System

Abstract

An object of a system according to an embodiment is to select an appropriate analysis technique based on data provided by a user and provide an analysis result in an easy-to-understand report format.SOLUTION: A system according to an embodiment includes a data providing unit, an analysis technique selection unit, an analysis result generation unit, and a report providing unit. The data provider receives data from a user. The analysis technique selection unit selects an appropriate analysis technique based on the data received by the data provision unit. The analysis result generation unit analyzes the data using the analysis technique selected by the analysis technique selection unit and generates an analysis result. The report providing unit provides the analysis result generated by the analysis result generation unit in a report format.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 technologies have had the problem of making it difficult to select an appropriate analysis method based on data provided by the user and to provide the analysis results in an easy-to-understand report format.

[0005] The system according to the embodiment aims to select an appropriate analysis method based on data provided by a user and provide the analysis results in an easy-to-understand report format. [Means for solving the problem]

[0006] The system according to the embodiment includes a data providing unit, an analysis method selecting unit, an analysis result generating unit, and a report providing unit. The data providing unit receives data from a user. The analysis method selecting unit selects an appropriate analysis method based on the data received by the data providing unit. The analysis result generating unit analyzes the data using the analysis method selected by the analysis method selecting unit and generates analysis results. The report providing unit provides the analysis results generated by the analysis result generating unit in report format. [Effects of the Invention]

[0007] The system according to the embodiment can select an appropriate analysis method based on data provided by the user and provide the analysis results in an easy-to-understand report format. [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 report generation system according to an embodiment of the present invention is a system in which a generation AI selects an appropriate analysis method based on data provided by a user and provides the analysis results in a report format. This enables the report generation system to automatically select an appropriate analysis method based on data provided by a user and provide the analysis results in a report format.

[0029] A report generation system according to an embodiment includes a data providing unit, an analysis method selecting unit, an analysis result generating unit, and a report providing unit. The data providing unit receives data from a user. For example, the user can provide sales data, customer data, marketing data, etc. The data providing unit can also convert the data provided by the user into an appropriate format. The analysis method selecting unit selects an appropriate analysis method based on the data received by the data providing unit. For example, it can select time series analysis for sales data and clustering analysis for customer data. The analysis method selecting unit automatically selects the optimal analysis method according to the characteristics of the data, for example, using a model fine-tuned in advance by the generation AI. The analysis result generating unit analyzes the data using the analysis method selected by the analysis method selecting unit and generates analysis results. For example, it summarizes the results of the time series analysis of the sales data in graphs or tables and explains the analysis results in text. For example, the generation AI automatically generates graphs or charts to make the analysis results visually easy to understand and incorporates them into a report. The report providing unit provides the analysis results generated by the analysis result generating unit in report format. For example, the generated report can be downloaded or received by email. The report providing unit can also customize the format and content of the report according to the user's requests, for example, by using a generation AI. This allows the report generation system according to the embodiment to automatically select an appropriate analysis method based on the data provided by the user and provide the analysis results in report format.

[0030] The data provision unit automatically evaluates the quality of data provided by users and performs data cleaning as necessary. For example, in sales data provided by users, the generation AI automatically detects missing values ​​and outliers and performs appropriate interpolation or correction. For example, it detects abnormally high or low values ​​in sales data and interpolates them with an average value. In addition, in customer data, the generation AI automatically detects and integrates duplicate data. For example, if the same customer is divided into multiple records, they are combined into one. In addition, in marketing data, the generation AI removes noise data and maintains data consistency. For example, it converts data entered in different formats into a unified format. This automatically evaluates data quality and performs data cleaning as necessary, thereby improving the accuracy of analysis results.

[0031] The data providing unit can propose an optimal data preprocessing method according to the format and content of the data entered by the user, and provide the user with options. For example, in the data providing unit, the generation AI proposes a data normalization and standardization method for sales data provided by the user, and provides the user with options. For example, it presents a method for unifying the scale of sales data. In addition, in the data providing unit, the generation AI proposes a data encoding method for customer data, and provides the user with options. For example, it presents a method for converting categorical data into numerical data. In addition, in the data providing unit, the generation AI proposes a data sampling method for marketing data, and provides the user with options. For example, it presents a method for extracting a representative sample from a large amount of data. In this way, by proposing an optimal data preprocessing method for the data entered by the user and providing the user with options, data preprocessing can be performed efficiently.

[0032] The data providing unit can automatically search related external data sources for data provided by a user and collect complementary data. For example, in the data providing unit, the generation AI automatically searches market data for sales data provided by a user and collects complementary data. For example, it collects sales data from competitors and performs comparative analysis. In addition, in the data providing unit, the generation AI automatically searches social media data for customer data and collects complementary data. For example, it analyzes customers' social media activities and complements customer profiles. In addition, in the data providing unit, the generation AI automatically searches advertising data for marketing data and collects complementary data. For example, it collects advertising campaign data from competitors and complements marketing strategies. In this way, the accuracy of the analysis results can be improved by automatically searching related external data sources for data provided by a user and collecting complementary data.

[0033] The data providing unit allows a user to provide data using voice input or image input, and the generation AI can analyze the data and convert it into text data. The data providing unit, for example, allows a user to provide sales data using voice input, and the generation AI analyzes the voice data and converts it into text data. For example, sales figures are entered by voice and converted into text data. The data providing unit also allows a user to provide customer data using image input, and the generation AI analyzes the image data and converts it into text data. For example, an image of a customer's business card is analyzed and converted into text data. The data providing unit also allows a user to provide marketing data using voice input or image input, and the generation AI analyzes the data and converts it into text data. For example, an image of an advertising poster is analyzed and converted into text data. This allows a user to provide data using voice input or image input, and the generation AI analyzes the data and converts it into text data, thereby improving the diversity and efficiency of data input.

[0034] The analysis method selection unit can explain to the user the basis for the analysis method selected by the generation AI and visualize it in a way that is easy for the user to understand. For example, if the generation AI selects time series analysis for sales data, the analysis method selection unit will explain the basis for that selection to the user and visualize it in a graph or chart. For example, it will display a graph showing past sales trends. Furthermore, if the analysis method selection unit selects clustering analysis for customer data, it will explain to the user the basis for that selection and visualize it. For example, it will display clustering results showing customer segments in a graph. Furthermore, if the analysis method selection unit selects regression analysis for marketing data, it will explain to the user the basis for that selection and visualize it. For example, it will display a regression line showing the relationship between advertising expenses and sales. In this way, by explaining to the user the basis for the analysis method selected by the generation AI and visualizing it, the user can more easily understand the analysis results.

[0035] The analysis method selection unit can apply multiple analysis methods simultaneously, compare the results, and select the optimal method. For example, the analysis method selection unit has the generation AI simultaneously apply time series analysis and regression analysis to sales data, compare the results, and select the optimal method. For example, it selects a method with high prediction accuracy. The analysis method selection unit also simultaneously applies clustering analysis and decision tree analysis to customer data, compares the results, and selects the optimal method. For example, it selects a method with high accuracy in customer segmentation. The analysis method selection unit also simultaneously applies regression analysis and factor analysis to marketing data, compares the results, and selects the optimal method. For example, it selects a method with high explanatory power for advertising effectiveness. In this way, by applying multiple analysis methods simultaneously, comparing the results, and selecting the optimal method, the accuracy of the analysis results can be improved.

[0036] The analysis result generation unit can automatically apply terminology and formats specific to the user's industry when generating analysis results. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit automatically applies terminology and formats specific to the retail industry. For example, it uses indicators such as sales revenue, profit margin, and inventory turnover. Furthermore, when generating analysis results for customer data, the analysis result generation unit automatically applies terminology and formats specific to the financial industry. For example, it uses indicators such as customer credit score, risk assessment, and investment portfolio. Furthermore, when generating analysis results for marketing data, the analysis result generation unit automatically applies terminology and formats specific to the advertising industry. For example, it uses indicators such as click rate, conversion rate, and return on advertising spend. In this way, by automatically applying terminology and formats specific to the user's industry when generating analysis results, it is possible to provide reports that are easier for users to understand.

[0037] When generating analysis results, the analysis result generation unit can display the results from multiple angles using multiple visualization techniques. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit displays the results using multiple visualization techniques, such as line graphs, bar graphs, and pie charts. For example, it displays sales trends using a line graph and sales breakdowns using a pie chart. When generating analysis results for customer data, the analysis result generation unit displays the results using multiple visualization techniques, such as heat maps, scatter plots, and tree maps. For example, it displays the distribution of customer segments using a heat map and customer attributes using a scatter plot. When generating analysis results for marketing data, the analysis result generation unit displays the results using multiple visualization techniques, such as bubble charts, radar charts, and network diagrams. For example, it displays the effectiveness of an advertising campaign using a bubble chart and the relevance of advertisements using a network diagram. In this way, when generating analysis results, the analysis results can be displayed from multiple angles using multiple visualization techniques, making it easier for users to understand the analysis results.

[0038] The report providing unit can collect user feedback in real time when the generation AI provides a report and reflect it in the generation of the next report. For example, when the generation AI provides a sales data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, additional information desired by the user is included in the next report. Furthermore, when providing a customer data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, errors pointed out by the user are corrected in the next report. Furthermore, when providing a marketing data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, detailed analysis desired by the user is added to the next report. In this way, by collecting user feedback in real time and reflecting it in the generation of the next report, it is possible to improve the quality of the report and user satisfaction.

[0039] When the generation AI provides a report, the report providing unit can refer to the user's browsing history and prioritize displaying the parts that interest the user most. For example, when the generation AI provides a sales data report, the report providing unit refers to the user's browsing history and prioritize displaying the sales trends and peaks that interest the user most. For example, it highlights items that the user has frequently viewed in the past. Furthermore, when providing a customer data report, the report providing unit refers to the user's browsing history and prioritize displaying the customer segments and behavior patterns that interest the user most. For example, it highlights segments that the user has paid attention to in the past. Furthermore, when providing a marketing data report, the report providing unit refers to the user's browsing history and prioritize displaying the effects of advertising campaigns that interest the user most. For example, it highlights campaigns that the user has been interested in in the past. In this way, it is possible to provide a report that matches the user's interests by referring to the user's browsing history and prioritize displaying the parts that interest the user most.

[0040] When the generation AI provides a report, the report providing unit can provide it in a format optimized for different devices. For example, when the generation AI provides a sales data report, the report providing unit provides it in a format optimized for smartphones. For example, it applies a layout that matches the screen size of a smartphone. Furthermore, when the report providing unit provides a customer data report, it provides it in a format optimized for tablets. For example, it applies an interface that matches the screen size of a tablet. Furthermore, when the report providing unit provides a marketing data report, it provides it in a format optimized for PCs. For example, it displays detailed graphs and charts that are suitable for the large screen of a PC. In this way, by providing reports in formats optimized for different devices, users can comfortably view the reports on any device.

[0041] When the generation AI provides a report, the report providing unit works in conjunction with the user's calendar, and can automatically generate the report before an important meeting or presentation. For example, when the generation AI provides a sales data report, the report providing unit works in conjunction with the user's calendar, and automatically generates the report before an important meeting. For example, the latest sales report is provided the day before a monthly meeting. Furthermore, when the report providing unit provides a customer data report, it works in conjunction with the user's calendar, and automatically generates the report before an important presentation. For example, the latest customer report is provided the day before a customer analysis presentation. Furthermore, when the report providing unit provides a marketing data report, it works in conjunction with the user's calendar, and automatically generates the report before an important meeting. For example, the latest marketing report is provided the day before an advertising campaign review meeting. In this way, by working in conjunction with the user's calendar and automatically generating reports before important meetings or presentations, the user can obtain the latest reports when they need them.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data providing unit can automatically search related external data sources for data provided by a user and collect complementary data. For example, in the data providing unit, the generation AI automatically searches market data for sales data provided by a user and collects complementary data. For example, it collects sales data from competitors and performs comparative analysis. In addition, in the data providing unit, the generation AI automatically searches social media data for customer data and collects complementary data. For example, it analyzes customers' social media activities and complements customer profiles. In addition, in the data providing unit, the generation AI automatically searches advertising data for marketing data and collects complementary data. For example, it collects advertising campaign data from competitors and complements marketing strategies. In this way, the accuracy of the analysis results can be improved by automatically searching related external data sources for data provided by a user and collecting complementary data.

[0044] The data providing unit allows a user to provide data using voice input or image input, and the generation AI can analyze the data and convert it into text data. The data providing unit, for example, allows a user to provide sales data using voice input, and the generation AI analyzes the voice data and converts it into text data. For example, sales figures are entered by voice and converted into text data. The data providing unit also allows a user to provide customer data using image input, and the generation AI analyzes the image data and converts it into text data. For example, an image of a customer's business card is analyzed and converted into text data. The data providing unit also allows a user to provide marketing data using voice input or image input, and the generation AI analyzes the data and converts it into text data. For example, an image of an advertising poster is analyzed and converted into text data. This allows a user to provide data using voice input or image input, and the generation AI analyzes the data and converts it into text data, thereby improving the diversity and efficiency of data input.

[0045] The analysis method selection unit can explain to the user the basis for the analysis method selected by the generation AI and visualize it in a way that is easy for the user to understand. For example, if the generation AI selects time series analysis for sales data, the analysis method selection unit will explain the basis for that selection to the user and visualize it in a graph or chart. For example, it will display a graph showing past sales trends. Furthermore, if the analysis method selection unit selects clustering analysis for customer data, it will explain to the user the basis for that selection and visualize it. For example, it will display clustering results showing customer segments in a graph. Furthermore, if the analysis method selection unit selects regression analysis for marketing data, it will explain to the user the basis for that selection and visualize it. For example, it will display a regression line showing the relationship between advertising expenses and sales. In this way, by explaining to the user the basis for the analysis method selected by the generation AI and visualizing it, the user can more easily understand the analysis results.

[0046] The analysis method selection unit can apply multiple analysis methods simultaneously, compare the results, and select the optimal method. For example, the analysis method selection unit has the generation AI simultaneously apply time series analysis and regression analysis to sales data, compare the results, and select the optimal method. For example, it selects a method with high prediction accuracy. The analysis method selection unit also simultaneously applies clustering analysis and decision tree analysis to customer data, compares the results, and selects the optimal method. For example, it selects a method with high accuracy in customer segmentation. The analysis method selection unit also simultaneously applies regression analysis and factor analysis to marketing data, compares the results, and selects the optimal method. For example, it selects a method with high explanatory power for advertising effectiveness. In this way, by applying multiple analysis methods simultaneously, comparing the results, and selecting the optimal method, the accuracy of the analysis results can be improved.

[0047] The analysis result generation unit can automatically apply terminology and formats specific to the user's industry when generating analysis results. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit automatically applies terminology and formats specific to the retail industry. For example, it uses indicators such as sales revenue, profit margin, and inventory turnover. Furthermore, when generating analysis results for customer data, the analysis result generation unit automatically applies terminology and formats specific to the financial industry. For example, it uses indicators such as customer credit score, risk assessment, and investment portfolio. Furthermore, when generating analysis results for marketing data, the analysis result generation unit automatically applies terminology and formats specific to the advertising industry. For example, it uses indicators such as click rate, conversion rate, and return on advertising spend. In this way, by automatically applying terminology and formats specific to the user's industry when generating analysis results, it is possible to provide reports that are easier for users to understand.

[0048] When generating analysis results, the analysis result generation unit can display the results from multiple angles using multiple visualization techniques. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit displays the results using multiple visualization techniques, such as line graphs, bar graphs, and pie charts. For example, it displays sales trends using a line graph and sales breakdowns using a pie chart. When generating analysis results for customer data, the analysis result generation unit displays the results using multiple visualization techniques, such as heat maps, scatter plots, and tree maps. For example, it displays the distribution of customer segments using a heat map and customer attributes using a scatter plot. When generating analysis results for marketing data, the analysis result generation unit displays the results using multiple visualization techniques, such as bubble charts, radar charts, and network diagrams. For example, it displays the effectiveness of an advertising campaign using a bubble chart and the relevance of advertisements using a network diagram. In this way, when generating analysis results, the analysis results can be displayed from multiple angles using multiple visualization techniques, making it easier for users to understand the analysis results.

[0049] The report providing unit can collect user feedback in real time when the generation AI provides a report and reflect it in the generation of the next report. For example, when the generation AI provides a sales data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, additional information desired by the user is included in the next report. Furthermore, when providing a customer data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, errors pointed out by the user are corrected in the next report. Furthermore, when providing a marketing data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, detailed analysis desired by the user is added to the next report. In this way, by collecting user feedback in real time and reflecting it in the generation of the next report, it is possible to improve the quality of the report and user satisfaction.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The data provider receives data from the user. For example, the user can provide sales data, customer data, marketing data, etc. The data provider can also convert the data provided by the user into an appropriate format. Step 2: The analysis method selection unit selects an appropriate analysis method based on the data received by the data provision unit. For example, it can select time series analysis for sales data and clustering analysis for customer data. The analysis method selection unit automatically selects the optimal analysis method according to the characteristics of the data using a model that has been fine-tuned in advance by the generation AI. Step 3: The analysis result generation unit analyzes the data using the analysis method selected by the analysis method selection unit and generates analysis results. For example, it summarizes the results of a time-series analysis of sales data in graphs and tables and explains the analysis results in text. The analysis result generation unit automatically generates graphs and charts so that the generation AI can make the analysis results visually easy to understand, and incorporates them into the report. Step 4: The report provider provides the analysis results generated by the analysis result generator in the form of a report. For example, the generated report can be downloaded or received by email. The report provider's generation AI can also customize the format and content of the report according to the user's requests.

[0052] (Example 2) A report generation system according to an embodiment of the present invention is a system in which a generation AI selects an appropriate analysis method based on data provided by a user and provides the analysis results in a report format. This enables the report generation system to automatically select an appropriate analysis method based on data provided by a user and provide the analysis results in a report format.

[0053] A report generation system according to an embodiment includes a data providing unit, an analysis method selecting unit, an analysis result generating unit, and a report providing unit. The data providing unit receives data from a user. For example, the user can provide sales data, customer data, marketing data, etc. The data providing unit can also convert the data provided by the user into an appropriate format. The analysis method selecting unit selects an appropriate analysis method based on the data received by the data providing unit. For example, it can select time series analysis for sales data and clustering analysis for customer data. The analysis method selecting unit automatically selects the optimal analysis method according to the characteristics of the data, for example, using a model fine-tuned in advance by the generation AI. The analysis result generating unit analyzes the data using the analysis method selected by the analysis method selecting unit and generates analysis results. For example, it summarizes the results of the time series analysis of the sales data in graphs or tables and explains the analysis results in text. For example, the generation AI automatically generates graphs or charts to make the analysis results visually easy to understand and incorporates them into a report. The report providing unit provides the analysis results generated by the analysis result generating unit in report format. For example, the generated report can be downloaded or received by email. The report providing unit can also customize the format and content of the report according to the user's requests, for example, by using a generation AI. This allows the report generation system according to the embodiment to automatically select an appropriate analysis method based on the data provided by the user and provide the analysis results in report format.

[0054] The data provision unit automatically evaluates the quality of data provided by users and performs data cleaning as necessary. For example, in sales data provided by users, the generation AI automatically detects missing values ​​and outliers and performs appropriate interpolation or correction. For example, it detects abnormally high or low values ​​in sales data and interpolates them with an average value. In addition, in customer data, the generation AI automatically detects and integrates duplicate data. For example, if the same customer is divided into multiple records, they are combined into one. In addition, in marketing data, the generation AI removes noise data and maintains data consistency. For example, it converts data entered in different formats into a unified format. This automatically evaluates data quality and performs data cleaning as necessary, thereby improving the accuracy of analysis results.

[0055] The data providing unit can propose an optimal data preprocessing method according to the format and content of the data entered by the user, and provide the user with options. For example, in the data providing unit, the generation AI proposes a data normalization and standardization method for sales data provided by the user, and provides the user with options. For example, it presents a method for unifying the scale of sales data. In addition, in the data providing unit, the generation AI proposes a data encoding method for customer data, and provides the user with options. For example, it presents a method for converting categorical data into numerical data. In addition, in the data providing unit, the generation AI proposes a data sampling method for marketing data, and provides the user with options. For example, it presents a method for extracting a representative sample from a large amount of data. In this way, by proposing an optimal data preprocessing method for the data entered by the user and providing the user with options, data preprocessing can be performed efficiently.

[0056] The data providing unit can use the emotion estimation function to analyze the emotions a user feels when providing input data and provide an interface for eliciting positive emotions. For example, when a user enters sales data, the data providing unit allows the generation AI to analyze the user's facial expressions and voice and provide an interface for eliciting positive emotions. For example, an encouraging message may be displayed. Furthermore, when customer data is entered, the data providing unit allows the generation AI to analyze the user's emotions in real time and provide an interface for eliciting positive emotions. For example, success stories may be presented. Furthermore, when marketing data is entered, the data providing unit allows the generation AI to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, compliments may be displayed according to the input content. This allows for eliciting positive emotions when a user provides input data, thereby improving the efficiency and accuracy of data entry.

[0057] The data providing unit can automatically search related external data sources for data provided by a user and collect complementary data. For example, in the data providing unit, the generation AI automatically searches market data for sales data provided by a user and collects complementary data. For example, it collects sales data from competitors and performs comparative analysis. In addition, in the data providing unit, the generation AI automatically searches social media data for customer data and collects complementary data. For example, it analyzes customers' social media activities and complements customer profiles. In addition, in the data providing unit, the generation AI automatically searches advertising data for marketing data and collects complementary data. For example, it collects advertising campaign data from competitors and complements marketing strategies. In this way, the accuracy of the analysis results can be improved by automatically searching related external data sources for data provided by a user and collecting complementary data.

[0058] The data providing unit allows a user to provide data using voice input or image input, and the generation AI can analyze the data and convert it into text data. The data providing unit, for example, allows a user to provide sales data using voice input, and the generation AI analyzes the voice data and converts it into text data. For example, sales figures are entered by voice and converted into text data. The data providing unit also allows a user to provide customer data using image input, and the generation AI analyzes the image data and converts it into text data. For example, an image of a customer's business card is analyzed and converted into text data. The data providing unit also allows a user to provide marketing data using voice input or image input, and the generation AI analyzes the data and converts it into text data. For example, an image of an advertising poster is analyzed and converted into text data. This allows a user to provide data using voice input or image input, and the generation AI analyzes the data and converts it into text data, thereby improving the diversity and efficiency of data input.

[0059] The data providing unit uses the emotion estimation function to provide real-time feedback on the emotions of the user when providing input data, thereby improving the input process. For example, when the user is inputting sales data, the data providing unit has the generation AI analyze the user's emotions in real time and provide feedback. For example, if the user is feeling stressed, the generation AI makes suggestions to help the user relax. Furthermore, when the data providing unit inputs customer data, the generation AI analyzes the user's emotions in real time and provides feedback. For example, if the user is tired, the generation AI displays a message encouraging the user to take a break. Furthermore, when the data providing unit inputs marketing data, the generation AI analyzes the user's emotions in real time and provides feedback. For example, if the user is lacking concentration, the generation AI makes suggestions to help the user improve their concentration. In this way, by providing real-time feedback on the emotions of the user when providing input data, the input process can be improved and user satisfaction can be increased.

[0060] The analysis method selection unit can explain to the user the basis for the analysis method selected by the generation AI and visualize it in a way that is easy for the user to understand. For example, if the generation AI selects time series analysis for sales data, the analysis method selection unit will explain the basis for that selection to the user and visualize it in a graph or chart. For example, it will display a graph showing past sales trends. Furthermore, if the analysis method selection unit selects clustering analysis for customer data, it will explain to the user the basis for that selection and visualize it. For example, it will display clustering results showing customer segments in a graph. Furthermore, if the analysis method selection unit selects regression analysis for marketing data, it will explain to the user the basis for that selection and visualize it. For example, it will display a regression line showing the relationship between advertising expenses and sales. In this way, by explaining to the user the basis for the analysis method selected by the generation AI and visualizing it, the user can more easily understand the analysis results.

[0061] The analysis method selection unit can apply multiple analysis methods simultaneously, compare the results, and select the optimal method. For example, the analysis method selection unit has the generation AI simultaneously apply time series analysis and regression analysis to sales data, compare the results, and select the optimal method. For example, it selects a method with high prediction accuracy. The analysis method selection unit also simultaneously applies clustering analysis and decision tree analysis to customer data, compares the results, and selects the optimal method. For example, it selects a method with high accuracy in customer segmentation. The analysis method selection unit also simultaneously applies regression analysis and factor analysis to marketing data, compares the results, and selects the optimal method. For example, it selects a method with high explanatory power for advertising effectiveness. In this way, by applying multiple analysis methods simultaneously, comparing the results, and selecting the optimal method, the accuracy of the analysis results can be improved.

[0062] The analysis method selection unit uses the emotion estimation function to adjust the selection of analysis method based on the user's emotions, and can select the method that is most convincing to the user. For example, when the generation AI selects time series analysis for sales data, the analysis method selection unit analyzes the user's emotions and selects a method that is highly convincing. For example, it prioritizes a method that the user feels comfortable with. Furthermore, when selecting clustering analysis for customer data, the analysis method selection unit analyzes the user's emotions and selects a method that is highly convincing. For example, it prioritizes a method that the user feels trustworthy. Furthermore, when selecting regression analysis for marketing data, the analysis method selection unit analyzes the user's emotions and selects a method that is highly convincing. For example, it prioritizes a method that the user can easily understand. In this way, by adjusting the selection of analysis method based on the user's emotions, it is possible to select the method that is most convincing to the user.

[0063] The analysis result generation unit can automatically apply terminology and formats specific to the user's industry when generating analysis results. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit automatically applies terminology and formats specific to the retail industry. For example, it uses indicators such as sales revenue, profit margin, and inventory turnover. Furthermore, when generating analysis results for customer data, the analysis result generation unit automatically applies terminology and formats specific to the financial industry. For example, it uses indicators such as customer credit score, risk assessment, and investment portfolio. Furthermore, when generating analysis results for marketing data, the analysis result generation unit automatically applies terminology and formats specific to the advertising industry. For example, it uses indicators such as click rate, conversion rate, and return on advertising spend. In this way, by automatically applying terminology and formats specific to the user's industry when generating analysis results, it is possible to provide reports that are easier for users to understand.

[0064] When generating analysis results, the analysis result generation unit can display the results from multiple angles using multiple visualization techniques. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit displays the results using multiple visualization techniques, such as line graphs, bar graphs, and pie charts. For example, it displays sales trends using a line graph and sales breakdowns using a pie chart. When generating analysis results for customer data, the analysis result generation unit displays the results using multiple visualization techniques, such as heat maps, scatter plots, and tree maps. For example, it displays the distribution of customer segments using a heat map and customer attributes using a scatter plot. When generating analysis results for marketing data, the analysis result generation unit displays the results using multiple visualization techniques, such as bubble charts, radar charts, and network diagrams. For example, it displays the effectiveness of an advertising campaign using a bubble chart and the relevance of advertisements using a network diagram. In this way, when generating analysis results, the analysis results can be displayed from multiple angles using multiple visualization techniques, making it easier for users to understand the analysis results.

[0065] The analysis result generation unit can use the emotion estimation function to provide the analysis results in a format that is easiest for the user to understand. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit analyzes the user's emotions and provides the results in the format that is easiest for the user to understand. For example, it selects a graph format that the user prefers. Furthermore, when generating analysis results for customer data, the analysis result generation unit analyzes the user's emotions and provides the results in the format that is easiest for the user to understand. For example, it generates explanatory text in words that the user can easily understand. Furthermore, when generating analysis results for marketing data, the analysis result generation unit analyzes the user's emotions and provides the results in the format that is easiest for the user to understand. For example, it applies colors and designs that the user prefers. In this way, by providing analysis results in a format that is easiest for the user to understand, the user's level of understanding can be improved.

[0066] The report providing unit can collect user feedback in real time when the generation AI provides a report and reflect it in the generation of the next report. For example, when the generation AI provides a sales data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, additional information desired by the user is included in the next report. Furthermore, when providing a customer data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, errors pointed out by the user are corrected in the next report. Furthermore, when providing a marketing data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, detailed analysis desired by the user is added to the next report. In this way, by collecting user feedback in real time and reflecting it in the generation of the next report, it is possible to improve the quality of the report and user satisfaction.

[0067] When the generation AI provides a report, the report providing unit can refer to the user's browsing history and prioritize displaying the parts that interest the user most. For example, when the generation AI provides a sales data report, the report providing unit refers to the user's browsing history and prioritize displaying the sales trends and peaks that interest the user most. For example, it highlights items that the user has frequently viewed in the past. Furthermore, when providing a customer data report, the report providing unit refers to the user's browsing history and prioritize displaying the customer segments and behavior patterns that interest the user most. For example, it highlights segments that the user has paid attention to in the past. Furthermore, when providing a marketing data report, the report providing unit refers to the user's browsing history and prioritize displaying the effects of advertising campaigns that interest the user most. For example, it highlights campaigns that the user has been interested in in the past. In this way, it is possible to provide a report that matches the user's interests by referring to the user's browsing history and prioritize displaying the parts that interest the user most.

[0068] The report providing unit can use the emotion estimation function to provide a report in a format that is most satisfactory to the user. For example, when the generation AI provides a sales data report, the report providing unit analyzes the user's emotions and provides the report in a format that is most satisfactory to the user. For example, it applies the graph format and colors that the user prefers. Furthermore, when providing a customer data report, the report providing unit analyzes the user's emotions and provides the report in a format that is most satisfactory to the user. For example, it generates explanations in words that are easy for the user to understand. Furthermore, when providing a marketing data report, the report providing unit analyzes the user's emotions and provides the report in a format that is most satisfactory to the user. For example, it applies the design and layout that the user prefers. In this way, by providing the report in a format that is most satisfactory to the user, user satisfaction can be improved.

[0069] When the generation AI provides a report, the report providing unit can provide it in a format optimized for different devices. For example, when the generation AI provides a sales data report, the report providing unit provides it in a format optimized for smartphones. For example, it applies a layout that matches the screen size of a smartphone. Furthermore, when the report providing unit provides a customer data report, it provides it in a format optimized for tablets. For example, it applies an interface that matches the screen size of a tablet. Furthermore, when the report providing unit provides a marketing data report, it provides it in a format optimized for PCs. For example, it displays detailed graphs and charts that are suitable for the large screen of a PC. In this way, by providing reports in formats optimized for different devices, users can comfortably view the reports on any device.

[0070] When the generation AI provides a report, the report providing unit works in conjunction with the user's calendar, and can automatically generate the report before an important meeting or presentation. For example, when the generation AI provides a sales data report, the report providing unit works in conjunction with the user's calendar, and automatically generates the report before an important meeting. For example, the latest sales report is provided the day before a monthly meeting. Furthermore, when the report providing unit provides a customer data report, it works in conjunction with the user's calendar, and automatically generates the report before an important presentation. For example, the latest customer report is provided the day before a customer analysis presentation. Furthermore, when the report providing unit provides a marketing data report, it works in conjunction with the user's calendar, and automatically generates the report before an important meeting. For example, the latest marketing report is provided the day before an advertising campaign review meeting. In this way, by working in conjunction with the user's calendar and automatically generating reports before important meetings or presentations, the user can obtain the latest reports when they need them.

[0071] The report providing unit can use the emotion estimation function to provide the report at the time when the user is most relaxed. For example, when the generation AI provides a sales data report, the report providing unit analyzes the user's emotions and provides the report at the time when the user is most relaxed. For example, the report is sent at night when the user is relaxed. Furthermore, when providing a customer data report, the report providing unit analyzes the user's emotions and provides the report at the time when the user is most relaxed. For example, the report is sent on the weekend when the user is relaxed. Furthermore, when providing a marketing data report, the report providing unit analyzes the user's emotions and provides the report at the time when the user is most relaxed. For example, the report is sent during the user's lunch break when the user is relaxed. In this way, by providing the report at the time when the user is most relaxed, the user can receive the report without stress.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The data providing unit can automatically search related external data sources for data provided by a user and collect complementary data. For example, in the data providing unit, the generation AI automatically searches market data for sales data provided by a user and collects complementary data. For example, it collects sales data from competitors and performs comparative analysis. In addition, in the data providing unit, the generation AI automatically searches social media data for customer data and collects complementary data. For example, it analyzes customers' social media activities and complements customer profiles. In addition, in the data providing unit, the generation AI automatically searches advertising data for marketing data and collects complementary data. For example, it collects advertising campaign data from competitors and complements marketing strategies. In this way, the accuracy of the analysis results can be improved by automatically searching related external data sources for data provided by a user and collecting complementary data.

[0074] The data providing unit allows a user to provide data using voice input or image input, and the generation AI can analyze the data and convert it into text data. The data providing unit, for example, allows a user to provide sales data using voice input, and the generation AI analyzes the voice data and converts it into text data. For example, sales figures are entered by voice and converted into text data. The data providing unit also allows a user to provide customer data using image input, and the generation AI analyzes the image data and converts it into text data. For example, an image of a customer's business card is analyzed and converted into text data. The data providing unit also allows a user to provide marketing data using voice input or image input, and the generation AI analyzes the data and converts it into text data. For example, an image of an advertising poster is analyzed and converted into text data. This allows a user to provide data using voice input or image input, and the generation AI analyzes the data and converts it into text data, thereby improving the diversity and efficiency of data input.

[0075] The data providing unit uses the emotion estimation function to provide real-time feedback on the emotions of the user when providing input data, thereby improving the input process. For example, when the user is inputting sales data, the data providing unit has the generation AI analyze the user's emotions in real time and provide feedback. For example, if the user is feeling stressed, the generation AI makes suggestions to help the user relax. Furthermore, when the data providing unit inputs customer data, the generation AI analyzes the user's emotions in real time and provides feedback. For example, if the user is tired, the generation AI displays a message encouraging the user to take a break. Furthermore, when the data providing unit inputs marketing data, the generation AI analyzes the user's emotions in real time and provides feedback. For example, if the user is lacking concentration, the generation AI makes suggestions to help the user improve their concentration. In this way, by providing real-time feedback on the emotions of the user when providing input data, the input process can be improved and user satisfaction can be increased.

[0076] The analysis method selection unit can explain to the user the basis for the analysis method selected by the generation AI and visualize it in a way that is easy for the user to understand. For example, if the generation AI selects time series analysis for sales data, the analysis method selection unit will explain the basis for that selection to the user and visualize it in a graph or chart. For example, it will display a graph showing past sales trends. Furthermore, if the analysis method selection unit selects clustering analysis for customer data, it will explain to the user the basis for that selection and visualize it. For example, it will display clustering results showing customer segments in a graph. Furthermore, if the analysis method selection unit selects regression analysis for marketing data, it will explain to the user the basis for that selection and visualize it. For example, it will display a regression line showing the relationship between advertising expenses and sales. In this way, by explaining to the user the basis for the analysis method selected by the generation AI and visualizing it, the user can more easily understand the analysis results.

[0077] The analysis method selection unit can apply multiple analysis methods simultaneously, compare the results, and select the optimal method. For example, the analysis method selection unit has the generation AI simultaneously apply time series analysis and regression analysis to sales data, compare the results, and select the optimal method. For example, it selects a method with high prediction accuracy. The analysis method selection unit also simultaneously applies clustering analysis and decision tree analysis to customer data, compares the results, and selects the optimal method. For example, it selects a method with high accuracy in customer segmentation. The analysis method selection unit also simultaneously applies regression analysis and factor analysis to marketing data, compares the results, and selects the optimal method. For example, it selects a method with high explanatory power for advertising effectiveness. In this way, by applying multiple analysis methods simultaneously, comparing the results, and selecting the optimal method, the accuracy of the analysis results can be improved.

[0078] The analysis method selection unit uses the emotion estimation function to adjust the selection of analysis method based on the user's emotions, and can select the method that is most convincing to the user. For example, when the generation AI selects time series analysis for sales data, the analysis method selection unit analyzes the user's emotions and selects a method that is highly convincing. For example, it prioritizes a method that the user feels comfortable with. Furthermore, when selecting clustering analysis for customer data, the analysis method selection unit analyzes the user's emotions and selects a method that is highly convincing. For example, it prioritizes a method that the user feels trustworthy. Furthermore, when selecting regression analysis for marketing data, the analysis method selection unit analyzes the user's emotions and selects a method that is highly convincing. For example, it prioritizes a method that the user can easily understand. In this way, by adjusting the selection of analysis method based on the user's emotions, it is possible to select the method that is most convincing to the user.

[0079] The analysis result generation unit can automatically apply terminology and formats specific to the user's industry when generating analysis results. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit automatically applies terminology and formats specific to the retail industry. For example, it uses indicators such as sales revenue, profit margin, and inventory turnover. Furthermore, when generating analysis results for customer data, the analysis result generation unit automatically applies terminology and formats specific to the financial industry. For example, it uses indicators such as customer credit score, risk assessment, and investment portfolio. Furthermore, when generating analysis results for marketing data, the analysis result generation unit automatically applies terminology and formats specific to the advertising industry. For example, it uses indicators such as click rate, conversion rate, and return on advertising spend. In this way, by automatically applying terminology and formats specific to the user's industry when generating analysis results, it is possible to provide reports that are easier for users to understand.

[0080] When generating analysis results, the analysis result generation unit can display the results from multiple angles using multiple visualization techniques. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit displays the results using multiple visualization techniques, such as line graphs, bar graphs, and pie charts. For example, it displays sales trends using a line graph and sales breakdowns using a pie chart. When generating analysis results for customer data, the analysis result generation unit displays the results using multiple visualization techniques, such as heat maps, scatter plots, and tree maps. For example, it displays the distribution of customer segments using a heat map and customer attributes using a scatter plot. When generating analysis results for marketing data, the analysis result generation unit displays the results using multiple visualization techniques, such as bubble charts, radar charts, and network diagrams. For example, it displays the effectiveness of an advertising campaign using a bubble chart and the relevance of advertisements using a network diagram. In this way, when generating analysis results, the analysis results can be displayed from multiple angles using multiple visualization techniques, making it easier for users to understand the analysis results.

[0081] The analysis result generation unit can use the emotion estimation function to provide the analysis results in a format that is easiest for the user to understand. For example, when the generation AI generates analysis results for sales data, the analysis result generation unit analyzes the user's emotions and provides the results in the format that is easiest for the user to understand. For example, it selects a graph format that the user prefers. Furthermore, when generating analysis results for customer data, the analysis result generation unit analyzes the user's emotions and provides the results in the format that is easiest for the user to understand. For example, it generates explanatory text in words that the user can easily understand. Furthermore, when generating analysis results for marketing data, the analysis result generation unit analyzes the user's emotions and provides the results in the format that is easiest for the user to understand. For example, it applies colors and designs that the user prefers. In this way, by providing analysis results in a format that is easiest for the user to understand, the user's level of understanding can be improved.

[0082] The report providing unit can collect user feedback in real time when the generation AI provides a report and reflect it in the generation of the next report. For example, when the generation AI provides a sales data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, additional information desired by the user is included in the next report. Furthermore, when providing a customer data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, errors pointed out by the user are corrected in the next report. Furthermore, when providing a marketing data report, the report providing unit collects user feedback in real time and reflects it in the generation of the next report. For example, detailed analysis desired by the user is added to the next report. In this way, by collecting user feedback in real time and reflecting it in the generation of the next report, it is possible to improve the quality of the report and user satisfaction.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The data provider receives data from the user. For example, the user can provide sales data, customer data, marketing data, etc. The data provider can also convert the data provided by the user into an appropriate format. Step 2: The analysis method selection unit selects an appropriate analysis method based on the data received by the data provision unit. For example, it can select time series analysis for sales data and clustering analysis for customer data. The analysis method selection unit automatically selects the optimal analysis method according to the characteristics of the data using a model that has been fine-tuned in advance by the generation AI. Step 3: The analysis result generation unit analyzes the data using the analysis method selected by the analysis method selection unit and generates analysis results. For example, it summarizes the results of a time-series analysis of sales data in graphs and tables and explains the analysis results in text. The analysis result generation unit automatically generates graphs and charts so that the generation AI can make the analysis results visually easy to understand, and incorporates them into the report. Step 4: The report provider provides the analysis results generated by the analysis result generator in the form of a report. For example, the generated report can be downloaded or received by email. The report provider's generation AI can also customize the format and content of the report according to the user's requests.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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 AI 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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, in order to avoid confusion and to 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 data providing unit that receives data from a user; an analysis method selection unit that selects an appropriate analysis method based on the data received by the data providing unit; an analysis result generation unit that analyzes data using the analysis method selected by the analysis method selection unit and generates an analysis result; a report providing unit that provides the analysis results generated by the analysis result generating unit in a report format. A system characterized by:

2. The data providing unit Automatically assess the quality of user-provided data and clean it as needed 2. The system of claim 1.

3. The data providing unit Proposes the optimal data preprocessing method based on the format and content of the data entered by the user, and provides the user with a choice of options.

2. The system of claim 1.

4. The data providing unit Analyzing the emotions of the user when providing input data, and providing an interface for eliciting positive emotions 2. The system of claim 1.

5. The data providing unit Automatically search relevant external data sources for user-provided data and collect complementary data 2. The system of claim 1.

6. The data providing unit Users can provide data using voice or image input, and the generative AI analyzes and converts that data into text.

2. The system of claim 1.

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