system

Abstract

The system according to this embodiment aims to automatically analyze the characteristics and distribution of a dataset and propose and apply the optimal statistical model. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, an application unit, and a visualization unit. The analysis unit automatically analyzes the characteristics and distribution of the dataset. The proposal unit proposes a statistical model based on the analysis results obtained by the analysis unit. The application unit applies the statistical model proposed by the proposal unit to the dataset. The visualization unit interprets and visualizes the results obtained by the application unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately grasp the characteristics and distribution of a dataset and to select and apply an optimal statistical model.

[0005] The system according to the embodiment aims to automatically analyze the characteristics and distribution of a dataset and to propose and apply an optimal statistical model.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, an application unit, and a visualization unit. The analysis unit automatically analyzes the characteristics and distribution of the dataset. The proposal unit proposes a statistical model based on the analysis results obtained by the analysis unit. The application unit applies the statistical model proposed by the proposal unit to the dataset. The visualization unit interprets and visualizes the results obtained by the application unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze the characteristics and distribution of a dataset and propose and apply the optimal statistical model. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant for statistical processing according to an embodiment of the present invention is a system that automatically analyzes the characteristics and distribution of a dataset, proposes and applies the optimal statistical model, and supports the interpretation and visualization of results. The AI ​​assistant for statistical processing automatically analyzes the characteristics and distribution of a dataset, proposes the optimal statistical model, applies that model to the dataset, and supports the interpretation and visualization of results. For example, the AI ​​assistant for statistical processing automatically analyzes the characteristics and distribution of a dataset. In this process, the AI ​​analyzes the data type, distribution, presence or absence of missing values, etc. For example, the AI ​​can calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. Next, the AI ​​assistant for statistical processing proposes the optimal statistical model. Based on the characteristics of the dataset, the AI ​​selects the optimal statistical model, such as regression analysis, clustering, or classification. For example, the AI ​​can analyze the correlation between variables in the dataset and propose a regression model. The AI ​​can also group the dataset using a clustering algorithm. Furthermore, the AI ​​assistant for statistical processing applies the statistical model proposed by the AI ​​to the dataset. The AI ​​analyzes the dataset using the selected statistical model and outputs the results. For example, AI can use regression models to calculate predicted values ​​and display the results in graphs or tables. Finally, an AI assistant for statistical processing supports the interpretation and visualization of results. AI provides tools to interpret analysis results in an easy-to-understand and visually display manner. For example, AI can display analysis results in formats such as graphs and heatmaps, allowing users to intuitively understand the results. This enables an AI assistant for statistical processing to automatically analyze the characteristics and distribution of a dataset, propose and apply the optimal statistical model, and support the interpretation and visualization of results. This allows users to easily analyze datasets and understand results even without expertise in data analysis. This enables an AI assistant for statistical processing to automatically analyze the characteristics and distribution of a dataset, propose and apply the optimal statistical model, and support the interpretation and visualization of results.

[0029] The AI ​​assistant for statistical processing according to this embodiment comprises an analysis unit, a proposal unit, an application unit, and a visualization unit. The analysis unit automatically analyzes the characteristics and distribution of a dataset. For example, the analysis unit analyzes the type and distribution of data, the presence or absence of missing values, etc. The analysis unit can, for example, calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. The proposal unit proposes the optimal statistical model based on the analysis results obtained by the analysis unit. The proposal unit selects the optimal statistical model, for example, regression analysis, clustering, classification, etc. The proposal unit can, for example, analyze the correlation between variables in the dataset and propose a regression model. The proposal unit can also group the dataset using a clustering algorithm. The application unit applies the statistical model proposed by the proposal unit to the dataset. For example, the application unit analyzes the dataset using the selected statistical model and outputs the results. For example, the application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. The visualization unit interprets and visualizes the results obtained by the application unit. The visualization unit provides tools for, for example, interpreting analysis results in an easy-to-understand manner and displaying them visually. The visualization unit displays analysis results in formats such as graphs and heatmaps, enabling users to intuitively understand the results. This allows the AI ​​assistant for statistical processing according to the embodiment to automatically analyze the characteristics and distribution of the dataset, propose and apply the optimal statistical model, and support the interpretation and visualization of the results.

[0030] The analysis unit automatically analyzes the characteristics and distribution of the dataset. Specifically, it calculates basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and analyzes the data type, distribution, and presence of missing values. For example, it can visually display the distribution of numerical data in the dataset using histograms and box plots, and show the frequency distribution of categorical data using bar graphs and pie charts. Furthermore, the analysis unit can detect outliers and anomalies within the dataset and evaluate their impact. The analysis unit uses AI to automate these analyses, quickly and accurately understanding the characteristics of the data. For example, AI can use techniques such as kernel density estimation and histogram smoothing to analyze the distribution of data. It can also analyze patterns of missing values ​​and suggest the optimal method for missing value imputation. As a result, the analysis unit can quickly grasp the overall picture of the dataset and provide the information necessary for subsequent processing.

[0031] The proposal unit proposes the optimal statistical model based on the analysis results obtained by the analysis unit. Specifically, the proposal unit selects the most suitable statistical model, such as regression analysis, clustering, or classification, depending on the characteristics and distribution of the dataset. For example, it can analyze the correlation between variables in the dataset and propose a regression model. The proposal unit uses AI to automate these model selections and quickly propose the optimal model. The AI ​​analyzes the characteristics of the dataset and uses algorithms to select the optimal model based on historical data and similar datasets. For example, the AI ​​calculates a correlation matrix between variables in the dataset and proposes a regression model using strongly correlated variables. It can also use clustering algorithms to group the dataset and analyze the characteristics of each group. This allows the proposal unit to quickly and accurately propose the most suitable statistical model for the dataset, improving the accuracy of subsequent analyses and predictions.

[0032] The application unit applies the statistical model proposed by the proposal unit to the dataset. Specifically, the application unit analyzes the dataset using the selected statistical model and outputs the results. For example, it can calculate predicted values ​​using a regression model and display the results in graph or tabular format. The application unit automates these analyses using AI and outputs results quickly and accurately. The AI ​​analyzes the dataset based on the selected model and uses algorithms to calculate predicted values ​​and classification results. For example, the AI ​​uses a regression model to calculate predicted values ​​based on specific variables and displays the results in graph or tabular format. It can also use a clustering algorithm to group the dataset and analyze the characteristics of each group. This allows the application unit to output analysis results for the dataset quickly and accurately, making the results easy for the user to understand.

[0033] The visualization unit interprets and visualizes the results obtained by the application unit. Specifically, the visualization unit provides tools for interpreting analysis results in an easy-to-understand manner and displaying them visually. For example, it displays analysis results in formats such as graphs and heatmaps, allowing users to intuitively understand the results. The visualization unit uses AI to automate these visualizations and display results quickly and accurately. The AI ​​selects the optimal visualization method based on the analysis results and uses algorithms to make the results easy for users to understand. For example, the AI ​​displays regression model results as scatter plots and regression lines, and clustering results as heatmaps and dendrograms. It can also display analysis results in an interactive dashboard format, allowing users to investigate the results in detail. In this way, the visualization unit displays analysis results intuitively and visually, allowing users to easily understand the results and use them to aid in decision-making.

[0034] The analysis unit can analyze the type and distribution of data, as well as the presence or absence of missing values. For example, the analysis unit can analyze the type and distribution of data, as well as the presence or absence of missing values. For example, the analysis unit can calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. This allows for understanding the characteristics of the dataset by analyzing the type and distribution of data, as well as the presence or absence of missing values. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to calculate basic statistics for each variable in the dataset and to visually display the data distribution.

[0035] The proposal unit can select statistical models for regression analysis, clustering, and classification. For example, the proposal unit can select statistical models for regression analysis, clustering, and classification. For example, the proposal unit can analyze the correlations between variables in a dataset and propose a regression model. The proposal unit can also group datasets using clustering algorithms. This allows for the selection of the optimal statistical model based on the characteristics of the dataset. Some or all of the above-described processes in the proposal unit may be performed using, for example, generative AI, or not. For example, the proposal unit can use generative AI to analyze the correlations between variables in a dataset and propose a regression model.

[0036] The application unit can analyze a dataset using a selected statistical model and output the results. For example, the application unit can analyze a dataset using a selected statistical model and output the results. For example, the application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. This allows the application unit to analyze a dataset using a selected statistical model and output the results. Some or all of the above-described processes in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to calculate predicted values ​​using a regression model and display the results in graph or tabular format.

[0037] The visualization unit can display the analysis results in the form of graphs or heatmaps. For example, the visualization unit displays the analysis results in the form of graphs or heatmaps. The visualization unit provides tools for easily interpreting and visually displaying the analysis results. For example, the visualization unit displays the analysis results in the form of graphs or heatmaps, allowing the user to intuitively understand the results. This allows the user to intuitively understand the results by visually displaying them. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to display the analysis results in the form of graphs or heatmaps.

[0038] The analysis unit can calculate basic statistics for each variable in the dataset and display the data distribution. For example, the analysis unit can calculate basic statistics for each variable in the dataset and display the data distribution. For example, the analysis unit can calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. This allows for understanding the characteristics of the data by calculating basic statistics for each variable in the dataset and visually displaying the data distribution. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to calculate basic statistics for each variable in the dataset and visually display the data distribution.

[0039] The proposal unit can analyze the correlations between variables in a dataset and propose a regression model. For example, the proposal unit can analyze the correlations between variables in a dataset and propose a regression model. This allows for an understanding of the relationships between data by analyzing the correlations between variables in a dataset and proposing a regression model. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use AI to analyze the correlations between variables in a dataset and propose a regression model.

[0040] The application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. For example, the application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. The application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. This allows for a visual understanding of the prediction results by calculating predicted values ​​using a regression model and displaying the results in graph or tabular format. Some or all of the above-described processes in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to calculate predicted values ​​using a regression model and display the results in graph or tabular format.

[0041] The visualization unit can provide tools for interpreting and displaying analysis results. For example, the visualization unit provides tools for interpreting and displaying analysis results. For example, the visualization unit provides tools for interpreting analysis results in an easy-to-understand manner and displaying them visually. For example, the visualization unit displays analysis results in the form of graphs or heatmaps, allowing users to intuitively understand the results. This allows users to intuitively understand the results by providing tools for interpreting analysis results in an easy-to-understand manner and displaying them visually. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to provide tools for interpreting and displaying analysis results.

[0042] The analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform time series analysis considering the temporal variation of the dataset. The analysis unit can also perform seasonal adjustment considering the seasonal variation of the data. The analysis unit can also analyze the trends of the data and grasp long-term variations. By performing analysis while considering the temporal variation of the data, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis while considering the temporal variation of the data when analyzing the characteristics of a dataset.

[0043] The analysis unit can analyze outliers and their impact when analyzing the characteristics of a dataset. For example, the analysis unit can analyze outliers and their impact when analyzing the characteristics of a dataset. For example, the analysis unit can perform an analysis excluding outliers in order to detect outliers and evaluate their impact. The analysis unit can also compare an analysis including outliers with an analysis excluding outliers in order to evaluate the impact of outliers. The analysis unit can also identify the cause of outliers and take measures to minimize their impact. In this way, the quality of the data can be improved by detecting outliers and evaluating their impact. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to detect outliers and evaluate their impact when analyzing the characteristics of a dataset.

[0044] The analysis unit can perform analysis on a dataset based on the data characteristics specific to the user's industry. For example, the analysis unit can perform analysis on a dataset based on the data characteristics specific to the user's industry. For example, the analysis unit can apply industry-standard analysis methods, taking into account the data characteristics specific to the user's industry. The analysis unit can also develop customized analysis methods, taking into account the data characteristics specific to the industry. The analysis unit can also display the analysis results in an industry-standard format, taking into account the data characteristics specific to the industry. This allows for analysis results that are appropriate for the industry by performing analysis while considering the data characteristics specific to the user's industry. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis on a dataset while considering the data characteristics specific to the user's industry.

[0045] The analysis unit can improve its analysis method by referring to the user's past analysis history when analyzing a dataset. For example, the analysis unit can improve its analysis method by referring to the user's past analysis history when analyzing a dataset. For example, the analysis unit can refer to the user's past analysis history and select the optimal analysis method. The analysis unit can also analyze the user's past analysis history and improve the analysis method. The analysis unit can also optimize how the analysis results are displayed based on the user's past analysis history. This makes it possible to perform more effective analysis by optimizing the analysis method by referring to the user's past analysis history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to improve the analysis method by referring to the user's past analysis history when analyzing a dataset.

[0046] The proposal unit can select an appropriate statistical model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, the proposal unit can select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, if the dataset is large, the proposal unit can select an efficient statistical model. If the dataset is complex, the proposal unit can also select a detailed statistical model. The proposal unit can also select a balanced statistical model, taking into account the size and complexity of the dataset. This allows for more effective analysis by selecting the optimal model considering the size and complexity of the dataset. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use AI to select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset.

[0047] The proposal unit can select a statistical model by referring to the past performance of the dataset when selecting a proposed statistical model. For example, the proposal unit selects a model by referring to the past performance of the dataset when selecting a proposed statistical model. For example, the proposal unit selects the optimal statistical model by referring to the past performance of the dataset. The proposal unit can also analyze past performance and identify areas for improvement in the model. The proposal unit can also optimize the model selection criteria based on past performance. This allows for the proposal of a more effective model by selecting a model by referring to the past performance of the dataset. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select a statistical model by referring to the past performance of the dataset when selecting a proposed statistical model.

[0048] The proposal unit can select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select a model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select an industry-standard statistical model considering the user's industry-specific requirements. The proposal unit can also develop a customized statistical model considering industry-specific requirements. The proposal unit can also optimize the model selection criteria considering industry-specific requirements. This allows the proposal unit to propose a model suitable for the industry by selecting a model considering the user's industry-specific requirements. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose.

[0049] The proposal unit can select an appropriate statistical model by referring to the user's past model usage history when selecting a statistical model to propose. For example, the proposal unit can select an appropriate model by referring to the user's past model usage history when selecting a statistical model to propose. For example, the proposal unit can select the optimal statistical model by referring to the user's past model usage history. The proposal unit can also analyze past model usage history and identify areas for improvement in the model. The proposal unit can also optimize the model selection criteria based on past model usage history. This allows for the proposal of a more effective model by selecting the optimal model by referring to the user's past model usage history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select an appropriate model by referring to the user's past model usage history when selecting a statistical model to propose.

[0050] The application unit can apply statistical models based on real-time updates of the dataset. For example, the application unit can apply statistical models based on real-time updates of the dataset. The application unit can adjust the application method considering real-time updates of the dataset. The application unit can also reapply statistical models based on real-time updated data. The application unit can also evaluate the impact of real-time updates and optimize the application method. This enables analysis based on the latest data by considering real-time updates of the dataset during application. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to consider real-time updates of the dataset when applying statistical models.

[0051] The application unit can apply methods to reduce the impact of outliers in the dataset when applying a statistical model. For example, the application unit can apply methods to reduce the impact of outliers in the dataset when applying a statistical model. For example, the application unit can provide an application method that excludes outliers in order to minimize the impact of outliers. The application unit can also evaluate the impact of outliers and adjust the application method. The application unit can also identify the cause of the outliers and take measures to minimize their impact. By minimizing the impact of outliers in the dataset, more accurate analysis results can be obtained. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to apply methods to reduce the impact of outliers in the dataset when applying a statistical model.

[0052] The application unit can apply statistical models based on the user's industry-specific data characteristics. For example, the application unit can apply statistical models based on the user's industry-specific data characteristics. For example, the application unit can consider the user's industry-specific data characteristics and provide industry-standard application methods. The application unit can also consider industry-specific data characteristics and develop customized application methods. The application unit can also consider industry-specific data characteristics and display application results in an industry-standard format. This allows users to obtain industry-appropriate analysis results by applying statistical models while considering their industry-specific data characteristics. Some or all of the above-described processes in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to apply statistical models while considering the user's industry-specific data characteristics.

[0053] The application unit can improve its application method by referring to the user's past application history when applying a statistical model. For example, the application unit can improve its application method by referring to the user's past application history when applying a statistical model. For example, the application unit can refer to the user's past application history and select the optimal application method. The application unit can also analyze the user's past application history and improve the application method. The application unit can also optimize how the application results are displayed based on the user's past application history. This makes it possible to perform more effective applications by optimizing the application method by referring to the user's past application history. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to improve the application method by referring to the user's past application history when applying a statistical model.

[0054] The visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can provide a time-series graph that takes into account the temporal variation of the dataset. The visualization unit can also provide a seasonally adjusted graph that takes into account the seasonal variation of the data. The visualization unit can also provide a graph that visualizes data trends and helps to understand long-term variations. By performing visualizations that take into account the temporal variation of the dataset, more accurate visualization results can be obtained. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to perform visualizations that take into account the temporal variation of the dataset.

[0055] The visualization unit can apply methods to emphasize the impact of outliers when visualizing. For example, the visualization unit can display outliers in red to highlight them. The visualization unit can also display warning marks around outliers to visually indicate their impact. The visualization unit can also compare graphs containing outliers with graphs excluding them to evaluate their impact. This allows for an intuitive understanding of the impact of outliers by visually highlighting their effects. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to apply methods to emphasize the impact of outliers when visualizing.

[0056] The visualization unit can perform visualizations based on the data characteristics specific to the user's industry. For example, the visualization unit can perform visualizations based on the data characteristics specific to the user's industry. For example, the visualization unit can consider the data characteristics specific to the user's industry and apply industry-standard visualization methods. The visualization unit can also consider industry-specific data characteristics and develop customized visualization methods. The visualization unit can also consider industry-specific data characteristics and display the visualization results in an industry-standard format. This allows for visualization results that are appropriate for the industry by considering the data characteristics specific to the user's industry. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to perform visualizations while considering the data characteristics specific to the user's industry.

[0057] The visualization unit can improve its visualization method by referring to the user's past visualization history when performing visualization. For example, the visualization unit can improve its visualization method by referring to the user's past visualization history when performing visualization. For example, the visualization unit can refer to the user's past visualization history and select the optimal visualization method. The visualization unit can also analyze the user's past visualization history and improve the visualization method. The visualization unit can also optimize the display method of the visualization results based on the user's past visualization history. This makes it possible to perform more effective visualization by optimizing the visualization method by referring to the user's past visualization history. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can use AI to improve the visualization method by referring to the user's past visualization history when performing visualization.

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

[0059] The analysis unit can perform analysis based on the geographical distribution of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform analysis based on the geographical distribution of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform region-specific analysis considering the geographical distribution of the dataset. The analysis unit can also consider geographical factors to grasp region-specific trends. The analysis unit can also visually display geographical data to clarify differences between regions. This makes it easier to grasp region-specific trends by performing analysis while considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis while considering the geographical distribution of the data when analyzing the characteristics of a dataset.

[0060] The application unit can apply a method to emphasize the impact of outliers in the dataset when applying a statistical model. For example, the application unit can apply a method to emphasize the impact of outliers in the dataset when applying a statistical model. For example, the application unit can provide an application method that excludes outliers in order to minimize the impact of outliers. The application unit can also evaluate the impact of outliers and adjust the application method. The application unit can also identify the causes of outliers and take measures to minimize their impact. By minimizing the impact of outliers in the dataset, more accurate analysis results can be obtained. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to apply a method to emphasize the impact of outliers in the dataset when applying a statistical model.

[0061] The analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform time series analysis considering the temporal variation of the dataset. The analysis unit can also perform seasonal adjustment considering the seasonal variation of the data. The analysis unit can also analyze the trends of the data and grasp long-term variations. By performing analysis while considering the temporal variation of the data, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis while considering the temporal variation of the data when analyzing the characteristics of a dataset.

[0062] The proposal unit can select an appropriate statistical model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, the proposal unit can select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, if the dataset is large, the proposal unit can select an efficient statistical model. If the dataset is complex, the proposal unit can also select a detailed statistical model. The proposal unit can also select a balanced statistical model, taking into account the size and complexity of the dataset. This allows for more effective analysis by selecting the optimal model considering the size and complexity of the dataset. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use AI to select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset.

[0063] The application unit can apply statistical models based on real-time updates of the dataset. For example, the application unit can apply statistical models based on real-time updates of the dataset. The application unit can adjust the application method considering real-time updates of the dataset. The application unit can also reapply statistical models based on real-time updated data. The application unit can also evaluate the impact of real-time updates and optimize the application method. This enables analysis based on the latest data by considering real-time updates of the dataset during application. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to consider real-time updates of the dataset when applying statistical models.

[0064] The visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can provide a time-series graph that takes into account the temporal variation of the dataset. The visualization unit can also provide a seasonally adjusted graph that takes into account the seasonal variation of the data. The visualization unit can also provide a graph that visualizes data trends and helps to understand long-term variations. By performing visualizations that take into account the temporal variation of the dataset, more accurate visualization results can be obtained. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to perform visualizations that take into account the temporal variation of the dataset.

[0065] The proposal unit can select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select a model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select an industry-standard statistical model considering the user's industry-specific requirements. The proposal unit can also develop a customized statistical model considering industry-specific requirements. The proposal unit can also optimize the model selection criteria considering industry-specific requirements. This allows the proposal unit to propose a model suitable for the industry by selecting a model considering the user's industry-specific requirements. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis unit automatically analyzes the characteristics and distribution of the dataset. For example, the analysis unit analyzes the data type, distribution, and presence of missing values, calculates basic statistics for each variable in the dataset (mean, median, standard deviation, etc.), and visually displays the data distribution. Step 2: The proposal unit proposes the optimal statistical model based on the analysis results obtained by the analysis unit. For example, the proposal unit selects the most suitable statistical model, such as regression analysis, clustering, or classification, and proposes a regression model by analyzing the correlation between variables in the dataset, or groups the dataset using a clustering algorithm. Step 3: The application unit applies the statistical model proposed by the proposal unit to the dataset. The application unit, for example, analyzes the dataset using the selected statistical model and outputs the results. The application unit calculates predicted values ​​using a regression model and displays the results in graph or tabular format. Step 4: The visualization unit interprets and visualizes the results obtained by the application unit. The visualization unit provides tools for interpreting the analysis results in an easy-to-understand manner and displaying them visually. The visualization unit displays the analysis results in formats such as graphs and heatmaps, enabling users to intuitively understand the results.

[0068] (Example of form 2) An AI assistant for statistical processing according to an embodiment of the present invention is a system that automatically analyzes the characteristics and distribution of a dataset, proposes and applies the optimal statistical model, and supports the interpretation and visualization of results. The AI ​​assistant for statistical processing automatically analyzes the characteristics and distribution of a dataset, proposes the optimal statistical model, applies that model to the dataset, and supports the interpretation and visualization of results. For example, the AI ​​assistant for statistical processing automatically analyzes the characteristics and distribution of a dataset. In this process, the AI ​​analyzes the data type, distribution, presence or absence of missing values, etc. For example, the AI ​​can calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. Next, the AI ​​assistant for statistical processing proposes the optimal statistical model. Based on the characteristics of the dataset, the AI ​​selects the optimal statistical model, such as regression analysis, clustering, or classification. For example, the AI ​​can analyze the correlation between variables in the dataset and propose a regression model. The AI ​​can also group the dataset using a clustering algorithm. Furthermore, the AI ​​assistant for statistical processing applies the statistical model proposed by the AI ​​to the dataset. The AI ​​analyzes the dataset using the selected statistical model and outputs the results. For example, AI can use regression models to calculate predicted values ​​and display the results in graphs or tables. Finally, an AI assistant for statistical processing supports the interpretation and visualization of results. AI provides tools to interpret analysis results in an easy-to-understand and visually display manner. For example, AI can display analysis results in formats such as graphs and heatmaps, allowing users to intuitively understand the results. This enables an AI assistant for statistical processing to automatically analyze the characteristics and distribution of a dataset, propose and apply the optimal statistical model, and support the interpretation and visualization of results. This allows users to easily analyze datasets and understand results even without expertise in data analysis. This enables an AI assistant for statistical processing to automatically analyze the characteristics and distribution of a dataset, propose and apply the optimal statistical model, and support the interpretation and visualization of results.

[0069] The AI ​​assistant for statistical processing according to this embodiment comprises an analysis unit, a proposal unit, an application unit, and a visualization unit. The analysis unit automatically analyzes the characteristics and distribution of a dataset. For example, the analysis unit analyzes the type and distribution of data, the presence or absence of missing values, etc. The analysis unit can, for example, calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. The proposal unit proposes the optimal statistical model based on the analysis results obtained by the analysis unit. The proposal unit selects the optimal statistical model, for example, regression analysis, clustering, classification, etc. The proposal unit can, for example, analyze the correlation between variables in the dataset and propose a regression model. The proposal unit can also group the dataset using a clustering algorithm. The application unit applies the statistical model proposed by the proposal unit to the dataset. For example, the application unit analyzes the dataset using the selected statistical model and outputs the results. For example, the application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. The visualization unit interprets and visualizes the results obtained by the application unit. The visualization unit provides tools for, for example, interpreting analysis results in an easy-to-understand manner and displaying them visually. The visualization unit displays analysis results in formats such as graphs and heatmaps, enabling users to intuitively understand the results. This allows the AI ​​assistant for statistical processing according to the embodiment to automatically analyze the characteristics and distribution of the dataset, propose and apply the optimal statistical model, and support the interpretation and visualization of the results.

[0070] The analysis unit automatically analyzes the characteristics and distribution of the dataset. Specifically, it calculates basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and analyzes the data type, distribution, and presence of missing values. For example, it can visually display the distribution of numerical data in the dataset using histograms and box plots, and show the frequency distribution of categorical data using bar graphs and pie charts. Furthermore, the analysis unit can detect outliers and anomalies within the dataset and evaluate their impact. The analysis unit uses AI to automate these analyses, quickly and accurately understanding the characteristics of the data. For example, AI can use techniques such as kernel density estimation and histogram smoothing to analyze the distribution of data. It can also analyze patterns of missing values ​​and suggest the optimal method for missing value imputation. As a result, the analysis unit can quickly grasp the overall picture of the dataset and provide the information necessary for subsequent processing.

[0071] The proposal unit proposes the optimal statistical model based on the analysis results obtained by the analysis unit. Specifically, the proposal unit selects the most suitable statistical model, such as regression analysis, clustering, or classification, depending on the characteristics and distribution of the dataset. For example, it can analyze the correlation between variables in the dataset and propose a regression model. The proposal unit uses AI to automate these model selections and quickly propose the optimal model. The AI ​​analyzes the characteristics of the dataset and uses algorithms to select the optimal model based on historical data and similar datasets. For example, the AI ​​calculates a correlation matrix between variables in the dataset and proposes a regression model using strongly correlated variables. It can also use clustering algorithms to group the dataset and analyze the characteristics of each group. This allows the proposal unit to quickly and accurately propose the most suitable statistical model for the dataset, improving the accuracy of subsequent analyses and predictions.

[0072] The application unit applies the statistical model proposed by the proposal unit to the dataset. Specifically, the application unit analyzes the dataset using the selected statistical model and outputs the results. For example, it can calculate predicted values ​​using a regression model and display the results in graph or tabular format. The application unit automates these analyses using AI and outputs results quickly and accurately. The AI ​​analyzes the dataset based on the selected model and uses algorithms to calculate predicted values ​​and classification results. For example, the AI ​​uses a regression model to calculate predicted values ​​based on specific variables and displays the results in graph or tabular format. It can also use a clustering algorithm to group the dataset and analyze the characteristics of each group. This allows the application unit to output analysis results for the dataset quickly and accurately, making the results easy for the user to understand.

[0073] The visualization unit interprets and visualizes the results obtained by the application unit. Specifically, the visualization unit provides tools for interpreting analysis results in an easy-to-understand manner and displaying them visually. For example, it displays analysis results in formats such as graphs and heatmaps, allowing users to intuitively understand the results. The visualization unit uses AI to automate these visualizations and display results quickly and accurately. The AI ​​selects the optimal visualization method based on the analysis results and uses algorithms to make the results easy for users to understand. For example, the AI ​​displays regression model results as scatter plots and regression lines, and clustering results as heatmaps and dendrograms. It can also display analysis results in an interactive dashboard format, allowing users to investigate the results in detail. In this way, the visualization unit displays analysis results intuitively and visually, allowing users to easily understand the results and use them to aid in decision-making.

[0074] The analysis unit can analyze the type and distribution of data, as well as the presence or absence of missing values. For example, the analysis unit can analyze the type and distribution of data, as well as the presence or absence of missing values. For example, the analysis unit can calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. This allows for understanding the characteristics of the dataset by analyzing the type and distribution of data, as well as the presence or absence of missing values. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to calculate basic statistics for each variable in the dataset and to visually display the data distribution.

[0075] The proposal unit can select statistical models for regression analysis, clustering, and classification. For example, the proposal unit can select statistical models for regression analysis, clustering, and classification. For example, the proposal unit can analyze the correlations between variables in a dataset and propose a regression model. The proposal unit can also group datasets using clustering algorithms. This allows for the selection of the optimal statistical model based on the characteristics of the dataset. Some or all of the above-described processes in the proposal unit may be performed using, for example, generative AI, or not. For example, the proposal unit can use generative AI to analyze the correlations between variables in a dataset and propose a regression model.

[0076] The application unit can analyze a dataset using a selected statistical model and output the results. For example, the application unit can analyze a dataset using a selected statistical model and output the results. For example, the application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. This allows the application unit to analyze a dataset using a selected statistical model and output the results. Some or all of the above-described processes in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to calculate predicted values ​​using a regression model and display the results in graph or tabular format.

[0077] The visualization unit can display the analysis results in the form of graphs or heatmaps. For example, the visualization unit displays the analysis results in the form of graphs or heatmaps. The visualization unit provides tools for easily interpreting and visually displaying the analysis results. For example, the visualization unit displays the analysis results in the form of graphs or heatmaps, allowing the user to intuitively understand the results. This allows the user to intuitively understand the results by visually displaying them. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to display the analysis results in the form of graphs or heatmaps.

[0078] The analysis unit can calculate basic statistics for each variable in the dataset and display the data distribution. For example, the analysis unit can calculate basic statistics for each variable in the dataset and display the data distribution. For example, the analysis unit can calculate basic statistics (mean, median, standard deviation, etc.) for each variable in the dataset and visually display the data distribution. This allows for understanding the characteristics of the data by calculating basic statistics for each variable in the dataset and visually displaying the data distribution. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to calculate basic statistics for each variable in the dataset and visually display the data distribution.

[0079] The proposal unit can analyze the correlations between variables in a dataset and propose a regression model. For example, the proposal unit can analyze the correlations between variables in a dataset and propose a regression model. This allows for an understanding of the relationships between data by analyzing the correlations between variables in a dataset and proposing a regression model. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use AI to analyze the correlations between variables in a dataset and propose a regression model.

[0080] The application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. For example, the application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. The application unit can calculate predicted values ​​using a regression model and display the results in graph or tabular format. This allows for a visual understanding of the prediction results by calculating predicted values ​​using a regression model and displaying the results in graph or tabular format. Some or all of the above-described processes in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to calculate predicted values ​​using a regression model and display the results in graph or tabular format.

[0081] The visualization unit can provide tools for interpreting and displaying analysis results. For example, the visualization unit provides tools for interpreting and displaying analysis results. For example, the visualization unit provides tools for interpreting analysis results in an easy-to-understand manner and displaying them visually. For example, the visualization unit displays analysis results in the form of graphs or heatmaps, allowing users to intuitively understand the results. This allows users to intuitively understand the results by providing tools for interpreting analysis results in an easy-to-understand manner and displaying them visually. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to provide tools for interpreting and displaying analysis results.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method of the dataset based on the estimated user emotions. For example, if the user is stressed, the analysis unit can simplify the analysis method and calculate only key statistics. If the user is relaxed, the analysis unit can also perform a detailed analysis to get a complete picture of the dataset. If the user is in a hurry, the analysis unit can omit parts of the analysis to obtain results quickly. This allows for more appropriate analysis by adjusting the analysis method of the dataset according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to estimate the user's emotions and adjust the analysis method of the dataset based on the estimated user emotions.

[0083] The analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform time series analysis considering the temporal variation of the dataset. The analysis unit can also perform seasonal adjustment considering the seasonal variation of the data. The analysis unit can also analyze the trends of the data and grasp long-term variations. By performing analysis while considering the temporal variation of the data, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis while considering the temporal variation of the data when analyzing the characteristics of a dataset.

[0084] The analysis unit can analyze outliers and their impact when analyzing the characteristics of a dataset. For example, the analysis unit can analyze outliers and their impact when analyzing the characteristics of a dataset. For example, the analysis unit can perform an analysis excluding outliers in order to detect outliers and evaluate their impact. The analysis unit can also compare an analysis including outliers with an analysis excluding outliers in order to evaluate the impact of outliers. The analysis unit can also identify the cause of outliers and take measures to minimize their impact. In this way, the quality of the data can be improved by detecting outliers and evaluating their impact. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to detect outliers and evaluate their impact when analyzing the characteristics of a dataset.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also sequentially display detailed analysis results. If the user is in a hurry, the analysis unit can also display only the main analysis results to obtain results quickly. This allows for the prioritization of important results by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use AI to estimate the user's emotions and determine the priority of analysis results based on the estimated emotions.

[0086] The analysis unit can perform analysis on a dataset based on the data characteristics specific to the user's industry. For example, the analysis unit can perform analysis on a dataset based on the data characteristics specific to the user's industry. For example, the analysis unit can apply industry-standard analysis methods, taking into account the data characteristics specific to the user's industry. The analysis unit can also develop customized analysis methods, taking into account the data characteristics specific to the industry. The analysis unit can also display the analysis results in an industry-standard format, taking into account the data characteristics specific to the industry. This allows for analysis results that are appropriate for the industry by performing analysis while considering the data characteristics specific to the user's industry. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis on a dataset while considering the data characteristics specific to the user's industry.

[0087] The analysis unit can improve its analysis method by referring to the user's past analysis history when analyzing a dataset. For example, the analysis unit can improve its analysis method by referring to the user's past analysis history when analyzing a dataset. For example, the analysis unit can refer to the user's past analysis history and select the optimal analysis method. The analysis unit can also analyze the user's past analysis history and improve the analysis method. The analysis unit can also optimize how the analysis results are displayed based on the user's past analysis history. This makes it possible to perform more effective analysis by optimizing the analysis method by referring to the user's past analysis history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to improve the analysis method by referring to the user's past analysis history when analyzing a dataset.

[0088] The suggestion unit can estimate the user's emotions and adjust the type of statistical model it suggests based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions and adjust the type of statistical model it suggests based on the estimated emotions. For example, if the user is stressed, the suggestion unit can suggest a simple statistical model. If the user is relaxed, the suggestion unit can also suggest a detailed statistical model. If the user is in a hurry, the suggestion unit can suggest a simplified statistical model to obtain results quickly. This allows for the suggestion of a more appropriate model by adjusting the type of statistical model suggested according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can use AI to estimate the user's emotions and adjust the type of statistical model it suggests based on the estimated emotions.

[0089] The proposal unit can select an appropriate statistical model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, the proposal unit can select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, if the dataset is large, the proposal unit can select an efficient statistical model. If the dataset is complex, the proposal unit can also select a detailed statistical model. The proposal unit can also select a balanced statistical model, taking into account the size and complexity of the dataset. This allows for more effective analysis by selecting the optimal model considering the size and complexity of the dataset. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use AI to select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset.

[0090] The proposal unit can select a statistical model by referring to the past performance of the dataset when selecting a proposed statistical model. For example, the proposal unit selects a model by referring to the past performance of the dataset when selecting a proposed statistical model. For example, the proposal unit selects the optimal statistical model by referring to the past performance of the dataset. The proposal unit can also analyze past performance and identify areas for improvement in the model. The proposal unit can also optimize the model selection criteria based on past performance. This allows for the proposal of a more effective model by selecting a model by referring to the past performance of the dataset. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select a statistical model by referring to the past performance of the dataset when selecting a proposed statistical model.

[0091] The suggestion unit can estimate the user's emotions and determine the priority of statistical models to suggest based on the estimated emotions. For example, the suggestion unit estimates the user's emotions and determines the priority of statistical models to suggest based on the estimated emotions. For example, if the user is stressed, the suggestion unit may prioritize suggesting important statistical models. If the user is relaxed, the suggestion unit may also sequentially suggest detailed statistical models. If the user is in a hurry, the suggestion unit may also suggest only key statistical models to obtain results quickly. This allows for the priority of important models to be suggested by determining the priority of statistical models to suggest according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can use AI to estimate the user's emotions and determine the priority of statistical models to suggest based on the estimated emotions.

[0092] The proposal unit can select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select a model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select an industry-standard statistical model considering the user's industry-specific requirements. The proposal unit can also develop a customized statistical model considering industry-specific requirements. The proposal unit can also optimize the model selection criteria considering industry-specific requirements. This allows the proposal unit to propose a model suitable for the industry by selecting a model considering the user's industry-specific requirements. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose.

[0093] The proposal unit can select an appropriate statistical model by referring to the user's past model usage history when selecting a statistical model to propose. For example, the proposal unit can select an appropriate model by referring to the user's past model usage history when selecting a statistical model to propose. For example, the proposal unit can select the optimal statistical model by referring to the user's past model usage history. The proposal unit can also analyze past model usage history and identify areas for improvement in the model. The proposal unit can also optimize the model selection criteria based on past model usage history. This allows for the proposal of a more effective model by selecting the optimal model by referring to the user's past model usage history. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select an appropriate model by referring to the user's past model usage history when selecting a statistical model to propose.

[0094] The application unit can estimate the user's emotions and adjust the application method of the statistical model based on the estimated user emotions. For example, the application unit estimates the user's emotions and adjusts the application method of the statistical model based on the estimated user emotions. For example, if the user is stressed, the application unit may provide a simplified application method. If the user is relaxed, the application unit may also provide a detailed application method. If the user is in a hurry, the application unit may also provide a simple application method to obtain results quickly. This allows for more appropriate application by adjusting the application method of the statistical model according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can use AI to estimate the user's emotions and adjust the application method of the statistical model based on the estimated user emotions.

[0095] The application unit can apply statistical models based on real-time updates of the dataset. For example, the application unit can apply statistical models based on real-time updates of the dataset. The application unit can adjust the application method considering real-time updates of the dataset. The application unit can also reapply statistical models based on real-time updated data. The application unit can also evaluate the impact of real-time updates and optimize the application method. This enables analysis based on the latest data by considering real-time updates of the dataset during application. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to consider real-time updates of the dataset when applying statistical models.

[0096] The application unit can apply methods to reduce the impact of outliers in the dataset when applying a statistical model. For example, the application unit can apply methods to reduce the impact of outliers in the dataset when applying a statistical model. For example, the application unit can provide an application method that excludes outliers in order to minimize the impact of outliers. The application unit can also evaluate the impact of outliers and adjust the application method. The application unit can also identify the cause of the outliers and take measures to minimize their impact. By minimizing the impact of outliers in the dataset, more accurate analysis results can be obtained. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to apply methods to reduce the impact of outliers in the dataset when applying a statistical model.

[0097] The application unit can estimate the user's emotions and determine the priority of the statistical model's application results based on the estimated user emotions. For example, the application unit estimates the user's emotions and determines the priority of the statistical model's application results based on the estimated user emotions. For example, if the user is stressed, the application unit prioritizes displaying important application results. If the user is relaxed, the application unit can also sequentially display detailed application results. If the user is in a hurry, the application unit can also display only the main application results to obtain results quickly. This allows for the priority display of important results by determining the priority of the statistical model's application results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the application unit may be performed using AI, for example, or not using AI. For example, the application unit can use AI to estimate the user's emotions and determine the priority of the statistical model's application results based on the estimated user emotions.

[0098] The application unit can apply statistical models based on the user's industry-specific data characteristics. For example, the application unit can apply statistical models based on the user's industry-specific data characteristics. For example, the application unit can consider the user's industry-specific data characteristics and provide industry-standard application methods. The application unit can also consider industry-specific data characteristics and develop customized application methods. The application unit can also consider industry-specific data characteristics and display application results in an industry-standard format. This allows users to obtain industry-appropriate analysis results by applying statistical models while considering their industry-specific data characteristics. Some or all of the above-described processes in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to apply statistical models while considering the user's industry-specific data characteristics.

[0099] The application unit can improve its application method by referring to the user's past application history when applying a statistical model. For example, the application unit can improve its application method by referring to the user's past application history when applying a statistical model. For example, the application unit can refer to the user's past application history and select the optimal application method. The application unit can also analyze the user's past application history and improve the application method. The application unit can also optimize how the application results are displayed based on the user's past application history. This makes it possible to perform more effective applications by optimizing the application method by referring to the user's past application history. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to improve the application method by referring to the user's past application history when applying a statistical model.

[0100] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, the visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit can provide a simple graph. If the user is relaxed, the visualization unit can also provide a detailed graph. If the user is in a hurry, the visualization unit can provide a simplified graph to obtain results quickly. This allows for more appropriate visualization by adjusting the visualization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can use AI to estimate the user's emotions and adjust the visualization method based on the estimated emotions.

[0101] The visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can provide a time-series graph that takes into account the temporal variation of the dataset. The visualization unit can also provide a seasonally adjusted graph that takes into account the seasonal variation of the data. The visualization unit can also provide a graph that visualizes data trends and helps to understand long-term variations. By performing visualizations that take into account the temporal variation of the dataset, more accurate visualization results can be obtained. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to perform visualizations that take into account the temporal variation of the dataset.

[0102] The visualization unit can apply methods to emphasize the impact of outliers when visualizing. For example, the visualization unit can display outliers in red to highlight them. The visualization unit can also display warning marks around outliers to visually indicate their impact. The visualization unit can also compare graphs containing outliers with graphs excluding them to evaluate their impact. This allows for an intuitive understanding of the impact of outliers by visually highlighting their effects. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to apply methods to emphasize the impact of outliers when visualizing.

[0103] The visualization unit can estimate the user's emotions and determine the priority of visualization results based on the estimated user emotions. For example, the visualization unit can estimate the user's emotions and determine the priority of visualization results based on the estimated user emotions. For example, if the user is stressed, the visualization unit can prioritize displaying important visualization results. If the user is relaxed, the visualization unit can also sequentially display detailed visualization results. If the user is in a hurry, the visualization unit can also display only the main visualization results to obtain results quickly. This allows for the priority display of important results by determining the priority of visualization results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can use AI to estimate the user's emotions and determine the priority of visualization results based on the estimated user emotions.

[0104] The visualization unit can perform visualizations based on the data characteristics specific to the user's industry. For example, the visualization unit can perform visualizations based on the data characteristics specific to the user's industry. For example, the visualization unit can consider the data characteristics specific to the user's industry and apply industry-standard visualization methods. The visualization unit can also consider industry-specific data characteristics and develop customized visualization methods. The visualization unit can also consider industry-specific data characteristics and display the visualization results in an industry-standard format. This allows for visualization results that are appropriate for the industry by considering the data characteristics specific to the user's industry. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to perform visualizations while considering the data characteristics specific to the user's industry.

[0105] The visualization unit can improve its visualization method by referring to the user's past visualization history when performing visualization. For example, the visualization unit can improve its visualization method by referring to the user's past visualization history when performing visualization. For example, the visualization unit can refer to the user's past visualization history and select the optimal visualization method. The visualization unit can also analyze the user's past visualization history and improve the visualization method. The visualization unit can also optimize the display method of the visualization results based on the user's past visualization history. This makes it possible to perform more effective visualization by optimizing the visualization method by referring to the user's past visualization history. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can use AI to improve the visualization method by referring to the user's past visualization history when performing visualization.

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

[0107] The analysis unit can perform analysis based on the geographical distribution of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform analysis based on the geographical distribution of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform region-specific analysis considering the geographical distribution of the dataset. The analysis unit can also consider geographical factors to grasp region-specific trends. The analysis unit can also visually display geographical data to clarify differences between regions. This makes it easier to grasp region-specific trends by performing analysis while considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis while considering the geographical distribution of the data when analyzing the characteristics of a dataset.

[0108] The proposal unit can estimate the user's emotions and adjust the complexity of the proposed statistical model based on the estimated user emotions. For example, the proposal unit can estimate the user's emotions and adjust the complexity of the proposed statistical model based on the estimated user emotions. For example, if the user is stressed, the proposal unit can propose a simple statistical model. If the user is relaxed, the proposal unit can also propose a detailed statistical model. If the user is in a hurry, the proposal unit can propose a simplified statistical model to obtain results quickly. This allows for the proposal of a more appropriate model by adjusting the complexity of the proposed statistical model according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not using AI. For example, the proposal unit can use AI to estimate the user's emotions and adjust the complexity of the proposed statistical model based on the estimated user emotions.

[0109] The application unit can apply a method to emphasize the impact of outliers in the dataset when applying a statistical model. For example, the application unit can apply a method to emphasize the impact of outliers in the dataset when applying a statistical model. For example, the application unit can provide an application method that excludes outliers in order to minimize the impact of outliers. The application unit can also evaluate the impact of outliers and adjust the application method. The application unit can also identify the causes of outliers and take measures to minimize their impact. By minimizing the impact of outliers in the dataset, more accurate analysis results can be obtained. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to apply a method to emphasize the impact of outliers in the dataset when applying a statistical model.

[0110] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, the visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit can provide a simple graph. If the user is relaxed, the visualization unit can also provide a detailed graph. If the user is in a hurry, the visualization unit can provide a simplified graph to obtain results quickly. This allows for more appropriate visualization by adjusting the visualization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can use AI to estimate the user's emotions and adjust the visualization method based on the estimated emotions.

[0111] The analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform analysis based on the temporal variation of the data when analyzing the characteristics of a dataset. For example, the analysis unit can perform time series analysis considering the temporal variation of the dataset. The analysis unit can also perform seasonal adjustment considering the seasonal variation of the data. The analysis unit can also analyze the trends of the data and grasp long-term variations. By performing analysis while considering the temporal variation of the data, more accurate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform analysis while considering the temporal variation of the data when analyzing the characteristics of a dataset.

[0112] The proposal unit can select an appropriate statistical model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, the proposal unit can select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset. For example, if the dataset is large, the proposal unit can select an efficient statistical model. If the dataset is complex, the proposal unit can also select a detailed statistical model. The proposal unit can also select a balanced statistical model, taking into account the size and complexity of the dataset. This allows for more effective analysis by selecting the optimal model considering the size and complexity of the dataset. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can use AI to select an appropriate model when selecting a proposed statistical model, taking into account the size and complexity of the dataset.

[0113] The application unit can apply statistical models based on real-time updates of the dataset. For example, the application unit can apply statistical models based on real-time updates of the dataset. The application unit can adjust the application method considering real-time updates of the dataset. The application unit can also reapply statistical models based on real-time updated data. The application unit can also evaluate the impact of real-time updates and optimize the application method. This enables analysis based on the latest data by considering real-time updates of the dataset during application. Some or all of the above processing in the application unit may be performed using AI, for example, or without AI. For example, the application unit can use AI to consider real-time updates of the dataset when applying statistical models.

[0114] The visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can perform visualizations based on the temporal variation of the dataset. For example, the visualization unit can provide a time-series graph that takes into account the temporal variation of the dataset. The visualization unit can also provide a seasonally adjusted graph that takes into account the seasonal variation of the data. The visualization unit can also provide a graph that visualizes data trends and helps to understand long-term variations. By performing visualizations that take into account the temporal variation of the dataset, more accurate visualization results can be obtained. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can use AI to perform visualizations that take into account the temporal variation of the dataset.

[0115] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important analysis results. If the user is relaxed, the analysis unit can also sequentially display detailed analysis results. If the user is in a hurry, the analysis unit can also display only the main analysis results to obtain results quickly. This allows for the prioritization of important results by determining the priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use AI to estimate the user's emotions and determine the priority of analysis results based on the estimated emotions.

[0116] The proposal unit can select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select a model based on the user's industry-specific requirements when selecting a statistical model to propose. For example, the proposal unit can select an industry-standard statistical model considering the user's industry-specific requirements. The proposal unit can also develop a customized statistical model considering industry-specific requirements. The proposal unit can also optimize the model selection criteria considering industry-specific requirements. This allows the proposal unit to propose a model suitable for the industry by selecting a model considering the user's industry-specific requirements. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can use AI to select a statistical model based on the user's industry-specific requirements when selecting a statistical model to propose.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The analysis unit automatically analyzes the characteristics and distribution of the dataset. For example, the analysis unit analyzes the data type, distribution, and presence of missing values, calculates basic statistics for each variable in the dataset (mean, median, standard deviation, etc.), and visually displays the data distribution. Step 2: The proposal unit proposes the optimal statistical model based on the analysis results obtained by the analysis unit. For example, the proposal unit selects the most suitable statistical model, such as regression analysis, clustering, or classification, and proposes a regression model by analyzing the correlation between variables in the dataset, or groups the dataset using a clustering algorithm. Step 3: The application unit applies the statistical model proposed by the proposal unit to the dataset. The application unit, for example, analyzes the dataset using the selected statistical model and outputs the results. The application unit calculates predicted values ​​using a regression model and displays the results in graph or tabular format. Step 4: The visualization unit interprets and visualizes the results obtained by the application unit. The visualization unit provides tools for interpreting the analysis results in an easy-to-understand manner and displaying them visually. The visualization unit displays the analysis results in formats such as graphs and heatmaps, enabling users to intuitively understand the results.

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

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0122] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the proposal unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the application unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. For example, the visualization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the proposal unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the application unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. For example, the visualization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. For example, the proposal unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. For example, the application unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. For example, the visualization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

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

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the proposal unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the application unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. For example, the visualization unit is implemented by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

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

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) An analysis unit that automatically analyzes the characteristics and distribution of the dataset, A proposal unit proposes a statistical model based on the analysis results obtained by the analysis unit, An application unit that applies the statistical model proposed by the proposal unit to a dataset, The system includes a visualization unit that interprets and visualizes the results obtained by the application unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the data type, distribution, and presence of missing values. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Choose a statistical model: regression analysis, clustering, or classification. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned application unit is The dataset is analyzed using the selected statistical model, and the results are output. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned visualization unit, Display analysis results in the form of graphs or heatmaps. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Calculate basic statistics for each variable in the dataset and display the data distribution. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We analyze the correlations between variables in a dataset and propose a regression model. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned application unit is The regression model is used to calculate predicted values, and the results are displayed in graphs and tables. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned visualization unit, Provides tools for interpreting and displaying analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, We estimate user sentiment and adjust the dataset analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing the characteristics of a dataset, the analysis is performed based on the temporal variation of the data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing the characteristics of a dataset, we analyze the detection of outliers and their impact. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing a dataset, the analysis is performed based on data characteristics specific to the user's industry. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing datasets, we improve the analysis method by referring to the user's past analysis history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the type of statistical model suggested based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When selecting a statistical model to propose, choose an appropriate model considering the size and complexity of the dataset. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When selecting a statistical model to propose, the model is chosen by referring to the historical performance of the dataset. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate user sentiment and determine the priority of proposed statistical models based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When selecting a statistical model to propose, the model should be chosen based on the user's industry-specific requirements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When selecting a statistical model to propose, we refer to the user's past model usage history to select the most appropriate model. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned application unit is We estimate the user's emotions and adjust how the statistical model is applied based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned application unit is When applying statistical models, the application is based on real-time updates of the dataset. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned application unit is When applying statistical models, apply methods to reduce the impact of outliers in the dataset. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned application unit is It estimates user sentiment and prioritizes the application of statistical models based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned application unit is When applying statistical models, the application should be based on the data characteristics specific to the user's industry. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned application unit is When applying statistical models, we refer to the user's past application history to improve the application method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned visualization unit, When visualizing, the visualization is based on the temporal variation of the dataset. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visualization unit, When visualizing, apply a method to highlight the impact of outliers. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned visualization unit, The system estimates the user's emotions and prioritizes the visualization results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned visualization unit, When performing visualization, the visualization should be based on the data characteristics specific to the user's industry. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned visualization unit, When creating visualizations, we refer to the user's past visualization history to improve the visualization method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. An analysis unit that automatically analyzes the characteristics and distribution of the dataset, A proposal unit proposes a statistical model based on the analysis results obtained by the analysis unit, An application unit that applies the statistical model proposed by the proposal unit to a dataset, The system includes a visualization unit that interprets and visualizes the results obtained by the application unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the data type, distribution, and presence of missing values. The system according to feature 1.

3. The aforementioned proposal section is, Choose a statistical model: regression analysis, clustering, or classification. The system according to feature 1.

4. The aforementioned application unit is The dataset is analyzed using the selected statistical model, and the results are output. The system according to feature 1.

5. The aforementioned visualization unit, Display analysis results in the form of graphs or heatmaps. The system according to feature 1.

6. The aforementioned analysis unit, Calculate basic statistics for each variable in the dataset and display the data distribution. The system according to feature 1.

7. The aforementioned proposal section is, We analyze the correlations between variables in a dataset and propose a regression model. The system according to feature 1.

8. The aforementioned application unit is The regression model is used to calculate predicted values, and the results are displayed in graphs and tables. The system according to feature 1.

Images