System

Abstract

An object of a system according to an embodiment is to improve the efficiency of data analysis and to select an appropriate analysis method.SOLUTION: A system according to an embodiment includes a reception part, an analysis part, an analysis part and a provision part. The reception unit inputs data. The analysis unit analyzes the data input by the reception unit and selects an appropriate analysis method. The analysis unit analyzes the data based on the method selected by the analysis unit. The providing unit provides an analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to efficiently analyze large amounts of data and select an appropriate analysis method.

[0005] The system according to the embodiment aims to improve the efficiency of data analysis and to select an appropriate analysis method. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, an analysis section, and a providing unit. The reception unit inputs data. The analysis unit analyzes the data input by the reception unit and selects an appropriate analysis method. The analysis unit analyzes the data based on the method selected by the analysis unit. The providing unit provides the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of data analysis and select an appropriate analysis method. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A data analysis system according to an embodiment of the present invention inputs data, analyzes it with a generation AI, selects the optimal data analysis method, and provides numerical analysis results. In this data analysis system, a user inputs data, and the generation AI analyzes the input data and selects the optimal data analysis method. Examples of methods include regression analysis, clustering, and classification. The generation AI analyzes the data based on the selected method and provides numerical analysis results. The generation AI then provides the analysis results to the user. The user can make decisions based on the provided analysis results. For example, if sales data is input, the generation AI analyzes sales trends and provides future sales forecasts. This allows the user to develop effective sales strategies. Furthermore, the generation AI ensures consistency of the analysis results. While manual analysis can vary in results due to the analyst's skill and experience, the generation AI applies the same method to the same data, ensuring consistency of results. This allows users to obtain reliable analysis results. This system allows even users without analytical knowledge to easily perform data analysis, improving the data utilization capabilities of the entire organization. For example, if a marketing department employee inputs customer data and a generation AI performs customer segmentation, the accuracy of target marketing will improve. Furthermore, if management inputs financial data and a generation AI performs financial analysis, the data will be useful in formulating business strategies. This data analysis system is expected to improve the quality and effectiveness of decision-making and have a significant impact on organizational strategy planning and performance improvement. This allows users to input data, have the generation AI analyze it, select the optimal data analysis method, and provide numerical analysis results. For example, by analyzing sales data trends and providing future sales forecasts, users can formulate effective sales strategies. Furthermore, customer segmentation based on customer data will improve the accuracy of target marketing. Furthermore, financial analysis based on financial data will be useful in formulating business strategies.This allows data analysis systems to improve the quality and effectiveness of decision-making, and can have a significant impact on organizational strategy planning and performance improvement.

[0029] A data analysis system according to an embodiment includes a receiving unit, an analysis unit, and a providing unit. The receiving unit receives data from a user. The data includes, but is not limited to, numerical data, text data, and image data. The receiving unit receives the data entered by the user and passes it to the analysis unit. The analysis unit uses a generation AI to analyze the data entered by the receiving unit and selects an optimal analysis method. Examples of analysis methods include, but are not limited to, regression analysis, clustering, and classification. For example, the generation AI selects an appropriate analysis method depending on the type and purpose of the data. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI and selects an optimal method. The analysis unit analyzes the data based on the method selected by the analysis unit. The analysis is performed using, for example, regression analysis, clustering, classification, and other methods, but is not limited to these examples. For example, the generation AI analyzes the data based on the selected method and generates a numerical analysis result. The providing unit provides the analysis result obtained by the analysis unit to a user. The analysis results may be provided in the form of a report, a dashboard display, or other methods, but are not limited to these examples. For example, the generation AI generates the analysis results in the form of a report and provides them to the user. The generation AI can also display the analysis results in real time using a dashboard display. This allows the data analysis system according to the embodiment to automate the process from data input to analysis, analysis, and provision, thereby achieving efficient data analysis. For example, a user inputs data, the generation AI analyzes it, selects the optimal method, and provides the analysis results, enabling rapid and accurate data analysis. This allows the user to obtain reliable analysis results, improving the quality and effectiveness of decision-making.

[0030] The analysis unit can select a method from regression analysis, clustering, and classification. The analysis unit selects, for example, regression analysis. Examples of regression analysis include, but are not limited to, linear regression and logistic regression. For example, the generation AI can select linear regression to analyze data trends and make future predictions. The generation AI can also select logistic regression for binary classification problems. The analysis unit selects, for example, clustering. Examples of clustering include, but are not limited to, K-means and hierarchical clustering. For example, the generation AI can select K-means to group data and aggregate similar data. The generation AI can also select hierarchical clustering to analyze the hierarchical structure of data. The analysis unit selects, for example, classification. Examples of classification include, but are not limited to, decision trees and support vector machines. For example, the generation AI can select a decision tree to classify data into categories. The generation AI can also select a support vector machine for classifying high-dimensional data. This allows the analysis unit to select from a variety of data analysis methods to perform optimal analysis according to the characteristics of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data into a generation AI and have the generation AI select the optimal analysis method.

[0031] The providing unit can analyze trends in sales data and provide future sales forecasts. The providing unit, for example, analyzes trends in sales data. Sales data includes, but is not limited to, monthly sales and sales by product. For example, the generation AI can perform time series analysis of sales data to extract trends. The generation AI can also analyze seasonal fluctuations in sales data to forecast future sales. The providing unit, for example, provides future sales forecasts. Forecasts include, but are not limited to, forecasting models and simulations. For example, the generation AI can build forecasting models based on sales data to forecast future sales. The generation AI can also use simulations to forecast sales based on different scenarios. This enables the providing unit to develop effective sales strategies by analyzing and forecasting trends in sales data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input sales data to the generation AI and cause the generation AI to generate a sales forecast.

[0032] The providing unit can classify customers based on the customer data. The providing unit, for example, classifies customers based on the customer data. Customer data includes, for example, purchase history and customer attribute information, but is not limited to these examples. For example, the generation AI can cluster the customer data and group similar customers. The generation AI can also perform segmentation based on the customer data to improve the accuracy of target marketing. As a result, the providing unit improves the accuracy of target marketing through segmentation based on the customer data. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input customer data to the generation AI and have the generation AI perform customer classification.

[0033] The providing unit can perform financial analysis based on the financial data. The providing unit, for example, performs financial analysis based on the financial data. Financial data includes, for example, income statements and balance sheets, but is not limited to these examples. For example, the generation AI can perform financial ratio analysis on the financial data to evaluate the financial situation of a company. The generation AI can also perform cash flow analysis on the financial data to evaluate the company's cash flow. This allows the providing unit to use analysis based on the financial data to help formulate a management strategy. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input financial data into the generation AI and have the generation AI perform financial analysis.

[0034] The reception unit can analyze the user's past data input history and select the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the generation AI can analyze the user's past input history and select the optimal input method. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the generation AI can suggest the optimal input method for a specific time period based on the user's input history. The reception unit can also analyze the format of data previously input by the user and suggest the optimal input format. For example, the generation AI can analyze the user's input data and suggest the optimal input format. As a result, the reception unit selects the optimal input method based on the user's past data input history, thereby improving the user's input efficiency. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0035] The reception unit can filter data based on the user's current project or area of ​​interest when inputting data. For example, the reception unit displays only data related to the project the user is currently working on on the input screen. For example, the generation AI can analyze the user's project information and filter related data. The reception unit can also prioritize input of related data based on the user's area of ​​interest. For example, the generation AI can analyze the user's area of ​​interest and filter related data. The reception unit can also filter and display related data based on areas in which the user has previously shown interest. For example, the generation AI can analyze the user's past activity history and filter related data. This enables the reception unit to input highly relevant data by filtering based on the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project information and area of ​​interest data to the generation AI and have the generation AI perform filtering.

[0036] When inputting data, the reception unit can select the optimal input means depending on the user's input method. For example, if the user requests voice input, the reception unit inputs data using voice recognition technology. For example, the generation AI can analyze the user's voice and convert it into text data. Furthermore, if the user requests text input, the reception unit can also prioritize keyboard input. For example, the generation AI can analyze the user's input method and select the optimal input means. Furthermore, if the user requests image input, the reception unit can input data using image recognition technology. For example, the generation AI can analyze the user's image data and convert it into text data. This allows the reception unit to select the optimal input means depending on the user's input method, thereby improving the efficiency of data input. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0037] When inputting data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting data related to that area. For example, the generation AI can analyze the user's geographical location information and filter relevant data. Furthermore, if the user is moving, the reception unit can also input relevant data based on the user's current location. For example, the generation AI can analyze the user's current location information and filter relevant data. Furthermore, if the user is in a specific location, the reception unit can also prioritize inputting data related to that location. For example, the generation AI can analyze the user's location information and filter relevant data. This allows the reception unit to efficiently input highly relevant data by inputting data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data to the generation AI and have the generation AI filter the relevant data.

[0038] The reception unit can analyze the user's social media activity and input relevant data when inputting data. The reception unit can input relevant data based on, for example, information shared by the user on social media. For example, the generation AI can analyze the user's social media activity and filter relevant data. The reception unit can also analyze the user's social media activity history and input relevant data. For example, the generation AI can analyze the user's social media activity history and filter relevant data. The reception unit can also input relevant data based on the activities of the user's friends on social media. For example, the generation AI can analyze the social media activity of the user's friends and filter relevant data. This allows the reception unit to efficiently input highly relevant data by inputting data based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and have the generation AI filter the relevant data.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when entering data. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the generation AI can analyze the user's past feedback and suggest an optimal input method. The reception unit can also customize and provide an input method based on the user's past feedback. For example, the generation AI can customize the input method based on the user's feedback. The reception unit can also avoid input methods that the user has previously expressed dissatisfaction with and provide an input method that provides high satisfaction. For example, the generation AI can analyze the user's feedback and avoid input methods that the user has previously expressed dissatisfaction with. This allows the reception unit to customize the input method based on the user's past feedback, thereby improving user satisfaction. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to the generation AI and cause the generation AI to customize the input method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis method based on the importance of the data. For example, the analysis unit applies a detailed analysis method to data with high importance. For example, the generation AI can analyze the importance of the data and select a detailed analysis method. The analysis unit can also apply a simple analysis method to data with low importance. For example, the generation AI can analyze the importance of the data and select a simple analysis method. The analysis unit can also gradually adjust the level of detail of the analysis method according to the importance of the data. For example, the generation AI can analyze the importance of the data and adjust the level of detail. This enables the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis method according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis method.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies regression analysis to numerical data. For example, the generation AI can analyze the numerical data and apply regression analysis. The analysis unit can also apply natural language processing to text data. For example, the generation AI can analyze the text data and apply natural language processing. The analysis unit can also apply an image recognition algorithm to image data. For example, the generation AI can analyze the image data and apply an image recognition algorithm. This enables the analysis unit to perform appropriate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis method based on the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and select the optimal analysis method. The analysis unit can also select a highly accurate analysis method from the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and select a highly accurate analysis method. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and improve the accuracy of the analysis. As a result, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis methods based on the time of data submission. The analysis unit, for example, prioritizes application of analysis methods to the most recent data. For example, the generation AI can analyze the time of data submission and prioritize application of analysis methods to the most recent data. The analysis unit can also postpone application of analysis methods to older data. For example, the generation AI can analyze the time of data submission and postpone application of analysis methods to older data. The analysis unit can also gradually adjust the priority of analysis methods depending on the time of data submission. For example, the generation AI can analyze the time of data submission and adjust the priority. This enables the analysis unit to perform efficient analysis by determining the priority of analysis methods based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of analysis methods.

[0044] During analysis, the analysis unit can adjust the order of analysis methods based on the relevance of the data. The analysis unit, for example, prioritizes application of analysis methods to highly relevant data. For example, the generation AI can analyze the relevance of the data and prioritize application of analysis methods to highly relevant data. The analysis unit can also postpone application of analysis methods to less relevant data. For example, the generation AI can analyze the relevance of the data and postpone application of analysis methods to less relevant data. The analysis unit can also gradually adjust the order of analysis methods according to the relevance of the data. For example, the generation AI can analyze the relevance of the data and adjust the order. This enables the analysis unit to perform efficient analysis by adjusting the order of analysis methods based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of the analysis methods.

[0045] During analysis, the analysis unit can adjust the selection of the analysis method according to the user's level of expertise. For example, if the user has expertise, the analysis unit applies a detailed analysis method. For example, the generation AI can analyze the user's level of expertise and select a detailed analysis method. Furthermore, if the user does not have expertise, the analysis unit can apply a simple analysis method. For example, the generation AI can analyze the user's level of expertise and select a simple analysis method. Furthermore, the analysis unit can gradually adjust the selection of the analysis method according to the user's level of expertise. For example, the generation AI can analyze the user's level of expertise and adjust the selection of the analysis method. This enables the analysis unit to perform an appropriate analysis by selecting an analysis method according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI select an analysis method.

[0046] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit, for example, analyzes the interrelationships between data and prioritizes the analysis of highly correlated data. For example, the generation AI can analyze the interrelationships between data and prioritize the analysis of highly correlated data. The analysis unit can also optimize the analysis method by taking into account the interrelationships between data. For example, the generation AI can analyze the interrelationships between data and select the optimal analysis method. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between data. For example, the generation AI can analyze the interrelationships between data and improve the accuracy of the analysis. As a result, the analysis unit improves the accuracy of the analysis by taking the interrelationships between data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. The analysis unit, for example, selects the optimal analysis method based on the attribute information of the data submitter. For example, the generation AI can analyze the attribute information of the data submitter and select the optimal analysis method. The analysis unit can also improve the accuracy of the analysis by taking into account the attribute information of the data submitter. For example, the generation AI can analyze the attribute information of the data submitter and improve the accuracy of the analysis. The analysis unit can also customize the analysis results based on the attribute information of the data submitter. For example, the generation AI can analyze the attribute information of the data submitter and customize the analysis results. This enables the analysis unit to perform a more appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the attribute information data of the data submitter into the generation AI and have the generation AI perform the analysis.

[0048] During analysis, the analysis unit can weight the analysis based on the frequency of data submission. For example, the analysis unit may assign a higher weight to data submitted frequently. For example, the generation AI may analyze the frequency of data submission and assign a higher weight to the data for analysis. The analysis unit may also assign a lower weight to data submitted infrequently. For example, the generation AI may analyze the frequency of data submission and assign a lower weight to the data for analysis. The analysis unit may also gradually adjust the weighting of the analysis according to the frequency of data submission. For example, the generation AI may analyze the frequency of data submission and adjust the weighting. This enables the analysis unit to perform efficient analysis by weighting based on the frequency of data submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data on the frequency of data submission to the generation AI and have the generation AI adjust the weighting.

[0049] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit analyzes the geographical distribution of the data and considers the characteristics of each region when performing the analysis. For example, the generation AI can analyze the geographical distribution of the data and consider the characteristics of each region when performing the analysis. The analysis unit can also compare and analyze data from different regions based on the geographical distribution. For example, the generation AI can analyze the geographical distribution of the data and compare and analyze data from different regions. The analysis unit can also provide analysis results for each region taking into account the geographical distribution. For example, the generation AI can analyze the geographical distribution of the data and provide analysis results for each region. This enables the analysis unit to perform an analysis that reflects the characteristics of each region by considering the geographical distribution of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input geographical distribution data of the data to the generation AI and have the generation AI perform the analysis.

[0050] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. For example, the analysis unit can refer to literature related to the data and perform the analysis based on the latest research results. For example, the generation AI can analyze literature related to the data and perform the analysis based on the latest research results. The analysis unit can also optimize the analysis method based on the related literature. For example, the generation AI can analyze literature related to the data and select the optimal analysis method. The analysis unit can also improve the reliability of the analysis results by referring to the related literature. For example, the generation AI can analyze literature related to the data and improve the reliability of the analysis results. As a result, the analysis unit can perform a highly accurate analysis based on the latest research results by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input literature data related to the data into the generation AI and have the generation AI perform the analysis.

[0051] The analysis unit can perform the analysis taking into account the market value of the data. For example, the analysis unit evaluates the market value of the data and prioritizes analysis of high-value data. For example, the generation AI can analyze the market value of the data and prioritize analysis of high-value data. The analysis unit can also optimize the analysis method based on the market value. For example, the generation AI can analyze the market value of the data and select the optimal analysis method. The analysis unit can also provide analysis results taking into account the market value of the data. For example, the generation AI can analyze the market value of the data and provide the analysis results. This allows the analysis unit to prioritize analysis of high-value data by considering the market value of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input market value data of the data to the generation AI and have the generation AI perform the analysis.

[0052] The providing unit can adjust the level of detail of the provided information based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for analysis results with high importance. For example, the generation AI can analyze the importance of the analysis result and provide detailed information. The providing unit can also provide simple information for analysis results with low importance. For example, the generation AI can analyze the importance of the analysis result and provide simple information. The providing unit can also gradually adjust the level of detail of the provided information according to the importance of the analysis result. For example, the generation AI can analyze the importance of the analysis result and adjust the level of detail. This enables the providing unit to provide appropriate information by adjusting the level of detail according to the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail.

[0053] The providing unit can apply different providing algorithms depending on the category of the analysis results when providing the results. For example, the providing unit can provide the analysis results of numerical data using graphs or charts. For example, the generation AI can analyze the numerical data and generate graphs or charts. The providing unit can also provide the analysis results of text data using summaries or keywords. For example, the generation AI can analyze the text data and extract summaries or keywords. The providing unit can also provide the analysis results of image data using visual effects. For example, the generation AI can analyze the image data and generate visual effects. This enables the providing unit to provide appropriate information by applying a providing algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the analysis results to the generation AI and cause the generation AI to apply the providing algorithm.

[0054] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. The providing unit, for example, optimizes the provision method based on the provision results received by the user in the past. For example, the generation AI can analyze the user's past provision results and select the optimal provision method. The providing unit can also select a highly accurate provision method from the user's past provision results. For example, the generation AI can analyze the user's past provision results and select a highly accurate provision method. The providing unit can also improve the accuracy of the provision by referring to the user's past provision results. For example, the generation AI can analyze the user's past provision results and improve the accuracy of the provision. As a result, the providing unit improves the accuracy of the provision by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0055] The providing unit can determine the priority of provision based on the time of submission of the analysis results when the analysis results are provided. The providing unit, for example, prioritizes providing the most recent analysis results. For example, the generation AI can analyze the time of submission of the analysis results and prioritize providing the most recent analysis results. The providing unit can also postpone providing older analysis results. For example, the generation AI can analyze the time of submission of the analysis results and postpone providing older analysis results. The providing unit can also gradually adjust the priority of provision depending on the time of submission of the analysis results. For example, the generation AI can analyze the time of submission of the analysis results and adjust the priority. This enables the providing unit to provide information efficiently by determining the priority of provision based on the time of submission of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the time of submission of the analysis results to the generation AI and have the generation AI determine the priority of provision.

[0056] The providing unit can adjust the order of provision based on the relevance of the analysis results when providing the results. For example, the providing unit can prioritize providing highly relevant analysis results. For example, the generation AI can analyze the relevance of the analysis results and prioritize providing highly relevant analysis results. The providing unit can also postpone providing less relevant analysis results. For example, the generation AI can analyze the relevance of the analysis results and postpone providing less relevant analysis results. The providing unit can also gradually adjust the order of provision based on the relevance of the analysis results. For example, the generation AI can analyze the relevance of the analysis results and adjust the order. This enables the providing unit to provide information efficiently by adjusting the order of provision based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0057] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information using a lot of technical terminology. For example, the generation AI can analyze the user's level of expertise and provide information using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide information in simpler terms. For example, the generation AI can analyze the user's level of expertise and provide information in simpler terms. Furthermore, the providing unit can gradually adjust the use of technical terminology provided according to the user's level of expertise. For example, the generation AI can analyze the user's level of expertise and adjust the use of technical terminology. This enables the providing unit to provide appropriate information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

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

[0059] The reception unit can select the optimal data preprocessing method depending on the type of data input by the user. For example, normalization and standardization can be performed on numerical data. Furthermore, tokenization and stop word removal can be performed on text data. Furthermore, resizing and noise removal can be performed on image data. In this way, the reception unit can improve the analysis accuracy of the analysis unit by performing preprocessing according to the type of data. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select a preprocessing method.

[0060] The analysis unit can evaluate the reliability of the data and exclude unreliable data. For example, if the data contains many missing values ​​or many outliers, the data can be excluded from the analysis. Data can also be excluded as unreliable data if the source of the data is unclear or if there are problems with the data collection method. Furthermore, the analysis unit can check the consistency and integrity of the data to evaluate its reliability. This allows the analysis unit to perform analysis using only highly reliable data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can have the generation AI perform the data reliability evaluation.

[0061] The providing unit can analyze the user's past behavioral history and provide the analysis results at the optimal timing. For example, if the user previously checked the analysis results at a specific time period, the analysis results can be provided according to that time period. Furthermore, if the user previously checked the analysis results on a specific day of the week, the analysis results can be provided according to that day of the week. Furthermore, if the user previously checked the analysis results after a specific event, the analysis results can be provided according to the event. This allows the providing unit to provide the analysis results at the optimal timing based on the user's behavioral history, thereby improving user convenience. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's behavioral history data into a generation AI and cause the generation AI to select the optimal timing.

[0062] The analysis unit can analyze correlations in data and prioritize analysis of highly correlated data. For example, the analysis unit can analyze the correlation between sales data and advertising expense data and prioritize analysis of highly correlated data. The analysis unit can also analyze the correlation between customer data and purchase history data and prioritize analysis of highly correlated data. Furthermore, the analysis unit can analyze the correlation between financial data and market data and prioritize analysis of highly correlated data. This enables the analysis unit to perform efficient analysis by setting priorities based on data correlations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on data correlations into a generation AI and have the generation AI set the priorities.

[0063] The reception unit can select the optimal data conversion method depending on the format of the user's input data. For example, scaling and normalization can be performed on numerical data. Furthermore, vectorization and encoding can be performed on text data. Furthermore, pixel value normalization and filtering can be performed on image data. In this way, the reception unit can improve the analysis accuracy of the analysis unit by performing conversion according to the data format. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select a conversion method.

[0064] The providing unit can customize the format of the analysis results to be provided based on the user's past feedback. For example, it can provide formats that the user has previously preferred (graphs, charts, text, etc.) preferentially. It can also avoid formats that the user has previously expressed dissatisfaction with. Furthermore, it can suggest new formats based on the user's past feedback. In this way, the providing unit can improve user satisfaction by customizing the format based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI and cause the generating AI to customize the format.

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

[0066] Step 1: The user inputs data into the reception unit. The data includes numerical data, text data, image data, etc. The reception unit receives the data input by the user and passes it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the data entered by the reception unit and select the optimal analysis method. Analysis methods include regression analysis, clustering, classification, etc. The generation AI selects the appropriate analysis method depending on the type of data and purpose. Step 3: The analysis unit analyzes the data based on the method selected by the analysis unit. The analysis is performed using methods such as regression analysis, clustering, and classification. The generation AI analyzes the data based on the selected method and generates numerical analysis results. Step 4: The provision unit provides the analysis results obtained by the analysis unit to the user. The provision is performed in the form of a report, a dashboard display, or other methods. The generation AI generates the analysis results in the form of a report and provides it to the user. The analysis results can also be displayed in real time using a dashboard display.

[0067] (Example 2) A data analysis system according to an embodiment of the present invention inputs data, analyzes it with a generation AI, selects the optimal data analysis method, and provides numerical analysis results. In this data analysis system, a user inputs data, and the generation AI analyzes the input data and selects the optimal data analysis method. Examples of methods include regression analysis, clustering, and classification. The generation AI analyzes the data based on the selected method and provides numerical analysis results. The generation AI then provides the analysis results to the user. The user can make decisions based on the provided analysis results. For example, if sales data is input, the generation AI analyzes sales trends and provides future sales forecasts. This allows the user to develop effective sales strategies. Furthermore, the generation AI ensures consistency of the analysis results. While manual analysis can vary in results due to the analyst's skill and experience, the generation AI applies the same method to the same data, ensuring consistency of results. This allows users to obtain reliable analysis results. This system allows even users without analytical knowledge to easily perform data analysis, improving the data utilization capabilities of the entire organization. For example, if a marketing department employee inputs customer data and a generation AI performs customer segmentation, the accuracy of target marketing will improve. Furthermore, if management inputs financial data and a generation AI performs financial analysis, the data will be useful in formulating business strategies. This data analysis system is expected to improve the quality and effectiveness of decision-making and have a significant impact on organizational strategy planning and performance improvement. This allows users to input data, have the generation AI analyze it, select the optimal data analysis method, and provide numerical analysis results. For example, by analyzing sales data trends and providing future sales forecasts, users can formulate effective sales strategies. Furthermore, customer segmentation based on customer data will improve the accuracy of target marketing. Furthermore, financial analysis based on financial data will be useful in formulating business strategies.This allows data analysis systems to improve the quality and effectiveness of decision-making, and can have a significant impact on organizational strategy planning and performance improvement.

[0068] A data analysis system according to an embodiment includes a receiving unit, an analysis unit, and a providing unit. The receiving unit receives data from a user. The data includes, but is not limited to, numerical data, text data, and image data. The receiving unit receives the data entered by the user and passes it to the analysis unit. The analysis unit uses a generation AI to analyze the data entered by the receiving unit and selects an optimal analysis method. Examples of analysis methods include, but are not limited to, regression analysis, clustering, and classification. For example, the generation AI selects an appropriate analysis method depending on the type and purpose of the data. The generation AI analyzes the data using a text generation AI (e.g., LLM) or a multimodal generation AI and selects an optimal method. The analysis unit analyzes the data based on the method selected by the analysis unit. The analysis is performed using, for example, regression analysis, clustering, classification, and other methods, but is not limited to these examples. For example, the generation AI analyzes the data based on the selected method and generates a numerical analysis result. The providing unit provides the analysis result obtained by the analysis unit to a user. The analysis results may be provided in the form of a report, a dashboard display, or other methods, but are not limited to these examples. For example, the generation AI generates the analysis results in the form of a report and provides them to the user. The generation AI can also display the analysis results in real time using a dashboard display. This allows the data analysis system according to the embodiment to automate the process from data input to analysis, analysis, and provision, thereby achieving efficient data analysis. For example, a user inputs data, the generation AI analyzes it, selects the optimal method, and provides the analysis results, enabling rapid and accurate data analysis. This allows the user to obtain reliable analysis results, improving the quality and effectiveness of decision-making.

[0069] The analysis unit can select a method from regression analysis, clustering, and classification. The analysis unit selects, for example, regression analysis. Examples of regression analysis include, but are not limited to, linear regression and logistic regression. For example, the generation AI can select linear regression to analyze data trends and make future predictions. The generation AI can also select logistic regression for binary classification problems. The analysis unit selects, for example, clustering. Examples of clustering include, but are not limited to, K-means and hierarchical clustering. For example, the generation AI can select K-means to group data and aggregate similar data. The generation AI can also select hierarchical clustering to analyze the hierarchical structure of data. The analysis unit selects, for example, classification. Examples of classification include, but are not limited to, decision trees and support vector machines. For example, the generation AI can select a decision tree to classify data into categories. The generation AI can also select a support vector machine for classifying high-dimensional data. This allows the analysis unit to select from a variety of data analysis methods to perform optimal analysis according to the characteristics of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data into a generation AI and have the generation AI select the optimal analysis method.

[0070] The providing unit can analyze trends in sales data and provide future sales forecasts. The providing unit, for example, analyzes trends in sales data. Sales data includes, but is not limited to, monthly sales and sales by product. For example, the generation AI can perform time series analysis of sales data to extract trends. The generation AI can also analyze seasonal fluctuations in sales data to forecast future sales. The providing unit, for example, provides future sales forecasts. Forecasts include, but are not limited to, forecasting models and simulations. For example, the generation AI can build forecasting models based on sales data to forecast future sales. The generation AI can also use simulations to forecast sales based on different scenarios. This enables the providing unit to develop effective sales strategies by analyzing and forecasting trends in sales data. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input sales data to the generation AI and cause the generation AI to generate a sales forecast.

[0071] The providing unit can classify customers based on the customer data. The providing unit, for example, classifies customers based on the customer data. Customer data includes, for example, purchase history and customer attribute information, but is not limited to these examples. For example, the generation AI can cluster the customer data and group similar customers. The generation AI can also perform segmentation based on the customer data to improve the accuracy of target marketing. As a result, the providing unit improves the accuracy of target marketing through segmentation based on the customer data. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input customer data to the generation AI and have the generation AI perform customer classification.

[0072] The providing unit can perform financial analysis based on the financial data. The providing unit, for example, performs financial analysis based on the financial data. Financial data includes, for example, income statements and balance sheets, but is not limited to these examples. For example, the generation AI can perform financial ratio analysis on the financial data to evaluate the financial situation of a company. The generation AI can also perform cash flow analysis on the financial data to evaluate the company's cash flow. This allows the providing unit to use analysis based on the financial data to help formulate a management strategy. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input financial data into the generation AI and have the generation AI perform financial analysis.

[0073] The reception unit can estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the timing of data input to allow the user to input in a relaxed state. For example, the generation AI can analyze the user's facial expression to estimate whether the user is feeling stressed. Furthermore, if the user is concentrating, the reception unit can also speed up the timing of data input to allow for efficient input. For example, the generation AI can analyze the user's voice to estimate whether the user is concentrating. Furthermore, if the user is tired, the reception unit can adjust the timing of data input to allow the user to input while taking breaks. For example, the generation AI can analyze the user's biometric data to estimate whether the user is tired. This enables the reception unit to adjust the timing of data input according to the user's emotions, thereby enabling efficient data input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0074] The reception unit can analyze the user's past data input history and select the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. For example, the generation AI can analyze the user's past input history and select the optimal input method. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. For example, the generation AI can suggest the optimal input method for a specific time period based on the user's input history. The reception unit can also analyze the format of data previously input by the user and suggest the optimal input format. For example, the generation AI can analyze the user's input data and suggest the optimal input format. As a result, the reception unit selects the optimal input method based on the user's past data input history, thereby improving the user's input efficiency. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal input method.

[0075] The reception unit can filter data based on the user's current project or area of ​​interest when inputting data. For example, the reception unit displays only data related to the project the user is currently working on on the input screen. For example, the generation AI can analyze the user's project information and filter related data. The reception unit can also prioritize input of related data based on the user's area of ​​interest. For example, the generation AI can analyze the user's area of ​​interest and filter related data. The reception unit can also filter and display related data based on areas in which the user has previously shown interest. For example, the generation AI can analyze the user's past activity history and filter related data. This enables the reception unit to input highly relevant data by filtering based on the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's project information and area of ​​interest data to the generation AI and have the generation AI perform filtering.

[0076] When inputting data, the reception unit can select the optimal input means depending on the user's input method. For example, if the user requests voice input, the reception unit inputs data using voice recognition technology. For example, the generation AI can analyze the user's voice and convert it into text data. Furthermore, if the user requests text input, the reception unit can also prioritize keyboard input. For example, the generation AI can analyze the user's input method and select the optimal input means. Furthermore, if the user requests image input, the reception unit can input data using image recognition technology. For example, the generation AI can analyze the user's image data and convert it into text data. This allows the reception unit to select the optimal input means depending on the user's input method, thereby improving the efficiency of data input. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without AI. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0077] The reception unit can estimate the user's emotions and determine the priority of data to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit postpones data of lower importance and prioritizes input of data of higher importance. For example, the generation AI can analyze the user's facial expression to estimate whether the user is feeling stressed. Furthermore, if the user is relaxed, the reception unit can input all data equally. For example, the generation AI can analyze the user's voice to estimate whether the user is relaxed. Furthermore, if the user is in a hurry, the reception unit can prioritize input of the most important data. For example, the generation AI can analyze the user's biometric data to estimate whether the user is in a hurry. This allows the reception unit to prioritize data according to the user's emotions and prioritize input of important data. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0078] When inputting data, the reception unit can prioritize inputting highly relevant data by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting data related to that area. For example, the generation AI can analyze the user's geographical location information and filter relevant data. Furthermore, if the user is moving, the reception unit can also input relevant data based on the user's current location. For example, the generation AI can analyze the user's current location information and filter relevant data. Furthermore, if the user is in a specific location, the reception unit can also prioritize inputting data related to that location. For example, the generation AI can analyze the user's location information and filter relevant data. This allows the reception unit to efficiently input highly relevant data by inputting data based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information data to the generation AI and have the generation AI filter the relevant data.

[0079] The reception unit can analyze the user's social media activity and input relevant data when inputting data. The reception unit can input relevant data based on, for example, information shared by the user on social media. For example, the generation AI can analyze the user's social media activity and filter relevant data. The reception unit can also analyze the user's social media activity history and input relevant data. For example, the generation AI can analyze the user's social media activity history and filter relevant data. The reception unit can also input relevant data based on the activities of the user's friends on social media. For example, the generation AI can analyze the social media activity of the user's friends and filter relevant data. This allows the reception unit to efficiently input highly relevant data by inputting data based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and have the generation AI filter the relevant data.

[0080] The reception unit can customize the input method by reflecting the user's past feedback when entering data. The reception unit, for example, suggests an optimal input method based on feedback provided by the user in the past. For example, the generation AI can analyze the user's past feedback and suggest an optimal input method. The reception unit can also customize and provide an input method based on the user's past feedback. For example, the generation AI can customize the input method based on the user's feedback. The reception unit can also avoid input methods that the user has previously expressed dissatisfaction with and provide an input method that provides high satisfaction. For example, the generation AI can analyze the user's feedback and avoid input methods that the user has previously expressed dissatisfaction with. This allows the reception unit to customize the input method based on the user's past feedback, thereby improving user satisfaction. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data to the generation AI and cause the generation AI to customize the input method.

[0081] The analysis unit can estimate the user's emotions and adjust the criteria for selecting an analysis method based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit selects a detailed analysis method. For example, the generation AI can analyze the user's facial expressions to estimate whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can select a simple analysis method. For example, the generation AI can analyze the user's voice to estimate whether the user is in a hurry. Furthermore, if the user is excited, the analysis unit can select a visually stimulating analysis method. For example, the generation AI can analyze the user's biometric data to estimate whether the user is excited. This enables the analysis unit to perform appropriate analysis by adjusting the criteria for selecting an analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis method based on the importance of the data. For example, the analysis unit applies a detailed analysis method to data with high importance. For example, the generation AI can analyze the importance of the data and select a detailed analysis method. The analysis unit can also apply a simple analysis method to data with low importance. For example, the generation AI can analyze the importance of the data and select a simple analysis method. The analysis unit can also gradually adjust the level of detail of the analysis method according to the importance of the data. For example, the generation AI can analyze the importance of the data and adjust the level of detail. This enables the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis method according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis method.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies regression analysis to numerical data. For example, the generation AI can analyze the numerical data and apply regression analysis. The analysis unit can also apply natural language processing to text data. For example, the generation AI can analyze the text data and apply natural language processing. The analysis unit can also apply an image recognition algorithm to image data. For example, the generation AI can analyze the image data and apply an image recognition algorithm. This enables the analysis unit to perform appropriate analysis by applying an analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data on the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, optimizes the analysis method based on the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and select the optimal analysis method. The analysis unit can also select a highly accurate analysis method from the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and select a highly accurate analysis method. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the generation AI can analyze the user's past analysis results and improve the accuracy of the analysis. As a result, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the selection order of analysis methods based on the estimated user emotions. For example, if the user is relaxed, the analysis unit prioritizes the application of a detailed analysis method. For example, the generation AI can analyze the user's facial expression to estimate whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can prioritize the application of a simple analysis method. For example, the generation AI can analyze the user's voice to estimate whether the user is in a hurry. Furthermore, if the user is excited, the analysis unit can prioritize the application of a visually stimulating analysis method. For example, the generation AI can analyze the user's biometric data to estimate whether the user is excited. This enables the analysis unit to perform appropriate analysis by adjusting the selection order of analysis methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0086] During analysis, the analysis unit can determine the priority of analysis methods based on the time of data submission. The analysis unit, for example, prioritizes application of analysis methods to the most recent data. For example, the generation AI can analyze the time of data submission and prioritize application of analysis methods to the most recent data. The analysis unit can also postpone application of analysis methods to older data. For example, the generation AI can analyze the time of data submission and postpone application of analysis methods to older data. The analysis unit can also gradually adjust the priority of analysis methods depending on the time of data submission. For example, the generation AI can analyze the time of data submission and adjust the priority. This enables the analysis unit to perform efficient analysis by determining the priority of analysis methods based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of analysis methods.

[0087] During analysis, the analysis unit can adjust the order of analysis methods based on the relevance of the data. The analysis unit, for example, prioritizes application of analysis methods to highly relevant data. For example, the generation AI can analyze the relevance of the data and prioritize application of analysis methods to highly relevant data. The analysis unit can also postpone application of analysis methods to less relevant data. For example, the generation AI can analyze the relevance of the data and postpone application of analysis methods to less relevant data. The analysis unit can also gradually adjust the order of analysis methods according to the relevance of the data. For example, the generation AI can analyze the relevance of the data and adjust the order. This enables the analysis unit to perform efficient analysis by adjusting the order of analysis methods based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and cause the generation AI to adjust the order of the analysis methods.

[0088] During analysis, the analysis unit can adjust the selection of the analysis method according to the user's level of expertise. For example, if the user has expertise, the analysis unit applies a detailed analysis method. For example, the generation AI can analyze the user's level of expertise and select a detailed analysis method. Furthermore, if the user does not have expertise, the analysis unit can apply a simple analysis method. For example, the generation AI can analyze the user's level of expertise and select a simple analysis method. Furthermore, the analysis unit can gradually adjust the selection of the analysis method according to the user's level of expertise. For example, the generation AI can analyze the user's level of expertise and adjust the selection of the analysis method. This enables the analysis unit to perform an appropriate analysis by selecting an analysis method according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI select an analysis method.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit applies a detailed analysis method. For example, the generation AI can analyze the user's facial expressions to estimate whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can apply a simple analysis method. For example, the generation AI can analyze the user's voice to estimate whether the user is in a hurry. Furthermore, if the user is excited, the analysis unit can apply a visually stimulating analysis method. For example, the generation AI can analyze the user's biometric data to estimate whether the user is excited. This enables the analysis unit to perform appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0090] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. The analysis unit, for example, analyzes the interrelationships between data and prioritizes the analysis of highly correlated data. For example, the generation AI can analyze the interrelationships between data and prioritize the analysis of highly correlated data. The analysis unit can also optimize the analysis method by taking into account the interrelationships between data. For example, the generation AI can analyze the interrelationships between data and select the optimal analysis method. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between data. For example, the generation AI can analyze the interrelationships between data and improve the accuracy of the analysis. As a result, the analysis unit improves the accuracy of the analysis by taking the interrelationships between data into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. The analysis unit, for example, selects the optimal analysis method based on the attribute information of the data submitter. For example, the generation AI can analyze the attribute information of the data submitter and select the optimal analysis method. The analysis unit can also improve the accuracy of the analysis by taking into account the attribute information of the data submitter. For example, the generation AI can analyze the attribute information of the data submitter and improve the accuracy of the analysis. The analysis unit can also customize the analysis results based on the attribute information of the data submitter. For example, the generation AI can analyze the attribute information of the data submitter and customize the analysis results. This enables the analysis unit to perform a more appropriate analysis by taking into account the attribute information of the data submitter. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the attribute information data of the data submitter into the generation AI and have the generation AI perform the analysis.

[0092] During analysis, the analysis unit can weight the analysis based on the frequency of data submission. For example, the analysis unit may assign a higher weight to data submitted frequently. For example, the generation AI may analyze the frequency of data submission and assign a higher weight to the data for analysis. The analysis unit may also assign a lower weight to data submitted infrequently. For example, the generation AI may analyze the frequency of data submission and assign a lower weight to the data for analysis. The analysis unit may also gradually adjust the weighting of the analysis according to the frequency of data submission. For example, the generation AI may analyze the frequency of data submission and adjust the weighting. This enables the analysis unit to perform efficient analysis by weighting based on the frequency of data submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data on the frequency of data submission to the generation AI and have the generation AI adjust the weighting.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the generation AI can analyze the user's facial expressions to estimate whether the user is nervous. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the generation AI can analyze the user's voice to estimate whether the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the key points. For example, the generation AI can analyze the user's biometric data to estimate whether the user is in a hurry. This enables the analysis unit to provide appropriate analysis results by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0094] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit analyzes the geographical distribution of the data and considers the characteristics of each region when performing the analysis. For example, the generation AI can analyze the geographical distribution of the data and consider the characteristics of each region when performing the analysis. The analysis unit can also compare and analyze data from different regions based on the geographical distribution. For example, the generation AI can analyze the geographical distribution of the data and compare and analyze data from different regions. The analysis unit can also provide analysis results for each region taking into account the geographical distribution. For example, the generation AI can analyze the geographical distribution of the data and provide analysis results for each region. This enables the analysis unit to perform an analysis that reflects the characteristics of each region by considering the geographical distribution of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input geographical distribution data of the data to the generation AI and have the generation AI perform the analysis.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. For example, the analysis unit can refer to literature related to the data and perform the analysis based on the latest research results. For example, the generation AI can analyze literature related to the data and perform the analysis based on the latest research results. The analysis unit can also optimize the analysis method based on the related literature. For example, the generation AI can analyze literature related to the data and select the optimal analysis method. The analysis unit can also improve the reliability of the analysis results by referring to the related literature. For example, the generation AI can analyze literature related to the data and improve the reliability of the analysis results. As a result, the analysis unit can perform a highly accurate analysis based on the latest research results by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input literature data related to the data into the generation AI and have the generation AI perform the analysis.

[0096] The analysis unit can perform the analysis taking into account the market value of the data. For example, the analysis unit evaluates the market value of the data and prioritizes analysis of high-value data. For example, the generation AI can analyze the market value of the data and prioritize analysis of high-value data. The analysis unit can also optimize the analysis method based on the market value. For example, the generation AI can analyze the market value of the data and select the optimal analysis method. The analysis unit can also provide analysis results taking into account the market value of the data. For example, the generation AI can analyze the market value of the data and provide the analysis results. This allows the analysis unit to prioritize analysis of high-value data by considering the market value of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input market value data of the data to the generation AI and have the generation AI perform the analysis.

[0097] The providing unit can estimate the user's emotions and adjust the presentation method of the analysis results to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible presentation method. For example, the generation AI can analyze the user's facial expression to estimate whether the user is nervous. Furthermore, if the user is relaxed, the providing unit can also provide a presentation method that includes detailed information. For example, the generation AI can analyze the user's voice to estimate whether the user is relaxed. Furthermore, if the user is in a hurry, the providing unit can also provide a presentation method that focuses on the main points. For example, the generation AI can analyze the user's biometric data to estimate whether the user is in a hurry. This enables the providing unit to provide appropriate analysis results by adjusting the presentation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0098] The providing unit can adjust the level of detail of the provided information based on the importance of the analysis result when providing the information. For example, the providing unit provides detailed information for analysis results with high importance. For example, the generation AI can analyze the importance of the analysis result and provide detailed information. The providing unit can also provide simple information for analysis results with low importance. For example, the generation AI can analyze the importance of the analysis result and provide simple information. The providing unit can also gradually adjust the level of detail of the provided information according to the importance of the analysis result. For example, the generation AI can analyze the importance of the analysis result and adjust the level of detail. This enables the providing unit to provide appropriate information by adjusting the level of detail according to the importance of the analysis result. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the analysis result to the generation AI and cause the generation AI to adjust the level of detail.

[0099] The providing unit can apply different providing algorithms depending on the category of the analysis results when providing the results. For example, the providing unit can provide the analysis results of numerical data using graphs or charts. For example, the generation AI can analyze the numerical data and generate graphs or charts. The providing unit can also provide the analysis results of text data using summaries or keywords. For example, the generation AI can analyze the text data and extract summaries or keywords. The providing unit can also provide the analysis results of image data using visual effects. For example, the generation AI can analyze the image data and generate visual effects. This enables the providing unit to provide appropriate information by applying a providing algorithm depending on the category of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of the analysis results to the generation AI and cause the generation AI to apply the providing algorithm.

[0100] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the data. The providing unit, for example, optimizes the provision method based on the provision results received by the user in the past. For example, the generation AI can analyze the user's past provision results and select the optimal provision method. The providing unit can also select a highly accurate provision method from the user's past provision results. For example, the generation AI can analyze the user's past provision results and select a highly accurate provision method. The providing unit can also improve the accuracy of the provision by referring to the user's past provision results. For example, the generation AI can analyze the user's past provision results and improve the accuracy of the provision. As a result, the providing unit improves the accuracy of the provision by referring to the user's past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0101] The providing unit can estimate the user's emotions and adjust the length of the analysis results to be provided based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide a short, concise analysis result. For example, the generation AI can analyze the user's facial expression to estimate whether the user is in a hurry. Furthermore, if the user is relaxed, the providing unit can provide a longer analysis result with detailed explanations. For example, the generation AI can analyze the user's voice to estimate whether the user is relaxed. Furthermore, if the user is excited, the providing unit can provide an analysis result with visually stimulating effects. For example, the generation AI can analyze the user's biometric data to estimate whether the user is excited. This enables the providing unit to provide appropriate information by adjusting the length of the analysis result according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0102] The providing unit can determine the priority of provision based on the time of submission of the analysis results when the analysis results are provided. The providing unit, for example, prioritizes providing the most recent analysis results. For example, the generation AI can analyze the time of submission of the analysis results and prioritize providing the most recent analysis results. The providing unit can also postpone providing older analysis results. For example, the generation AI can analyze the time of submission of the analysis results and postpone providing older analysis results. The providing unit can also gradually adjust the priority of provision depending on the time of submission of the analysis results. For example, the generation AI can analyze the time of submission of the analysis results and adjust the priority. This enables the providing unit to provide information efficiently by determining the priority of provision based on the time of submission of the analysis results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the time of submission of the analysis results to the generation AI and have the generation AI determine the priority of provision.

[0103] The providing unit can adjust the order of provision based on the relevance of the analysis results when providing the results. For example, the providing unit can prioritize providing highly relevant analysis results. For example, the generation AI can analyze the relevance of the analysis results and prioritize providing highly relevant analysis results. The providing unit can also postpone providing less relevant analysis results. For example, the generation AI can analyze the relevance of the analysis results and postpone providing less relevant analysis results. The providing unit can also gradually adjust the order of provision based on the relevance of the analysis results. For example, the generation AI can analyze the relevance of the analysis results and adjust the order. This enables the providing unit to provide information efficiently by adjusting the order of provision based on the relevance of the analysis results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0104] The providing unit can adjust the use of technical terminology provided during provision according to the user's level of expertise. For example, if the user has technical expertise, the providing unit can provide information using a lot of technical terminology. For example, the generation AI can analyze the user's level of expertise and provide information using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide information in simpler terms. For example, the generation AI can analyze the user's level of expertise and provide information in simpler terms. Furthermore, the providing unit can gradually adjust the use of technical terminology provided according to the user's level of expertise. For example, the generation AI can analyze the user's level of expertise and adjust the use of technical terminology. This enables the providing unit to provide appropriate information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, analysis unit, and providing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, receives data input by a user, and passes the data to the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the input data using a generation AI, and selects an optimal analysis method. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the data based on the selected method. For example, the providing unit is realized by the output device 40 of the smart device 14, and provides the analysis results to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, receives data input by a user, and passes it to the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the input data using a generation AI, and selects an optimal analysis method. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the data based on the selected method. For example, the provision unit is realized by the speaker 240 of the smart glasses 214, and provides the analysis results to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, receives data input by a user, and passes it to the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the input data using a generation AI, and selects an optimal analysis method. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the data based on the selected method. For example, the provision unit is realized by the display 343 of the headset-type terminal 314, and provides the analysis results to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, analysis section, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, receives data input by a user, and passes it to the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the input data using a generation AI, and selects an optimal analysis method. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the data based on the selected method. For example, the provision unit is realized by the speaker 240 of the robot 414, and provides the analysis results to the user.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can select the optimal data preprocessing method depending on the type of data input by the user. For example, normalization and standardization can be performed on numerical data. Furthermore, tokenization and stop word removal can be performed on text data. Furthermore, resizing and noise removal can be performed on image data. In this way, the reception unit can improve the analysis accuracy of the analysis unit by performing preprocessing according to the type of data. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select a preprocessing method.

[0107] The analysis unit can evaluate the reliability of the data and exclude unreliable data. For example, if the data contains many missing values ​​or many outliers, the data can be excluded from the analysis. Data can also be excluded as unreliable data if the source of the data is unclear or if there are problems with the data collection method. Furthermore, the analysis unit can check the consistency and integrity of the data to evaluate its reliability. This allows the analysis unit to perform analysis using only highly reliable data, thereby improving the accuracy of the analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can have the generation AI perform the data reliability evaluation.

[0108] The providing unit can estimate the user's emotions and adjust the format of the analysis results to be provided based on the estimated user emotions. For example, if the user is feeling stressed, a simple, visually easy-to-understand format can be provided. If the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is in a hurry, a short format that focuses on the main points can be provided. This enables the providing unit to provide appropriate analysis results by adjusting the format according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0109] The providing unit can analyze the user's past behavioral history and provide the analysis results at the optimal timing. For example, if the user previously checked the analysis results at a specific time period, the analysis results can be provided according to that time period. Furthermore, if the user previously checked the analysis results on a specific day of the week, the analysis results can be provided according to that day of the week. Furthermore, if the user previously checked the analysis results after a specific event, the analysis results can be provided according to the event. This allows the providing unit to provide the analysis results at the optimal timing based on the user's behavioral history, thereby improving user convenience. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's behavioral history data into a generation AI and cause the generation AI to select the optimal timing.

[0110] The providing unit can estimate the user's emotions and adjust the level of detail of the analysis results to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a concise and to-the-point analysis result. Furthermore, if the user is relaxed, the providing unit can provide an analysis result including detailed information. Furthermore, if the user is excited, the providing unit can provide an analysis result with a visually stimulating effect. This enables the providing unit to provide appropriate analysis results by adjusting the level of detail according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0111] The analysis unit can analyze correlations in data and prioritize analysis of highly correlated data. For example, the analysis unit can analyze the correlation between sales data and advertising expense data and prioritize analysis of highly correlated data. The analysis unit can also analyze the correlation between customer data and purchase history data and prioritize analysis of highly correlated data. Furthermore, the analysis unit can analyze the correlation between financial data and market data and prioritize analysis of highly correlated data. This enables the analysis unit to perform efficient analysis by setting priorities based on data correlations. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on data correlations into a generation AI and have the generation AI set the priorities.

[0112] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This enables the analysis unit to provide appropriate analysis results by adjusting the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0113] The reception unit can select the optimal data conversion method depending on the format of the user's input data. For example, scaling and normalization can be performed on numerical data. Furthermore, vectorization and encoding can be performed on text data. Furthermore, pixel value normalization and filtering can be performed on image data. In this way, the reception unit can improve the analysis accuracy of the analysis unit by performing conversion according to the data format. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select a conversion method.

[0114] The providing unit can estimate the user's emotions and adjust the visual presentation of the analysis results to be provided based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible graph or chart can be provided. If the user is relaxed, a graph or chart containing detailed information can be provided. Furthermore, if the user is excited, a graph or chart with a visually stimulating effect can be provided. This enables the providing unit to provide appropriate analysis results by adjusting the visual presentation according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0115] The providing unit can customize the format of the analysis results to be provided based on the user's past feedback. For example, it can provide formats that the user has previously preferred (graphs, charts, text, etc.) preferentially. It can also avoid formats that the user has previously expressed dissatisfaction with. Furthermore, it can suggest new formats based on the user's past feedback. In this way, the providing unit can improve user satisfaction by customizing the format based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generating AI and cause the generating AI to customize the format.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The user inputs data into the reception unit. The data includes numerical data, text data, image data, etc. The reception unit receives the data input by the user and passes it to the analysis unit. Step 2: The analysis unit uses the generation AI to analyze the data entered by the reception unit and select the optimal analysis method. Analysis methods include regression analysis, clustering, classification, etc. The generation AI selects the appropriate analysis method depending on the type of data and purpose. Step 3: The analysis unit analyzes the data based on the method selected by the analysis unit. The analysis is performed using methods such as regression analysis, clustering, and classification. The generation AI analyzes the data based on the selected method and generates numerical analysis results. Step 4: The provision unit provides the analysis results obtained by the analysis unit to the user. The provision is performed in the form of a report, a dashboard display, or other methods. The generation AI generates the analysis results in the form of a report and provides it to the user. The analysis results can also be displayed in real time using a dashboard display.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting data; an analysis unit that analyzes the data input by the reception unit and selects an appropriate analysis method; an analysis unit that analyzes data based on the method selected by the analysis unit; a providing unit that provides the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Choose regression, clustering, and classification methods The system of claim 1 .

3. The providing unit Analyze trends in sales data and provide future sales forecasts The system of claim 1 .

4. The providing unit Segment customers based on customer data The system of claim 1 .

5. The providing unit Conduct financial analysis based on financial data The system of claim 1 .

6. The reception unit Estimate the user's emotions and adjust the timing of data input based on the estimated user emotions. The system of claim 1 .

7. The reception unit Analyze the user's past data entry history and select the optimal entry method The system of claim 1 .

8. The reception unit Filter data entry based on the user's current projects or areas of interest The system of claim 1 .

9. The reception unit When entering data, select the most appropriate input method depending on the user's input method. The system of claim 1 .

10. The reception unit Estimate the user's emotions and prioritize the data to be input based on the estimated user emotions. The system of claim 1 .

Images