security and reduces the risk of confidential information leaks. Furthermore, the encryption unit can automatically select the data encryption algorithm and apply the optimal encryption method. For example, encryption algorithms such as AES and RSA can be used.

[0108] The analysis unit may include a reliability evaluation unit that evaluates the reliability of the data when analyzing the data. The reliability evaluation unit evaluates the source of the data, the collection method, the consistency of the data, etc., and calculates a reliability score for the data. For example, the reliability evaluation unit evaluates whether the source of the data is reliable and assigns a reliability score. It can also evaluate whether the data collection method is appropriate and adjust the reliability score. Furthermore, it can check the consistency of the data and assign a high reliability score to data with few outliers and missing values. This allows the analysis unit to prioritize analysis of highly reliable data and improve the accuracy of the analysis results.

[0109] The processing unit can be equipped with a version management unit that manages data versions when data is processed. The version management unit records the data processing history and manages each version of the data. For example, when the processing unit complements data, it records the data before and after the complement in the version management unit. Also, when data is modified, it can record the data before and after the modification in the version management unit. Furthermore, the version management unit can refer to past versions of data and revert to a specific version if necessary. This makes the data processing history clear and makes it easier to track the data change history.

[0110] The analysis unit may include a visualization unit that visualizes data during data analysis. The visualization unit displays the analysis results in a visual format, such as a graph or chart, allowing the user to intuitively understand the analysis results. For example, when the analysis unit detects missing values ​​in the data, it displays the distribution of the missing values ​​in a heat map. It can also display the results of abnormal value detection in a box plot. It can also display the correlation of data in a scatter plot, allowing the user to visually grasp the trends in the data. This allows the user to easily understand the analysis results and intuitively grasp the characteristics of the data.

[0111] The processing unit may include a quality evaluation unit that evaluates the quality of the data when processing the data. The quality evaluation unit evaluates the quality of the processed data and calculates a quality score. For example, the quality evaluation unit evaluates the consistency and accuracy of the data and assigns a quality score. It can also check the data for missing values ​​or abnormal values ​​and adjust the quality score. It can also evaluate whether the data processing method is appropriate and assign a quality score. This allows the processing unit to provide high-quality data and improve the reliability of the data.

[0112] The input unit can estimate the user's emotions and customize the data entry interface based on the estimated user emotions. For example, if the user is feeling stressed, the input unit can provide a simple and easy-to-use interface. If the user is relaxed, the input unit can provide an interface that allows for detailed settings. Furthermore, if the user is in a hurry, the input unit can provide an interface that allows for quick data entry. In this way, by customizing the data entry interface according to the user's emotions, it is possible to reduce the burden on the user and improve the efficiency of data entry.

[0113] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can emphasize positive feedback. If the user is relaxed, the analysis unit can provide detailed feedback. Furthermore, if the user is in a hurry, the analysis unit can provide concise feedback that focuses on the main points. In this way, adjusting the method of providing feedback on the analysis results according to the user's emotions can promote the user's understanding and make it easier for them to accept the analysis results.

[0114] The processing unit can estimate the user's emotions and display the progress of data processing based on the estimated user emotions. For example, if the user is feeling stressed, the processing unit can display the progress in a simple manner, reducing the user's burden. Alternatively, if the user is relaxed, the processing unit can display the progress in detail. Furthermore, if the user is in a hurry, the processing unit can quickly display the progress, giving the user a sense of security. In this way, by displaying the progress of data processing according to the user's emotions, it is possible to reduce the user's stress and make it easier for them to grasp the progress of data processing.

[0115] The output unit can estimate the user's emotion and adjust the format of the output data based on the estimated user's emotion. For example, if the user is nervous, the output unit can provide a simple, highly visible format. If the user is relaxed, the output unit can provide a format including detailed information. Furthermore, if the user is in a hurry, the output unit can provide a format that focuses on the main points. In this way, adjusting the format of the output data according to the user's emotion can reduce the burden on the user and promote understanding of the output data.

[0116] The output unit can estimate the user's emotions and adjust the notification method of the output data based on the estimated user's emotions. For example, the output unit can provide a more subtle notification if the user is feeling stressed. Alternatively, the output unit can provide a more detailed notification if the user is relaxed. Furthermore, the output unit can provide a more rapid notification if the user is in a hurry. In this way, by adjusting the notification method of the output data according to the user's emotions, the burden on the user can be reduced and the output data can be received more smoothly.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The input section is where data scientists input the data they want to process. The appropriate input method is selected depending on the format and content of the data. For example, the input section can input data in various formats, such as CSV files and Excel files. Step 2: The analysis unit uses the generation AI to analyze the data entered by the input unit and propose the optimal processing method. The generation AI learns the history and patterns of past data processing, and selects the optimal processing method based on this. For example, the analysis unit detects missing values ​​in the data and proposes methods to fill them in. The analysis unit can also detect abnormal values ​​in the data and propose methods to correct them. Step 3: The processing unit uses the generating AI to process the data based on the processing method proposed by the analysis unit. For example, the processing unit complements the data based on the proposed complementation method. The processing unit can also correct the data based on the proposed correction method. Step 4: The output unit uses the generation AI to output the data processed by the processing unit. For example, the output unit outputs processed data, such as data with missing values ​​filled in or data with outliers corrected. This allows the data processing system according to the embodiment to consistently perform processes from data input to analysis, processing, and output. For example, the output unit displays the processed data through a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results via email provides rapid feedback by sending the results directly to the data scientist. Some or all of the above-described processing in the output unit may be performed, for example, using AI, or may be performed without using AI.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting data; an analysis unit that analyzes the data input by the input unit and proposes an appropriate processing method; a processing unit that processes data based on the processing method proposed by the analysis unit; an output unit that outputs the data processed by the processing unit; Equipped with A system characterized by:

2. The analysis unit Detect missing values ​​in your data and suggest ways to impute them 2. The system of claim 1.

3. The processing unit is Impute data based on the proposed imputation method 2. The system of claim 1.

4. The analysis unit Detects data anomalies and suggests ways to fix them 2. The system of claim 1.

5. The processing unit is Correct the data based on the suggested corrections 2. The system of claim 1.

6. The output unit Output the processed data 2. The system of claim 1.

7. The input unit Estimate the user's emotions and adjust the timing of data input based on the estimated user emotions.

2. The system of claim 1.

8. The input unit Automatically selects the appropriate input method depending on the data format and content 2. The system of claim 1.

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