system

Abstract

We provide a system that efficiently and automatically generates high-quality documents. [Solution] A system comprising: means for a user to input data and keywords; means for a server to receive and temporarily store the input data; means for the server to perform a cleansing process on the received data; means for the server to analyze the cleansed data and generate analysis results; means for the server to automatically generate materials based on the analysis results; means for the server to apply visual formatting and design to the generated materials; means for the server to save the completed materials in a specified format and provide them to the user; and means for the user to download the materials via a provided link.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The creation of conventional presentation materials and tabulated materials requires a great deal of time and effort. Also, high skills are required for data analysis and formatting, which contributes to reducing the efficiency of the work. Furthermore, it is not easy to generate visually consistent materials, and it is difficult to create high-quality materials without specialized knowledge. As a result, quick and accurate decision-making in a business environment is hindered. To solve these problems, a system that can efficiently and automatically generate high-quality materials is required.

Means for Solving the Problems

[0005] The present invention solves the above problems by the following means. First, a means is provided for the user to input data and keywords. The server receives the input data and stores it temporarily. The received data is cleansed and missing values ​​are imputed with the mean or median. Next, the cleansed data is analyzed and analysis results are generated. Based on the analysis results, a document is automatically generated, and the generated document is visually formatted and designed by applying the company's brand colors and a consistent font size. Finally, the server saves the completed document in a specified format and provides it to the user, who downloads the document via the provided link.

[0006] A "user" is a person or group that operates a terminal used to input data and generate documents using a system.

[0007] A "server" is a computer system that receives, stores, analyzes, and automatically generates and provides data sent by users.

[0008] "Data" refers to numerical information, text information, or information in file format provided by the user, and serves as the basis for generating materials.

[0009] "Keywords" are specific terms or phrases that users specify when generating data, and they influence the analysis and content of the data.

[0010] "Data cleansing" is the process of correcting or removing missing or outlier values ​​in the data and converting it into a format suitable for analysis.

[0011] "Analysis" is the process of extracting useful information from cleansed data using statistical methods and machine learning models.

[0012] "Documents" refer to documents and charts that are automatically generated based on the analysis results, such as presentations and summary tables, tailored to the user's purpose.

[0013] "Automatic generation" refers to the process by which a system creates documents using data and keywords received from the user, without human intervention.

[0014] "Visual formatting and design" refers to the process of adjusting fonts, colors, layouts, and other elements of generated materials to apply a visually appealing and consistent design.

[0015] "Specified format" refers to the file format in which the document is saved, and examples include PowerPoint (.pptx) and Excel (.xlsx).

[0016] A "link" refers to a URL or download address that is sent to a user to provide them with materials. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map where multiple emotions are mapped. [Figure 10] Shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[0039] Program Processing Overview

[0040] 1. User: Data entry

[0041] Users enter the necessary data and keywords on the terminal's interface. They can also upload data files.

[0042] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[0043] 2. Server: Data reception and storage

[0044] The server receives data sent from the user's terminal and stores it temporarily. This ensures that data is available for subsequent processing.

[0045] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[0046] 3. Server: Data cleansing

[0047] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[0048] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[0049] 4. Server: Data Analysis

[0050] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[0051] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[0052] 5. Server: Data generation

[0053] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[0054] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[0055] 6. Server: Document formatting and design

[0056] Apply visual formatting and design to the generated document. This includes unifying fonts and adjusting colors.

[0057] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[0058] 7. Server: Data storage and distribution

[0059] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[0060] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[0061] 8. User: Downloading and using materials

[0062] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0063] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[0064] Example Flowchart

[0065] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[0066] 2. The server receives and stores the uploaded data.

[0067] 3. The server performs a cleansing process on the data and imputes missing values.

[0068] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[0069] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[0070] 6. The server formats the generated documents by applying the company's brand colors and a unified font.

[0071] 7. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[0072] 8. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[0073] The embodiments of the present invention have now been specifically described. This system enables users to effectively and efficiently generate and provide high-quality materials.

[0074] The following describes the processing flow.

[0075] Step 1:

[0076] The user enters the necessary data and keywords on the terminal interface. Specifically, they enter sales data files or product lists into input fields and select the desired type of document.

[0077] Step 2:

[0078] The server receives data sent by the user. Specifically, it saves the file to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[0079] Step 3:

[0080] The server detects missing or outlier values ​​in the received data and performs the necessary cleansing processes. For example, it might impute missing values ​​with the mean or median, or remove abnormally high or low values.

[0081] Step 4:

[0082] The server analyzes the cleansed data using AI models and statistical methods. Specifically, it calculates monthly sales summaries and product-specific sales trends, and generates analysis results.

[0083] Step 5:

[0084] The server automatically generates documents based on the analysis results. Specifically, it creates presentation slides using the PowerPoint library and places graphs and text. For example, it might place a monthly sales trend graph on the first slide and a product-specific sales pie chart on the second slide.

[0085] Step 6:

[0086] The server applies visual formatting and design to the generated materials. Specifically, it applies the company's brand colors and a consistent font, and adjusts the slide layout. This results in a visually appealing and consistent design.

[0087] Step 7:

[0088] The server saves the completed document in the specified format. Specifically, it saves the generated presentation as a file named "SalesPresentation.pptx" to the server's storage.

[0089] Step 8:

[0090] The server generates a download link for the stored materials and provides it to the user. Specifically, this involves either sending the link to the user's email address or displaying it on the interface.

[0091] Step 9:

[0092] Users download materials by clicking on the provided links. Specifically, they use the downloaded materials for their own presentations and reports. For example, they might use a presentation generated during a sales meeting to give a presentation.

[0093] (Example 1)

[0094] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] Traditional data creation systems require users to manually organize and analyze large amounts of data to create documents, which is time-consuming and labor-intensive. Furthermore, inconsistencies in document quality and consistency are a problem. Additionally, data loss and outlier handling may not be performed properly, potentially reducing the reliability of analysis results.

[0096] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0097] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, and means for the user to download the materials via a provided link. This enables the user to generate and provide high-quality materials effectively and efficiently.

[0098] A "user" is the entity that uses the system to input data and keywords and generate materials.

[0099] A "terminal" is a device operated by a user, equipped with an interface for data input and file uploads.

[0100] A "server" is a computer system that receives data sent by users, stores it, cleanses it, analyzes it, and generates and provides data.

[0101] "Data" refers to information that users input or upload to the system, which forms the basis for analysis and data generation.

[0102] "Keywords" are words or phrases that users input into the system and are used to specify the policy and purpose of creating documents.

[0103] "Data cleansing" is a process that involves imputing or removing missing or outlier values ​​from data.

[0104] "Analysis" is the process of analyzing cleansed data using AI models and statistical methods to extract useful information.

[0105] "Documents" are automatically generated by the server based on the analysis results and take the form of presentations or reports.

[0106] "Visual formatting and design" refers to the process of improving the appearance of generated materials by unifying fonts and adjusting colors.

[0107] A "specified format" refers to the file format used when saving completed documents, such as .pptx or .xlsx.

[0108] A "download link" is a URL that allows users to obtain the completed document.

[0109] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for carrying out this invention will be described with a system program flow and specific examples.

[0110] This system uses the following hardware and software.

[0111] hardware

[0112] Device: A device used by the user (e.g., PC, tablet, smartphone)

[0113] Server: A computer system that receives, stores, analyzes, and generates data.

[0114] software

[0115] Frontend Interface: A web application for users to input data and keywords.

[0116] Backend system: Server-side programs that receive, store, cleanse, analyze, and generate data.

[0117] Database: A data storage system used for data persistence (e.g., MySQL®, PostgreSQL)

[0118] Analysis tools: Software used for data analysis (e.g., Python, TENSORFLOW®, Pandas, Matplotlib)

[0119] Presentation generation tools: Software used to generate presentation materials (e.g., Microsoft® PowerPoint, Python-pptx)

[0120] Processing flow and specific operating procedures

[0121] Data entry

[0122] The user accesses the web application on their device, uploads the necessary data files, and enters keywords for the document they want to generate. For example, a user might upload "Sales Data from January to December 2023.xlsx" and enter keywords to generate a document titled "Sales Presentation by Product."

[0123] Data reception and storage

[0124] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory. It then securely stores the received data in the database. A data backup is taken at this point.

[0125] Data cleansing

[0126] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median. For example, if sales data contains missing values, it will be imputed with the average sales value for the same month of the previous year.

[0127] Data Analysis

[0128] Based on the cleansed data, data analysis is performed using TensorFlow and statistical methods. This analysis includes time series analysis and regression analysis. As a result of the analysis, aggregated results and trends of monthly sales data are generated. Based on the analysis results, data for graphing is generated using the Matplotlib library in Python.

[0129] Material generation

[0130] The server automatically generates presentation materials based on the analysis results. It uses libraries such as Python-pptx to create slides according to pre-prepared templates. For example, it can insert line graphs showing monthly sales trends and pie charts showing sales by product into the slides.

[0131] Document formatting and design

[0132] Apply visual formatting and design to the generated documents. Reflect the company's brand colors and consistent fonts to improve their appearance. For example, use the company's corporate colors as the background and arrange titles and text using a consistent font.

[0133] Document preservation and distribution

[0134] The completed document is saved in the specified format (.pptx, .xlsx, etc.), and a download link is generated for the user. This link is sent to the user via email or other means, and the user can click it to download the document.

[0135] Download and use the materials

[0136] Users can download materials by clicking the provided download link and use them for presentations and reports. For example, they might use "Sales Presentation.pptx" to explain sales figures in a sales meeting.

[0137] This system allows users to quickly create and provide high-quality materials without requiring extra effort.

[0138] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0139] Step 1:

[0140] The user accesses the web application on their device, uploads the necessary data files, and enters keywords.

[0141] Input: Sales data file (e.g., SalesData.xlsx), Keywords for the generated document (e.g., Product Sales Presentation)

[0142] Specific actions: The user clicks the "File Upload" button, selects a local sales data file, and uploads it. They also enter the keyword "Product Sales Presentation" into the text box.

[0143] Output: The uploaded data file and the entered keyword are sent to the server.

[0144] Step 2:

[0145] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory.

[0146] Input: Uploaded data file and entered keywords

[0147] Specific operation: The server receives an HTTP request and performs file handling to save the data file to a temporary directory. Then, it saves the data to the database.

[0148] Output: Data files and keywords stored in the database

[0149] Step 3:

[0150] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median.

[0151] Input: Sales data file stored in the database

[0152] Specific operation: The cleansing script uses Pandas to read the data file and imputes missing values ​​with the mean or median. Outliers are filtered or corrected.

[0153] Output: Cleansed dataset

[0154] Step 4:

[0155] The server performs data analysis using TensorFlow and statistical methods based on the cleansed data. This includes time series analysis and regression analysis. As a result of the analysis, aggregated monthly sales data and trends are generated.

[0156] Input: Cleansed dataset

[0157] Specific operation: The analysis script uses TensorFlow and other analysis tools to analyze the data and extract useful information (e.g., monthly sales trends).

[0158] Output: Data analysis results (e.g., monthly sales summary data, data for graphs)

[0159] Step 5:

[0160] The server automatically generates presentation materials based on the analysis results. It creates slides according to pre-prepared templates using libraries such as Python-pptx.

[0161] Input: Data analysis results

[0162] Specific operation: The generation script uses the Python-pptx library to insert data into the template and automatically generates slides containing graphs and text.

[0163] Output: Generated presentation materials (e.g., Sales Presentation.pptx)

[0164] Step 6:

[0165] The server applies visual formatting and design to the generated documents. It incorporates the company's brand colors and consistent fonts to improve their appearance.

[0166] Input: Generated presentation materials

[0167] Specific actions: The design script applies the company's brand colors to the entire document and unifies the font style and size.

[0168] Output: Final presentation materials with the design applied.

[0169] Step 7:

[0170] The server saves the completed document in the specified format and generates a download link to provide to the user.

[0171] Input: Final presentation materials with design applied.

[0172] Specific operation: The server saves the document in the specified format (e.g., .pptx) and generates a download link to send to the user.

[0173] Output: Download link

[0174] Step 8:

[0175] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0176] Input: Download link

[0177] Specific operation: The user clicks on a download link provided via email or notification and downloads the document through their browser.

[0178] Output: The final version of the presentation materials saved on the user's device (e.g., Sales Presentation.pptx)

[0179] (Application Example 1)

[0180] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0181] Conventional data generation systems struggled to automatically analyze viewer trends and content evaluations by analyzing viewing and comment data. Furthermore, when large amounts of data were available, there was a lack of means to summarize the analysis results and provide them in a visually easy-to-understand format. As a result, content providers were unable to quickly obtain data-driven insights, making effective content revisions and new content creation difficult.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0183] In this invention, the server includes means for users to upload viewing data and comment data, for the server to analyze this data to automatically analyze viewer trends and content evaluations and generate a report, for users to input data and keywords, for the server to receive and temporarily store the input data, for the server to perform a cleansing process on the received data, for the server to analyze the cleansed data and generate analysis results, for the server to automatically generate materials based on the analysis results, for the server to apply visual formatting and design to the generated materials, for the server to save the completed materials in a specified format and provide them to the user, and for the user to download the materials via a provided link. This makes it possible to easily analyze viewing data and comment data and automatically generate visually easy-to-understand materials.

[0184] A "user" refers to a person who uses the system to input data and keywords.

[0185] "Data" refers to information that is analyzed on the server, such as viewing data and comment data.

[0186] A "keyword" refers to a specific word or phrase used in data analysis.

[0187] A "server" refers to a device or system that receives data, temporarily stores it, analyzes it, and generates and provides materials.

[0188] "Temporarily saving" refers to saving the entered data for a short period before analysis.

[0189] "Data cleansing" refers to the process of preparing data by removing or imputing missing or outlier values.

[0190] "Analysis" refers to the process of extracting information from data using statistical or AI technology.

[0191] "Analysis results" refer to useful information and trends derived from the analysis of data.

[0192] "Documents" refers to visually formatted documents and reports generated based on data analysis results.

[0193] "Visual formatting" refers to actions taken to improve the appearance of a document, such as unifying fonts and adjusting colors.

[0194] "Specified format" refers to a specific file format (such as .pptx or .xlsx) used when saving documents.

[0195] A "link" refers to the URL or path that allows a user to download materials.

[0196] "Viewing data" refers to data about how viewers use specific content.

[0197] "Comment data" refers to text data such as opinions and impressions left by viewers regarding specific content.

[0198] "Viewer trends" refer to viewer behavior patterns and preferences derived from viewing data and comment data.

[0199] "Content evaluation" refers to the assessment of content quality based on comment data and viewing data.

[0200] A "report" refers to a document that summarizes the results of the analysis of viewing data and comment data.

[0201] This invention relates to a system in which users upload viewing data and comment data, a server analyzes this data to automatically analyze viewer trends and content evaluations, and generates a report based on the analysis results. Several main hardware and software components are required to implement this system. Their configurations and the specific processes at each step are described below.

[0202] Users first upload viewing and comment data to the system using their own devices. The data can be in formats such as CSV or text files. The uploaded data is sent to the server and temporarily stored.

[0203] The server performs a cleansing process on the received data. Specifically, it removes or imputes missing or outlier values ​​in the data. The Python pandas library is used for this process to ensure high-quality data.

[0204] Next, the server analyzes the cleansed data. This analysis includes counting the number of views and counting the frequency of specific keywords. The pandas library is used to count the number of views, and Python's string manipulation functions are used for keyword analysis.

[0205] The analysis results are automatically generated as visually easy-to-understand materials. This involves creating graphs using the matplotlib and seaborn libraries, and compiling them into a PDF report using the FPDF library. This allows for a visual confirmation of trends in viewing data and the frequency of keyword occurrences.

[0206] Finally, the server saves the generated document in the specified format (e.g., PDF) and provides the user with a download link. By clicking the provided link, the user can download the generated document and use it for sales meetings or report writing.

[0207] As a concrete example, a user uploads a "views_and_comments.csv" file and specifies the keywords "great" and "interesting". The system analyzes the uploaded data and generates a report that includes a graph of viewing data trends and the frequency of keyword occurrences. An example of a prompt message corresponding to this specific example is as follows:

[0208] Example of a prompt:

[0209] "Please analyze viewing trends and the frequency of specific keywords based on viewing and comment data from June 2023. The keywords are "great" and "interesting." Please provide the results in a PDF report."

[0210] This system will allow users to easily analyze viewing and comment data, quickly gaining insights for effective content revisions and the creation of new content.

[0211] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0212] Step 1:

[0213] Users upload viewing data and comment data files (e.g., "views_and_comments.csv") from their terminal to the system. They also enter keywords for additional analysis. The input data consists of viewing data and comment data files, as well as keywords. If the user specifies keywords such as "great" or "interesting," these keywords are sent to the server.

[0214] Step 2:

[0215] The server receives data files sent by users and stores them temporarily. The temporarily stored data consists of viewing data and comment data in CSV format. This storage prepares the data for subsequent cleansing and analysis.

[0216] Step 3:

[0217] The server performs a cleansing process on the received data. Specifically, it uses the pandas library to remove or impute missing or outlier values. The input is temporarily stored viewing data and comment data, and the output is the cleansed data.

[0218] Step 4:

[0219] The server analyzes the cleaned data. Specifically, it aggregates view counts and counts keyword occurrences. The input consists of cleaned data and keywords specified by the user, and the output is aggregated daily view counts and keyword occurrences. Pandas is used for view count aggregation, and Python string manipulation is used for keyword analysis.

[0220] Step 5:

[0221] The server automatically generates visually easy-to-understand materials based on the analysis results. Specifically, it creates graphs using matplotlib and seaborn with the analysis results and compiles them into a PDF report using the FPDF library. The input is the analysis results (view count trend data, keyword frequency data), and the output is a visual material (PDF report).

[0222] Step 6:

[0223] The server applies visual formatting and design to the generated document. Specifically, this includes unifying fonts, adjusting colors, and refining graph layouts. The input is an initial version of the PDF report, and the output is a formatted, final version of the PDF report.

[0224] Step 7:

[0225] The server saves the completed document in the specified format (e.g., PDF) and generates a download link. The user downloads the document via the provided link. The input is a formatted, final PDF report, and the output is a download link provided to the user.

[0226] Step 8:

[0227] The user downloads the generated document using a download link provided by the server. This document is then used for sales meetings and report writing. The input is the download link provided by the server, and the output is the final downloaded document.

[0228] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0229] This invention relates to a system in which a user inputs data and keywords, a server receives, analyzes, and formats that data to automatically generate materials, and further recognizes the user's emotions to adjust the content and style of the materials before providing them. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[0230] Program Processing Overview

[0231] 1. User: Data entry

[0232] Users enter the necessary data and keywords on the terminal interface. They can also upload data files.

[0233] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[0234] 2. Server: Data reception and storage

[0235] The server receives data sent from the user's terminal and stores it temporarily. This makes the data available for subsequent processing.

[0236] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[0237] 3. Server: Data cleansing

[0238] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[0239] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[0240] 4. Server: Data Analysis

[0241] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[0242] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[0243] 5. Server: Data generation

[0244] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[0245] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[0246] 6. Server: Adjustment by the emotion engine

[0247] The emotion engine recognizes the user's emotions and adjusts the content and style of the materials based on that emotional information. Emotions are detected from the user's facial expressions and tone of voice.

[0248] Specific example: If a user is feeling stressed, the system will use relaxing colors and fonts in the material.

[0249] 7. Server: Document formatting and design

[0250] Apply visual formatting and design to the generated materials. This includes applying the company's brand colors and a consistent font, and adjusting the slide layout.

[0251] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[0252] 8. Server: Data storage and distribution

[0253] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[0254] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[0255] 9. User: Downloading and using materials

[0256] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0257] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[0258] Example Flowchart

[0259] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[0260] 2. The server receives and stores the uploaded data.

[0261] 3. The server performs a cleansing process on the data and imputes missing values.

[0262] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[0263] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[0264] 6. The emotion engine analyzes the user's facial expressions and tone of voice, and adjusts the design of the materials accordingly. For example, if it detects that the user is tired, it applies a calming color scheme to the materials.

[0265] 7. The server formats the generated documents by applying the company's brand colors and a unified font.

[0266] 8. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[0267] 9. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[0268] The embodiments of the present invention are described in detail above. This system enables users to efficiently and effectively generate and provide high-quality materials, and by combining it with emotion recognition, it is possible to propose content and styles that better suit the user's needs.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] The user enters the necessary data and keywords on the device's interface. Specifically, they upload sales data files using the file upload function and select the type of document they want to generate. The user also grants permission for the camera and microphone to be used, and prepares input for emotion recognition.

[0272] Step 2:

[0273] The server receives data sent by the user. The file is saved to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[0274] Step 3:

[0275] The server executes cleansing processing on the received data. Specifically, it fills in missing values with the average or median value, detects and removes abnormally high or low values. Also, it unifies the data format and formats it into a form suitable for analysis.

[0276] Step 4:

[0277] The server analyzes the cleansed data using an AI model or statistical methods. Specifically, it aggregates the sales data monthly and calculates the sales trend for each product. It extracts the necessary information based on the analysis results and generates data for graph creation.

[0278] Step 5:

[0279] The server automatically generates materials based on the analysis results. Specifically, it uses the PowerPoint library to create slides and arranges various graphs and texts in the necessary places. For example, it inserts a monthly sales trend graph on the first slide and a pie chart of sales by product on the second slide.

[0280] Step 6:

[0281] The emotion engine recognizes the user's emotion. The server collects the user's expression and voice tone with a camera and a microphone and analyzes the user's current emotional state using an emotion recognition algorithm.

[0282] Step 7:

[0283] The server adjusts the content and style of the materials based on the emotion recognition results. Specifically, when the user is feeling stressed, it changes the color scheme of the materials to calming colors and makes the font easy to read. Also, when the user has a positive emotion, it applies bright color schemes and lively designs.

[0284] Step 8:

[0285] Apply visual formatting and design to the materials generated by the server. Specifically, use the company's brand color and unified font to adjust the layout of the entire slide. This provides consistent and easy-to-read materials.

[0286] Step 9:

[0287] Save the materials completed by the server in a specified format (e.g.,.pptx,.xlsx). Save the generated presentation materials as the "Sales Presentation.pptx" file in the storage within the server.

[0288] Step 10:

[0289] Generate a download link for the materials saved by the server and provide it to the user. Specifically, send the download link to the user's email address or display a download button on the UI.

[0290] Step 11:

[0291] The user clicks the provided link to download the materials. Use the obtained materials for various presentations and report creations. For example, use the presentation generated at the sales meeting for the presentation.

[0292] As described above, the user can efficiently generate and use high-quality materials that reflect their emotional state.

[0293] (Example 2)

[0294] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0295] Traditional document creation systems have the problem of requiring users to manually input data, perform analysis, and generate documents, which is time-consuming and labor-intensive. Furthermore, the generated documents may not align with the user's emotions and needs, resulting in low user satisfaction. Therefore, there is a need for a system that can automatically generate efficient and high-quality documents, and further adjust the content and style based on the user's emotions.

[0296] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform cleansing processing on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply overall visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for the server to recognize the user's emotions and adjust the content and visual style of the materials based on the emotional information. This enables the efficient and automatic generation of high-quality materials, and further enables the provision of personalized materials that respond to the user's emotions.

[0297] "User" refers to the end user who uses this system to input data and generate documents.

[0298] A "server" refers to a central computing system that receives, stores, analyzes, generates data from users, and performs sentiment recognition.

[0299] "Data" refers to the information and files that users input into the system, and specifically includes sales data, business data, and so on.

[0300] "Keywords" refer to words or phrases that represent topics or subjects specified by the user when generating materials.

[0301] "Data cleansing" refers to a process of correcting or removing missing values and outliers from the received data.

[0302] "Analysis" refers to a process of analyzing the cleansed data using AI models or statistical methods to extract meaningful information.

[0303] "Materials" refer to reports or presentation materials generated based on the analysis results, specifically including graphs and text.

[0304] "Visual formatting and design" refer to the work of applying a good-looking layout and color scheme to the generated materials.

[0305] "Specified format" refers to a specific file format used when saving the generated materials, including, for example,.pptx and.xlsx.

[0306] "Link" refers to the URL or web link provided for the user to download the materials.

[0307] "Emotion recognition" refers to a technology that analyzes the user's emotion from their expression and voice tone, and as a result, adjusts the content and style of the materials.

[0308] "Automatic generation" refers to a process in which the system programmatically creates materials based on the user's input information.

[0309] The present invention is a system in which a user inputs data and keywords, the server receives, analyzes, and formats the data to automatically generate materials, and further recognizes the user's emotion to adjust and provide the content and style of the materials. The embodiments for implementing the present invention will be specifically described below.

[0310] Overview

[0311] This system consists of three main elements: the user's terminal, the server, and the emotion recognition engine.

[0312] User terminal

[0313] The user enters the necessary data and keywords using the terminal interface. The terminal is assumed to be a common device such as a PC or smartphone. For example, the user might upload "Sales Data from January to December 2023.xlsx" and enter "Sales Presentation by Product" as the keyword. This allows the user to clearly specify their requirements for the system.

[0314] server

[0315] The server has multiple functions. First, it receives data sent from users and stores it temporarily. After receiving the data, the server performs a cleansing process. This process includes imputing missing values ​​with the mean or median using the Pandas library. Next, it analyzes the cleansed data and extracts specific information. This analysis uses the Sci-kit Learn library to analyze data trends and patterns and generate data for graphing.

[0316] Subsequently, the system automatically generates documents based on the analysis results. In this step, graphs are created using the Matplotlib library, and PowerPoint slides are automatically generated using the python-pptx library. The generated documents are then formatted and designed visually, applying the company's brand colors and a unified font.

[0317] Emotion recognition engine

[0318] The emotion recognition engine analyzes the user's emotions in real time. Specifically, it uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and tone of voice to recognize emotions such as stress and fatigue. Based on this, it adjusts the color scheme and fonts of the materials to provide a style that is optimal for the user's state.

[0319] Specific example

[0320] When a user accesses the system interface and uploads "Sales Data.xlsx," the server receives and saves the data. Next, the server performs data cleansing, imputing missing values ​​with the average sales value for the same month of the previous year. Then, the server analyzes the cleansed data and generates aggregated monthly sales data. Based on the analysis results, a presentation document is automatically generated in PowerPoint format. An emotion recognition engine analyzes the user's facial expressions and tone of voice, adjusting the design of the document accordingly. For example, if the system detects that the user is tired, it applies a calming color scheme to the document. Finally, the server saves the completed document in the specified format and provides the user with a download link. The user clicks the provided link to download the document and use it in their sales meeting.

[0321] As a result, this system can automatically generate efficient and high-quality materials and provide personalized materials that respond to the user's emotions.

[0322] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0323] Step 1:

[0324] The user uses the terminal interface to input the required data and keywords. The entered data is in a file format such as "Sales Data for January to December 2023.xlsx," and the keyword is "Sales Presentation by Product." The user then begins submitting this input data.

[0325] Input: Sales data.xlsx, Sales presentation by product

[0326] Output: The event where input data is sent from the terminal to the server.

[0327] Step 2:

[0328] The server receives the data sent by the user and temporarily stores it in the database. Once the storage is complete, it sends a confirmation message to the user acknowledging receipt of the data.

[0329] Input: Sales data.xlsx (data sent from the terminal), keywords

[0330] Output: Sales data.xlsx file successfully saved to the database; confirmation message sent to the user.

[0331] Step 3:

[0332] The server performs a cleansing process on the stored data. This process uses the Pandas library to impute missing values ​​and detect outliers. Means and medians are used as imputation methods. If outliers are detected, the user is notified and asked to correct them.

[0333] Input: SalesData.xlsx (Saved Data)

[0334] Output: Cleansed data (missing values ​​are imputed and outliers are removed)

[0335] Step 4:

[0336] The server analyzes the cleansed data and generates analysis results. It utilizes the Sci-kit Learn library to extract trend analysis and summary information. For example, it generates aggregated monthly sales data and creates data for graphing.

[0337] Input: Cleansed sales data

[0338] Output: Analysis results (trend analysis data, summary information)

[0339] Step 5:

[0340] The server automatically generates documents based on the analysis results. This process uses the Matplotlib library to create graphs and the python-pptx library to automatically generate PowerPoint slides. For example, monthly sales trend graphs and product-specific sales pie charts are inserted into the slides.

[0341] Input: Analysis results (trend analysis data, summary information)

[0342] Output: Automatically generated PowerPoint presentation (sales presentation)

[0343] Step 6:

[0344] The server uses an emotion recognition engine to recognize the user's emotions in real time. By combining OpenCV and TensorFlow libraries, it analyzes the user's facial expressions and tone of voice to determine emotions such as stress and fatigue. Based on this emotion information, the content and style of the materials are adjusted.

[0345] Input: User's facial expressions and voice tone (real-time data)

[0346] Output: Adjustments based on emotional information (changes to the style and color scheme of the document)

[0347] Step 7:

[0348] The server applies the company's brand colors and a consistent font to the generated documents, performing overall visual formatting and design. This ensures that the documents are visually appealing and compliant with the company's guidelines.

[0349] Input: Automated document

[0350] Output: A formatted document with the company's brand colors and fonts applied.

[0351] Step 8:

[0352] The server saves the completed document in the specified format (e.g., .pptx) and generates a download link to provide to the user. The download link is distributed via email or notification system.

[0353] Input: Formatted design document

[0354] Output: Saved document (.pptx), download link for the user

[0355] Step 9:

[0356] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing. These materials can then be used for presentations at sales meetings and reporting sessions.

[0357] Input: Download link

[0358] Output: Downloaded document (Sales Presentation.pptx)

[0359] (Application Example 2)

[0360] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0361] Current document generation systems sometimes fail to adequately address the user's psychological state when they receive automatically generated documents. This can lead to stress and difficulty in understanding the documents, resulting in a diminished user experience. Furthermore, it is difficult to properly visualize the analysis results of data from autonomous vehicles and driver evaluation data, making it challenging to communicate operational performance and driver evaluations efficiently and effectively.

[0362] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0363] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for detecting the user's emotions using an emotion recognition system and adjusting the content and design of the materials. This enables the generation of optimal materials that respond to the user's emotions, making the materials easier to understand and reducing stress during use. Furthermore, it enables the efficient and effective visualization of autonomous vehicle operation data and driver evaluation data, providing users with useful information.

[0364] "Means for users to input data and keywords" refers to a device or software that provides an interface for users to input operational data of autonomous vehicles, driver evaluation data, and keywords necessary for their analysis.

[0365] "Means by which a server receives and temporarily stores input data" refers to a function that receives data sent by a user and temporarily stores that data for use in subsequent processing.

[0366] "Means by which the server performs cleansing on received data" refers to a function that improves data quality by performing processes to impart or remove missing or abnormal values ​​from the received data.

[0367] "Means for a server to analyze cleansed data and generate analysis results" refers to a function that uses statistical analysis and machine learning models to analyze cleansed data and extract useful information and trends.

[0368] "A means for the server to automatically generate documents based on analysis results" refers to a function that automatically creates explanatory documents and reports according to a template based on the obtained analysis results.

[0369] "Means by which the server applies visual formatting and design to generated materials" refers to a function that applies a visually consistent design and layout to automatically generated materials.

[0370] "Means by which the server saves completed documents in a specified format and provides them to the user" refers to a function that saves the final documents in a specified format (e.g., .pptx, .pdf) and provides them to the user via an accessible link or other means.

[0371] "Means for users to download materials via provided links" refers to a function that allows users to download and obtain necessary materials by clicking or accessing provided links.

[0372] "A means of detecting a user's emotions using an emotion recognition system and adjusting the content and design of materials" refers to a function that uses sensing devices such as cameras and microphones to detect emotions from the user's facial expressions and tone of voice, and dynamically adjusts the content and design of materials based on that emotional information.

[0373] A specific embodiment of this invention is shown below. Here, we describe a system that generates data based on the operation data of an autonomous vehicle and the driver's evaluation data.

[0374] First, users input performance data, evaluation data, and keywords for analysis through the interface of a device such as a smartphone or tablet. This data includes, for example, "Autonomous Vehicle Data from January to December 2023.xlsx" and "Driver Evaluation Data.xlsx". Users can also choose to generate a "Monthly Performance Report".

[0375] The server receives data sent from the user's terminal and temporarily stores it in the database. In this stage, the received data undergoes cleansing processes, such as imputing or removing missing or outlier values. Specifically, if missing values ​​are detected, the corresponding data is imputed using the most recent mean value or similar.

[0376] The cleansed and processed data is analyzed using data analysis software such as Python, Pandas, and Scikit-learn. The results of the analysis include, for example, aggregated data on monthly operational performance and trend data on driver evaluations.

[0377] Next, the process moves to creating automatically generated materials based on the analysis results. Using tools such as Python-PPTX or Jinja2, PowerPoint presentation materials are created using templates suitable for the generated data. These materials include monthly operational performance graphs and pie charts of driver evaluations.

[0378] In addition, the emotion recognition system analyzes the user's facial expressions and tone of voice, and adjusts the design and content of the materials according to the user's psychological state. For example, if the user is feeling stressed, the color scheme and font of the materials can be changed to create a more relaxed atmosphere. This utilizes technologies such as OpenCV, TensorFlow, and the Microsoft Azure® Emotion Recognition API.

[0379] Finally, the completed document will have the company's brand colors and a unified font applied. The document will be saved in the specified format (e.g., .pptx, .pdf), and a download link will be generated using AWS® S3 or the Django Rest Framework. This link will be provided to the user, who can download and use the document via the link.

[0380] Specific example:

[0381] Input data: "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx"

[0382] Example of a prompt:

[0383] Please upload the operational performance data and driver evaluation data for autonomous vehicles from January to December 2023 and generate a monthly performance report.

[0384] In this way, the system automatically generates and provides high-quality analytical materials that are adapted to the user's psychological state, based on the user's input data.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] Users input autonomous vehicle operation data and driver evaluation data through a smartphone or tablet interface. Specifically, they upload "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx" files and select to generate a "Monthly Performance Report".

[0388] Input: Operation data and keywords, "Autonomous Vehicle Data.xlsx", and "Driver Evaluation Data.xlsx"

[0389] Output: Data sent from the user's device

[0390] Step 2:

[0391] The server receives data sent from the user's terminal and temporarily stores it in the database. This storage process makes the data available for subsequent processing.

[0392] Input: Data submitted by the user

[0393] Output: Data stored in the database

[0394] Step 3:

[0395] The server performs a cleansing process on the received data. Specifically, it detects missing values ​​and imputes them with the mean or median. Outliers are also identified and corrected or removed as necessary.

[0396] Input: Saved data

[0397] Output: Cleansed data

[0398] Step 4:

[0399] The server cleanses the data and analyzes it using data analysis software such as Python, Pandas, and Scikit-learn to generate analysis results. This includes aggregating monthly operational data and extracting trends in driver evaluations.

[0400] Input: Cleansed data

[0401] Output: Analysis results data (e.g., monthly operational performance graph, driver evaluation pie chart data)

[0402] Step 5:

[0403] The server automatically generates documents based on the analysis results according to a template. Tools such as Python-PPTX and Jinja2 are used for this process. Specifically, it creates a PowerPoint presentation based on the analysis results.

[0404] Input: Analysis result data

[0405] Output: Generated document (e.g., .pptx file)

[0406] Step 6:

[0407] The server uses an emotion recognition system to acquire user emotion information. This utilizes sensing devices such as cameras and microphones, and employs OpenCV, TensorFlow, and the Microsoft Azure Emotion Recognition API. It analyzes the user's facial expressions and voice tone to detect emotions.

[0408] Input: User facial expression data and voice data

[0409] Output: Detected emotion information

[0410] Step 7:

[0411] The server-generated materials are adjusted to reflect emotional information, making appropriate design and content changes. For example, if a user is feeling stressed, the color scheme and fonts of the materials are changed to a more relaxing style.

[0412] Input: Detected emotion information, generated data

[0413] Output: Adjusted document (e.g., a .pptx file with modified colors and fonts)

[0414] Step 8:

[0415] The server applies the company's brand colors and a unified font to the prepared documents and saves the final documents in the specified format (e.g., .pptx, .pdf). AWS S3 or Django Rest Framework is used for saving the documents and generating download links.

[0416] Input: Adjusted materials

[0417] Output: Saved final document, generated download link

[0418] Step 9:

[0419] Users download materials by clicking on the provided download link and use them in meetings, etc. Users access the link via their device and obtain the necessary materials.

[0420] Input: Download link

[0421] Output: Downloaded document (e.g., .pptx file)

[0422] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0423] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0424] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0425] [Second Embodiment]

[0426] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0427] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0428] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0429] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0430] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0431] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0432] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0433] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0434] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0435] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0436] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0437] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0438] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[0439] Program Processing Overview

[0440] 1. User: Data entry

[0441] Users enter the necessary data and keywords on the terminal's interface. They can also upload data files.

[0442] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[0443] 2. Server: Data reception and storage

[0444] The server receives data sent from the user's terminal and stores it temporarily. This ensures that data is available for subsequent processing.

[0445] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[0446] 3. Server: Data cleansing

[0447] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[0448] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[0449] 4. Server: Data Analysis

[0450] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[0451] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[0452] 5. Server: Data generation

[0453] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[0454] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[0455] 6. Server: Document formatting and design

[0456] Apply visual formatting and design to the generated document. This includes unifying fonts and adjusting colors.

[0457] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[0458] 7. Server: Data storage and distribution

[0459] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[0460] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[0461] 8. User: Downloading and using materials

[0462] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0463] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[0464] Example Flowchart

[0465] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[0466] 2. The server receives and stores the uploaded data.

[0467] 3. The server performs a cleansing process on the data and imputes missing values.

[0468] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[0469] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[0470] 6. The server formats the generated documents by applying the company's brand colors and a unified font.

[0471] 7. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[0472] 8. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[0473] The embodiments of the present invention have now been specifically described. This system enables users to effectively and efficiently generate and provide high-quality materials.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The user enters the necessary data and keywords on the terminal interface. Specifically, they enter sales data files or product lists into input fields and select the desired type of document.

[0477] Step 2:

[0478] The server receives data sent by the user. Specifically, it saves the file to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[0479] Step 3:

[0480] The server detects missing or outlier values ​​in the received data and performs the necessary cleansing processes. For example, it might impute missing values ​​with the mean or median, or remove abnormally high or low values.

[0481] Step 4:

[0482] The server analyzes the cleansed data using AI models and statistical methods. Specifically, it calculates monthly sales summaries and product-specific sales trends, and generates analysis results.

[0483] Step 5:

[0484] The server automatically generates documents based on the analysis results. Specifically, it creates presentation slides using the PowerPoint library and places graphs and text. For example, it might place a monthly sales trend graph on the first slide and a product-specific sales pie chart on the second slide.

[0485] Step 6:

[0486] The server applies visual formatting and design to the generated materials. Specifically, it applies the company's brand colors and a consistent font, and adjusts the slide layout. This results in a visually appealing and consistent design.

[0487] Step 7:

[0488] The server saves the completed document in the specified format. Specifically, it saves the generated presentation as a file named "SalesPresentation.pptx" to the server's storage.

[0489] Step 8:

[0490] The server generates a download link for the stored materials and provides it to the user. Specifically, this involves either sending the link to the user's email address or displaying it on the interface.

[0491] Step 9:

[0492] Users download materials by clicking on the provided links. Specifically, they use the downloaded materials for their own presentations and reports. For example, they might use a presentation generated during a sales meeting to give a presentation.

[0493] (Example 1)

[0494] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0495] Traditional data creation systems require users to manually organize and analyze large amounts of data to create documents, which is time-consuming and labor-intensive. Furthermore, inconsistencies in document quality and consistency are a problem. Additionally, data loss and outlier handling may not be performed properly, potentially reducing the reliability of analysis results.

[0496] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0497] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, and means for the user to download the materials via a provided link. This enables the user to generate and provide high-quality materials effectively and efficiently.

[0498] A "user" is the entity that uses the system to input data and keywords and generate materials.

[0499] A "terminal" is a device operated by a user, equipped with an interface for data input and file uploads.

[0500] A "server" is a computer system that receives data sent by users, stores it, cleanses it, analyzes it, and generates and provides data.

[0501] "Data" refers to information that users input or upload to the system, which forms the basis for analysis and data generation.

[0502] "Keywords" are words or phrases that users input into the system and are used to specify the policy and purpose of creating documents.

[0503] "Data cleansing" is a process that involves imputing or removing missing or outlier values ​​from data.

[0504] "Analysis" is the process of analyzing cleansed data using AI models and statistical methods to extract useful information.

[0505] "Documents" are automatically generated by the server based on the analysis results and take the form of presentations or reports.

[0506] "Visual formatting and design" refers to the process of improving the appearance of generated materials by unifying fonts and adjusting colors.

[0507] A "specified format" refers to the file format used when saving completed documents, such as .pptx or .xlsx.

[0508] A "download link" is a URL that allows users to obtain the completed document.

[0509] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for carrying out this invention will be described with a system program flow and specific examples.

[0510] This system uses the following hardware and software.

[0511] hardware

[0512] Device: A device used by the user (e.g., PC, tablet, smartphone)

[0513] Server: A computer system that receives, stores, analyzes, and generates data.

[0514] software

[0515] Frontend Interface: A web application for users to input data and keywords.

[0516] Backend system: Server-side programs that receive, store, cleanse, analyze, and generate data.

[0517] Database: A data storage system used for data persistence (e.g., MySQL, PostgreSQL)

[0518] Analysis tools: Software used for data analysis (e.g., Python, TensorFlow, Pandas, Matplotlib)

[0519] Presentation generation tools: Software used to generate presentation materials (e.g., Microsoft PowerPoint, Python-pptx)

[0520] Processing flow and specific operating procedures

[0521] Data entry

[0522] The user accesses the web application on their device, uploads the necessary data files, and enters keywords for the document they want to generate. For example, a user might upload "Sales Data from January to December 2023.xlsx" and enter keywords to generate a document titled "Sales Presentation by Product."

[0523] Data reception and storage

[0524] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory. It then securely stores the received data in the database. A data backup is taken at this point.

[0525] Data cleansing

[0526] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median. For example, if sales data contains missing values, it will be imputed with the average sales value for the same month of the previous year.

[0527] Data Analysis

[0528] Based on the cleansed data, data analysis is performed using TensorFlow and statistical methods. This analysis includes time series analysis and regression analysis. As a result of the analysis, aggregated results and trends of monthly sales data are generated. Based on the analysis results, data for graphing is generated using the Matplotlib library in Python.

[0529] Material generation

[0530] The server automatically generates presentation materials based on the analysis results. It uses libraries such as Python-pptx to create slides according to pre-prepared templates. For example, it can insert line graphs showing monthly sales trends and pie charts showing sales by product into the slides.

[0531] Document formatting and design

[0532] Apply visual formatting and design to the generated documents. Reflect the company's brand colors and consistent fonts to improve their appearance. For example, use the company's corporate colors as the background and arrange titles and text using a consistent font.

[0533] Document preservation and distribution

[0534] The completed document is saved in the specified format (.pptx, .xlsx, etc.), and a download link is generated for the user. This link is sent to the user via email or other means, and the user can click it to download the document.

[0535] Download and use the materials

[0536] Users can download materials by clicking the provided download link and use them for presentations and reports. For example, they might use "Sales Presentation.pptx" to explain sales figures in a sales meeting.

[0537] This system allows users to quickly create and provide high-quality materials without requiring extra effort.

[0538] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0539] Step 1:

[0540] The user accesses the web application on their device, uploads the necessary data files, and enters keywords.

[0541] Input: Sales data file (e.g., SalesData.xlsx), Keywords for the generated document (e.g., Product Sales Presentation)

[0542] Specific actions: The user clicks the "File Upload" button, selects a local sales data file, and uploads it. They also enter the keyword "Product Sales Presentation" into the text box.

[0543] Output: The uploaded data file and the entered keyword are sent to the server.

[0544] Step 2:

[0545] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory.

[0546] Input: Uploaded data file and entered keywords

[0547] Specific operation: The server receives an HTTP request and performs file handling to save the data file to a temporary directory. Then, it saves the data to the database.

[0548] Output: Data files and keywords stored in the database

[0549] Step 3:

[0550] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median.

[0551] Input: Sales data file stored in the database

[0552] Specific operation: The cleansing script uses Pandas to read the data file and imputes missing values ​​with the mean or median. Outliers are filtered or corrected.

[0553] Output: Cleansed dataset

[0554] Step 4:

[0555] The server performs data analysis using TensorFlow and statistical methods based on the cleansed data. This includes time series analysis and regression analysis. As a result of the analysis, aggregated monthly sales data and trends are generated.

[0556] Input: Cleansed dataset

[0557] Specific operation: The analysis script uses TensorFlow and other analysis tools to analyze the data and extract useful information (e.g., monthly sales trends).

[0558] Output: Data analysis results (e.g., monthly sales summary data, data for graphs)

[0559] Step 5:

[0560] The server automatically generates presentation materials based on the analysis results. It creates slides according to pre-prepared templates using libraries such as Python-pptx.

[0561] Input: Data analysis results

[0562] Specific operation: The generation script uses the Python-pptx library to insert data into the template and automatically generates slides containing graphs and text.

[0563] Output: Generated presentation materials (e.g., Sales Presentation.pptx)

[0564] Step 6:

[0565] The server applies visual formatting and design to the generated documents. It incorporates the company's brand colors and consistent fonts to improve their appearance.

[0566] Input: Generated presentation materials

[0567] Specific actions: The design script applies the company's brand colors to the entire document and unifies the font style and size.

[0568] Output: Final presentation materials with the design applied.

[0569] Step 7:

[0570] The server saves the completed document in the specified format and generates a download link to provide to the user.

[0571] Input: Final presentation materials with design applied.

[0572] Specific operation: The server saves the document in the specified format (e.g., .pptx) and generates a download link to send to the user.

[0573] Output: Download link

[0574] Step 8:

[0575] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0576] Input: Download link

[0577] Specific operation: The user clicks on a download link provided via email or notification and downloads the document through their browser.

[0578] Output: The final version of the presentation materials saved on the user's device (e.g., Sales Presentation.pptx)

[0579] (Application Example 1)

[0580] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0581] Conventional data generation systems struggled to automatically analyze viewer trends and content evaluations by analyzing viewing and comment data. Furthermore, when large amounts of data were available, there was a lack of means to summarize the analysis results and provide them in a visually easy-to-understand format. As a result, content providers were unable to quickly obtain data-driven insights, making effective content revisions and new content creation difficult.

[0582] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0583] In this invention, the server includes means for users to upload viewing data and comment data, for the server to analyze this data to automatically analyze viewer trends and content evaluations and generate a report, for users to input data and keywords, for the server to receive and temporarily store the input data, for the server to perform a cleansing process on the received data, for the server to analyze the cleansed data and generate analysis results, for the server to automatically generate materials based on the analysis results, for the server to apply visual formatting and design to the generated materials, for the server to save the completed materials in a specified format and provide them to the user, and for the user to download the materials via a provided link. This makes it possible to easily analyze viewing data and comment data and automatically generate visually easy-to-understand materials.

[0584] A "user" refers to a person who uses the system to input data and keywords.

[0585] "Data" refers to information that is analyzed on the server, such as viewing data and comment data.

[0586] A "keyword" refers to a specific word or phrase used in data analysis.

[0587] A "server" refers to a device or system that receives data, temporarily stores it, analyzes it, and generates and provides materials.

[0588] "Temporarily saving" refers to saving the entered data for a short period before analysis.

[0589] "Data cleansing" refers to the process of preparing data by removing or imputing missing or outlier values.

[0590] "Analysis" refers to the process of extracting information from data using statistical or AI technology.

[0591] "Analysis results" refer to useful information and trends derived from the analysis of data.

[0592] "Documents" refers to visually formatted documents and reports generated based on data analysis results.

[0593] "Visual formatting" refers to actions taken to improve the appearance of a document, such as unifying fonts and adjusting colors.

[0594] "Specified format" refers to a specific file format (such as .pptx or .xlsx) used when saving documents.

[0595] A "link" refers to the URL or path that allows a user to download materials.

[0596] "Viewing data" refers to data about how viewers use specific content.

[0597] "Comment data" refers to text data such as opinions and impressions left by viewers regarding specific content.

[0598] "Viewer trends" refer to viewer behavior patterns and preferences derived from viewing data and comment data.

[0599] "Content evaluation" refers to the assessment of content quality based on comment data and viewing data.

[0600] A "report" refers to a document that summarizes the results of the analysis of viewing data and comment data.

[0601] This invention relates to a system in which users upload viewing data and comment data, a server analyzes this data to automatically analyze viewer trends and content evaluations, and generates a report based on the analysis results. Several main hardware and software components are required to implement this system. Their configurations and the specific processes at each step are described below.

[0602] Users first upload viewing and comment data to the system using their own devices. The data can be in formats such as CSV or text files. The uploaded data is sent to the server and temporarily stored.

[0603] The server performs a cleansing process on the received data. Specifically, it removes or imputes missing or outlier values ​​in the data. The Python pandas library is used for this process to ensure high-quality data.

[0604] Next, the server analyzes the cleansed data. This analysis includes counting the number of views and counting the frequency of specific keywords. The pandas library is used to count the number of views, and Python's string manipulation functions are used for keyword analysis.

[0605] The analysis results are automatically generated as visually easy-to-understand materials. This involves creating graphs using the matplotlib and seaborn libraries, and compiling them into a PDF report using the FPDF library. This allows for a visual confirmation of trends in viewing data and the frequency of keyword occurrences.

[0606] Finally, the server saves the generated document in the specified format (e.g., PDF) and provides the user with a download link. By clicking the provided link, the user can download the generated document and use it for sales meetings or report writing.

[0607] As a concrete example, a user uploads a "views_and_comments.csv" file and specifies the keywords "great" and "interesting". The system analyzes the uploaded data and generates a report that includes a graph of viewing data trends and the frequency of keyword occurrences. An example of a prompt message corresponding to this specific example is as follows:

[0608] Example of a prompt:

[0609] "Please analyze viewing trends and the frequency of specific keywords based on viewing and comment data from June 2023. The keywords are "great" and "interesting." Please provide the results in a PDF report."

[0610] This system will allow users to easily analyze viewing and comment data, quickly gaining insights for effective content revisions and the creation of new content.

[0611] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0612] Step 1:

[0613] Users upload viewing data and comment data files (e.g., "views_and_comments.csv") from their terminal to the system. They also enter keywords for additional analysis. The input data consists of viewing data and comment data files, as well as keywords. If the user specifies keywords such as "great" or "interesting," these keywords are sent to the server.

[0614] Step 2:

[0615] The server receives data files sent by users and stores them temporarily. The temporarily stored data consists of viewing data and comment data in CSV format. This storage prepares the data for subsequent cleansing and analysis.

[0616] Step 3:

[0617] The server performs a cleansing process on the received data. Specifically, it uses the pandas library to remove or impute missing or outlier values. The input is temporarily stored viewing data and comment data, and the output is the cleansed data.

[0618] Step 4:

[0619] The server analyzes the cleaned data. Specifically, it aggregates view counts and counts keyword occurrences. The input consists of cleaned data and keywords specified by the user, and the output is aggregated daily view counts and keyword occurrences. Pandas is used for view count aggregation, and Python string manipulation is used for keyword analysis.

[0620] Step 5:

[0621] The server automatically generates visually easy-to-understand materials based on the analysis results. Specifically, it creates graphs using matplotlib and seaborn with the analysis results and compiles them into a PDF report using the FPDF library. The input is the analysis results (view count trend data, keyword frequency data), and the output is a visual material (PDF report).

[0622] Step 6:

[0623] The server applies visual formatting and design to the generated document. Specifically, this includes unifying fonts, adjusting colors, and refining graph layouts. The input is an initial version of the PDF report, and the output is the formatted, final version of the PDF report.

[0624] Step 7:

[0625] The server saves the completed document in the specified format (e.g., PDF) and generates a download link. The user downloads the document via the provided link. The input is a formatted, final PDF report, and the output is a download link provided to the user.

[0626] Step 8:

[0627] The user downloads the generated document using a download link provided by the server. This document is then used for sales meetings and report writing. The input is the download link provided by the server, and the output is the final downloaded document.

[0628] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0629] This invention relates to a system in which a user inputs data and keywords, a server receives, analyzes, and formats that data to automatically generate materials, and further recognizes the user's emotions to adjust the content and style of the materials before providing them. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[0630] Program Processing Overview

[0631] 1. User: Data entry

[0632] Users enter the necessary data and keywords on the terminal interface. They can also upload data files.

[0633] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[0634] 2. Server: Data reception and storage

[0635] The server receives data sent from the user's terminal and stores it temporarily. This makes the data available for subsequent processing.

[0636] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[0637] 3. Server: Data cleansing

[0638] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[0639] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[0640] 4. Server: Data Analysis

[0641] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[0642] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[0643] 5. Server: Data generation

[0644] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[0645] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[0646] 6. Server: Adjustment by the emotion engine

[0647] The emotion engine recognizes the user's emotions and adjusts the content and style of the materials based on that emotional information. Emotions are detected from the user's facial expressions and tone of voice.

[0648] Specific example: If a user is feeling stressed, the system will use relaxing colors and fonts in the material.

[0649] 7. Server: Document formatting and design

[0650] Apply visual formatting and design to the generated materials. This includes applying the company's brand colors and a consistent font, and adjusting the slide layout.

[0651] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[0652] 8. Server: Data storage and distribution

[0653] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[0654] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[0655] 9. User: Downloading and using materials

[0656] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0657] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[0658] Example Flowchart

[0659] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[0660] 2. The server receives and stores the uploaded data.

[0661] 3. The server performs a cleansing process on the data and imputes missing values.

[0662] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[0663] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[0664] 6. The emotion engine analyzes the user's facial expressions and tone of voice, and adjusts the design of the materials accordingly. For example, if it detects that the user is tired, it applies a calming color scheme to the materials.

[0665] 7. The server formats the generated documents by applying the company's brand colors and a unified font.

[0666] 8. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[0667] 9. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[0668] The embodiments of the present invention have now been specifically described. This system enables users to efficiently and effectively generate and provide high-quality materials, and by combining it with emotion recognition, it is possible to propose content and styles that better suit the user's needs.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] The user enters the necessary data and keywords on the device's interface. Specifically, they upload sales data files using the file upload function and select the type of document they want to generate. The user also grants permission for the camera and microphone to be used, and prepares input for emotion recognition.

[0672] Step 2:

[0673] The server receives data sent by the user. The file is saved to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[0674] Step 3:

[0675] The server performs a cleansing process on the received data. Specifically, it imputes missing values ​​with the mean or median, and detects and removes abnormally high or low values. It also standardizes the data format and shapes it into a form suitable for analysis.

[0676] Step 4:

[0677] The server analyzes the cleansed data using AI models and statistical methods. Specifically, it aggregates sales data by month and calculates sales trends for each product. Based on the analysis results, it extracts necessary information and generates data for creating graphs.

[0678] Step 5:

[0679] The server automatically generates documents based on the analysis results. Specifically, it creates slides using the PowerPoint library and places various graphs and text where needed. For example, it inserts a monthly sales trend graph on the first slide and a product-specific sales pie chart on the second slide.

[0680] Step 6:

[0681] The emotion engine recognizes the user's emotions. The server collects the user's facial expressions and voice tone using a camera and microphone, and analyzes the user's current emotional state using an emotion recognition algorithm.

[0682] Step 7:

[0683] The server adjusts the content and style of the materials based on the emotion recognition results. Specifically, if the user is feeling stressed, the color scheme of the materials will be changed to calmer colors and the font will be made easier to read. Conversely, if the user is feeling positive emotions, a bright color scheme and a lively design will be applied.

[0684] Step 8:

[0685] The server applies visual formatting and design to the generated materials. Specifically, it adjusts the overall layout of the slides using the company's brand colors and a consistent font. This results in consistent and easy-to-read materials.

[0686] Step 9:

[0687] The server saves the completed document in the specified format (e.g., .pptx, .xlsx). The generated presentation material is saved as a file named "Sales Presentation.pptx" on the server's storage.

[0688] Step 10:

[0689] The server generates a download link for the stored materials and provides it to the user. Specifically, this involves either sending the download link to the user's email address or displaying a download button on the UI.

[0690] Step 11:

[0691] Users download materials by clicking on the provided link. They then use the downloaded materials to create various presentations and reports. For example, they might use the generated presentation to give a presentation at a sales meeting.

[0692] As a result, users can efficiently generate and use high-quality materials that reflect their emotional state.

[0693] (Example 2)

[0694] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0695] Traditional document creation systems have the problem of requiring users to manually input data, perform analysis, and generate documents, which is time-consuming and labor-intensive. Furthermore, the generated documents may not align with the user's emotions and needs, resulting in low user satisfaction. Therefore, there is a need for a system that can efficiently and automatically generate high-quality documents, and further adjust the content and style based on the user's emotions.

[0696] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform cleansing processing on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply overall visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for the server to recognize the user's emotions and adjust the content and visual style of the materials based on the emotional information. This enables the efficient and automatic generation of high-quality materials, and further enables the provision of personalized materials that respond to the user's emotions.

[0697] "User" refers to the end user who uses this system to input data and generate documents.

[0698] A "server" refers to a central computing system that receives, stores, analyzes, generates data from users, and performs sentiment recognition.

[0699] "Data" refers to the information and files that users input into the system, and specifically includes sales data, business data, and so on.

[0700] "Keywords" refer to words or phrases that represent topics or subjects specified by the user when generating materials.

[0701] "Data cleansing" refers to the process of correcting or removing missing or outlier values ​​from received data.

[0702] "Analysis" refers to the process of analyzing cleansed data using AI models and statistical methods to extract meaningful information.

[0703] "Documents" refers to reports and presentation materials generated based on the analysis results, and specifically includes graphs and text.

[0704] "Visual formatting and design" refers to the process of applying an aesthetically pleasing layout and color scheme to the generated material.

[0705] "Specified format" refers to a specific file format used when saving the generated document, including, for example, .pptx and .xlsx.

[0706] A "link" refers to a URL or web link provided to a user for downloading materials.

[0707] "Emotion recognition" refers to a technology that analyzes a user's emotions from their facial expressions and tone of voice, and then adjusts the content and style of the materials accordingly.

[0708] "Automatic generation" refers to the process by which a system programmatically creates documents based on user input.

[0709] This invention is a system in which a user inputs data and keywords, a server receives, analyzes, and formats that data to automatically generate materials, and further recognizes the user's emotions to adjust the content and style of the materials before providing them. The embodiments for carrying out this invention are described in detail below.

[0710] overview

[0711] This system consists of three main elements: the user's terminal, the server, and the emotion recognition engine.

[0712] User terminal

[0713] The user enters the necessary data and keywords using the terminal interface. The terminal is assumed to be a common device such as a PC or smartphone. For example, the user might upload "Sales Data from January to December 2023.xlsx" and enter "Sales Presentation by Product" as the keyword. This allows the user to clearly specify their requirements for the system.

[0714] server

[0715] The server has multiple functions. First, it receives data sent from users and stores it temporarily. After receiving the data, the server performs a cleansing process. This process includes imputing missing values ​​with the mean or median using the Pandas library. Next, it analyzes the cleansed data and extracts specific information. This analysis uses the Sci-kit Learn library to analyze data trends and patterns and generate data for graphing.

[0716] Subsequently, the system automatically generates documents based on the analysis results. In this step, graphs are created using the Matplotlib library, and PowerPoint slides are automatically generated using the python-pptx library. The generated documents are then formatted and designed visually, applying the company's brand colors and a unified font.

[0717] Emotion recognition engine

[0718] The emotion recognition engine analyzes the user's emotions in real time. Specifically, it uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and tone of voice to recognize emotions such as stress and fatigue. Based on this, it adjusts the color scheme and fonts of the materials to provide a style that is optimal for the user's state.

[0719] Specific example

[0720] When a user accesses the system interface and uploads "Sales Data.xlsx," the server receives and saves the data. Next, the server performs data cleansing, imputing missing values ​​with the average sales value for the same month of the previous year. Then, the server analyzes the cleansed data and generates aggregated monthly sales data. Based on the analysis results, a presentation document is automatically generated in PowerPoint format. An emotion recognition engine analyzes the user's facial expressions and tone of voice, adjusting the design of the document accordingly. For example, if the system detects that the user is tired, it applies a calming color scheme to the document. Finally, the server saves the completed document in the specified format and provides the user with a download link. The user clicks the provided link to download the document and use it in their sales meeting.

[0721] As a result, this system can automatically generate efficient and high-quality materials and provide personalized materials that respond to the user's emotions.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The user uses the terminal interface to input the required data and keywords. The entered data is in a file format such as "Sales Data for January to December 2023.xlsx," and the keyword is "Sales Presentation by Product." The user then begins submitting this input data.

[0725] Input: Sales data.xlsx, Sales presentation by product

[0726] Output: The event where input data is sent from the terminal to the server.

[0727] Step 2:

[0728] The server receives the data sent by the user and temporarily stores it in the database. Once the storage is complete, it sends a confirmation message to the user acknowledging receipt of the data.

[0729] Input: Sales data.xlsx (data sent from the terminal), keywords

[0730] Output: Sales data.xlsx file successfully saved to the database; confirmation message sent to the user.

[0731] Step 3:

[0732] The server performs a cleansing process on the stored data. This process uses the Pandas library to impute missing values ​​and detect outliers. Means and medians are used as imputation methods. If outliers are detected, the user is notified and asked to correct them.

[0733] Input: SalesData.xlsx (Saved Data)

[0734] Output: Cleansed data (missing values ​​are imputed and outliers are removed)

[0735] Step 4:

[0736] The server analyzes the cleansed data and generates analysis results. It utilizes the Sci-kit Learn library to extract trend analysis and summary information. For example, it generates aggregated monthly sales data and creates data for graphing.

[0737] Input: Cleansed sales data

[0738] Output: Analysis results (trend analysis data, summary information)

[0739] Step 5:

[0740] The server automatically generates documents based on the analysis results. This process uses the Matplotlib library to create graphs and the python-pptx library to automatically generate PowerPoint slides. For example, monthly sales trend graphs and product-specific sales pie charts are inserted into the slides.

[0741] Input: Analysis results (trend analysis data, summary information)

[0742] Output: Automatically generated PowerPoint presentation (sales presentation)

[0743] Step 6:

[0744] The server uses an emotion recognition engine to recognize the user's emotions in real time. By combining OpenCV and TensorFlow libraries, it analyzes the user's facial expressions and tone of voice to determine emotions such as stress and fatigue. Based on this emotion information, it adjusts the content and style of the materials.

[0745] Input: User's facial expressions and voice tone (real-time data)

[0746] Output: Adjustments based on emotional information (changes to the style and color scheme of the document)

[0747] Step 7:

[0748] The server applies the company's brand colors and a consistent font to the generated documents, performing overall visual formatting and design. This ensures that the documents are visually appealing and compliant with the company's guidelines.

[0749] Input: Automated document

[0750] Output: A formatted document with the company's brand colors and fonts applied.

[0751] Step 8:

[0752] The server saves the completed document in the specified format (e.g., .pptx) and generates a download link to provide to the user. The download link is distributed via email or notification system.

[0753] Input: Formatted design document

[0754] Output: Saved document (.pptx), download link for the user

[0755] Step 9:

[0756] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing. These materials can then be used for presentations at sales meetings and reporting sessions.

[0757] Input: Download link

[0758] Output: Downloaded document (Sales Presentation.pptx)

[0759] (Application Example 2)

[0760] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0761] Current document generation systems sometimes fail to adequately address the user's psychological state when they receive automatically generated documents. This can lead to stress and difficulty in understanding the documents, resulting in a diminished user experience. Furthermore, it is difficult to properly visualize the analysis results of data from autonomous vehicles and driver evaluation data, making it challenging to communicate operational performance and driver evaluations efficiently and effectively.

[0762] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0763] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for detecting the user's emotions using an emotion recognition system and adjusting the content and design of the materials. This enables the generation of optimal materials that respond to the user's emotions, making the materials easier to understand and reducing stress during use. Furthermore, it enables the efficient and effective visualization of autonomous vehicle operation data and driver evaluation data, providing users with useful information.

[0764] "Means for users to input data and keywords" refers to a device or software that provides an interface for users to input operational data of autonomous vehicles, driver evaluation data, and keywords necessary for their analysis.

[0765] "A means by which a server receives and temporarily stores input data" refers to a function that receives data sent by a user and temporarily stores that data for use in subsequent processing.

[0766] "Means by which the server performs cleansing on received data" refers to a function that improves data quality by performing processes to impart or remove missing or abnormal values ​​from the received data.

[0767] "Means for a server to analyze cleansed data and generate analysis results" refers to a function that uses statistical analysis and machine learning models to analyze cleansed data and extract useful information and trends.

[0768] "A means for the server to automatically generate documents based on analysis results" refers to a function that automatically creates explanatory documents and reports according to a template based on the obtained analysis results.

[0769] "Means by which the server applies visual formatting and design to generated materials" refers to a function that applies a visually consistent design and layout to automatically generated materials.

[0770] "Means by which the server saves completed documents in a specified format and provides them to the user" refers to a function that saves the final documents in a specified format (e.g., .pptx, .pdf) and provides them to the user via an accessible link or other means.

[0771] "Means for users to download materials via provided links" refers to a function that allows users to download and obtain necessary materials by clicking or accessing provided links.

[0772] "A means of detecting a user's emotions using an emotion recognition system and adjusting the content and design of materials" refers to a function that uses sensing devices such as cameras and microphones to detect emotions from the user's facial expressions and tone of voice, and dynamically adjusts the content and design of materials based on that emotional information.

[0773] A specific embodiment of this invention is shown below. Here, we describe a system that generates data based on the operation data of an autonomous vehicle and the driver's evaluation data.

[0774] First, users input performance data, evaluation data, and keywords for analysis through the interface of a device such as a smartphone or tablet. This data includes, for example, "Autonomous Vehicle Data from January to December 2023.xlsx" and "Driver Evaluation Data.xlsx". Users can also choose to generate a "Monthly Performance Report".

[0775] The server receives data sent from the user's terminal and temporarily stores it in the database. In this stage, the received data undergoes cleansing processes, such as imputing or removing missing or outlier values. Specifically, if missing values ​​are detected, the corresponding data is imputed using the most recent mean value or similar.

[0776] The cleansed and processed data is analyzed using data analysis software such as Python, Pandas, and Scikit-learn. The results of the analysis include, for example, aggregated data on monthly operational performance and trend data on driver evaluations.

[0777] Next, the process moves to creating automatically generated materials based on the analysis results. Using tools such as Python-PPTX or Jinja2, PowerPoint presentation materials are created using templates suitable for the generated data. These materials include monthly operational performance graphs and pie charts of driver evaluations.

[0778] In addition, the emotion recognition system analyzes the user's facial expressions and tone of voice, and adjusts the design and content of the materials according to the user's psychological state. For example, if the user is feeling stressed, the color scheme and font of the materials can be changed to create a more relaxed atmosphere. This utilizes technologies such as OpenCV, TensorFlow, and the Microsoft Azure Emotion Recognition API.

[0779] Finally, the completed document will have the company's brand colors and a unified font applied. The document will be saved in the specified format (e.g., .pptx, .pdf), and a download link will be generated using AWS S3 or the Django Rest Framework. This link will be provided to the user, who can download and use the document via the link.

[0780] Specific example:

[0781] Input data: "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx"

[0782] Example of a prompt:

[0783] Please upload the operational performance data and driver evaluation data for autonomous vehicles from January to December 2023 and generate a monthly performance report.

[0784] In this way, the system automatically generates and provides high-quality analytical materials that are adapted to the user's psychological state, based on the user's input data.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] Users input autonomous vehicle operation data and driver evaluation data through a smartphone or tablet interface. Specifically, they upload "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx" files and select to generate a "Monthly Performance Report".

[0788] Input: Operation data and keywords, "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx"

[0789] Output: Data sent from the user's device

[0790] Step 2:

[0791] The server receives data sent from the user's terminal and temporarily stores it in the database. This storage process makes the data available for subsequent processing.

[0792] Input: Data submitted by the user

[0793] Output: Data stored in the database

[0794] Step 3:

[0795] The server performs a cleansing process on the received data. Specifically, it detects missing values ​​and imputes them with the mean or median. Outliers are also identified and corrected or removed as necessary.

[0796] Input: Saved data

[0797] Output: Cleansed data

[0798] Step 4:

[0799] The server cleanses the data and analyzes it using data analysis software such as Python, Pandas, and Scikit-learn to generate analysis results. This includes aggregating monthly operational data and extracting trends in driver evaluations.

[0800] Input: Cleansed data

[0801] Output: Analysis results data (e.g., monthly operational performance graph, driver evaluation pie chart data)

[0802] Step 5:

[0803] The server automatically generates documents based on the analysis results according to a template. Tools such as Python-PPTX and Jinja2 are used for this process. Specifically, it creates a PowerPoint presentation based on the analysis results.

[0804] Input: Analysis result data

[0805] Output: Generated document (e.g., .pptx file)

[0806] Step 6:

[0807] The server acquires user emotional information using an emotion recognition system. This utilizes sensing devices such as cameras and microphones, and employs OpenCV, TensorFlow, and the Microsoft Azure Emotion Recognition API. It analyzes the user's facial expressions and voice tone to detect emotions.

[0808] Input: User facial expression data and voice data

[0809] Output: Detected emotion information

[0810] Step 7:

[0811] The server-generated materials are adjusted to reflect emotional information, making appropriate design and content changes. For example, if a user is feeling stressed, the color scheme and fonts of the materials are changed to a more relaxing style.

[0812] Input: Detected emotion information, generated data

[0813] Output: Adjusted document (e.g., a .pptx file with modified colors and fonts)

[0814] Step 8:

[0815] The server applies the company's brand colors and a unified font to the prepared documents and saves the final documents in the specified format (e.g., .pptx, .pdf). AWS S3 or Django Rest Framework is used for saving the documents and generating download links.

[0816] Input: Adjusted materials

[0817] Output: Saved final document, generated download link

[0818] Step 9:

[0819] Users download materials by clicking the provided download link and use them in meetings, etc. Users access the link via their device and obtain the necessary materials.

[0820] Input: Download link

[0821] Output: Downloaded document (e.g., .pptx file)

[0822] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0823] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0824] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0825] [Third Embodiment]

[0826] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0827] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0828] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0829] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0830] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0831] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0832] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0833] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0834] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0835] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0836] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0837] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0838] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[0839] Program Processing Overview

[0840] 1. User: Data entry

[0841] Users enter the necessary data and keywords on the terminal's interface. They can also upload data files.

[0842] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[0843] 2. Server: Data reception and storage

[0844] The server receives data sent from the user's terminal and stores it temporarily. This ensures that data is available for subsequent processing.

[0845] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[0846] 3. Server: Data cleansing

[0847] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[0848] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[0849] 4. Server: Data Analysis

[0850] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[0851] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[0852] 5. Server: Data generation

[0853] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[0854] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[0855] 6. Server: Document formatting and design

[0856] Apply visual formatting and design to the generated document. This includes unifying fonts and adjusting colors.

[0857] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[0858] 7. Server: Data storage and distribution

[0859] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[0860] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[0861] 8. User: Downloading and using materials

[0862] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0863] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[0864] Example Flowchart

[0865] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[0866] 2. The server receives and stores the uploaded data.

[0867] 3. The server performs a cleansing process on the data and imputes missing values.

[0868] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[0869] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[0870] 6. The server formats the generated documents by applying the company's brand colors and a unified font.

[0871] 7. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[0872] 8. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[0873] The embodiments of the present invention have now been specifically described. This system enables users to effectively and efficiently generate and provide high-quality materials.

[0874] The following describes the processing flow.

[0875] Step 1:

[0876] The user enters the necessary data and keywords on the terminal interface. Specifically, they enter sales data files or product lists into input fields and select the desired type of document.

[0877] Step 2:

[0878] The server receives data sent by the user. Specifically, it saves the file to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[0879] Step 3:

[0880] The server detects missing or outlier values ​​in the received data and performs the necessary cleansing processes. For example, it might impute missing values ​​with the mean or median, or remove abnormally high or low values.

[0881] Step 4:

[0882] The server analyzes the cleansed data using AI models and statistical methods. Specifically, it calculates monthly sales summaries and product-specific sales trends, and generates analysis results.

[0883] Step 5:

[0884] The server automatically generates documents based on the analysis results. Specifically, it creates presentation slides using the PowerPoint library and places graphs and text. For example, it might place a monthly sales trend graph on the first slide and a product-specific sales pie chart on the second slide.

[0885] Step 6:

[0886] The server applies visual formatting and design to the generated materials. Specifically, it applies the company's brand colors and a consistent font, and adjusts the slide layout. This results in a visually appealing and consistent design.

[0887] Step 7:

[0888] The server saves the completed document in the specified format. Specifically, it saves the generated presentation as a file named "SalesPresentation.pptx" to the server's storage.

[0889] Step 8:

[0890] The server generates a download link for the stored materials and provides it to the user. Specifically, this involves either sending the link to the user's email address or displaying it on the interface.

[0891] Step 9:

[0892] Users download materials by clicking on the provided links. Specifically, they use the downloaded materials for their own presentations and reports. For example, they might use a presentation generated during a sales meeting to give a presentation.

[0893] (Example 1)

[0894] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0895] Traditional data creation systems require users to manually organize and analyze large amounts of data to create documents, which is time-consuming and labor-intensive. Furthermore, inconsistencies in document quality and consistency are a problem. Additionally, data loss and outlier handling may not be performed properly, potentially reducing the reliability of analysis results.

[0896] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0897] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, and means for the user to download the materials via a provided link. This enables the user to generate and provide high-quality materials effectively and efficiently.

[0898] A "user" is the entity that uses the system to input data and keywords and generate materials.

[0899] A "terminal" is a device operated by a user, equipped with an interface for data input and file uploads.

[0900] A "server" is a computer system that receives data sent by users, stores it, cleanses it, analyzes it, and generates and provides data.

[0901] "Data" refers to information that users input or upload to the system, which forms the basis for analysis and data generation.

[0902] "Keywords" are words or phrases that users input into the system and are used to specify the policy and purpose of creating documents.

[0903] "Data cleansing" is a process that involves imputing or removing missing or outlier values ​​from data.

[0904] "Analysis" is the process of analyzing cleansed data using AI models and statistical methods to extract useful information.

[0905] "Documents" are automatically generated by the server based on the analysis results and take the form of presentations or reports.

[0906] "Visual formatting and design" refers to the process of improving the appearance of generated materials by unifying fonts and adjusting colors.

[0907] A "specified format" refers to the file format used when saving completed documents, such as .pptx or .xlsx.

[0908] A "download link" is a URL that allows users to obtain the completed document.

[0909] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for carrying out this invention will be described with a system program flow and specific examples.

[0910] This system uses the following hardware and software.

[0911] hardware

[0912] Device: A device used by the user (e.g., PC, tablet, smartphone)

[0913] Server: A computer system that receives, stores, analyzes, and generates data.

[0914] software

[0915] Frontend Interface: A web application for users to input data and keywords.

[0916] Backend system: Server-side programs that receive, store, cleanse, analyze, and generate data.

[0917] Database: A data storage system used for data persistence (e.g., MySQL, PostgreSQL)

[0918] Analysis tools: Software used for data analysis (e.g., Python, TensorFlow, Pandas, Matplotlib)

[0919] Presentation generation tools: Software used to generate presentation materials (e.g., Microsoft PowerPoint, Python-pptx)

[0920] Processing flow and specific operating procedures

[0921] Data entry

[0922] The user accesses the web application on their device, uploads the necessary data files, and enters keywords for the document they want to generate. For example, a user might upload "Sales Data from January to December 2023.xlsx" and enter keywords to generate a document titled "Sales Presentation by Product."

[0923] Data reception and storage

[0924] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory. It then securely stores the received data in the database. A data backup is taken at this point.

[0925] Data cleansing

[0926] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median. For example, if sales data contains missing values, it will be imputed with the average sales value for the same month of the previous year.

[0927] Data Analysis

[0928] Based on the cleansed data, data analysis is performed using TensorFlow and statistical methods. This analysis includes time series analysis and regression analysis. As a result of the analysis, aggregated results and trends of monthly sales data are generated. Based on the analysis results, data for graphing is generated using the Matplotlib library in Python.

[0929] Material generation

[0930] The server automatically generates presentation materials based on the analysis results. It uses libraries such as Python-pptx to create slides according to pre-prepared templates. For example, it can insert line graphs showing monthly sales trends and pie charts showing sales by product into the slides.

[0931] Document formatting and design

[0932] Apply visual formatting and design to the generated documents. Reflect the company's brand colors and consistent fonts to improve their appearance. For example, use the company's corporate colors as the background and arrange titles and text using a consistent font.

[0933] Document preservation and distribution

[0934] The completed document is saved in the specified format (.pptx, .xlsx, etc.), and a download link is generated for the user. This link is sent to the user via email or other means, and the user can click it to download the document.

[0935] Download and use the materials

[0936] Users can download materials by clicking the provided download link and use them for presentations and reports. For example, they might use "Sales Presentation.pptx" to explain sales figures in a sales meeting.

[0937] This system allows users to quickly create and provide high-quality materials without requiring extra effort.

[0938] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0939] Step 1:

[0940] The user accesses the web application on their device, uploads the necessary data files, and enters keywords.

[0941] Input: Sales data file (e.g., SalesData.xlsx), Keywords for the generated document (e.g., Product Sales Presentation)

[0942] Specific actions: The user clicks the "File Upload" button, selects a local sales data file, and uploads it. They also enter the keyword "Product Sales Presentation" into the text box.

[0943] Output: The uploaded data file and the entered keyword are sent to the server.

[0944] Step 2:

[0945] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory.

[0946] Input: Uploaded data file and entered keywords

[0947] Specific operation: The server receives an HTTP request and performs file handling to save the data file to a temporary directory. Then, it saves the data to the database.

[0948] Output: Data files and keywords stored in the database

[0949] Step 3:

[0950] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median.

[0951] Input: Sales data file stored in the database

[0952] Specific operation: The cleansing script uses Pandas to read the data file and imputes missing values ​​with the mean or median. Outliers are filtered or corrected.

[0953] Output: Cleansed dataset

[0954] Step 4:

[0955] The server performs data analysis using TensorFlow and statistical methods based on the cleansed data. This includes time series analysis and regression analysis. As a result of the analysis, aggregated monthly sales data and trends are generated.

[0956] Input: Cleansed dataset

[0957] Specific operation: The analysis script uses TensorFlow and other analysis tools to analyze the data and extract useful information (e.g., monthly sales trends).

[0958] Output: Data analysis results (e.g., monthly sales summary data, data for graphs)

[0959] Step 5:

[0960] The server automatically generates presentation materials based on the analysis results. It creates slides according to pre-prepared templates using libraries such as Python-pptx.

[0961] Input: Data analysis results

[0962] Specific operation: The generation script uses the Python-pptx library to insert data into the template and automatically generates slides containing graphs and text.

[0963] Output: Generated presentation materials (e.g., Sales Presentation.pptx)

[0964] Step 6:

[0965] The server applies visual formatting and design to the generated documents. It incorporates the company's brand colors and consistent fonts to improve their appearance.

[0966] Input: Generated presentation materials

[0967] Specific actions: The design script applies the company's brand colors to the entire document and unifies the font style and size.

[0968] Output: Final presentation materials with the design applied.

[0969] Step 7:

[0970] The server saves the completed document in the specified format and generates a download link to provide to the user.

[0971] Input: Final presentation materials with design applied.

[0972] Specific operation: The server saves the document in the specified format (e.g., .pptx) and generates a download link to send to the user.

[0973] Output: Download link

[0974] Step 8:

[0975] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[0976] Input: Download link

[0977] Specific operation: The user clicks on a download link provided via email or notification and downloads the document through their browser.

[0978] Output: The final version of the presentation materials saved on the user's device (e.g., Sales Presentation.pptx)

[0979] (Application Example 1)

[0980] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0981] Conventional data generation systems struggled to automatically analyze viewer trends and content evaluations by analyzing viewing and comment data. Furthermore, when large amounts of data were available, there was a lack of means to summarize the analysis results and provide them in a visually easy-to-understand format. As a result, content providers were unable to quickly obtain data-driven insights, making effective content revisions and new content creation difficult.

[0982] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0983] In this invention, the server includes means for users to upload viewing data and comment data, for the server to analyze this data to automatically analyze viewer trends and content evaluations and generate a report, for users to input data and keywords, for the server to receive and temporarily store the input data, for the server to perform a cleansing process on the received data, for the server to analyze the cleansed data and generate analysis results, for the server to automatically generate materials based on the analysis results, for the server to apply visual formatting and design to the generated materials, for the server to save the completed materials in a specified format and provide them to the user, and for the user to download the materials via a provided link. This makes it possible to easily analyze viewing data and comment data and automatically generate visually easy-to-understand materials.

[0984] A "user" refers to a person who uses the system to input data and keywords.

[0985] "Data" refers to information that is analyzed on the server, such as viewing data and comment data.

[0986] A "keyword" refers to a specific word or phrase used in data analysis.

[0987] A "server" refers to a device or system that receives data, temporarily stores it, analyzes it, and generates and provides materials.

[0988] "Temporarily saving" refers to saving the entered data for a short period before analysis.

[0989] "Data cleansing" refers to the process of preparing data by removing or imputing missing or outlier values.

[0990] "Analysis" refers to the process of extracting information from data using statistical or AI technology.

[0991] "Analysis results" refer to useful information and trends derived from the analysis of data.

[0992] "Documents" refers to visually formatted documents and reports generated based on data analysis results.

[0993] "Visual formatting" refers to actions taken to improve the appearance of a document, such as unifying fonts and adjusting colors.

[0994] "Specified format" refers to a specific file format (such as .pptx or .xlsx) used when saving documents.

[0995] A "link" refers to the URL or path that allows a user to download materials.

[0996] "Viewing data" refers to data about how viewers use specific content.

[0997] "Comment data" refers to text data such as opinions and impressions left by viewers regarding specific content.

[0998] "Viewer trends" refer to viewer behavior patterns and preferences derived from viewing data and comment data.

[0999] "Content evaluation" refers to the assessment of content quality based on comment data and viewing data.

[1000] A "report" refers to a document that summarizes the results of the analysis of viewing data and comment data.

[1001] This invention relates to a system in which users upload viewing data and comment data, a server analyzes this data to automatically analyze viewer trends and content evaluations, and generates a report based on the analysis results. Several main hardware and software components are required to implement this system. Their configurations and the specific processes at each step are described below.

[1002] Users first upload viewing and comment data to the system using their own devices. The data can be in formats such as CSV or text files. The uploaded data is sent to the server and temporarily stored.

[1003] The server performs a cleansing process on the received data. Specifically, it removes or imputes missing or outlier values ​​in the data. The Python pandas library is used for this process to ensure high-quality data.

[1004] Next, the server analyzes the cleansed data. This analysis includes counting the number of views and counting the frequency of specific keywords. The pandas library is used to count the number of views, and Python's string manipulation functions are used for keyword analysis.

[1005] The analysis results are automatically generated as visually easy-to-understand materials. This involves creating graphs using the matplotlib and seaborn libraries, and compiling them into a PDF report using the FPDF library. This allows for a visual confirmation of trends in viewing data and the frequency of keyword occurrences.

[1006] Finally, the server saves the generated document in the specified format (e.g., PDF) and provides the user with a download link. By clicking the provided link, the user can download the generated document and use it for sales meetings or report writing.

[1007] As a concrete example, a user uploads a "views_and_comments.csv" file and specifies the keywords "great" and "interesting". The system analyzes the uploaded data and generates a report that includes a graph of viewing data trends and the frequency of keyword occurrences. An example of a prompt message corresponding to this specific example is as follows:

[1008] Example of a prompt:

[1009] "Please analyze viewing trends and the frequency of specific keywords based on viewing and comment data from June 2023. The keywords are "great" and "interesting." Please provide the results in a PDF report."

[1010] This system will allow users to easily analyze viewing and comment data, quickly gaining insights for effective content revisions and the creation of new content.

[1011] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1012] Step 1:

[1013] Users upload viewing data and comment data files (e.g., "views_and_comments.csv") from their terminal to the system. They also enter keywords for additional analysis. The input data consists of viewing data and comment data files, as well as keywords. If the user specifies keywords such as "great" or "interesting," these keywords are sent to the server.

[1014] Step 2:

[1015] The server receives data files sent by users and stores them temporarily. The temporarily stored data consists of viewing data and comment data in CSV format. This storage prepares the data for subsequent cleansing and analysis.

[1016] Step 3:

[1017] The server performs a cleansing process on the received data. Specifically, it uses the pandas library to remove or impute missing or outlier values. The input is temporarily stored viewing data and comment data, and the output is the cleansed data.

[1018] Step 4:

[1019] The server analyzes the cleaned data. Specifically, it aggregates view counts and counts keyword occurrences. The input consists of cleaned data and keywords specified by the user, and the output is aggregated daily view counts and keyword occurrences. Pandas is used for view count aggregation, and Python string manipulation is used for keyword analysis.

[1020] Step 5:

[1021] The server automatically generates visually easy-to-understand materials based on the analysis results. Specifically, it creates graphs using matplotlib and seaborn with the analysis results and compiles them into a PDF report using the FPDF library. The input is the analysis results (view count trend data, keyword frequency data), and the output is a visual material (PDF report).

[1022] Step 6:

[1023] The server applies visual formatting and design to the generated document. Specifically, this includes unifying fonts, adjusting colors, and refining graph layouts. The input is an initial version of the PDF report, and the output is the formatted, final version of the PDF report.

[1024] Step 7:

[1025] The server saves the completed document in the specified format (e.g., PDF) and generates a download link. The user downloads the document via the provided link. The input is a formatted, final PDF report, and the output is a download link provided to the user.

[1026] Step 8:

[1027] The user downloads the generated document using a download link provided by the server. This document is then used for sales meetings and report writing. The input is the download link provided by the server, and the output is the final downloaded document.

[1028] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1029] This invention relates to a system in which a user inputs data and keywords, a server receives, analyzes, and formats that data to automatically generate materials, and further recognizes the user's emotions to adjust the content and style of the materials before providing them. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[1030] Program Processing Overview

[1031] 1. User: Data entry

[1032] Users enter the necessary data and keywords on the terminal interface. They can also upload data files.

[1033] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[1034] 2. Server: Data reception and storage

[1035] The server receives data sent from the user's terminal and stores it temporarily. This makes the data available for subsequent processing.

[1036] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[1037] 3. Server: Data cleansing

[1038] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[1039] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[1040] 4. Server: Data Analysis

[1041] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[1042] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[1043] 5. Server: Data generation

[1044] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[1045] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[1046] 6. Server: Adjustment by the emotion engine

[1047] The emotion engine recognizes the user's emotions and adjusts the content and style of the materials based on that emotional information. Emotions are detected from the user's facial expressions and tone of voice.

[1048] Specific example: If a user is feeling stressed, the system will use relaxing colors and fonts in the material.

[1049] 7. Server: Document formatting and design

[1050] Apply visual formatting and design to the generated materials. This includes applying the company's brand colors and a consistent font, and adjusting the slide layout.

[1051] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[1052] 8. Server: Data storage and distribution

[1053] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[1054] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[1055] 9. User: Downloading and using materials

[1056] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[1057] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[1058] Example Flowchart

[1059] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[1060] 2. The server receives and stores the uploaded data.

[1061] 3. The server performs a cleansing process on the data and imputes missing values.

[1062] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[1063] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[1064] 6. The emotion engine analyzes the user's facial expressions and tone of voice, and adjusts the design of the materials accordingly. For example, if it detects that the user is tired, it applies a calming color scheme to the materials.

[1065] 7. The server formats the generated documents by applying the company's brand colors and a unified font.

[1066] 8. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[1067] 9. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[1068] The embodiments of the present invention have now been specifically described. This system enables users to efficiently and effectively generate and provide high-quality materials, and by combining it with emotion recognition, it is possible to propose content and styles that better suit the user's needs.

[1069] The following describes the processing flow.

[1070] Step 1:

[1071] The user enters the necessary data and keywords on the device's interface. Specifically, they upload sales data files using the file upload function and select the type of document they want to generate. The user also grants permission for the camera and microphone to be used, and prepares input for emotion recognition.

[1072] Step 2:

[1073] The server receives data sent by the user. The file is saved to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[1074] Step 3:

[1075] The server performs a cleansing process on the received data. Specifically, it imputes missing values ​​with the mean or median, and detects and removes abnormally high or low values. It also standardizes the data format and shapes it into a form suitable for analysis.

[1076] Step 4:

[1077] The server analyzes the cleansed data using AI models and statistical methods. Specifically, it aggregates sales data by month and calculates sales trends for each product. Based on the analysis results, it extracts necessary information and generates data for creating graphs.

[1078] Step 5:

[1079] The server automatically generates documents based on the analysis results. Specifically, it creates slides using the PowerPoint library and places various graphs and text where needed. For example, it inserts a monthly sales trend graph on the first slide and a product-specific sales pie chart on the second slide.

[1080] Step 6:

[1081] The emotion engine recognizes the user's emotions. The server collects the user's facial expressions and voice tone using a camera and microphone, and analyzes the user's current emotional state using an emotion recognition algorithm.

[1082] Step 7:

[1083] The server adjusts the content and style of the materials based on the emotion recognition results. Specifically, if the user is feeling stressed, the color scheme of the materials will be changed to calmer colors and the font will be made easier to read. Conversely, if the user is feeling positive emotions, a bright color scheme and a lively design will be applied.

[1084] Step 8:

[1085] The server applies visual formatting and design to the generated materials. Specifically, it adjusts the overall layout of the slides using the company's brand colors and a consistent font. This results in consistent and easy-to-read materials.

[1086] Step 9:

[1087] The server saves the completed document in the specified format (e.g., .pptx, .xlsx). The generated presentation material is saved as a file named "Sales Presentation.pptx" on the server's storage.

[1088] Step 10:

[1089] The server generates a download link for the stored materials and provides it to the user. Specifically, this involves either sending the download link to the user's email address or displaying a download button on the UI.

[1090] Step 11:

[1091] Users download materials by clicking on the provided link. They then use the downloaded materials to create various presentations and reports. For example, they might use the generated presentation to give a presentation at a sales meeting.

[1092] As a result, users can efficiently generate and use high-quality materials that reflect their emotional state.

[1093] (Example 2)

[1094] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1095] Traditional document creation systems have the problem of requiring users to manually input data, perform analysis, and generate documents, which is time-consuming and labor-intensive. Furthermore, the generated documents may not align with the user's emotions and needs, resulting in low user satisfaction. Therefore, there is a need for a system that can efficiently and automatically generate high-quality documents, and further adjust the content and style based on the user's emotions.

[1096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform cleansing processing on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply overall visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for the server to recognize the user's emotions and adjust the content and visual style of the materials based on the emotional information. This enables the efficient and automatic generation of high-quality materials, and further enables the provision of personalized materials that respond to the user's emotions.

[1097] "User" refers to the end user who uses this system to input data and generate documents.

[1098] A "server" refers to a central computing system that receives, stores, analyzes, generates data from users, and performs sentiment recognition.

[1099] "Data" refers to the information and files that users input into the system, and specifically includes sales data, business data, and so on.

[1100] "Keywords" refer to words or phrases that represent topics or subjects specified by the user when generating materials.

[1101] "Data cleansing" refers to the process of correcting or removing missing or outlier values ​​from received data.

[1102] "Analysis" refers to the process of analyzing cleansed data using AI models and statistical methods to extract meaningful information.

[1103] "Documents" refers to reports and presentation materials generated based on the analysis results, and specifically includes graphs and text.

[1104] "Visual formatting and design" refers to the process of applying an aesthetically pleasing layout and color scheme to the generated material.

[1105] "Specified format" refers to a specific file format used when saving the generated document, including, for example, .pptx and .xlsx.

[1106] A "link" refers to a URL or web link provided to a user for downloading materials.

[1107] "Emotion recognition" refers to a technology that analyzes a user's emotions from their facial expressions and tone of voice, and then adjusts the content and style of the materials accordingly.

[1108] "Automatic generation" refers to the process by which a system programmatically creates documents based on user input.

[1109] This invention is a system in which a user inputs data and keywords, a server receives, analyzes, and formats that data to automatically generate materials, and further recognizes the user's emotions to adjust the content and style of the materials before providing them. The embodiments for carrying out this invention are described in detail below.

[1110] overview

[1111] This system consists of three main elements: the user's terminal, the server, and the emotion recognition engine.

[1112] User terminal

[1113] The user enters the necessary data and keywords using the terminal interface. The terminal is assumed to be a common device such as a PC or smartphone. For example, the user might upload "Sales Data from January to December 2023.xlsx" and enter "Sales Presentation by Product" as the keyword. This allows the user to clearly specify their requirements for the system.

[1114] server

[1115] The server has multiple functions. First, it receives data sent from users and stores it temporarily. After receiving the data, the server performs a cleansing process. This process includes imputing missing values ​​with the mean or median using the Pandas library. Next, it analyzes the cleansed data and extracts specific information. This analysis uses the Sci-kit Learn library to analyze data trends and patterns and generate data for graphing.

[1116] Subsequently, the system automatically generates documents based on the analysis results. In this step, graphs are created using the Matplotlib library, and PowerPoint slides are automatically generated using the python-pptx library. The generated documents are then formatted and designed visually, applying the company's brand colors and a unified font.

[1117] Emotion recognition engine

[1118] The emotion recognition engine analyzes the user's emotions in real time. Specifically, it uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and tone of voice to recognize emotions such as stress and fatigue. Based on this, it adjusts the color scheme and fonts of the materials to provide a style that is optimal for the user's state.

[1119] Specific example

[1120] When a user accesses the system interface and uploads "Sales Data.xlsx," the server receives and saves the data. Next, the server performs data cleansing, imputing missing values ​​with the average sales value for the same month of the previous year. Then, the server analyzes the cleansed data and generates aggregated monthly sales data. Based on the analysis results, a presentation document is automatically generated in PowerPoint format. An emotion recognition engine analyzes the user's facial expressions and tone of voice, adjusting the design of the document accordingly. For example, if the system detects that the user is tired, it applies a calming color scheme to the document. Finally, the server saves the completed document in the specified format and provides the user with a download link. The user clicks the provided link to download the document and use it in their sales meeting.

[1121] As a result, this system can automatically generate efficient and high-quality materials and provide personalized materials that respond to the user's emotions.

[1122] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1123] Step 1:

[1124] The user uses the terminal interface to input the required data and keywords. The entered data is in a file format such as "Sales Data for January to December 2023.xlsx," and the keyword is "Sales Presentation by Product." The user then begins submitting this input data.

[1125] Input: Sales data.xlsx, Sales presentation by product

[1126] Output: The event where input data is sent from the terminal to the server.

[1127] Step 2:

[1128] The server receives the data sent by the user and temporarily stores it in the database. Once the storage is complete, it sends a confirmation message to the user acknowledging receipt of the data.

[1129] Input: Sales data.xlsx (data sent from the terminal), keywords

[1130] Output: Sales data.xlsx file successfully saved to the database; confirmation message sent to the user.

[1131] Step 3:

[1132] The server performs a cleansing process on the stored data. This process uses the Pandas library to impute missing values ​​and detect outliers. Means and medians are used as imputation methods. If outliers are detected, the user is notified and asked to correct them.

[1133] Input: SalesData.xlsx (Saved Data)

[1134] Output: Cleansed data (missing values ​​are imputed and outliers are removed)

[1135] Step 4:

[1136] The server analyzes the cleansed data and generates analysis results. It utilizes the Sci-kit Learn library to extract trend analysis and summary information. For example, it generates aggregated monthly sales data and creates data for graphing.

[1137] Input: Cleansed sales data

[1138] Output: Analysis results (trend analysis data, summary information)

[1139] Step 5:

[1140] The server automatically generates documents based on the analysis results. This process uses the Matplotlib library to create graphs and the python-pptx library to automatically generate PowerPoint slides. For example, monthly sales trend graphs and product-specific sales pie charts are inserted into the slides.

[1141] Input: Analysis results (trend analysis data, summary information)

[1142] Output: Automatically generated PowerPoint presentation (sales presentation)

[1143] Step 6:

[1144] The server uses an emotion recognition engine to recognize the user's emotions in real time. By combining OpenCV and TensorFlow libraries, it analyzes the user's facial expressions and tone of voice to determine emotions such as stress and fatigue. Based on this emotion information, it adjusts the content and style of the materials.

[1145] Input: User's facial expressions and voice tone (real-time data)

[1146] Output: Adjustments based on emotional information (changes to the style and color scheme of the document)

[1147] Step 7:

[1148] The server applies the company's brand colors and a consistent font to the generated documents, performing overall visual formatting and design. This ensures that the documents are visually appealing and compliant with the company's guidelines.

[1149] Input: Automated document

[1150] Output: A formatted document with the company's brand colors and fonts applied.

[1151] Step 8:

[1152] The server saves the completed document in the specified format (e.g., .pptx) and generates a download link to provide to the user. The download link is distributed via email or notification system.

[1153] Input: Formatted design document

[1154] Output: Saved document (.pptx), download link for the user

[1155] Step 9:

[1156] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing. These materials can then be used for presentations at sales meetings and reporting sessions.

[1157] Input: Download link

[1158] Output: Downloaded document (Sales Presentation.pptx)

[1159] (Application Example 2)

[1160] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1161] Current document generation systems sometimes fail to adequately address the user's psychological state when they receive automatically generated documents. This can lead to stress and difficulty in understanding the documents, resulting in a diminished user experience. Furthermore, it is difficult to properly visualize the analysis results of data from autonomous vehicles and driver evaluation data, making it challenging to communicate operational performance and driver evaluations efficiently and effectively.

[1162] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1163] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for detecting the user's emotions using an emotion recognition system and adjusting the content and design of the materials. This enables the generation of optimal materials that respond to the user's emotions, making the materials easier to understand and reducing stress during use. Furthermore, it enables the efficient and effective visualization of autonomous vehicle operation data and driver evaluation data, providing users with useful information.

[1164] "Means for users to input data and keywords" refers to a device or software that provides an interface for users to input operational data of autonomous vehicles, driver evaluation data, and keywords necessary for their analysis.

[1165] "A means by which a server receives and temporarily stores input data" refers to a function that receives data sent by a user and temporarily stores that data for use in subsequent processing.

[1166] "Means by which the server performs cleansing on received data" refers to a function that improves data quality by performing processes to impart or remove missing or abnormal values ​​from the received data.

[1167] "Means for a server to analyze cleansed data and generate analysis results" refers to a function that uses statistical analysis and machine learning models to analyze cleansed data and extract useful information and trends.

[1168] "A means for the server to automatically generate documents based on analysis results" refers to a function that automatically creates explanatory documents and reports according to a template based on the obtained analysis results.

[1169] "Means by which the server applies visual formatting and design to generated materials" refers to a function that applies a visually consistent design and layout to automatically generated materials.

[1170] "Means by which the server saves completed documents in a specified format and provides them to the user" refers to a function that saves the final documents in a specified format (e.g., .pptx, .pdf) and provides them to the user via an accessible link or other means.

[1171] "Means for users to download materials via provided links" refers to a function that allows users to download and obtain necessary materials by clicking or accessing provided links.

[1172] "A means of detecting a user's emotions using an emotion recognition system and adjusting the content and design of materials" refers to a function that uses sensing devices such as cameras and microphones to detect emotions from the user's facial expressions and tone of voice, and dynamically adjusts the content and design of materials based on that emotional information.

[1173] A specific embodiment of this invention is shown below. Here, we describe a system that generates data based on the operation data of an autonomous vehicle and the driver's evaluation data.

[1174] First, users input performance data, evaluation data, and keywords for analysis through the interface of a device such as a smartphone or tablet. This data includes, for example, "Autonomous Vehicle Data from January to December 2023.xlsx" and "Driver Evaluation Data.xlsx". Users can also choose to generate a "Monthly Performance Report".

[1175] The server receives data sent from the user's terminal and temporarily stores it in the database. In this stage, the received data undergoes cleansing processes, such as imputing or removing missing or outlier values. Specifically, if missing values ​​are detected, the corresponding data is imputed using the most recent mean value or similar.

[1176] The cleansed and processed data is analyzed using data analysis software such as Python, Pandas, and Scikit-learn. The results of the analysis include, for example, aggregated data on monthly operational performance and trend data on driver evaluations.

[1177] Next, the process moves to creating automatically generated materials based on the analysis results. Using tools such as Python-PPTX or Jinja2, PowerPoint presentation materials are created using templates suitable for the generated data. These materials include monthly operational performance graphs and pie charts of driver evaluations.

[1178] In addition, the emotion recognition system analyzes the user's facial expressions and tone of voice, and adjusts the design and content of the materials according to the user's psychological state. For example, if the user is feeling stressed, the color scheme and font of the materials can be changed to create a more relaxed atmosphere. This utilizes technologies such as OpenCV, TensorFlow, and the Microsoft Azure Emotion Recognition API.

[1179] Finally, the completed document will have the company's brand colors and a unified font applied. The document will be saved in the specified format (e.g., .pptx, .pdf), and a download link will be generated using AWS S3 or the Django Rest Framework. This link will be provided to the user, who can download and use the document via the link.

[1180] Specific example:

[1181] Input data: "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx"

[1182] Example of a prompt:

[1183] Please upload the operational performance data and driver evaluation data for autonomous vehicles from January to December 2023 and generate a monthly performance report.

[1184] In this way, the system automatically generates and provides high-quality analytical materials that are adapted to the user's psychological state, based on the user's input data.

[1185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1186] Step 1:

[1187] Users input autonomous vehicle operation data and driver evaluation data through a smartphone or tablet interface. Specifically, they upload "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx" files and select to generate a "Monthly Performance Report".

[1188] Input: Operation data and keywords, "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx"

[1189] Output: Data sent from the user's device

[1190] Step 2:

[1191] The server receives data sent from the user's terminal and temporarily stores it in the database. This storage process makes the data available for subsequent processing.

[1192] Input: Data submitted by the user

[1193] Output: Data stored in the database

[1194] Step 3:

[1195] The server performs a cleansing process on the received data. Specifically, it detects missing values ​​and imputes them with the mean or median. Outliers are also identified and corrected or removed as necessary.

[1196] Input: Saved data

[1197] Output: Cleansed data

[1198] Step 4:

[1199] The server cleanses the data and analyzes it using data analysis software such as Python, Pandas, and Scikit-learn to generate analysis results. This includes aggregating monthly operational data and extracting trends in driver evaluations.

[1200] Input: Cleansed data

[1201] Output: Analysis results data (e.g., monthly operational performance graph, driver evaluation pie chart data)

[1202] Step 5:

[1203] The server automatically generates documents based on the analysis results according to a template. Tools such as Python-PPTX and Jinja2 are used for this process. Specifically, it creates a PowerPoint presentation based on the analysis results.

[1204] Input: Analysis result data

[1205] Output: Generated document (e.g., .pptx file)

[1206] Step 6:

[1207] The server acquires user emotional information using an emotion recognition system. This utilizes sensing devices such as cameras and microphones, and employs OpenCV, TensorFlow, and the Microsoft Azure Emotion Recognition API. It analyzes the user's facial expressions and voice tone to detect emotions.

[1208] Input: User facial expression data and voice data

[1209] Output: Detected emotion information

[1210] Step 7:

[1211] The server-generated materials are adjusted to reflect emotional information, making appropriate design and content changes. For example, if a user is feeling stressed, the color scheme and fonts of the materials are changed to a more relaxing style.

[1212] Input: Detected emotion information, generated data

[1213] Output: Adjusted document (e.g., a .pptx file with modified colors and fonts)

[1214] Step 8:

[1215] The server applies the company's brand colors and a unified font to the prepared documents and saves the final documents in the specified format (e.g., .pptx, .pdf). AWS S3 or Django Rest Framework is used for saving the documents and generating download links.

[1216] Input: Adjusted materials

[1217] Output: Saved final document, generated download link

[1218] Step 9:

[1219] Users download materials by clicking the provided download link and use them in meetings, etc. Users access the link via their device and obtain the necessary materials.

[1220] Input: Download link

[1221] Output: Downloaded document (e.g., .pptx file)

[1222] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1223] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1224] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1225] [Fourth Embodiment]

[1226] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1227] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1228] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1229] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1230] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1231] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1232] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1233] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1234] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1235] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1236] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1237] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1238] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1239] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[1240] Program Processing Overview

[1241] 1. User: Data entry

[1242] Users enter the necessary data and keywords on the terminal's interface. They can also upload data files.

[1243] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[1244] 2. Server: Data reception and storage

[1245] The server receives data sent from the user's terminal and stores it temporarily. This ensures that data is available for subsequent processing.

[1246] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[1247] 3. Server: Data cleansing

[1248] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[1249] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[1250] 4. Server: Data Analysis

[1251] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[1252] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[1253] 5. Server: Data generation

[1254] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[1255] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[1256] 6. Server: Document formatting and design

[1257] Apply visual formatting and design to the generated document. This includes unifying fonts and adjusting colors.

[1258] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[1259] 7. Server: Data storage and distribution

[1260] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[1261] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[1262] 8. User: Downloading and using materials

[1263] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[1264] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[1265] Example Flowchart

[1266] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[1267] 2. The server receives and stores the uploaded data.

[1268] 3. The server performs a cleansing process on the data and imputes missing values.

[1269] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[1270] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[1271] 6. The server formats the generated documents by applying the company's brand colors and a unified font.

[1272] 7. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[1273] 8. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[1274] The embodiments of the present invention have now been specifically described. This system enables users to effectively and efficiently generate and provide high-quality materials.

[1275] The following describes the processing flow.

[1276] Step 1:

[1277] The user enters the necessary data and keywords on the terminal interface. Specifically, they enter sales data files or product lists into input fields and select the desired type of document.

[1278] Step 2:

[1279] The server receives data sent by the user. Specifically, it saves the file to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[1280] Step 3:

[1281] The server detects missing or outlier values ​​in the received data and performs the necessary cleansing processes. For example, it might impute missing values ​​with the mean or median, or remove abnormally high or low values.

[1282] Step 4:

[1283] The server analyzes the cleansed data using AI models and statistical methods. Specifically, it calculates monthly sales summaries and product-specific sales trends, and generates analysis results.

[1284] Step 5:

[1285] The server automatically generates documents based on the analysis results. Specifically, it creates presentation slides using the PowerPoint library and places graphs and text. For example, it might place a monthly sales trend graph on the first slide and a product-specific sales pie chart on the second slide.

[1286] Step 6:

[1287] The server applies visual formatting and design to the generated materials. Specifically, it applies the company's brand colors and a consistent font, and adjusts the slide layout. This results in a visually appealing and consistent design.

[1288] Step 7:

[1289] The server saves the completed document in the specified format. Specifically, it saves the generated presentation as a file named "SalesPresentation.pptx" to the server's storage.

[1290] Step 8:

[1291] The server generates a download link for the stored materials and provides it to the user. Specifically, this involves either sending the link to the user's email address or displaying it on the interface.

[1292] Step 9:

[1293] Users download materials by clicking on the provided links. Specifically, they use the downloaded materials for their own presentations and reports. For example, they might use a presentation generated during a sales meeting to give a presentation.

[1294] (Example 1)

[1295] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1296] Traditional data creation systems require users to manually organize and analyze large amounts of data to create documents, which is time-consuming and labor-intensive. Furthermore, inconsistencies in document quality and consistency are a problem. Additionally, data loss and outlier handling may not be performed properly, potentially reducing the reliability of analysis results.

[1297] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1298] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, and means for the user to download the materials via a provided link. This enables the user to generate and provide high-quality materials effectively and efficiently.

[1299] A "user" is the entity that uses the system to input data and keywords and generate materials.

[1300] A "terminal" is a device operated by a user, equipped with an interface for data input and file uploads.

[1301] A "server" is a computer system that receives data sent by users, stores it, cleanses it, analyzes it, and generates and provides data.

[1302] "Data" refers to information that users input or upload to the system, which forms the basis for analysis and data generation.

[1303] "Keywords" are words or phrases that users input into the system and are used to specify the policy and purpose of creating documents.

[1304] "Data cleansing" is a process that involves imputing or removing missing or outlier values ​​from data.

[1305] "Analysis" is the process of analyzing cleansed data using AI models and statistical methods to extract useful information.

[1306] "Documents" are automatically generated by the server based on the analysis results and take the form of presentations or reports.

[1307] "Visual formatting and design" refers to the process of improving the appearance of generated materials by unifying fonts and adjusting colors.

[1308] A "specified format" refers to the file format used when saving completed documents, such as .pptx or .xlsx.

[1309] A "download link" is a URL that allows users to obtain the completed document.

[1310] This invention relates to a system in which a user inputs data and keywords, and a server receives, analyzes, and formats that data to automatically generate and provide materials to the user. The embodiments for carrying out this invention will be described with a system program flow and specific examples.

[1311] This system uses the following hardware and software.

[1312] hardware

[1313] Device: A device used by the user (e.g., PC, tablet, smartphone)

[1314] Server: A computer system that receives, stores, analyzes, and generates data.

[1315] software

[1316] Frontend Interface: A web application for users to input data and keywords.

[1317] Backend system: Server-side programs that receive, store, cleanse, analyze, and generate data.

[1318] Database: A data storage system used for data persistence (e.g., MySQL, PostgreSQL)

[1319] Analysis tools: Software used for data analysis (e.g., Python, TensorFlow, Pandas, Matplotlib)

[1320] Presentation generation tools: Software used to generate presentation materials (e.g., Microsoft PowerPoint, Python-pptx)

[1321] Processing flow and specific operating procedures

[1322] Data entry

[1323] The user accesses the web application on their device, uploads the necessary data files, and enters keywords for the document they want to generate. For example, a user might upload "Sales Data from January to December 2023.xlsx" and enter keywords to generate a document titled "Sales Presentation by Product."

[1324] Data reception and storage

[1325] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory. It then securely stores the received data in the database. A data backup is taken at this point.

[1326] Data cleansing

[1327] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median. For example, if sales data contains missing values, it will be imputed with the average sales value for the same month of the previous year.

[1328] Data Analysis

[1329] Based on the cleansed data, data analysis is performed using TensorFlow and statistical methods. This analysis includes time series analysis and regression analysis. As a result of the analysis, aggregated results and trends of monthly sales data are generated. Based on the analysis results, data for graphing is generated using the Matplotlib library in Python.

[1330] Material generation

[1331] The server automatically generates presentation materials based on the analysis results. It uses libraries such as Python-pptx to create slides according to pre-prepared templates. For example, it can insert line graphs showing monthly sales trends and pie charts showing sales by product into the slides.

[1332] Document formatting and design

[1333] Apply visual formatting and design to the generated documents. Reflect the company's brand colors and consistent fonts to improve their appearance. For example, use the company's corporate colors as the background and arrange titles and text using a consistent font.

[1334] Document preservation and distribution

[1335] The completed document is saved in the specified format (.pptx, .xlsx, etc.), and a download link is generated for the user. This link is sent to the user via email or other means, and the user can click it to download the document.

[1336] Download and use the materials

[1337] Users can download materials by clicking the provided download link and use them for presentations and reports. For example, they might use "Sales Presentation.pptx" to explain sales figures in a sales meeting.

[1338] This system allows users to quickly create and provide high-quality materials without requiring extra effort.

[1339] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1340] Step 1:

[1341] The user accesses the web application on their device, uploads the necessary data files, and enters keywords.

[1342] Input: Sales data file (e.g., SalesData.xlsx), Keywords for the generated document (e.g., Product Sales Presentation)

[1343] Specific actions: The user clicks the "File Upload" button, selects a local sales data file, and uploads it. They also enter the keyword "Product Sales Presentation" into the text box.

[1344] Output: The uploaded data file and the entered keyword are sent to the server.

[1345] Step 2:

[1346] The server receives data sent by the user via an HTTP POST request and stores it in a temporary directory.

[1347] Input: Uploaded data file and entered keywords

[1348] Specific operation: The server receives an HTTP request and performs file handling to save the data file to a temporary directory. Then, it saves the data to the database.

[1349] Output: Data files and keywords stored in the database

[1350] Step 3:

[1351] The server performs data cleansing on the stored data, removing missing and outlier values. It uses the Python Pandas library to impute missing values ​​with the mean or median.

[1352] Input: Sales data file stored in the database

[1353] Specific operation: The cleansing script uses Pandas to read the data file and imputes missing values ​​with the mean or median. Outliers are filtered or corrected.

[1354] Output: Cleansed dataset

[1355] Step 4:

[1356] The server performs data analysis using TensorFlow and statistical methods based on the cleansed data. This includes time series analysis and regression analysis. As a result of the analysis, aggregated monthly sales data and trends are generated.

[1357] Input: Cleansed dataset

[1358] Specific operation: The analysis script uses TensorFlow and other analysis tools to analyze the data and extract useful information (e.g., monthly sales trends).

[1359] Output: Data analysis results (e.g., monthly sales summary data, data for graphs)

[1360] Step 5:

[1361] The server automatically generates presentation materials based on the analysis results. It creates slides according to pre-prepared templates using libraries such as Python-pptx.

[1362] Input: Data analysis results

[1363] Specific operation: The generation script uses the Python-pptx library to insert data into the template and automatically generates slides containing graphs and text.

[1364] Output: Generated presentation materials (e.g., Sales Presentation.pptx)

[1365] Step 6:

[1366] The server applies visual formatting and design to the generated documents. It incorporates the company's brand colors and consistent fonts to improve their appearance.

[1367] Input: Generated presentation materials

[1368] Specific actions: The design script applies the company's brand colors to the entire document and unifies the font style and size.

[1369] Output: Final presentation materials with the design applied.

[1370] Step 7:

[1371] The server saves the completed document in the specified format and generates a download link to provide to the user.

[1372] Input: Final presentation materials with design applied.

[1373] Specific operation: The server saves the document in the specified format (e.g., .pptx) and generates a download link to send to the user.

[1374] Output: Download link

[1375] Step 8:

[1376] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[1377] Input: Download link

[1378] Specific operation: The user clicks on a download link provided via email or notification and downloads the document through their browser.

[1379] Output: The final version of the presentation materials saved on the user's device (e.g., Sales Presentation.pptx)

[1380] (Application Example 1)

[1381] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1382] Conventional data generation systems struggled to automatically analyze viewer trends and content evaluations by analyzing viewing and comment data. Furthermore, when large amounts of data were available, there was a lack of means to summarize the analysis results and provide them in a visually easy-to-understand format. As a result, content providers were unable to quickly obtain data-driven insights, making effective content revisions and new content creation difficult.

[1383] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1384] In this invention, the server includes means for users to upload viewing data and comment data, for the server to analyze this data to automatically analyze viewer trends and content evaluations and generate a report, for users to input data and keywords, for the server to receive and temporarily store the input data, for the server to perform a cleansing process on the received data, for the server to analyze the cleansed data and generate analysis results, for the server to automatically generate materials based on the analysis results, for the server to apply visual formatting and design to the generated materials, for the server to save the completed materials in a specified format and provide them to the user, and for the user to download the materials via a provided link. This makes it possible to easily analyze viewing data and comment data and automatically generate visually easy-to-understand materials.

[1385] A "user" refers to a person who uses the system to input data and keywords.

[1386] "Data" refers to information that is analyzed on the server, such as viewing data and comment data.

[1387] A "keyword" refers to a specific word or phrase used in data analysis.

[1388] A "server" refers to a device or system that receives data, temporarily stores it, analyzes it, and generates and provides materials.

[1389] "Temporarily saving" refers to saving the entered data for a short period before analysis.

[1390] "Data cleansing" refers to the process of preparing data by removing or imputing missing or outlier values.

[1391] "Analysis" refers to the process of extracting information from data using statistical or AI technology.

[1392] "Analysis results" refer to useful information and trends derived from the analysis of data.

[1393] "Documents" refers to visually formatted documents and reports generated based on data analysis results.

[1394] "Visual formatting" refers to actions taken to improve the appearance of a document, such as unifying fonts and adjusting colors.

[1395] "Specified format" refers to a specific file format (such as .pptx or .xlsx) used when saving documents.

[1396] A "link" refers to the URL or path that allows a user to download materials.

[1397] "Viewing data" refers to data about how viewers use specific content.

[1398] "Comment data" refers to text data such as opinions and impressions left by viewers regarding specific content.

[1399] "Viewer trends" refer to viewer behavior patterns and preferences derived from viewing data and comment data.

[1400] "Content evaluation" refers to the assessment of content quality based on comment data and viewing data.

[1401] A "report" refers to a document that summarizes the results of the analysis of viewing data and comment data.

[1402] This invention relates to a system in which users upload viewing data and comment data, a server analyzes this data to automatically analyze viewer trends and content evaluations, and generates a report based on the analysis results. Several main hardware and software components are required to implement this system. Their configurations and the specific processes at each step are described below.

[1403] Users first upload viewing and comment data to the system using their own devices. The data can be in formats such as CSV or text files. The uploaded data is sent to the server and temporarily stored.

[1404] The server performs a cleansing process on the received data. Specifically, it removes or imputes missing or outlier values ​​in the data. The Python pandas library is used for this process to ensure high-quality data.

[1405] Next, the server analyzes the cleansed data. This analysis includes counting the number of views and counting the frequency of specific keywords. The pandas library is used to count the number of views, and Python's string manipulation functions are used for keyword analysis.

[1406] The analysis results are automatically generated as visually easy-to-understand materials. This involves creating graphs using the matplotlib and seaborn libraries, and compiling them into a PDF report using the FPDF library. This allows for a visual confirmation of trends in viewing data and the frequency of keyword occurrences.

[1407] Finally, the server saves the generated document in the specified format (e.g., PDF) and provides the user with a download link. By clicking the provided link, the user can download the generated document and use it for sales meetings or report writing.

[1408] As a concrete example, a user uploads a "views_and_comments.csv" file and specifies the keywords "great" and "interesting". The system analyzes the uploaded data and generates a report that includes a graph of viewing data trends and the frequency of keyword occurrences. An example of a prompt message corresponding to this specific example is as follows:

[1409] Example of a prompt:

[1410] "Please analyze viewing trends and the frequency of specific keywords based on viewing and comment data from June 2023. The keywords are "great" and "interesting." Please provide the results in a PDF report."

[1411] This system will allow users to easily analyze viewing and comment data, quickly gaining insights for effective content revisions and the creation of new content.

[1412] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1413] Step 1:

[1414] Users upload viewing data and comment data files (e.g., "views_and_comments.csv") from their terminal to the system. They also enter keywords for additional analysis. The input data consists of viewing data and comment data files, as well as keywords. If the user specifies keywords such as "great" or "interesting," these keywords are sent to the server.

[1415] Step 2:

[1416] The server receives data files sent by users and stores them temporarily. The temporarily stored data consists of viewing data and comment data in CSV format. This storage prepares the data for subsequent cleansing and analysis.

[1417] Step 3:

[1418] The server performs a cleansing process on the received data. Specifically, it uses the pandas library to remove or impute missing or outlier values. The input is temporarily stored viewing data and comment data, and the output is the cleansed data.

[1419] Step 4:

[1420] The server analyzes the cleaned data. Specifically, it aggregates view counts and counts keyword occurrences. The input consists of cleaned data and keywords specified by the user, and the output is aggregated daily view counts and keyword occurrences. Pandas is used for view count aggregation, and Python string manipulation is used for keyword analysis.

[1421] Step 5:

[1422] The server automatically generates visually easy-to-understand materials based on the analysis results. Specifically, it creates graphs using matplotlib and seaborn with the analysis results and compiles them into a PDF report using the FPDF library. The input is the analysis results (view count trend data, keyword frequency data), and the output is a visual material (PDF report).

[1423] Step 6:

[1424] The server applies visual formatting and design to the generated document. Specifically, this includes unifying fonts, adjusting colors, and refining graph layouts. The input is an initial version of the PDF report, and the output is the formatted, final version of the PDF report.

[1425] Step 7:

[1426] The server saves the completed document in the specified format (e.g., PDF) and generates a download link. The user downloads the document via the provided link. The input is a formatted, final PDF report, and the output is a download link provided to the user.

[1427] Step 8:

[1428] The user downloads the generated document using a download link provided by the server. This document is then used for sales meetings and report writing. The input is the download link provided by the server, and the output is the final downloaded document.

[1429] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1430] This invention relates to a system in which a user inputs data and keywords, a server receives, analyzes, and formats that data to automatically generate materials, and further recognizes the user's emotions to adjust the content and style of the materials before providing them. The embodiments for implementing this invention will be described with a system program flow and specific examples.

[1431] Program Processing Overview

[1432] 1. User: Data entry

[1433] Users enter the necessary data and keywords on the terminal interface. They can also upload data files.

[1434] Example: A user uploads "Sales data from January to December 2023.xlsx" and selects to generate a "Sales presentation by product".

[1435] 2. Server: Data reception and storage

[1436] The server receives data sent from the user's terminal and stores it temporarily. This makes the data available for subsequent processing.

[1437] Specific example: The server receives "Sales Data.xlsx" and saves it to the database.

[1438] 3. Server: Data cleansing

[1439] The stored data is cleansed to impute or remove missing or outlier values. Missing values ​​are typically imputed using the mean or median.

[1440] Specific example: If there are missing values ​​in the sales data, they will be imputed using the average sales value for the same month of the previous year.

[1441] 4. Server: Data Analysis

[1442] Cleansed data is analyzed using AI models and statistical methods to extract useful information. The analysis results generate data summaries and trends.

[1443] Specific example: Calculate the aggregated results of monthly sales data and generate data for graphing those results.

[1444] 5. Server: Data generation

[1445] Based on the analysis results, the system automatically generates documents according to a template. This process includes creating graphs and arranging text.

[1446] Specific example: Insert a monthly sales trend graph or a pie chart of sales by product into a PowerPoint slide.

[1447] 6. Server: Adjustment by the emotion engine

[1448] The emotion engine recognizes the user's emotions and adjusts the content and style of the materials based on that emotional information. Emotions are detected from the user's facial expressions and tone of voice.

[1449] Specific example: If a user is feeling stressed, the system will use relaxing colors and fonts in the material.

[1450] 7. Server: Document formatting and design

[1451] Apply visual formatting and design to the generated materials. This includes applying the company's brand colors and a consistent font, and adjusting the slide layout.

[1452] Specific example: Apply the company's brand colors and a consistent font throughout the entire presentation.

[1453] 8. Server: Data storage and distribution

[1454] Save the completed document in the specified format (e.g., .pptx, .xlsx), generate a download link, and provide it to the user.

[1455] Specific example: Save the completed "Sales Presentation.pptx" file to the server and send the download link to the user's email address.

[1456] 9. User: Downloading and using materials

[1457] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing.

[1458] Specific example: A user clicks a download link to obtain "Sales Presentation.pptx" and uses it in a sales meeting.

[1459] Example Flowchart

[1460] 1. The user accesses the system interface and uploads "Sales Data.xlsx".

[1461] 2. The server receives and stores the uploaded data.

[1462] 3. The server performs a cleansing process on the data and imputes missing values.

[1463] 4. The server analyzes the cleansed data and generates aggregated monthly sales data.

[1464] 5. The server automatically generates presentation materials in PowerPoint format using the analysis results.

[1465] 6. The emotion engine analyzes the user's facial expressions and tone of voice, and adjusts the design of the materials accordingly. For example, if it detects that the user is tired, it applies a calming color scheme to the materials.

[1466] 7. The server formats the generated documents by applying the company's brand colors and a unified font.

[1467] 8. The server saves the completed document as "Sales Presentation.pptx" and provides the user with a download link.

[1468] 9. The user clicks the download link to obtain the materials and gives a presentation at the sales meeting.

[1469] The embodiments of the present invention have now been specifically described. This system enables users to efficiently and effectively generate and provide high-quality materials, and by combining it with emotion recognition, it is possible to propose content and styles that better suit the user's needs.

[1470] The following describes the processing flow.

[1471] Step 1:

[1472] The user enters the necessary data and keywords on the device's interface. Specifically, they upload sales data files using the file upload function and select the type of document they want to generate. The user also grants permission for the camera and microphone to be used, and prepares input for emotion recognition.

[1473] Step 2:

[1474] The server receives data sent by the user. The file is saved to a specified folder on the server via the data upload function. This makes the data available for subsequent processing.

[1475] Step 3:

[1476] The server performs a cleansing process on the received data. Specifically, it imputes missing values ​​with the mean or median, and detects and removes abnormally high or low values. It also standardizes the data format and shapes it into a form suitable for analysis.

[1477] Step 4:

[1478] The server analyzes the cleansed data using AI models and statistical methods. Specifically, it aggregates sales data by month and calculates sales trends for each product. Based on the analysis results, it extracts necessary information and generates data for creating graphs.

[1479] Step 5:

[1480] The server automatically generates documents based on the analysis results. Specifically, it creates slides using the PowerPoint library and places various graphs and text where needed. For example, it inserts a monthly sales trend graph on the first slide and a product-specific sales pie chart on the second slide.

[1481] Step 6:

[1482] The emotion engine recognizes the user's emotions. The server collects the user's facial expressions and voice tone using a camera and microphone, and analyzes the user's current emotional state using an emotion recognition algorithm.

[1483] Step 7:

[1484] The server adjusts the content and style of the materials based on the emotion recognition results. Specifically, if the user is feeling stressed, the color scheme of the materials will be changed to calmer colors and the font will be made easier to read. Conversely, if the user is feeling positive emotions, a bright color scheme and a lively design will be applied.

[1485] Step 8:

[1486] The server applies visual formatting and design to the generated materials. Specifically, it adjusts the overall layout of the slides using the company's brand colors and a consistent font. This results in consistent and easy-to-read materials.

[1487] Step 9:

[1488] The server saves the completed document in the specified format (e.g., .pptx, .xlsx). The generated presentation material is saved as a file named "Sales Presentation.pptx" on the server's storage.

[1489] Step 10:

[1490] The server generates a download link for the stored materials and provides it to the user. Specifically, this involves either sending the download link to the user's email address or displaying a download button on the UI.

[1491] Step 11:

[1492] Users download materials by clicking on the provided link. They then use the downloaded materials to create various presentations and reports. For example, they might use the generated presentation to give a presentation at a sales meeting.

[1493] As a result, users can efficiently generate and use high-quality materials that reflect their emotional state.

[1494] (Example 2)

[1495] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1496] Traditional document creation systems have the problem of requiring users to manually input data, perform analysis, and generate documents, which is time-consuming and labor-intensive. Furthermore, the generated documents may not align with the user's emotions and needs, resulting in low user satisfaction. Therefore, there is a need for a system that can efficiently and automatically generate high-quality documents, and further adjust the content and style based on the user's emotions.

[1497] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform cleansing processing on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply overall visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for the server to recognize the user's emotions and adjust the content and visual style of the materials based on the emotional information. This enables the efficient and automatic generation of high-quality materials, and further enables the provision of personalized materials that respond to the user's emotions.

[1498] "User" refers to the end user who uses this system to input data and generate documents.

[1499] A "server" refers to a central computing system that receives, stores, analyzes, generates data from users, and performs sentiment recognition.

[1500] "Data" refers to the information and files that users input into the system, and specifically includes sales data, business data, and so on.

[1501] "Keywords" refer to words or phrases that represent topics or subjects specified by the user when generating materials.

[1502] "Data cleansing" refers to the process of correcting or removing missing or outlier values ​​from received data.

[1503] "Analysis" refers to the process of analyzing cleansed data using AI models and statistical methods to extract meaningful information.

[1504] "Documents" refers to reports and presentation materials generated based on the analysis results, and specifically includes graphs and text.

[1505] "Visual formatting and design" refers to the process of applying an aesthetically pleasing layout and color scheme to the generated material.

[1506] "Specified format" refers to a specific file format used when saving the generated document, including, for example, .pptx and .xlsx.

[1507] A "link" refers to a URL or web link provided to a user for downloading materials.

[1508] "Emotion recognition" refers to a technology that analyzes a user's emotions from their facial expressions and tone of voice, and then adjusts the content and style of the materials accordingly.

[1509] "Automatic generation" refers to the process by which a system programmatically creates documents based on user input.

[1510] This invention is a system in which a user inputs data and keywords, a server receives, analyzes, and formats that data to automatically generate materials, and further recognizes the user's emotions to adjust the content and style of the materials before providing them. The embodiments for carrying out this invention are described in detail below.

[1511] overview

[1512] This system consists of three main elements: the user's terminal, the server, and the emotion recognition engine.

[1513] User terminal

[1514] The user enters the necessary data and keywords using the terminal interface. The terminal is assumed to be a common device such as a PC or smartphone. For example, the user might upload "Sales Data from January to December 2023.xlsx" and enter "Sales Presentation by Product" as the keyword. This allows the user to clearly specify their requirements for the system.

[1515] server

[1516] The server has multiple functions. First, it receives data sent from users and stores it temporarily. After receiving the data, the server performs a cleansing process. This process includes imputing missing values ​​with the mean or median using the Pandas library. Next, it analyzes the cleansed data and extracts specific information. This analysis uses the Sci-kit Learn library to analyze data trends and patterns and generate data for graphing.

[1517] Subsequently, the system automatically generates documents based on the analysis results. In this step, graphs are created using the Matplotlib library, and PowerPoint slides are automatically generated using the python-pptx library. The generated documents are then formatted and designed visually, applying the company's brand colors and a unified font.

[1518] Emotion recognition engine

[1519] The emotion recognition engine analyzes the user's emotions in real time. Specifically, it uses OpenCV and TensorFlow libraries to analyze the user's facial expressions and tone of voice to recognize emotions such as stress and fatigue. Based on this, it adjusts the color scheme and fonts of the materials to provide a style that is optimal for the user's state.

[1520] Specific example

[1521] When a user accesses the system interface and uploads "Sales Data.xlsx," the server receives and saves the data. Next, the server performs data cleansing, imputing missing values ​​with the average sales value for the same month of the previous year. Then, the server analyzes the cleansed data and generates aggregated monthly sales data. Based on the analysis results, a presentation document is automatically generated in PowerPoint format. An emotion recognition engine analyzes the user's facial expressions and tone of voice, adjusting the design of the document accordingly. For example, if the system detects that the user is tired, it applies a calming color scheme to the document. Finally, the server saves the completed document in the specified format and provides the user with a download link. The user clicks the provided link to download the document and use it in their sales meeting.

[1522] As a result, this system can automatically generate efficient and high-quality materials and provide personalized materials that respond to the user's emotions.

[1523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1524] Step 1:

[1525] The user uses the terminal interface to input the required data and keywords. The entered data is in a file format such as "Sales Data for January to December 2023.xlsx," and the keyword is "Sales Presentation by Product." The user then begins submitting this input data.

[1526] Input: Sales data.xlsx, Sales presentation by product

[1527] Output: The event where input data is sent from the terminal to the server.

[1528] Step 2:

[1529] The server receives the data sent by the user and temporarily stores it in the database. Once the storage is complete, it sends a confirmation message to the user acknowledging receipt of the data.

[1530] Input: Sales data.xlsx (data sent from the terminal), keywords

[1531] Output: Sales data.xlsx file successfully saved to the database; confirmation message sent to the user.

[1532] Step 3:

[1533] The server performs a cleansing process on the stored data. This process uses the Pandas library to impute missing values ​​and detect outliers. Means and medians are used as imputation methods. If outliers are detected, the user is notified and asked to correct them.

[1534] Input: SalesData.xlsx (Saved Data)

[1535] Output: Cleansed data (missing values ​​are imputed and outliers are removed)

[1536] Step 4:

[1537] The server analyzes the cleansed data and generates analysis results. It utilizes the Sci-kit Learn library to extract trend analysis and summary information. For example, it generates aggregated monthly sales data and creates data for graphing.

[1538] Input: Cleansed sales data

[1539] Output: Analysis results (trend analysis data, summary information)

[1540] Step 5:

[1541] The server automatically generates documents based on the analysis results. This process uses the Matplotlib library to create graphs and the python-pptx library to automatically generate PowerPoint slides. For example, monthly sales trend graphs and product-specific sales pie charts are inserted into the slides.

[1542] Input: Analysis results (trend analysis data, summary information)

[1543] Output: Automatically generated PowerPoint presentation (sales presentation)

[1544] Step 6:

[1545] The server uses an emotion recognition engine to recognize the user's emotions in real time. By combining OpenCV and TensorFlow libraries, it analyzes the user's facial expressions and tone of voice to determine emotions such as stress and fatigue. Based on this emotion information, it adjusts the content and style of the materials.

[1546] Input: User's facial expressions and voice tone (real-time data)

[1547] Output: Adjustments based on emotional information (changes to the style and color scheme of the document)

[1548] Step 7:

[1549] The server applies the company's brand colors and a consistent font to the generated documents, performing overall visual formatting and design. This ensures that the documents are visually appealing and compliant with the company's guidelines.

[1550] Input: Automated document

[1551] Output: A formatted document with the company's brand colors and fonts applied.

[1552] Step 8:

[1553] The server saves the completed document in the specified format (e.g., .pptx) and generates a download link to provide to the user. The download link is distributed via email or notification system.

[1554] Input: Formatted design document

[1555] Output: Saved document (.pptx), download link for the user

[1556] Step 9:

[1557] Users can obtain the materials by clicking the provided download link and use them for presentations and report writing. These materials can then be used for presentations at sales meetings and reporting sessions.

[1558] Input: Download link

[1559] Output: Downloaded document (Sales Presentation.pptx)

[1560] (Application Example 2)

[1561] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1562] Current document generation systems sometimes fail to adequately address the user's psychological state when they receive automatically generated documents. This can lead to stress and difficulty in understanding the documents, resulting in a diminished user experience. Furthermore, it is difficult to properly visualize the analysis results of data from autonomous vehicles and driver evaluation data, making it challenging to communicate operational performance and driver evaluations efficiently and effectively.

[1563] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1564] In this invention, the server includes means for the user to input data and keywords, means for the server to receive and temporarily store the input data, means for the server to perform a cleansing process on the received data, means for the server to analyze the cleansed data and generate analysis results, means for the server to automatically generate materials based on the analysis results, means for the server to apply visual formatting and design to the generated materials, means for the server to save the completed materials in a specified format and provide them to the user, means for the user to download the materials via a provided link, and means for detecting the user's emotions using an emotion recognition system and adjusting the content and design of the materials. This enables the generation of optimal materials that respond to the user's emotions, making the materials easier to understand and reducing stress during use. Furthermore, it enables the efficient and effective visualization of autonomous vehicle operation data and driver evaluation data, providing users with useful information.

[1565] "Means for users to input data and keywords" refers to a device or software that provides an interface for users to input operational data of autonomous vehicles, driver evaluation data, and keywords necessary for their analysis.

[1566] "A means by which a server receives and temporarily stores input data" refers to a function that receives data sent by a user and temporarily stores that data for use in subsequent processing.

[1567] "Means by which the server performs cleansing on received data" refers to a function that improves data quality by performing processes to impart or remove missing or abnormal values ​​from the received data.

[1568] "Means for a server to analyze cleansed data and generate analysis results" refers to a function that uses statistical analysis and machine learning models to analyze cleansed data and extract useful information and trends.

[1569] "A means for the server to automatically generate documents based on analysis results" refers to a function that automatically creates explanatory documents and reports according to a template based on the obtained analysis results.

[1570] "Means by which the server applies visual formatting and design to generated materials" refers to a function that applies a visually consistent design and layout to automatically generated materials.

[1571] "Means by which the server saves completed documents in a specified format and provides them to the user" refers to a function that saves the final documents in a specified format (e.g., .pptx, .pdf) and provides them to the user via an accessible link or other means.

[1572] "Means for users to download materials via provided links" refers to a function that allows users to download and obtain necessary materials by clicking or accessing provided links.

[1573] "A means of detecting a user's emotions using an emotion recognition system and adjusting the content and design of materials" refers to a function that uses sensing devices such as cameras and microphones to detect emotions from the user's facial expressions and tone of voice, and dynamically adjusts the content and design of materials based on that emotional information.

[1574] A specific embodiment of this invention is shown below. Here, we describe a system that generates data based on the operation data of an autonomous vehicle and the driver's evaluation data.

[1575] First, users input performance data, evaluation data, and keywords for analysis through the interface of a device such as a smartphone or tablet. This data includes, for example, "Autonomous Vehicle Data from January to December 2023.xlsx" and "Driver Evaluation Data.xlsx". Users can also choose to generate a "Monthly Performance Report".

[1576] The server receives data sent from the user's terminal and temporarily stores it in the database. In this stage, the received data undergoes cleansing processes, such as imputing or removing missing or outlier values. Specifically, if missing values ​​are detected, the corresponding data is imputed using the most recent mean value or similar.

[1577] The cleansed and processed data is analyzed using data analysis software such as Python, Pandas, and Scikit-learn. The results of the analysis include, for example, aggregated data on monthly operational performance and trend data on driver evaluations.

[1578] Next, the process moves to creating automatically generated materials based on the analysis results. Using tools such as Python-PPTX or Jinja2, PowerPoint presentation materials are created using templates suitable for the generated data. These materials include monthly operational performance graphs and pie charts of driver evaluations.

[1579] In addition, the emotion recognition system analyzes the user's facial expressions and tone of voice, and adjusts the design and content of the materials according to the user's psychological state. For example, if the user is feeling stressed, the color scheme and font of the materials can be changed to create a more relaxed atmosphere. This utilizes technologies such as OpenCV, TensorFlow, and the Microsoft Azure Emotion Recognition API.

[1580] Finally, the completed document will have the company's brand colors and a unified font applied. The document will be saved in the specified format (e.g., .pptx, .pdf), and a download link will be generated using AWS S3 or the Django Rest Framework. This link will be provided to the user, who can download and use the document via the link.

[1581] Specific example:

[1582] Input data: "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx"

[1583] Example of a prompt:

[1584] Please upload the operational performance data and driver evaluation data for autonomous vehicles from January to December 2023 and generate a monthly performance report.

[1585] In this way, the system automatically generates and provides high-quality analytical materials that are adapted to the user's psychological state, based on the user's input data.

[1586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1587] Step 1:

[1588] Users input autonomous vehicle operation data and driver evaluation data through a smartphone or tablet interface. Specifically, they upload "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx" files and select to generate a "Monthly Performance Report".

[1589] Input: Operation data and keywords, "Autonomous Vehicle Data.xlsx" and "Driver Evaluation Data.xlsx"

[1590] Output: Data sent from the user's device

[1591] Step 2:

[1592] The server receives data sent from the user's terminal and temporarily stores it in the database. This storage process makes the data available for subsequent processing.

[1593] Input: Data submitted by the user

[1594] Output: Data stored in the database

[1595] Step 3:

[1596] The server performs a cleansing process on the received data. Specifically, it detects missing values ​​and imputes them with the mean or median. Outliers are also identified and corrected or removed as necessary.

[1597] Input: Saved data

[1598] Output: Cleansed data

[1599] Step 4:

[1600] The server cleanses the data and analyzes it using data analysis software such as Python, Pandas, and Scikit-learn to generate analysis results. This includes aggregating monthly operational data and extracting trends in driver evaluations.

[1601] Input: Cleansed data

[1602] Output: Analysis results data (e.g., monthly operational performance graph, driver evaluation pie chart data)

[1603] Step 5:

[1604] The server automatically generates documents based on the analysis results according to a template. Tools such as Python-PPTX and Jinja2 are used for this process. Specifically, it creates a PowerPoint presentation based on the analysis results.

[1605] Input: Analysis result data

[1606] Output: Generated document (e.g., .pptx file)

[1607] Step 6:

[1608] The server acquires user emotional information using an emotion recognition system. This utilizes sensing devices such as cameras and microphones, and employs OpenCV, TensorFlow, and the Microsoft Azure Emotion Recognition API. It analyzes the user's facial expressions and voice tone to detect emotions.

[1609] Input: User facial expression data and voice data

[1610] Output: Detected emotion information

[1611] Step 7:

[1612] The server-generated materials are adjusted to reflect emotional information, making appropriate design and content changes. For example, if a user is feeling stressed, the color scheme and fonts of the materials are changed to a more relaxing style.

[1613] Input: Detected emotion information, generated data

[1614] Output: Adjusted document (e.g., a .pptx file with modified colors and fonts)

[1615] Step 8:

[1616] The server applies the company's brand colors and a unified font to the prepared documents and saves the final documents in the specified format (e.g., .pptx, .pdf). AWS S3 or Django Rest Framework is used for saving the documents and generating download links.

[1617] Input: Adjusted materials

[1618] Output: Saved final document, generated download link

[1619] Step 9:

[1620] Users download materials by clicking the provided download link and use them in meetings, etc. Users access the link via their device and obtain the necessary materials.

[1621] Input: Download link

[1622] Output: Downloaded document (e.g., .pptx file)

[1623] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1624] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1625] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1626] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1627] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1628] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1629] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1630] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1631] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1632] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1633] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1634] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1635] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1636] 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.

[1637] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1638] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1639] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1640] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1641] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1642] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1643] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1644] The following is further disclosed regarding the embodiments described above.

[1645] (Claim 1)

[1646] Means by which users input data and keywords,

[1647] A means by which the server receives the input data and temporarily stores it,

[1648] A means by which the server performs a cleansing process on the received data,

[1649] A means by which the server analyzes the cleansed data and generates analysis results,

[1650] A means by which the server automatically generates documents based on the analysis results,

[1651] A means by which the server applies visual formatting and design to the generated material,

[1652] A means by which the server saves the completed document in a specified format and provides it to the user,

[1653] A means for users to download materials via a provided link.

[1654] A system that includes this.

[1655] (Claim 2)

[1656] The system according to claim 1, wherein missing values ​​are imputed with the mean or median during the cleansing process.

[1657] (Claim 3)

[1658] The system according to claim 1, which applies a company's brand colors and a consistent font size to the generated materials.

[1659] "Example 1"

[1660] (Claim 1)

[1661] Means by which users input data and keywords,

[1662] A means by which the server receives the input data and temporarily stores it,

[1663] A means by which the server performs a cleansing process on the received data,

[1664] A means by which the server analyzes the cleansed data and generates analysis results,

[1665] A means by which the server automatically generates documents based on the analysis results,

[1666] A means by which the server applies visual formatting and design to the generated material,

[1667] A means by which the server saves the completed document in a specified format and provides it to the user,

[1668] A means for users to download materials via a provided link.

[1669] A system that includes this.

[1670] (Claim 2)

[1671] The system according to claim 1, wherein missing values ​​are imputed with the mean or median during the cleansing process.

[1672] (Claim 3)

[1673] The system according to claim 1, which applies a company's brand colors and a consistent font style to the generated materials.

[1674] "Application Example 1"

[1675] (Claim 1)

[1676] Means by which users input data and keywords,

[1677] A means by which the server receives the input data and temporarily stores it,

[1678] A means by which the server performs a cleansing process on the received data,

[1679] A means by which the server analyzes the cleansed data and generates analysis results,

[1680] A means by which the server automatically generates documents based on the analysis results,

[1681] A means by which the server applies visual formatting and design to the generated material,

[1682] A means by which the server saves the completed document in a specified format and provides it to the user,

[1683] A means for users to download materials via a provided link,

[1684] A means by which users upload viewing data and comment data, and a server analyzes that data to automatically analyze viewer trends and content evaluations, and generates reports.

[1685] A system that includes this.

[1686] (Claim 2)

[1687] The system according to claim 1, wherein missing values ​​are imputed with the mean or median during the cleansing process.

[1688] (Claim 3)

[1689] The system according to claim 1, which applies a company's brand colors and a consistent font size to the generated materials.

[1690] "Example 2 of combining an emotion engine"

[1691] (Claim 1)

[1692] Means by which users input data and keywords,

[1693] A means by which the server receives the input data and temporarily stores it,

[1694] A means by which the server performs a cleansing process on the received data,

[1695] A means by which the server analyzes the cleansed data and generates analysis results,

[1696] A means by which the server automatically generates documents based on the analysis results,

[1697] The server provides means for applying overall visual formatting and design to the generated materials,

[1698] A means by which the server saves the completed document in a specified format and provides it to the user,

[1699] A means for users to download materials via a provided link,

[1700] A means by which a server recognizes a user's emotions and adjusts the content and visual style of materials based on emotional information.

[1701] A system that includes this.

[1702] (Claim 2)

[1703] The system according to claim 1, wherein missing values ​​are imputed with the mean or median during the cleansing process.

[1704] (Claim 3)

[1705] The system according to claim 1, which applies a company's brand colors and a consistent font size to the generated materials.

[1706] "Application example 2 when combining with an emotional engine"

[1707] (Claim 1)

[1708] Means by which users input data and keywords,

[1709] A means by which the server receives the input data and temporarily stores it,

[1710] A means by which the server performs a cleansing process on the received data,

[1711] A means by which the server analyzes the cleansed data and generates analysis results,

[1712] A means by which the server automatically generates documents based on the analysis results,

[1713] A means by which the server applies visual formatting and design to the generated material,

[1714] A means by which the server saves the completed document in a specified format and provides it to the user,

[1715] A means for users to download materials via a provided link,

[1716] A means of detecting a user's emotions using an emotion recognition system and adjusting the content and design of the materials accordingly.

[1717] A system that includes this.

[1718] (Claim 2)

[1719] The system according to claim 1, wherein missing values ​​are imputed with the mean or median during the cleansing process.

[1720] (Claim 3)

[1721] The system according to claim 1, which applies a company's brand colors and a consistent font size to the generated materials. [Explanation of symbols]

[1722] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means by which users input data and keywords, A means by which the server receives the input data and temporarily stores it, A means by which the server performs a cleansing process on the received data, A means by which the server analyzes the cleansed data and generates analysis results, A means by which the server automatically generates documents based on the analysis results, A means by which the server applies visual formatting and design to the generated material, A means by which the server saves the completed document in a specified format and provides it to the user, A means for users to download materials via a provided link. A system that includes this.

2. The system according to claim 1, wherein missing values ​​are imputed with the mean or median during the cleansing process.

3. The system according to claim 1, which applies the company's brand colors and a consistent font size to the generated materials.

Images