system

Abstract

We provide the system. [Solution] A means for receiving PDF files from the user's terminal, A means of converting the contents of a received PDF file into text data, A means of extracting necessary information from converted text data and generating proposal materials for customers, Means to translate English documents into Japanese as needed, A means of analyzing market research report data and generating reports, A system that includes means for transmitting generated materials to the user's terminal.

Description

Technical Field

[0002] , , , , , , ,

[0004] , , , , , , ,

[0005] , , , , ,

[0003] , , , ,

[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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, as the improvement of business efficiency and accuracy is required, it has become difficult to quickly and accurately process a large amount of commercial material documents and market research reports and create proposal documents for customers. In particular, the work of translating documents corresponding to multiple languages and integrating and analyzing market research reports in various data formats is laborious and time-consuming, reducing the business efficiency of enterprises. In the conventional method, since a series of operations need to be performed manually, efficiency improvement is a major issue. The purpose of this invention is to improve business efficiency by automatically analyzing PDF-formatted documents, extracting necessary information, and generating documents.

Means for Solving the Problems

[0005] The present invention solves the above problems by the following means. First, it provides means for receiving PDF files from the user's terminal. Next, it includes means for converting the contents of the received PDF file into text data. Furthermore, it includes means for extracting necessary information from the converted text data and generating proposal materials for customers. In this case, it includes means for translating English materials into Japanese, and enables the creation of materials in multiple languages ​​as needed. It also includes means for analyzing data from market research reports and automatically generating reports, thereby enabling the automatic generation of graphs and charts. Finally, it includes means for sending the generated materials to the user's terminal. By automating this series of processes, the system aims to improve the efficiency and accuracy of operations.

[0006] A "terminal" is a computer device that a user operates to load and send PDF files and view received documents.

[0007] "PDF file" is an abbreviation for Portable Document Format, and it is an electronic document format that can preserve the layout of a document as is.

[0008] A "server" is a computing system that processes requests from terminals via a network and provides information.

[0009] "Text data" refers to character information extracted from a PDF file and is data stored in digital format.

[0010] A "proposal document for the customer" is a document intended to present a business proposal to a customer, based on product information provided by the vendor and restructured for that customer.

[0011] A "translation API" is an application programming interface for performing machine translation from one language to another.

[0012] A "market research report" is a report that analyzes market trends and statistical information and summarizes the results.

[0013] A "presentation slide" is a visual document consisting of multiple pages, created to convey information visually.

[0014] "Data analysis" refers to the techniques and methods used to examine and process data and extract useful information.

[0015] A graph is a diagram used to visually represent numerical data or relationships between different data points.

[0016] A "chart" or "chart" is a table or chart used to organize and visually represent information or data. [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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This 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 the 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 the emotion engine is combined.

Modes 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 labeled processor (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 CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0021] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a 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 is a system that automates tasks ranging from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials, in order to improve the work efficiency of users. This system can be implemented using the user's terminal, server, and network.

[0039] 1. Loading and sending PDF files

[0040] The user operates the device and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server.

[0041] 2. Analysis of PDF content

[0042] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[0043] 3. Generation and translation of product materials

[0044] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if the materials include English, a translation API is used to translate them into Japanese, matching the language specified by the user. The translated data is then formatted as a Japanese version of the proposal material and stored in storage.

[0045] 4. Generate market research reports

[0046] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format.

[0047] 5. Creating presentation materials

[0048] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations.

[0049] 6. Sending to the user

[0050] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[0051] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, and integrated analysis of market research reports.

[0052] The following describes the processing flow.

[0053] Step 1:

[0054] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[0055] Step 2:

[0056] The device reads the PDF file and sends it to the server. The server saves the received PDF file to its storage.

[0057] Step 3:

[0058] The server uses a PDF parsing library to convert the contents of the PDF file into text data. This process may also utilize optical character recognition (OCR) technology.

[0059] Step 4:

[0060] The server analyzes the converted text data and extracts the necessary information. For example, it analyzes keywords such as product features, pricing information, and specifications.

[0061] Step 5:

[0062] Based on the information extracted by the server, proposal documents for the customer are generated. The generated documents are formatted according to a predefined template.

[0063] Step 6:

[0064] The server detects whether the text data contains English. If it does, it uses a translation API to translate it into Japanese.

[0065] Step 7:

[0066] The server integrates the translated text data into the Japanese proposal document and reformats it as needed.

[0067] Step 8:

[0068] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts statistical information and trends.

[0069] Step 9:

[0070] The server automatically generates market research reports based on the extracted information. The generated reports automatically include graphs and charts.

[0071] Step 10:

[0072] The server extracts key information from market research reports and other materials and automatically generates presentation slides. The slides use a design optimized for presentations.

[0073] Step 11:

[0074] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[0075] Step 12:

[0076] Users can operate their devices to view, edit, and save various documents they receive. Based on these documents, users can then make proposals to clients.

[0077] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[0078] (Example 1)

[0079] 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."

[0080] In today's business environment, the ability to quickly process large volumes of data and accurately extract and analyze information is essential. However, performing a wide range of tasks individually—such as document content analysis, translation, market research report generation, and visual presentation material creation—is time-consuming and labor-intensive, leading to decreased operational efficiency. Therefore, there is a need for a system that automates these tasks consistently.

[0081] 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.

[0082] In this invention, the server includes means for receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating a proposal document, means for translating the document into different languages ​​as needed, means for formatting and saving the translated document, means for analyzing market research data and extracting important statistical information, means for automatically generating graphs and charts based on the important statistical information, means for extracting important information from market research reports and other materials and generating presentation slides, and means for sending the generated document to the user's terminal. This makes it possible to automate the entire process from document content analysis to document generation, translation, data analysis, and presentation material creation.

[0083] "User's device" refers to electronic devices such as computers and mobile devices used by the user.

[0084] A "server" is a central processing system that receives data transmitted from user terminals via a network and performs analysis and processing on it.

[0085] A "PDF file" is an abbreviation for Portable Document Format, an electronic file format that can save a document's formatting and images as they are.

[0086] "Text data" refers to character information extracted from a PDF file, and is data in a format that can be analyzed and edited.

[0087] "Information extraction" is the process of extracting necessary keywords or specific data related to the content of a document from text data.

[0088] A "proposal document" is a document used to explain or propose a product to a customer.

[0089] "Translation" is the process of converting information written in one language into another language.

[0090] "Different languages" refers to the source and target languages ​​specified by the user, such as English and Japanese.

[0091] "Formatting" refers to the process of formatting translated data or generated documents into a format that is easy to read and understand.

[0092] "Saving" refers to the process of recording data in a file system or database to prevent data loss.

[0093] "Market research data" refers to various types of information and statistical data related to a specific market.

[0094] "Important statistical information" refers to key numerical data and statistics needed for decision-making.

[0095] "Graphs and charts" are visual elements used to visually represent data, and include bar graphs, line graphs, pie charts, and so on.

[0096] A "presentation slide" is a slide-based document used to convey information visually.

[0097] This invention is a system that improves the work efficiency of users, automating the entire process from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials. This system can be implemented using the user's terminal, server, and network.

[0098] The user operates the terminal and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the terminal to the server. For example, a sales representative uploads a PDF file named "Market_Analysis_Report2019.pdf" to the system.

[0099] The server analyzes the received PDF file. This process uses PDF analysis libraries such as "Apache® PDFBox" or "PyMuPDF". The analysis library extracts the contents of the PDF file as text data. This extracted text data is used for preprocessing to make it easier to obtain the information needed for the next process. The server extracts and formats the text data from all pages of "Market_Analysis_Report2019.pdf".

[0100] The server extracts necessary information from text data and automatically generates product materials for customer proposals. If the user includes English materials, the server uses translation APIs such as "Google Cloud Translation API" or "Microsoft Azure Translator" to translate the English into Japanese. This translated data is formatted as a Japanese proposal document and stored in cloud storage. The server generates the English proposal document, translates it into Japanese, and stores it in "Amazon S3".

[0101] The server uses analysis libraries such as "pandas" and "openpyxl" to analyze the received market research data files (e.g., Excel files). It extracts important statistical information and compiles it into a report. During this process, it uses libraries such as "Matplotlib" and "Seaborn" to automatically generate graphs and charts to visualize key points, incorporating them into the report in a visually easy-to-understand format. The server analyzes "Market_Analysis_Data2019.xlsx" and generates a market research report in PDF format.

[0102] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. Here, libraries such as "python-pptx" are used to create the slides, which are then saved in PowerPoint or PDF format. These automatically generated slides are later used by the user for presentations. The server generates and saves PowerPoint slides based on market research data and proposal materials.

[0103] The server sends the generated materials to the user's terminal. The terminal saves the received materials to local storage and notifies the user. The server sends the proposal materials, market research report, and presentation slides to the user's terminal, which saves them and notifies the user.

[0104] Example of a prompt:

[0105] "Please use this PDF file to generate a customer proposal document, including a translation from English to Japanese."

[0106] This invention makes it possible to automate the entire process from document content analysis to material generation, translation, data analysis, and presentation material creation. Specifically, it enables users to quickly and efficiently create necessary materials even in busy work environments.

[0107] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0108] Step 1:

[0109] The user selects PDF files containing product information or market research reports from their device and sends them to the server.

[0110] Input: PDF file (e.g., "Market_Analysis_Report2019.pdf")

[0111] Output: PDF file sent to the server

[0112] Specific action: The user selects a PDF file using the terminal's file selection dialog and clicks the "Send" button. The terminal uploads the selected PDF file to the server.

[0113] Step 2:

[0114] The server analyzes the received PDF file and converts it into text data.

[0115] Input: Received PDF file

[0116] Output: Extracted text data

[0117] Specific operation: The server reads the PDF file using the "Apache PDFBox" library. It sequentially analyzes each page, extracts text data, and formats it into a parsable format by removing unnecessary line breaks and spaces.

[0118] Step 3:

[0119] The server extracts the necessary information from the text data and generates the proposal document.

[0120] Input: Parsed text data

[0121] Output: Proposal document

[0122] Specific operation: The server uses NLP technology to extract product names, prices, features, etc., from the analyzed text data, and automatically generates proposal documents by applying them to a template.

[0123] Step 4:

[0124] The server will use a translation API to translate the document into a different language as needed.

[0125] Input: Proposal document, source language, target language

[0126] Output: Translated proposal document

[0127] Specific operation: The server uses either the Google Cloud Translation API or Microsoft Azure Translator to translate the relevant portion of the proposal document into the specified language.

[0128] Step 5:

[0129] The server formats and saves the translated document.

[0130] Input: Translated proposal document

[0131] Output: Saved Japanese version of the proposal document

[0132] Specific operation: The server formats the translated data for readability and saves it to cloud storage. As a specific example, the translated documents are saved to "Amazon S3".

[0133] Step 6:

[0134] The server analyzes market research data, extracts key statistical information, and generates market research reports.

[0135] Input: Market research data file (e.g., Excel file)

[0136] Output: Market research report

[0137] Specific operation: The server uses "pandas" and "openpyxl" to read Excel files and extract important statistical information such as sales figures and growth rates. Next, it automatically generates graphs and charts using "Matplotlib" and "Seaborn" and incorporates them into a report in a visually easy-to-understand format.

[0138] Step 7:

[0139] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides.

[0140] Input: Market research reports, proposal documents, etc.

[0141] Output: Presentation slides

[0142] Specific operation: The server uses "python-pptx" to generate the slide storyline and adds important data and graphs to the slides. The generated slides are saved in PowerPoint and PDF formats.

[0143] Step 8:

[0144] The server sends the generated documents to the user's terminal.

[0145] Input: Various generated documents (proposal documents, market research reports, presentation slides)

[0146] Output: Documents sent to the user's terminal

[0147] Specific operation: The server encodes the data and sends it to the user's terminal as an HTTP response. The terminal saves the received data to local storage and notifies the user.

[0148] (Application Example 1)

[0149] 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."

[0150] Within factories, a wide variety of documents (maintenance procedures, operation manuals, quality control reports, etc.) are used on a daily basis. However, the lack of efficient means to manage these documents and to translate, analyze, and automatically generate them as needed can lead to decreased work efficiency and communication errors among workers. Furthermore, when there are many workers who speak different languages, it becomes difficult to smoothly share work procedures.

[0151] 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.

[0152] In this invention, the server includes means for receiving PDF files from a user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating the materials into a specified language as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for implementing the above functions in a factory document management robot, means for scanning documents used in the factory and sending them to the server, and means for sending scanned documents to the terminals of factory workers and notifying them. This enables consistent management, translation, analysis, and automatic generation of various documents, significantly improving work efficiency within the factory.

[0153] A "user" refers to a person or organization that operates the system and uses a device to receive and send PDF files.

[0154] A "terminal" is an electronic device used to read and send PDF files.

[0155] A "PDF file" is a fixed-layout electronic document format known as Portable Document Format.

[0156] "Text data" refers to the text information extracted from a PDF file.

[0157] "Proposal materials for customers" are documents that are automatically generated by a system and used to make proposals to customers.

[0158] "Translation" is the process of replacing text data from one language to another.

[0159] A "market research report" is a report that analyzes market data and summarizes the research findings.

[0160] A "factory document management robot" is an automated device that has the function of scanning documents used within a factory and sending them to a server.

[0161] "Scanning" is the process of reading a physical document into electronic data.

[0162] "Analysis" refers to the process of extracting necessary information from received data and processing that data.

[0163] A "server" is a computer system that processes and stores received PDF files and other data.

[0164] "Notification" refers to the process of sending generated materials and update information to a device to inform the user.

[0165] The system of the present invention automates the analysis of PDF files, the generation of documents, translation, and the creation of market research reports using the user's terminal, server, and network. The following describes specific embodiments for carrying out the present invention.

[0166] 1. Loading and sending PDF files

[0167] First, the user operates the device to load PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server. This loading and transmission process takes place via an internet connection.

[0168] 2. Analysis of PDF content

[0169] The server uses an appropriate PDF parsing library, such as pdftotext, to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then preprocessed for subsequent processing.

[0170] 3. Generation and translation of product materials

[0171] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API (such as Google Translate) to translate the necessary parts according to the language specified by the user. This automatically generates multilingual materials, which are then stored in storage.

[0172] 4. Generate market research reports

[0173] Furthermore, the server analyzes market research data files (e.g., Excel files) and extracts important statistical information. Based on this, reports, graphs, and charts are automatically generated. In this generation process, libraries such as openpyxl and matplotlib are used to incorporate the data into the reports in a visually easy-to-understand format.

[0174] 5. Creating presentation materials

[0175] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. These slides are created using python-pptx and saved in PowerPoint or PDF format.

[0176] 6. Sending to the user

[0177] The various documents generated by the server are ultimately sent to the user's terminal. The terminal saves the received documents and notifies the user, allowing them to review, edit, and save them.

[0178] Implementation of a document management robot within a factory

[0179] A characteristic application of this invention is its implementation in a robot used to manage documents within a factory. This robot has the function of scanning physical documents and sending them to a server. The scanned documents are sent to the user's terminal or the worker's tablet terminal, allowing for immediate access to the information when needed.

[0180] Specific example

[0181] For example, it is possible to automatically analyze quality control reports within a factory, translate them from English to Japanese, and generate new work procedure manuals. This allows for smooth sharing of work procedures among multilingual workers. Furthermore, the generated documents are automatically sent to terminals held by workers within the factory, significantly improving work efficiency.

[0182] Example of a prompt

[0183] "Please analyze the PDF report, extract the key points, and translate them into Japanese."

[0184] "Please automatically generate Excel reports and presentations based on this data."

[0185] Thus, the invention automates the entire process of analyzing PDF files, generating documents automatically, translating them, and creating reports, thereby significantly improving operational efficiency within the factory.

[0186] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0187] Step 1:

[0188] The user operates the device to load PDF files containing product information and market research reports. The input is a PDF file, and the output is the state in which that PDF file is loaded onto the device. Specifically, the user selects the PDF file from the file selection screen on the device and then performs the operation to load it.

[0189] Step 2:

[0190] The device reads a PDF file and sends it to the server. The input is the PDF file read by the device, and the output is the PDF data sent to the server. Specifically, the device uploads the PDF data to the server via the network.

[0191] Step 3:

[0192] The server parses the received PDF file and converts its contents into text data. The input is the PDF data received by the server, and the output is the converted text data. Specifically, the server uses the pdftotext library to convert the PDF file into text format.

[0193] Step 4:

[0194] The server extracts the necessary information from the converted text data and generates a proposal document for the customer. The input is the converted text data, and the output is the proposal document. Specifically, the server uses natural language processing technology to pick out the necessary information from the text data and format it into the proposal document format.

[0195] Step 5:

[0196] The server translates the document into the specified language as needed. The input is the language of the proposal document, and the output is the translated proposal document. Specifically, the server uses the googletrans API to translate the text data into the other language.

[0197] Step 6:

[0198] The server analyzes data from market research reports, extracts key statistical information, and generates a report. The input is a market research data file (e.g., an Excel file), and the output is the generated report. Specifically, the server uses openpyxl to analyze the Excel file and matplotlib to generate graphs and charts.

[0199] Step 7:

[0200] The server automatically generates presentation slides from market research reports and other relevant materials. The input is the market research report and related materials, and the output is the generated presentation slides. Specifically, the server uses the python-pptx library to create the slides and saves them.

[0201] Step 8:

[0202] The server generates various documents and sends them to the user's terminal. The input is the generated documents, and the output is the documents sent to the terminal. Specifically, the server sends the documents to the terminal over the network, and the terminal receives them.

[0203] Step 9:

[0204] The system saves received documents on the user's device and notifies the user. The input is the documents sent to the device, and the output is the received and saved documents. Specifically, the device saves the documents to local storage and displays a notification to the user.

[0205] Step 10:

[0206] A document management robot within the factory scans physical documents and sends them to a server. The input is the physical document, and the output is the scanned data sent to the server. Specifically, the robot reads the document with a scanner and sends the data to the server.

[0207] Step 11:

[0208] The server sends scanned documents to the worker's terminal and notifies them. The input is the scanned data, and the output is the document sent to the worker's terminal. Specifically, the server sends the scanned data to the worker's terminal via the network, and the terminal receives it and notifies the worker.

[0209] 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.

[0210] This invention aims to improve users' work efficiency by integrating an emotion engine into a system that automates everything from content analysis of PDF files to automatic document generation, translation, market research report summarization, and presentation material creation, thereby providing even more optimized materials. This system can be implemented using the user's terminal, server, and network.

[0211] 1. Loading and sending PDF files

[0212] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename. The loaded PDF file is then sent from the device to the server.

[0213] 2. Emotion recognition by an emotion engine

[0214] The device uses a built-in emotion engine to recognize the user's emotions. Sensors such as cameras and microphones are used to determine the user's emotional state from their facial expressions and tone of voice while they are viewing materials.

[0215] 3. Analysis of PDF content

[0216] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[0217] 4. Generation and translation of product materials

[0218] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if English materials are included, they are translated into Japanese using a translation API, according to the language specified by the user. The translated data is formatted as a Japanese version of the proposal material and stored in storage. Furthermore, the content of the proposal material is adjusted based on the user's emotional state recognized by the emotion engine.

[0219] 5. Generating Market Research Reports

[0220] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format. At this time, the user's emotional state, obtained by the emotion engine, is fed back, and the presentation method and content of the analysis results are adjusted.

[0221] 6. Creating presentation materials

[0222] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for the presentation. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[0223] 7. Sending to the user

[0224] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[0225] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials, and further optimizing the content by combining it with an emotion engine. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, integrated analysis of market research reports, and emotion-based optimization of documents.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[0229] Step 2:

[0230] The device uses a built-in emotion engine to recognize the user's emotions. This includes processing that analyzes the user's facial expressions and voice tone using sensors such as cameras and microphones.

[0231] Step 3:

[0232] The device sends the scanned PDF file and recognized emotion data to the server. The server saves the received PDF file to its storage.

[0233] Step 4:

[0234] The server uses a PDF parsing library to convert the contents of the PDF file into text data. Optical character recognition (OCR) technology may be used in this process.

[0235] Step 5:

[0236] The server analyzes the converted text data and extracts the necessary information. For example, it extracts keywords such as product features, pricing information, and specifications.

[0237] Step 6:

[0238] Based on the information extracted by the server, proposal materials for the customer are generated. This generation process is performed automatically according to a predefined template.

[0239] Step 7:

[0240] The server detects whether the parsed text data contains English. If it does, it uses a translation API to translate it into Japanese.

[0241] Step 8:

[0242] The server integrates the translated text data into the Japanese proposal document and reformats it as needed. In addition, the server adjusts the content of the proposal document based on the user's emotional state. For example, if the user is feeling stressed, the server will adjust the tone of the document to be more calming.

[0243] Step 9:

[0244] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts important statistics and trends.

[0245] Step 10:

[0246] The server automatically generates market research reports based on the extracted information. These reports automatically include graphs and charts, which are also adjusted based on the user's emotional state.

[0247] Step 11:

[0248] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The slides use a design optimized for presentations. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[0249] Step 12:

[0250] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[0251] Step 13:

[0252] The user operates the terminal to view, edit, and save various received documents. Based on these documents, the user can then make proposals to customers.

[0253] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[0254] (Example 2)

[0255] 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 device 14 as the "terminal".

[0256] In conventional business support systems, data extraction and translation from PDF files, as well as the generation of market research reports, are separate processes, resulting in significant time and effort for users. Furthermore, these processes fail to consider the user's feelings, making it difficult to provide optimal proposal materials.

[0257] 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 receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating proposal materials for the customer, means for translating between multiple languages ​​as necessary, means for recognizing the user's emotional state in real time, means for adjusting the content of the materials based on the emotional information obtained by the emotion recognition means, means for analyzing market research report data and generating a report, and means for sending the generated materials to the user's terminal. This automates the entire material creation process and enables the creation of optimal materials that reflect the user's emotions.

[0258] "User's device" refers to an information processing device such as a computer, smartphone, or tablet used by the user for operation.

[0259] A "PDF file" is an abbreviation for Portable Document Format, a file format for electronically displaying and printing documents.

[0260] "Means of receiving" refers to methods and functions for acquiring data via a network.

[0261] "Text data" refers to digital data expressed as character information.

[0262] "Means of conversion" refers to methods or functions for changing data in one format to another.

[0263] "Means for extracting necessary information" refers to methods or functions for extracting specific information from data.

[0264] "Proposal materials for clients" refer to documents and presentation materials used to make proposals to clients.

[0265] "Translation methods" refer to methods or functions for converting text from one language into another language.

[0266] "Emotion recognition means" refers to methods or functions that use sensors and analytical algorithms to determine the emotional state of a user.

[0267] "Real-time" refers to a state where data acquisition and processing occur instantly without delay.

[0268] "Emotional information" refers to data that represents the emotional state of a user.

[0269] "Means of adjusting content" refers to methods or functions for appropriately changing the content of data according to the situation.

[0270] A "market research report" is a report that compiles statistical information and analytical results regarding a specific market.

[0271] "Means of analyzing data" refers to the methods and functions used to perform analysis on data.

[0272] "Means for generating reports" refers to methods and functions for creating reports based on analysis results and information.

[0273] "Means of transmission" refers to methods or functions for sending data to other devices or systems.

[0274] This invention improves the user's work efficiency by combining an emotion recognition engine with a system that automates everything from content analysis of PDF files to automatic generation of documents, translation, summarization of market research reports, and creation of presentation materials. This system is implemented using the user's terminal, server, and network.

[0275] First, the user operates the device to select a specific PDF file, which is then loaded within the device. At this time, the device obtains the file path and filename of the selected PDF file and sends it to the server. The device also performs emotion recognition, using sensors such as a camera and microphone to capture and analyze the user's facial expressions and voice tone. OpenCV and speech recognition software are used for this emotion analysis.

[0276] The server analyzes the received PDF file and converts its content into text data using a PDF analysis library (e.g., PyPDF2 or PDFMiner). It then extracts the necessary information from the converted text data and generates a proposal document. During this process, it automatically translates the document using a translation API (e.g., Google Translate API) according to the language specified by the user. For example, English documents can be translated into Japanese.

[0277] Based on the user's emotional information obtained through emotion recognition, the content of the proposal document is optimized. For example, if the user is expressing surprise, specific points in the document can be emphasized. This makes the document more effective in communicating with the user.

[0278] The server also analyzes market research data files (e.g., Excel files) and extracts statistical information. The pandas library is used for the analysis, automatically generating visually clear graphs and charts, which are then incorporated into the report. During this process, the presentation method and content of the analysis results are adjusted to reflect the results of the sentiment engine.

[0279] Furthermore, the server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. This process uses the Python python-pptx library, and the generated slides are saved in PowerPoint or PDF format. The content of the slides is also adjusted to reflect the user's emotional state.

[0280] Finally, the generated various materials are sent from the server to the user's terminal. The terminal saves the received materials in the local storage and notifies the user, enabling the user to view, edit, and save the materials.

[0281] As a specific example, the functions of this system can be applied by inputting the following prompt text into the generation AI model.

[0282] Please create a proposal document using the following PDF file. The file name is "product_info.pdf". The proposal document should be in Japanese and in a form that is easy for the client to understand. Also, if the tone of the customer's voice is close to the emotion of surprise, please add emphasis expressions to the document.

[0283] In this way, the present invention aims to improve the efficiency and accuracy of the user's work by automating the processes from PDF file reading to material generation, translation, data analysis, and presentation material creation, and optimizing the content by combining emotion recognition.

[0284] The flow of the specific process in Example 2 will be described using FIG. 13.

[0285] Step 1:

[0286] The user operates the terminal to select and load a PDF file. The input is the PDF file selected by the user (e.g., "product_info.pdf"). The terminal obtains the file path (e.g., "C:\Users\username\Documents\product_info.pdf") and file name of this file. The output is data including the obtained file path and file name. As a specific operation, when the user selects a file using the file selection dialog, this information is sent to the terminal. <00,00906>

[0287] Step 2:

[0288] The terminal sends the acquired PDF file to the server. The input is data containing the file path and filename acquired in the previous step. The terminal sends this to the server over the network. The output is the PDF file sent to the server. Specifically, the file data is encoded and sent to the server via an HTTP request.

[0289] Step 3:

[0290] The server parses the received PDF file and converts it into text data. The input is the received PDF file. The server uses a PDF parsing library such as Python's PyPDF2 or PDFMiner to convert the file contents into text data. The output is the converted text data. Specifically, the server reads the PDF file and extracts its contents as text.

[0291] Step 4:

[0292] The server extracts necessary information from the converted text data and generates a proposal document. The input is the extracted text data. The server uses an algorithm to extract the necessary information from the text data and automatically generates a proposal document based on a template. The output is the generated proposal document. Specifically, the algorithm analyzes keywords and important phrases, and the document is constructed based on that.

[0293] Step 5:

[0294] The server translates the proposal document into the specified language as needed. The input consists of the generated proposal document and the target language information. The server uses a translation API, such as the Google Translate API, to translate the document. The output is the translated proposal document. Specifically, the text of the proposal document is sent to the API, and the translated text is received.

[0295] Step 6:

[0296] The device uses its built-in emotion recognition engine to recognize the user's emotions. The input is emotion data such as the user's facial expressions and voice tone. The device uses sensors such as cameras and microphones to capture and analyze this data in real time. The output is the analyzed emotion information. Specifically, the device uses emotion recognition software (e.g., OpenCV, Google Cloud Speech-to-Text API) to identify emotions.

[0297] Step 7:

[0298] The server adjusts the content of the proposal document based on the emotion recognition results. The input is emotion information and the generated proposal document. The server uses an algorithm to adjust the emphasis and expression of the document based on the emotion information. The output is the adjusted proposal document. Specifically, for example, if the user indicates surprise, the formatting is changed so that specific parts of the document are emphasized.

[0299] Step 8:

[0300] The server analyzes market research data and generates the report. The input is a data file for market research (e.g., an Excel file). The server uses the pandas library to analyze the data and extract key statistical information. The output is a market research report containing the statistical information. Specifically, it reads the data file, performs statistical analysis, and generates visual graphs and charts.

[0301] Step 9:

[0302] The server automatically generates presentation slides from market research reports and other relevant materials. The input is the market research report and related materials. The server uses the python-pptx library in Python to generate and save the slides. The output is the generated presentation slides. As a specific operation, a process of extracting important information and creating slides based on templates is performed.

[0303] Step 10:

[0304] The server sends various generated materials to the user's terminal. The input is the generated proposal materials, translated materials, adjusted materials, and presentation slides. The server sends these materials to the user's terminal in a batch. The output is the materials saved on the terminal. As a specific operation, the materials are packaged and sent to the terminal via the network.

[0305] Step 11:

[0306] The terminal saves the received materials and notifies the user. The input is the materials sent from the server. The terminal saves this in the local storage and notifies the user. The output is a user environment where the user can receive the notification and view the materials. As a specific operation, the materials are saved, and a pop-up notification or email notification is sent to the user.

[0307] (Application Example 2)

[0308] Next, Application 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".

[0309] Traditional systems lacked the ability to automate the entire process, from analyzing PDF file content to automatically generating documents, translating, summarizing market research reports, and creating presentation materials. This resulted in a significant amount of manual work and decreased operational efficiency. Furthermore, the systems failed to consider the emotional state of users, leading to unoptimized proposals and market research reports, making it difficult to provide compelling materials to clients.

[0310] 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.

[0311] In this invention, the server includes means for receiving PDF files from the user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating materials in different languages ​​as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for recognizing the user's emotional state and adjusting the content of the generated materials, and means for being installed on a smartphone. This enables the provision of optimized materials based on the user's emotions and improves work efficiency.

[0312] definition statement

[0313] "User's terminal" refers to an electronic device used for processing, including receiving and sending PDF files.

[0314] "PDF file" is an abbreviation for Portable Document Format, a file format that allows documents to be transferred while maintaining their layout.

[0315] "Text data" refers to text information converted from a PDF file.

[0316] "Extracting necessary information" refers to the process of extracting specific important data from the converted text data.

[0317] A "proposal document for the customer" is a document that is automatically generated based on extracted information and tailored to the customer's needs.

[0318] Translation is the process of converting information into a different language.

[0319] A "market research report" is a document that analyzes and summarizes market trends and statistical data.

[0320] "Analysis" is the process of examining data in detail to understand its patterns and structure.

[0321] "Emotional state" refers to the psychological state of a user, as judged from their facial expressions, tone of voice, and other factors.

[0322] "Adjustment" means optimizing the content of the material based on the perceived emotional state.

[0323] A "smartphone" is a portable electronic device that has communication capabilities and can run a variety of applications.

[0324] "Sending" refers to the act of sending generated materials as data to another device.

[0325] A "system" is a general term for the equipment and software used to combine the above-mentioned methods and execute a series of processes.

[0326] Modes for carrying out the invention

[0327] The present invention is a system implemented using a user's terminal, server, and network. Specific embodiments of this system will be described below.

[0328] 1. Receiving PDF files from the user's device.

[0329] The user's device selects and loads a specific PDF file, obtaining its file path and filename. This device is typically a mobile electronic device such as a smartphone. The device has a means of sending the loaded PDF file to a server; therefore, a network connection is required.

[0330] 2. Content analysis of PDF files

[0331] The server uses a PDF parsing library such as PyPDF2 to analyze the received PDF file. The contents of the PDF file are converted into text data, and the necessary information is extracted from the converted text data. Important keywords and data are extracted during this parsing process.

[0332] 3. Emotion recognition by an emotion engine

[0333] The user's device uses a built-in emotion engine to recognize the user's emotions. This involves using sensors such as cameras and microphones to determine the emotional state from the user's facial expressions and tone of voice while viewing materials. Emotion analysis tools like SentimentAnalyzer are used for emotion recognition.

[0334] 4. Generating and translating proposal materials for clients.

[0335] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API, such as Google Translator, to translate materials in different languages ​​(e.g., English) into a specified language (e.g., Japanese). The generated materials are then adjusted based on the user's emotional state, as determined by an emotion engine, and stored in storage.

[0336] 5. Generating Market Research Reports

[0337] The server analyzes market research data files (e.g., Excel files), extracts key statistical information, and automatically compiles a market research report. During this process, graphs and charts are automatically generated to present the analysis results in a visually easy-to-understand format. Furthermore, the results of the emotion engine are taken into consideration, and the analysis results are adjusted to reflect the user's emotional state.

[0338] 6. Creating presentation materials

[0339] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations. This process also incorporates the results of the emotion engine, adjusting the content to match the user's emotional state.

[0340] 7. Sending to the user's device

[0341] The server sends the generated documents to the user's terminal. The user's terminal saves the received documents and notifies the user, allowing them to view, edit, and save these documents.

[0342] Specific example

[0343] As a concrete example, imagine a user loading a PDF file named "product_introduction.pdf" from their device and sending it to the server. Also, consider a scenario where the user provides emotional input such as, "I'm interested in this product, but I want to know more." In this case, the following prompt message would be used:

[0344] Contents of the PDF file: Text content of product_introduction.pdf

[0345] User's emotional state: Interested in this product, but wants more information.

[0346] Product information generation and translation: Generated product description and translation results

[0347] Market Research Report: Generated Market Research Report

[0348] Presentation materials: Generated presentation materials

[0349] This system allows users to quickly and efficiently create the necessary documents and provides them with documents optimized based on their emotions.

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Program processing steps

[0352] Step 1:

[0353] Select and load the PDF file on the user's device.

[0354] The user selects a specific PDF file from their device and loads it. The device obtains the file path and filename. This data is sent to the server.

[0355] Input: PDF file

[0356] Output: File path and file name

[0357] Step 2:

[0358] Sending PDF files from the terminal to the server

[0359] The device sends the acquired PDF file to the server. The data is transferred over the network.

[0360] Input: File path and file name

[0361] Output: PDF file sent to the server

[0362] Step 3:

[0363] Content analysis of PDF files on the server

[0364] The server uses the PyPDF2 library to analyze the received PDF file, converting its contents into text data. It then extracts the necessary information from the converted text data.

[0365] Input: PDF file sent to the server

[0366] Output: Text data

[0367] Step 4:

[0368] Emotion recognition by devices

[0369] The device uses a built-in emotion engine to recognize the user's emotions. It uses the camera and microphone to analyze facial expressions and tone of voice while viewing materials to determine the user's emotional state.

[0370] Input: User's facial expressions and voice (sensor data)

[0371] Output: Emotional state

[0372] Step 5:

[0373] Server-based generation and translation of customer proposal documents.

[0374] The server extracts necessary information from the converted text data and generates proposal materials for the customer. It uses the Google Translator API to translate materials in different languages ​​into the specified language. It then uses the results of an emotion engine to refine the content of the materials.

[0375] Input: Text data, emotional state

[0376] Output: Proposal document (translated)

[0377] Step 6:

[0378] Server-based market research report generation

[0379] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. It automatically generates graphs and charts, and optimizes the report content by incorporating the results of the sentiment engine.

[0380] Input: Market research data files, emotional state

[0381] Output: Market research report

[0382] Step 7:

[0383] Server-based creation of presentation materials

[0384] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. It then incorporates the results of the emotion engine and adjusts the content to suit the user.

[0385] Input: Market research report, emotional state

[0386] Output: Presentation slides

[0387] Step 8:

[0388] Sending and notifying the terminal of the generated documents

[0389] The server sends the generated document to the user's terminal, which saves the received document and notifies the user. This allows the user to view, edit, and save the document.

[0390] Input: Generated documents (proposal documents, reports, slides)

[0391] Output: Documents and notifications sent to the user's device.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Second Embodiment]

[0396] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] This invention is a system that automates tasks ranging from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials, in order to improve the work efficiency of users. This system can be implemented using the user's terminal, server, and network.

[0409] 1. Loading and sending PDF files

[0410] The user operates the device and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server.

[0411] 2. Analysis of PDF content

[0412] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[0413] 3. Generation and translation of product materials

[0414] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if the materials include English, a translation API is used to translate them into Japanese, matching the language specified by the user. The translated data is then formatted as a Japanese version of the proposal material and stored in storage.

[0415] 4. Generate market research reports

[0416] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format.

[0417] 5. Creating presentation materials

[0418] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations.

[0419] 6. Sending to the user

[0420] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[0421] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, and integrated analysis of market research reports.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[0425] Step 2:

[0426] The device reads the PDF file and sends it to the server. The server saves the received PDF file to its storage.

[0427] Step 3:

[0428] The server uses a PDF parsing library to convert the contents of the PDF file into text data. This process may also utilize optical character recognition (OCR) technology.

[0429] Step 4:

[0430] The server analyzes the converted text data and extracts the necessary information. For example, it analyzes keywords such as product features, pricing information, and specifications.

[0431] Step 5:

[0432] Based on the information extracted by the server, proposal documents for the customer are generated. The generated documents are formatted according to a predefined template.

[0433] Step 6:

[0434] The server detects whether the text data contains English. If it does, it uses a translation API to translate it into Japanese.

[0435] Step 7:

[0436] The server integrates the translated text data into the Japanese proposal document and reformats it as needed.

[0437] Step 8:

[0438] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts statistical information and trends.

[0439] Step 9:

[0440] The server automatically generates market research reports based on the extracted information. The generated reports automatically include graphs and charts.

[0441] Step 10:

[0442] The server extracts key information from market research reports and other materials and automatically generates presentation slides. The slides use a design optimized for presentations.

[0443] Step 11:

[0444] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[0445] Step 12:

[0446] Users can operate their devices to view, edit, and save various documents they receive. Based on these documents, users can then make proposals to clients.

[0447] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[0448] (Example 1)

[0449] 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."

[0450] In today's business environment, the ability to quickly process large volumes of data and accurately extract and analyze information is essential. However, performing a wide range of tasks individually—such as document content analysis, translation, market research report generation, and visual presentation material creation—is time-consuming and labor-intensive, leading to decreased operational efficiency. Therefore, there is a need for a system that automates these tasks consistently.

[0451] 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.

[0452] In this invention, the server includes means for receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating a proposal document, means for translating the document into different languages ​​as needed, means for formatting and saving the translated document, means for analyzing market research data and extracting important statistical information, means for automatically generating graphs and charts based on the important statistical information, means for extracting important information from market research reports and other materials and generating presentation slides, and means for sending the generated document to the user's terminal. This makes it possible to automate the entire process from document content analysis to document generation, translation, data analysis, and presentation material creation.

[0453] "User's device" refers to electronic devices such as computers and mobile devices used by the user.

[0454] A "server" is a central processing system that receives data transmitted from user terminals via a network and performs analysis and processing on it.

[0455] A "PDF file" is an abbreviation for Portable Document Format, an electronic file format that can save a document's formatting and images as they are.

[0456] "Text data" refers to character information extracted from a PDF file, and is data in a format that can be analyzed and edited.

[0457] "Information extraction" is the process of extracting necessary keywords or specific data related to the content of a document from text data.

[0458] A "proposal document" is a document used to explain or propose a product to a customer.

[0459] "Translation" is the process of converting information written in one language into another language.

[0460] "Different languages" refers to the source and target languages ​​specified by the user, such as English and Japanese.

[0461] "Formatting" refers to the process of formatting translated data or generated documents into a format that is easy to read and understand.

[0462] "Saving" refers to the process of recording data in a file system or database to prevent data loss.

[0463] "Market research data" refers to various types of information and statistical data related to a specific market.

[0464] "Important statistical information" refers to key numerical data and statistics needed for decision-making.

[0465] "Graphs and charts" are visual elements used to visually represent data, and include bar graphs, line graphs, pie charts, and so on.

[0466] A "presentation slide" is a slide-based document used to convey information visually.

[0467] This invention is a system that improves the work efficiency of users, automating the entire process from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials. This system can be implemented using the user's terminal, server, and network.

[0468] The user operates the terminal and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the terminal to the server. For example, a sales representative uploads a PDF file named "Market_Analysis_Report2019.pdf" to the system.

[0469] The server analyzes the received PDF file. This process uses PDF analysis libraries such as "Apache PDFBox" or "PyMuPDF". The analysis library extracts the contents of the PDF file as text data. This extracted text data is used for preprocessing to make it easier to obtain the information needed for the next process. The server extracts and formats the text data from all pages of "Market_Analysis_Report2019.pdf".

[0470] The server extracts necessary information from text data and automatically generates product materials for customer proposals. If the user includes English materials, the server uses translation APIs such as "Google Cloud Translation API" or "Microsoft Azure Translator" to translate the English into Japanese. This translated data is formatted as a Japanese proposal document and stored in cloud storage. The server generates an English proposal document, translates it into Japanese, and stores it in "Amazon S3".

[0471] The server uses analysis libraries such as "pandas" and "openpyxl" to analyze the received market research data files (e.g., Excel files). It extracts important statistical information and compiles it into a report. During this process, it uses libraries such as "Matplotlib" and "Seaborn" to automatically generate graphs and charts to visualize key points, incorporating them into the report in a visually easy-to-understand format. The server analyzes "Market_Analysis_Data2019.xlsx" and generates a market research report in PDF format.

[0472] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. Here, libraries such as "python-pptx" are used to create the slides, which are then saved in PowerPoint or PDF format. These automatically generated slides are later used by the user for presentations. The server generates and saves PowerPoint slides based on market research data and proposal materials.

[0473] The server sends the generated materials to the user's terminal. The terminal saves the received materials to local storage and notifies the user. The server sends the proposal materials, market research report, and presentation slides to the user's terminal, which saves them and notifies the user.

[0474] Example of a prompt:

[0475] "Please use this PDF file to generate a customer proposal document, including a translation from English to Japanese."

[0476] This invention makes it possible to automate the entire process from document content analysis to material generation, translation, data analysis, and presentation material creation. Specifically, it enables users to quickly and efficiently create necessary materials even in busy work environments.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user selects PDF files containing product information or market research reports from their device and sends them to the server.

[0480] Input: PDF file (e.g., "Market_Analysis_Report2019.pdf")

[0481] Output: PDF file sent to the server

[0482] Specific action: The user selects a PDF file using the terminal's file selection dialog and clicks the "Send" button. The terminal uploads the selected PDF file to the server.

[0483] Step 2:

[0484] The server analyzes the received PDF file and converts it into text data.

[0485] Input: Received PDF file

[0486] Output: Extracted text data

[0487] Specific operation: The server reads the PDF file using the "Apache PDFBox" library. It sequentially analyzes each page, extracts text data, and formats it into a parsable format by removing unnecessary line breaks and spaces.

[0488] Step 3:

[0489] The server extracts the necessary information from the text data and generates the proposal document.

[0490] Input: Parsed text data

[0491] Output: Proposal document

[0492] Specific operation: The server uses NLP technology to extract product names, prices, features, etc., from the analyzed text data, and automatically generates proposal documents by applying them to a template.

[0493] Step 4:

[0494] The server will use a translation API to translate the document into a different language as needed.

[0495] Input: Proposal document, source language, target language

[0496] Output: Translated proposal document

[0497] Specific operation: The server uses either the Google Cloud Translation API or Microsoft Azure Translator to translate the relevant portion of the proposal document into the specified language.

[0498] Step 5:

[0499] The server formats and saves the translated document.

[0500] Input: Translated proposal document

[0501] Output: Saved Japanese version of the proposal document

[0502] Specific operation: The server formats the translated data for readability and saves it to cloud storage. As a specific example, the translated documents are saved to "Amazon S3".

[0503] Step 6:

[0504] The server analyzes market research data, extracts key statistical information, and generates market research reports.

[0505] Input: Market research data file (e.g., Excel file)

[0506] Output: Market research report

[0507] Specific operation: The server uses "pandas" and "openpyxl" to read Excel files and extract important statistical information such as sales figures and growth rates. Next, it automatically generates graphs and charts using "Matplotlib" and "Seaborn" and incorporates them into a report in a visually easy-to-understand format.

[0508] Step 7:

[0509] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides.

[0510] Input: Market research reports, proposal documents, etc.

[0511] Output: Presentation slides

[0512] Specific operation: The server uses "python-pptx" to generate the slide storyline and adds important data and graphs to the slides. The generated slides are saved in PowerPoint and PDF formats.

[0513] Step 8:

[0514] The server sends the generated documents to the user's terminal.

[0515] Input: Various generated documents (proposal documents, market research reports, presentation slides)

[0516] Output: Documents sent to the user's terminal

[0517] Specific operation: The server encodes the data and sends it to the user's terminal as an HTTP response. The terminal saves the received data to local storage and notifies the user.

[0518] (Application Example 1)

[0519] 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."

[0520] Within factories, a wide variety of documents (maintenance procedures, operation manuals, quality control reports, etc.) are used on a daily basis. However, the lack of efficient means to manage these documents and to translate, analyze, and automatically generate them as needed can lead to decreased work efficiency and communication errors among workers. Furthermore, when there are many workers who speak different languages, it becomes difficult to smoothly share work procedures.

[0521] 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.

[0522] In this invention, the server includes means for receiving PDF files from a user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating the materials into a specified language as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for implementing the above functions in a factory document management robot, means for scanning documents used in the factory and sending them to the server, and means for sending scanned documents to the terminals of factory workers and notifying them. This enables consistent management, translation, analysis, and automatic generation of various documents, significantly improving work efficiency within the factory.

[0523] A "user" refers to a person or organization that operates the system and uses a device to receive and send PDF files.

[0524] A "terminal" is an electronic device used to read and send PDF files.

[0525] A "PDF file" is a fixed-layout electronic document format known as Portable Document Format.

[0526] "Text data" refers to the text information extracted from a PDF file.

[0527] "Proposal materials for customers" are documents that are automatically generated by a system and used to make proposals to customers.

[0528] "Translation" is the process of replacing text data from one language to another.

[0529] A "market research report" is a report that analyzes market data and summarizes the research findings.

[0530] A "factory document management robot" is an automated device that has the function of scanning documents used within a factory and sending them to a server.

[0531] "Scanning" is the process of reading a physical document into electronic data.

[0532] "Analysis" refers to the process of extracting necessary information from received data and processing that data.

[0533] A "server" is a computer system that processes and stores received PDF files and other data.

[0534] "Notification" refers to the process of sending generated materials and update information to a device to inform the user.

[0535] The system of the present invention automates the analysis of PDF files, the generation of documents, translation, and the creation of market research reports using the user's terminal, server, and network. The following describes specific embodiments for carrying out the present invention.

[0536] 1. Loading and sending PDF files

[0537] First, the user operates the device to load PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server. This loading and transmission process takes place via an internet connection.

[0538] 2. Analysis of PDF content

[0539] The server uses an appropriate PDF parsing library, such as pdftotext, to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then preprocessed for subsequent processing.

[0540] 3. Generation and translation of product materials

[0541] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API (such as Google Translate) to translate the necessary parts according to the language specified by the user. This automatically generates multilingual materials, which are then stored in storage.

[0542] 4. Generate market research reports

[0543] Furthermore, the server analyzes market research data files (e.g., Excel files) and extracts important statistical information. Based on this, reports, graphs, and charts are automatically generated. In this generation process, libraries such as openpyxl and matplotlib are used to incorporate the data into the reports in a visually easy-to-understand format.

[0544] 5. Creating presentation materials

[0545] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. These slides are created using python-pptx and saved in PowerPoint or PDF format.

[0546] 6. Sending to the user

[0547] The various documents generated by the server are ultimately sent to the user's terminal. The terminal saves the received documents and notifies the user, allowing them to review, edit, and save them.

[0548] Implementation of a document management robot within a factory

[0549] A characteristic application of this invention is its implementation in a robot used to manage documents within a factory. This robot has the function of scanning physical documents and sending them to a server. The scanned documents are sent to the user's terminal or the worker's tablet terminal, allowing for immediate access to the information when needed.

[0550] Specific example

[0551] For example, it is possible to automatically analyze quality control reports within a factory, translate them from English to Japanese, and generate new work procedure manuals. This allows for smooth sharing of work procedures among multilingual workers. Furthermore, the generated documents are automatically sent to terminals held by workers within the factory, significantly improving work efficiency.

[0552] Example of a prompt

[0553] "Please analyze the PDF report, extract the key points, and translate them into Japanese."

[0554] "Please automatically generate Excel reports and presentations based on this data."

[0555] Thus, the invention automates the entire process of analyzing PDF files, generating documents automatically, translating them, and creating reports, thereby significantly improving operational efficiency within the factory.

[0556] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0557] Step 1:

[0558] The user operates the device to load PDF files containing product information and market research reports. The input is a PDF file, and the output is the state in which that PDF file is loaded onto the device. Specifically, the user selects the PDF file from the file selection screen on the device and then performs the operation to load it.

[0559] Step 2:

[0560] The device reads a PDF file and sends it to the server. The input is the PDF file read by the device, and the output is the PDF data sent to the server. Specifically, the device uploads the PDF data to the server via the network.

[0561] Step 3:

[0562] The server parses the received PDF file and converts its contents into text data. The input is the PDF data received by the server, and the output is the converted text data. Specifically, the server uses the pdftotext library to convert the PDF file into text format.

[0563] Step 4:

[0564] The server extracts the necessary information from the converted text data and generates a proposal document for the customer. The input is the converted text data, and the output is the proposal document. Specifically, the server uses natural language processing technology to pick out the necessary information from the text data and format it into the proposal document format.

[0565] Step 5:

[0566] The server translates the document into the specified language as needed. The input is the language of the proposal document, and the output is the translated proposal document. Specifically, the server uses the googletrans API to translate the text data into the other language.

[0567] Step 6:

[0568] The server analyzes data from market research reports, extracts key statistical information, and generates a report. The input is a market research data file (e.g., an Excel file), and the output is the generated report. Specifically, the server uses openpyxl to analyze the Excel file and matplotlib to generate graphs and charts.

[0569] Step 7:

[0570] The server automatically generates presentation slides from market research reports and other relevant materials. The input is the market research report and related materials, and the output is the generated presentation slides. Specifically, the server uses the python-pptx library to create the slides and saves them.

[0571] Step 8:

[0572] The server generates various documents and sends them to the user's terminal. The input is the generated documents, and the output is the documents sent to the terminal. Specifically, the server sends the documents to the terminal over the network, and the terminal receives them.

[0573] Step 9:

[0574] The system saves received documents on the user's device and notifies the user. The input is the documents sent to the device, and the output is the received and saved documents. Specifically, the device saves the documents to local storage and displays a notification to the user.

[0575] Step 10:

[0576] A document management robot within the factory scans physical documents and sends them to a server. The input is the physical document, and the output is the scanned data sent to the server. Specifically, the robot reads the document with a scanner and sends the data to the server.

[0577] Step 11:

[0578] The server sends scanned documents to the worker's terminal and notifies them. The input is the scanned data, and the output is the document sent to the worker's terminal. Specifically, the server sends the scanned data to the worker's terminal via the network, and the terminal receives it and notifies the worker.

[0579] 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.

[0580] This invention aims to improve users' work efficiency by integrating an emotion engine into a system that automates everything from content analysis of PDF files to automatic document generation, translation, market research report summarization, and presentation material creation, thereby providing even more optimized materials. This system can be implemented using the user's terminal, server, and network.

[0581] 1. Loading and sending PDF files

[0582] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename. The loaded PDF file is then sent from the device to the server.

[0583] 2. Emotion recognition by an emotion engine

[0584] The device uses a built-in emotion engine to recognize the user's emotions. Sensors such as cameras and microphones are used to determine the user's emotional state from their facial expressions and tone of voice while they are viewing materials.

[0585] 3. Analysis of PDF content

[0586] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[0587] 4. Generation and translation of product materials

[0588] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if English materials are included, they are translated into Japanese using a translation API, according to the language specified by the user. The translated data is formatted as a Japanese version of the proposal material and stored in storage. Furthermore, the content of the proposal material is adjusted based on the user's emotional state recognized by the emotion engine.

[0589] 5. Generating Market Research Reports

[0590] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format. At this time, the user's emotional state, obtained by the emotion engine, is fed back, and the presentation method and content of the analysis results are adjusted.

[0591] 6. Creating presentation materials

[0592] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for the presentation. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[0593] 7. Sending to the user

[0594] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[0595] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials, and further optimizing the content by combining it with an emotion engine. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, integrated analysis of market research reports, and emotion-based optimization of documents.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[0599] Step 2:

[0600] The device uses a built-in emotion engine to recognize the user's emotions. This includes processing that analyzes the user's facial expressions and voice tone using sensors such as cameras and microphones.

[0601] Step 3:

[0602] The device sends the scanned PDF file and recognized emotion data to the server. The server saves the received PDF file to its storage.

[0603] Step 4:

[0604] The server uses a PDF parsing library to convert the contents of the PDF file into text data. Optical character recognition (OCR) technology may be used in this process.

[0605] Step 5:

[0606] The server analyzes the converted text data and extracts the necessary information. For example, it extracts keywords such as product features, pricing information, and specifications.

[0607] Step 6:

[0608] Based on the information extracted by the server, proposal materials for the customer are generated. This generation process is performed automatically according to a predefined template.

[0609] Step 7:

[0610] The server detects whether the parsed text data contains English. If it does, it uses a translation API to translate it into Japanese.

[0611] Step 8:

[0612] The server integrates the translated text data into the Japanese proposal document and reformats it as needed. In addition, the server adjusts the content of the proposal document based on the user's emotional state. For example, if the user is feeling stressed, the server will adjust the tone of the document to be more calming.

[0613] Step 9:

[0614] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts important statistics and trends.

[0615] Step 10:

[0616] The server automatically generates market research reports based on the extracted information. These reports automatically include graphs and charts, which are also adjusted based on the user's emotional state.

[0617] Step 11:

[0618] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The slides use a design optimized for presentations. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[0619] Step 12:

[0620] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[0621] Step 13:

[0622] The user operates the terminal to view, edit, and save various received documents. Based on these documents, the user can then make proposals to customers.

[0623] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[0624] (Example 2)

[0625] 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".

[0626] In conventional business support systems, data extraction and translation from PDF files, as well as the generation of market research reports, are separate processes, resulting in significant time and effort for users. Furthermore, these processes fail to consider the user's feelings, making it difficult to provide optimal proposal materials.

[0627] 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 receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating proposal materials for the customer, means for translating between multiple languages ​​as necessary, means for recognizing the user's emotional state in real time, means for adjusting the content of the materials based on the emotional information obtained by the emotion recognition means, means for analyzing market research report data and generating a report, and means for sending the generated materials to the user's terminal. This automates the entire material creation process and enables the creation of optimal materials that reflect the user's emotions.

[0628] "User's device" refers to an information processing device such as a computer, smartphone, or tablet used by the user for operation.

[0629] A "PDF file" is an abbreviation for Portable Document Format, a file format for electronically displaying and printing documents.

[0630] "Means of receiving" refers to methods and functions for acquiring data via a network.

[0631] "Text data" refers to digital data expressed as character information.

[0632] "Means of conversion" refers to methods or functions for changing data in one format to another.

[0633] "Means for extracting necessary information" refers to methods or functions for extracting specific information from data.

[0634] "Proposal materials for clients" refer to documents and presentation materials used to make proposals to clients.

[0635] "Translation methods" refer to methods or functions for converting text from one language into another language.

[0636] "Emotion recognition means" refers to methods or functions that use sensors and analytical algorithms to determine the emotional state of a user.

[0637] "Real-time" refers to a state where data acquisition and processing occur instantly without delay.

[0638] "Emotional information" refers to data that represents the emotional state of a user.

[0639] "Means of adjusting content" refers to methods or functions for appropriately changing the content of data according to the situation.

[0640] A "market research report" is a report that compiles statistical information and analytical results regarding a specific market.

[0641] "Means of analyzing data" refers to the methods and functions used to perform analysis on data.

[0642] "Means for generating reports" refers to methods and functions for creating reports based on analysis results and information.

[0643] "Means of transmission" refers to methods or functions for sending data to other devices or systems.

[0644] This invention improves the user's work efficiency by combining an emotion recognition engine with a system that automates everything from content analysis of PDF files to automatic generation of documents, translation, summarization of market research reports, and creation of presentation materials. This system is implemented using the user's terminal, server, and network.

[0645] First, the user operates the device to select a specific PDF file, which is then loaded within the device. At this time, the device obtains the file path and filename of the selected PDF file and sends it to the server. The device also performs emotion recognition, using sensors such as a camera and microphone to capture and analyze the user's facial expressions and voice tone. OpenCV and speech recognition software are used for this emotion analysis.

[0646] The server analyzes the received PDF file and converts its content into text data using a PDF analysis library (e.g., PyPDF2 or PDFMiner). It then extracts the necessary information from the converted text data and generates a proposal document. During this process, it automatically translates the document using a translation API (e.g., Google Translate API) according to the language specified by the user. For example, English documents can be translated into Japanese.

[0647] Based on the user's emotional information obtained through emotion recognition, the content of the proposal document is optimized. For example, if the user is expressing surprise, specific points in the document can be emphasized. This makes the document more effective in communicating with the user.

[0648] The server also analyzes market research data files (e.g., Excel files) and extracts statistical information. The pandas library is used for the analysis, automatically generating visually clear graphs and charts, which are then incorporated into the report. During this process, the presentation method and content of the analysis results are adjusted to reflect the results of the sentiment engine.

[0649] Furthermore, the server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. This process uses the Python python-pptx library, and the generated slides are saved in PowerPoint or PDF format. The content of the slides is also adjusted to reflect the user's emotional state.

[0650] Finally, the generated documents are sent from the server to the user's terminal, which saves the received documents to its local storage and notifies the user, allowing the user to view, edit, and save the documents.

[0651] As a concrete example, the functionality of this system can be applied by inputting the following prompt sentence into the generating AI model.

[0652] Please create your proposal using the following PDF file. The file name is "product_info.pdf". The proposal should be in Japanese and in a format that is easy for the client to understand. Also, if the client's tone of voice is close to one of surprise, please add emphasis to the document.

[0653] Thus, this invention aims to improve the efficiency and accuracy of users' work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials, and by optimizing the content through the combination of sentiment recognition.

[0654] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0655] Step 1:

[0656] The user operates the terminal to select and load a PDF file. The input is the PDF file selected by the user (e.g., "product_info.pdf"). The terminal obtains the path (e.g., "C:\Users\username\Documents\product_info.pdf") and filename of this file. The output is data containing the obtained file path and filename. Specifically, when the user selects a file using a file selection dialog, this information is sent to the terminal.

[0657] Step 2:

[0658] The terminal sends the acquired PDF file to the server. The input is data containing the file path and filename acquired in the previous step. The terminal sends this to the server over the network. The output is the PDF file sent to the server. Specifically, the file data is encoded and sent to the server via an HTTP request.

[0659] Step 3:

[0660] The server parses the received PDF file and converts it into text data. The input is the received PDF file. The server uses a PDF parsing library such as Python's PyPDF2 or PDFMiner to convert the file contents into text data. The output is the converted text data. Specifically, the server reads the PDF file and extracts its contents as text.

[0661] Step 4:

[0662] The server extracts necessary information from the converted text data and generates a proposal document. The input is the extracted text data. The server uses an algorithm to extract the necessary information from the text data and automatically generates a proposal document based on a template. The output is the generated proposal document. Specifically, the algorithm analyzes keywords and important phrases, and the document is constructed based on that.

[0663] Step 5:

[0664] The server translates the proposal document into the specified language as needed. The input consists of the generated proposal document and the target language information. The server uses a translation API, such as the Google Translate API, to translate the document. The output is the translated proposal document. Specifically, the text of the proposal document is sent to the API, and the translated text is received.

[0665] Step 6:

[0666] The device uses its built-in emotion recognition engine to recognize the user's emotions. The input is emotion data such as the user's facial expressions and voice tone. The device uses sensors such as cameras and microphones to capture and analyze this data in real time. The output is the analyzed emotion information. Specifically, the device uses emotion recognition software (e.g., OpenCV, Google Cloud Speech-to-Text API) to identify emotions.

[0667] Step 7:

[0668] The server adjusts the content of the proposal document based on the emotion recognition results. The input is emotion information and the generated proposal document. The server uses an algorithm to adjust the emphasis and expression of the document based on the emotion information. The output is the adjusted proposal document. Specifically, for example, if the user indicates surprise, the formatting is changed so that specific parts of the document are emphasized.

[0669] Step 8:

[0670] The server analyzes market research data and generates the report. The input is a data file for market research (e.g., an Excel file). The server uses the pandas library to analyze the data and extract key statistical information. The output is a market research report containing the statistical information. Specifically, it reads the data file, performs statistical analysis, and generates visual graphs and charts.

[0671] Step 9:

[0672] The server automatically generates presentation slides from market research reports and other relevant materials. The input consists of market research reports and related materials. The server uses the Python python-pptx library to generate and save the slides. The output is the generated presentation slides. Specifically, the server extracts key information and creates slides based on a template.

[0673] Step 10:

[0674] The server sends various generated documents to the user's terminal. Inputs include generated proposal documents, translated documents, revised documents, and presentation slides. The server sends these documents to the user's terminal as a whole. Outputs are the documents saved on the terminal. Specifically, the documents are packaged and sent to the terminal via the network.

[0675] Step 11:

[0676] The terminal saves the received documents and notifies the user. The input is documents sent from the server. The terminal saves these to local storage and notifies the user. The output is the user environment where the notification is received and the documents can be viewed. Specifically, the documents are saved, and a pop-up notification or email notification is sent to the user.

[0677] (Application Example 2)

[0678] 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."

[0679] Traditional systems lacked the ability to automate the entire process, from analyzing PDF file content to automatically generating documents, translating, summarizing market research reports, and creating presentation materials. This resulted in a significant amount of manual work and decreased operational efficiency. Furthermore, the systems failed to consider the emotional state of users, leading to unoptimized proposals and market research reports, making it difficult to provide compelling materials to clients.

[0680] 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.

[0681] In this invention, the server includes means for receiving PDF files from the user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating materials in different languages ​​as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for recognizing the user's emotional state and adjusting the content of the generated materials, and means for being installed on a smartphone. This enables the provision of optimized materials based on the user's emotions and improves work efficiency.

[0682] definition statement

[0683] "User's terminal" refers to an electronic device used for processing, including receiving and sending PDF files.

[0684] "PDF file" is an abbreviation for Portable Document Format, a file format that allows documents to be transferred while maintaining their layout.

[0685] "Text data" refers to text information converted from a PDF file.

[0686] "Extracting necessary information" refers to the process of extracting specific important data from the converted text data.

[0687] A "proposal document for the customer" is a document that is automatically generated based on extracted information and tailored to the customer's needs.

[0688] Translation is the process of converting information into a different language.

[0689] A "market research report" is a document that analyzes and summarizes market trends and statistical data.

[0690] "Analysis" is the process of examining data in detail to understand its patterns and structure.

[0691] "Emotional state" refers to the psychological state of a user, as judged from their facial expressions, tone of voice, and other factors.

[0692] "Adjustment" means optimizing the content of the material based on the perceived emotional state.

[0693] A "smartphone" is a portable electronic device that has communication capabilities and can run a variety of applications.

[0694] "Sending" refers to the act of sending generated materials as data to another device.

[0695] A "system" is a general term for the equipment and software used to combine the above-mentioned methods and execute a series of processes.

[0696] Modes for carrying out the invention

[0697] The present invention is a system implemented using a user's terminal, server, and network. Specific embodiments of this system will be described below.

[0698] 1. Receiving PDF files from the user's device.

[0699] The user's device selects and loads a specific PDF file, obtaining its file path and filename. This device is typically a mobile electronic device such as a smartphone. The device has a means of sending the loaded PDF file to a server; therefore, a network connection is required.

[0700] 2. Content analysis of PDF files

[0701] The server uses a PDF parsing library such as PyPDF2 to analyze the received PDF file. The contents of the PDF file are converted into text data, and the necessary information is extracted from the converted text data. Important keywords and data are extracted during this parsing process.

[0702] 3. Emotion recognition by an emotion engine

[0703] The user's device uses a built-in emotion engine to recognize the user's emotions. This involves using sensors such as cameras and microphones to determine the emotional state from the user's facial expressions and tone of voice while viewing materials. Emotion analysis tools like SentimentAnalyzer are used for emotion recognition.

[0704] 4. Generating and translating proposal materials for clients.

[0705] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API, such as Google Translator, to translate materials in different languages ​​(e.g., English) into a specified language (e.g., Japanese). The generated materials are then adjusted based on the user's emotional state, as determined by an emotion engine, and stored in storage.

[0706] 5. Generating Market Research Reports

[0707] The server analyzes market research data files (e.g., Excel files), extracts key statistical information, and automatically compiles a market research report. During this process, graphs and charts are automatically generated to present the analysis results in a visually easy-to-understand format. Furthermore, the results of the emotion engine are taken into consideration, and the analysis results are adjusted to reflect the user's emotional state.

[0708] 6. Creating presentation materials

[0709] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations. This process also incorporates the results of the emotion engine, adjusting the content to match the user's emotional state.

[0710] 7. Sending to the user's device

[0711] The server sends the generated documents to the user's terminal. The user's terminal saves the received documents and notifies the user, allowing them to view, edit, and save these documents.

[0712] Specific example

[0713] As a concrete example, imagine a user loading a PDF file named "product_introduction.pdf" from their device and sending it to the server. Also, consider a scenario where the user provides emotional input such as, "I'm interested in this product, but I want to know more." In this case, the following prompt message would be used:

[0714] Contents of the PDF file: Text content of product_introduction.pdf

[0715] User's emotional state: Interested in this product, but wants more information.

[0716] Product information generation and translation: Generated product description and translation results

[0717] Market Research Report: Generated Market Research Report

[0718] Presentation materials: Generated presentation materials

[0719] This system allows users to quickly and efficiently create the necessary documents and provides them with documents optimized based on their emotions.

[0720] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0721] Program processing steps

[0722] Step 1:

[0723] Select and load the PDF file on the user's device.

[0724] The user selects a specific PDF file from their device and loads it. The device obtains the file path and filename. This data is sent to the server.

[0725] Input: PDF file

[0726] Output: File path and file name

[0727] Step 2:

[0728] Sending PDF files from the terminal to the server

[0729] The device sends the acquired PDF file to the server. The data is transferred over the network.

[0730] Input: File path and file name

[0731] Output: PDF file sent to the server

[0732] Step 3:

[0733] Content analysis of PDF files on the server

[0734] The server uses the PyPDF2 library to analyze the received PDF file, converting its contents into text data. It then extracts the necessary information from the converted text data.

[0735] Input: PDF file sent to the server

[0736] Output: Text data

[0737] Step 4:

[0738] Emotion recognition by devices

[0739] The device uses a built-in emotion engine to recognize the user's emotions. It uses the camera and microphone to analyze facial expressions and tone of voice while viewing materials to determine the user's emotional state.

[0740] Input: User's facial expressions and voice (sensor data)

[0741] Output: Emotional state

[0742] Step 5:

[0743] Server-based generation and translation of customer proposal documents.

[0744] The server extracts necessary information from the converted text data and generates proposal materials for the customer. It uses the Google Translator API to translate materials in different languages ​​into the specified language. It then uses the results of an emotion engine to refine the content of the materials.

[0745] Input: Text data, emotional state

[0746] Output: Proposal document (translated)

[0747] Step 6:

[0748] Server-based market research report generation

[0749] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. It automatically generates graphs and charts, and optimizes the report content by incorporating the results of the sentiment engine.

[0750] Input: Market research data files, emotional state

[0751] Output: Market research report

[0752] Step 7:

[0753] Server-based creation of presentation materials

[0754] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. It then incorporates the results of the emotion engine and adjusts the content to suit the user.

[0755] Input: Market research report, emotional state

[0756] Output: Presentation slides

[0757] Step 8:

[0758] Sending and notifying the terminal of the generated documents

[0759] The server sends the generated document to the user's terminal, which saves the received document and notifies the user. This allows the user to view, edit, and save the document.

[0760] Input: Generated documents (proposal documents, reports, slides)

[0761] Output: Documents and notifications sent to the user's device.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] [Third Embodiment]

[0766] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0767] 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.

[0768] 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).

[0769] 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.

[0770] 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.

[0771] 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).

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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".

[0778] This invention is a system that automates tasks ranging from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials, in order to improve the work efficiency of users. This system can be implemented using the user's terminal, server, and network.

[0779] 1. Loading and sending PDF files

[0780] The user operates the device and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server.

[0781] 2. Analysis of PDF content

[0782] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[0783] 3. Generation and translation of product materials

[0784] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if the materials include English, a translation API is used to translate them into Japanese, matching the language specified by the user. The translated data is then formatted as a Japanese version of the proposal material and stored in storage.

[0785] 4. Generate market research reports

[0786] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format.

[0787] 5. Creating presentation materials

[0788] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations.

[0789] 6. Sending to the user

[0790] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[0791] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, and integrated analysis of market research reports.

[0792] The following describes the processing flow.

[0793] Step 1:

[0794] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[0795] Step 2:

[0796] The device reads the PDF file and sends it to the server. The server saves the received PDF file to its storage.

[0797] Step 3:

[0798] The server uses a PDF parsing library to convert the contents of the PDF file into text data. This process may also utilize optical character recognition (OCR) technology.

[0799] Step 4:

[0800] The server analyzes the converted text data and extracts the necessary information. For example, it analyzes keywords such as product features, pricing information, and specifications.

[0801] Step 5:

[0802] Based on the information extracted by the server, proposal documents for the customer are generated. The generated documents are formatted according to a predefined template.

[0803] Step 6:

[0804] The server detects whether the text data contains English. If it does, it uses a translation API to translate it into Japanese.

[0805] Step 7:

[0806] The server integrates the translated text data into the Japanese proposal document and reformats it as needed.

[0807] Step 8:

[0808] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts statistical information and trends.

[0809] Step 9:

[0810] The server automatically generates market research reports based on the extracted information. The generated reports automatically include graphs and charts.

[0811] Step 10:

[0812] The server extracts key information from market research reports and other materials and automatically generates presentation slides. The slides use a design optimized for presentations.

[0813] Step 11:

[0814] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[0815] Step 12:

[0816] Users can operate their devices to view, edit, and save various documents they receive. Based on these documents, users can then make proposals to clients.

[0817] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[0818] (Example 1)

[0819] 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."

[0820] In today's business environment, the ability to quickly process large volumes of data and accurately extract and analyze information is essential. However, performing a wide range of tasks individually—such as document content analysis, translation, market research report generation, and visual presentation material creation—is time-consuming and labor-intensive, leading to decreased operational efficiency. Therefore, there is a need for a system that automates these tasks consistently.

[0821] 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.

[0822] In this invention, the server includes means for receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating a proposal document, means for translating the document into different languages ​​as needed, means for formatting and saving the translated document, means for analyzing market research data and extracting important statistical information, means for automatically generating graphs and charts based on the important statistical information, means for extracting important information from market research reports and other materials and generating presentation slides, and means for sending the generated document to the user's terminal. This makes it possible to automate the entire process from document content analysis to document generation, translation, data analysis, and presentation material creation.

[0823] "User's device" refers to electronic devices such as computers and mobile devices used by the user.

[0824] A "server" is a central processing system that receives data transmitted from user terminals via a network and performs analysis and processing on it.

[0825] A "PDF file" is an abbreviation for Portable Document Format, an electronic file format that can save a document's formatting and images as they are.

[0826] "Text data" refers to character information extracted from a PDF file, and is data in a format that can be analyzed and edited.

[0827] "Information extraction" is the process of extracting necessary keywords or specific data related to the content of a document from text data.

[0828] A "proposal document" is a document used to explain or propose a product to a customer.

[0829] "Translation" is the process of converting information written in one language into another language.

[0830] "Different languages" refers to the source and target languages ​​specified by the user, such as English and Japanese.

[0831] "Formatting" refers to the process of formatting translated data or generated documents into a format that is easy to read and understand.

[0832] "Saving" refers to the process of recording data in a file system or database to prevent data loss.

[0833] "Market research data" refers to various types of information and statistical data related to a specific market.

[0834] "Important statistical information" refers to key numerical data and statistics needed for decision-making.

[0835] "Graphs and charts" are visual elements used to visually represent data, and include bar graphs, line graphs, pie charts, and so on.

[0836] A "presentation slide" is a slide-based document used to convey information visually.

[0837] This invention is a system that improves the work efficiency of users, automating the entire process from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials. This system can be implemented using the user's terminal, server, and network.

[0838] The user operates the terminal and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the terminal to the server. For example, a sales representative uploads a PDF file named "Market_Analysis_Report2019.pdf" to the system.

[0839] The server analyzes the received PDF file. This process uses PDF analysis libraries such as "Apache PDFBox" or "PyMuPDF". The analysis library extracts the contents of the PDF file as text data. This extracted text data is used for preprocessing to make it easier to obtain the information needed for the next process. The server extracts and formats the text data from all pages of "Market_Analysis_Report2019.pdf".

[0840] The server extracts necessary information from text data and automatically generates product materials for customer proposals. If the user includes English materials, the server uses translation APIs such as "Google Cloud Translation API" or "Microsoft Azure Translator" to translate the English into Japanese. This translated data is formatted as a Japanese proposal document and stored in cloud storage. The server generates an English proposal document, translates it into Japanese, and stores it in "Amazon S3".

[0841] The server uses analysis libraries such as "pandas" and "openpyxl" to analyze the received market research data files (e.g., Excel files). It extracts important statistical information and compiles it into a report. During this process, it uses libraries such as "Matplotlib" and "Seaborn" to automatically generate graphs and charts to visualize key points, incorporating them into the report in a visually easy-to-understand format. The server analyzes "Market_Analysis_Data2019.xlsx" and generates a market research report in PDF format.

[0842] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. Here, libraries such as "python-pptx" are used to create the slides, which are then saved in PowerPoint or PDF format. These automatically generated slides are later used by the user for presentations. The server generates and saves PowerPoint slides based on market research data and proposal materials.

[0843] The server sends the generated materials to the user's terminal. The terminal saves the received materials to local storage and notifies the user. The server sends the proposal materials, market research report, and presentation slides to the user's terminal, which saves them and notifies the user.

[0844] Example of a prompt:

[0845] "Please use this PDF file to generate a customer proposal document, including a translation from English to Japanese."

[0846] This invention makes it possible to automate the entire process from document content analysis to material generation, translation, data analysis, and presentation material creation. Specifically, it enables users to quickly and efficiently create necessary materials even in busy work environments.

[0847] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0848] Step 1:

[0849] The user selects PDF files containing product information or market research reports from their device and sends them to the server.

[0850] Input: PDF file (e.g., "Market_Analysis_Report2019.pdf")

[0851] Output: PDF file sent to the server

[0852] Specific action: The user selects a PDF file using the terminal's file selection dialog and clicks the "Send" button. The terminal uploads the selected PDF file to the server.

[0853] Step 2:

[0854] The server analyzes the received PDF file and converts it into text data.

[0855] Input: Received PDF file

[0856] Output: Extracted text data

[0857] Specific operation: The server reads the PDF file using the "Apache PDFBox" library. It sequentially analyzes each page, extracts text data, and formats it into a parsable format by removing unnecessary line breaks and spaces.

[0858] Step 3:

[0859] The server extracts the necessary information from the text data and generates the proposal document.

[0860] Input: Parsed text data

[0861] Output: Proposal document

[0862] Specific operation: The server uses NLP technology to extract product names, prices, features, etc., from the analyzed text data, and automatically generates proposal documents by applying them to a template.

[0863] Step 4:

[0864] The server will use a translation API to translate the document into a different language as needed.

[0865] Input: Proposal document, source language, target language

[0866] Output: Translated proposal document

[0867] Specific operation: The server uses either the Google Cloud Translation API or Microsoft Azure Translator to translate the relevant portion of the proposal document into the specified language.

[0868] Step 5:

[0869] The server formats and saves the translated document.

[0870] Input: Translated proposal document

[0871] Output: Saved Japanese version of the proposal document

[0872] Specific operation: The server formats the translated data for readability and saves it to cloud storage. As a specific example, the translated documents are saved to "Amazon S3".

[0873] Step 6:

[0874] The server analyzes market research data, extracts key statistical information, and generates market research reports.

[0875] Input: Market research data file (e.g., Excel file)

[0876] Output: Market research report

[0877] Specific operation: The server uses "pandas" and "openpyxl" to read Excel files and extract important statistical information such as sales figures and growth rates. Next, it automatically generates graphs and charts using "Matplotlib" and "Seaborn" and incorporates them into a report in a visually easy-to-understand format.

[0878] Step 7:

[0879] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides.

[0880] Input: Market research reports, proposal documents, etc.

[0881] Output: Presentation slides

[0882] Specific operation: The server uses "python-pptx" to generate the slide storyline and adds important data and graphs to the slides. The generated slides are saved in PowerPoint and PDF formats.

[0883] Step 8:

[0884] The server sends the generated documents to the user's terminal.

[0885] Input: Various generated documents (proposal documents, market research reports, presentation slides)

[0886] Output: Documents sent to the user's terminal

[0887] Specific operation: The server encodes the data and sends it to the user's terminal as an HTTP response. The terminal saves the received data to local storage and notifies the user.

[0888] (Application Example 1)

[0889] 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."

[0890] Within factories, a wide variety of documents (maintenance procedures, operation manuals, quality control reports, etc.) are used on a daily basis. However, the lack of efficient means to manage these documents and to translate, analyze, and automatically generate them as needed can lead to decreased work efficiency and communication errors among workers. Furthermore, when there are many workers who speak different languages, it becomes difficult to smoothly share work procedures.

[0891] 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.

[0892] In this invention, the server includes means for receiving PDF files from a user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating the materials into a specified language as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for implementing the above functions in a factory document management robot, means for scanning documents used in the factory and sending them to the server, and means for sending scanned documents to the terminals of factory workers and notifying them. This enables consistent management, translation, analysis, and automatic generation of various documents, significantly improving work efficiency within the factory.

[0893] A "user" refers to a person or organization that operates the system and uses a device to receive and send PDF files.

[0894] A "terminal" is an electronic device used to read and send PDF files.

[0895] A "PDF file" is a fixed-layout electronic document format known as Portable Document Format.

[0896] "Text data" refers to the text information extracted from a PDF file.

[0897] "Proposal materials for customers" are documents that are automatically generated by a system and used to make proposals to customers.

[0898] "Translation" is the process of replacing text data from one language to another.

[0899] A "market research report" is a report that analyzes market data and summarizes the research findings.

[0900] A "factory document management robot" is an automated device that has the function of scanning documents used within a factory and sending them to a server.

[0901] "Scanning" is the process of reading a physical document into electronic data.

[0902] "Analysis" refers to the process of extracting necessary information from received data and processing that data.

[0903] A "server" is a computer system that processes and stores received PDF files and other data.

[0904] "Notification" refers to the process of sending generated materials and update information to a device to inform the user.

[0905] The system of the present invention automates the analysis of PDF files, the generation of documents, translation, and the creation of market research reports using the user's terminal, server, and network. The following describes specific embodiments for carrying out the present invention.

[0906] 1. Loading and sending PDF files

[0907] First, the user operates the device to load PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server. This loading and transmission process takes place via an internet connection.

[0908] 2. Analysis of PDF content

[0909] The server uses an appropriate PDF parsing library, such as pdftotext, to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then preprocessed for subsequent processing.

[0910] 3. Generation and translation of product materials

[0911] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API (such as Google Translate) to translate the necessary parts according to the language specified by the user. This automatically generates multilingual materials, which are then stored in storage.

[0912] 4. Generate market research reports

[0913] Furthermore, the server analyzes market research data files (e.g., Excel files) and extracts important statistical information. Based on this, reports, graphs, and charts are automatically generated. In this generation process, libraries such as openpyxl and matplotlib are used to incorporate the data into the reports in a visually easy-to-understand format.

[0914] 5. Creating presentation materials

[0915] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. These slides are created using python-pptx and saved in PowerPoint or PDF format.

[0916] 6. Sending to the user

[0917] The various documents generated by the server are ultimately sent to the user's terminal. The terminal saves the received documents and notifies the user, allowing them to review, edit, and save them.

[0918] Implementation of a document management robot within a factory

[0919] A characteristic application of this invention is its implementation in a robot used to manage documents within a factory. This robot has the function of scanning physical documents and sending them to a server. The scanned documents are sent to the user's terminal or the worker's tablet terminal, allowing for immediate access to the information when needed.

[0920] Specific example

[0921] For example, it is possible to automatically analyze quality control reports within a factory, translate them from English to Japanese, and generate new work procedure manuals. This allows for smooth sharing of work procedures among multilingual workers. Furthermore, the generated documents are automatically sent to terminals held by workers within the factory, significantly improving work efficiency.

[0922] Example of a prompt

[0923] "Please analyze the PDF report, extract the key points, and translate them into Japanese."

[0924] "Please automatically generate Excel reports and presentations based on this data."

[0925] Thus, the invention automates the entire process of analyzing PDF files, generating documents automatically, translating them, and creating reports, thereby significantly improving operational efficiency within the factory.

[0926] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0927] Step 1:

[0928] The user operates the device to load PDF files containing product information and market research reports. The input is a PDF file, and the output is the state in which that PDF file is loaded onto the device. Specifically, the user selects the PDF file from the file selection screen on the device and then performs the operation to load it.

[0929] Step 2:

[0930] The device reads a PDF file and sends it to the server. The input is the PDF file read by the device, and the output is the PDF data sent to the server. Specifically, the device uploads the PDF data to the server via the network.

[0931] Step 3:

[0932] The server parses the received PDF file and converts its contents into text data. The input is the PDF data received by the server, and the output is the converted text data. Specifically, the server uses the pdftotext library to convert the PDF file into text format.

[0933] Step 4:

[0934] The server extracts the necessary information from the converted text data and generates a proposal document for the customer. The input is the converted text data, and the output is the proposal document. Specifically, the server uses natural language processing technology to pick out the necessary information from the text data and format it into the proposal document format.

[0935] Step 5:

[0936] The server translates the document into the specified language as needed. The input is the language of the proposal document, and the output is the translated proposal document. Specifically, the server uses the googletrans API to translate the text data into the other language.

[0937] Step 6:

[0938] The server analyzes data from market research reports, extracts key statistical information, and generates a report. The input is a market research data file (e.g., an Excel file), and the output is the generated report. Specifically, the server uses openpyxl to analyze the Excel file and matplotlib to generate graphs and charts.

[0939] Step 7:

[0940] The server automatically generates presentation slides from market research reports and other relevant materials. The input is the market research report and related materials, and the output is the generated presentation slides. Specifically, the server uses the python-pptx library to create the slides and saves them.

[0941] Step 8:

[0942] The server generates various documents and sends them to the user's terminal. The input is the generated documents, and the output is the documents sent to the terminal. Specifically, the server sends the documents to the terminal over the network, and the terminal receives them.

[0943] Step 9:

[0944] The system saves received documents on the user's device and notifies the user. The input is the documents sent to the device, and the output is the received and saved documents. Specifically, the device saves the documents to local storage and displays a notification to the user.

[0945] Step 10:

[0946] A document management robot within the factory scans physical documents and sends them to a server. The input is the physical document, and the output is the scanned data sent to the server. Specifically, the robot reads the document with a scanner and sends the data to the server.

[0947] Step 11:

[0948] The server sends scanned documents to the worker's terminal and notifies them. The input is the scanned data, and the output is the document sent to the worker's terminal. Specifically, the server sends the scanned data to the worker's terminal via the network, and the terminal receives it and notifies the worker.

[0949] 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.

[0950] This invention aims to improve users' work efficiency by integrating an emotion engine into a system that automates everything from content analysis of PDF files to automatic document generation, translation, market research report summarization, and presentation material creation, thereby providing even more optimized materials. This system can be implemented using the user's terminal, server, and network.

[0951] 1. Loading and sending PDF files

[0952] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename. The loaded PDF file is then sent from the device to the server.

[0953] 2. Emotion recognition by an emotion engine

[0954] The device uses a built-in emotion engine to recognize the user's emotions. Sensors such as cameras and microphones are used to determine the user's emotional state from their facial expressions and tone of voice while they are viewing materials.

[0955] 3. Analysis of PDF content

[0956] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[0957] 4. Generation and translation of product materials

[0958] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if English materials are included, they are translated into Japanese using a translation API, according to the language specified by the user. The translated data is formatted as a Japanese version of the proposal material and stored in storage. Furthermore, the content of the proposal material is adjusted based on the user's emotional state recognized by the emotion engine.

[0959] 5. Generating Market Research Reports

[0960] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format. At this time, the user's emotional state, obtained by the emotion engine, is fed back, and the presentation method and content of the analysis results are adjusted.

[0961] 6. Creating presentation materials

[0962] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for the presentation. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[0963] 7. Sending to the user

[0964] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[0965] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials, and further optimizing the content by combining it with an emotion engine. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, integrated analysis of market research reports, and emotion-based optimization of documents.

[0966] The following describes the processing flow.

[0967] Step 1:

[0968] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[0969] Step 2:

[0970] The device uses a built-in emotion engine to recognize the user's emotions. This includes processing that analyzes the user's facial expressions and voice tone using sensors such as cameras and microphones.

[0971] Step 3:

[0972] The device sends the scanned PDF file and recognized emotion data to the server. The server saves the received PDF file to its storage.

[0973] Step 4:

[0974] The server uses a PDF parsing library to convert the contents of the PDF file into text data. Optical character recognition (OCR) technology may be used in this process.

[0975] Step 5:

[0976] The server analyzes the converted text data and extracts the necessary information. For example, it extracts keywords such as product features, pricing information, and specifications.

[0977] Step 6:

[0978] Based on the information extracted by the server, proposal materials for the customer are generated. This generation process is performed automatically according to a predefined template.

[0979] Step 7:

[0980] The server detects whether the parsed text data contains English. If it does, it uses a translation API to translate it into Japanese.

[0981] Step 8:

[0982] The server integrates the translated text data into the Japanese proposal document and reformats it as needed. In addition, the server adjusts the content of the proposal document based on the user's emotional state. For example, if the user is feeling stressed, the server will adjust the tone of the document to be more calming.

[0983] Step 9:

[0984] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts important statistics and trends.

[0985] Step 10:

[0986] The server automatically generates market research reports based on the extracted information. These reports automatically include graphs and charts, which are also adjusted based on the user's emotional state.

[0987] Step 11:

[0988] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The slides use a design optimized for presentations. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[0989] Step 12:

[0990] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[0991] Step 13:

[0992] The user operates the terminal to view, edit, and save various received documents. Based on these documents, the user can then make proposals to customers.

[0993] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[0994] (Example 2)

[0995] 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."

[0996] In conventional business support systems, data extraction and translation from PDF files, as well as the generation of market research reports, are separate processes, resulting in significant time and effort for users. Furthermore, these processes fail to consider the user's feelings, making it difficult to provide optimal proposal materials.

[0997] 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 receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating proposal materials for the customer, means for translating between multiple languages ​​as necessary, means for recognizing the user's emotional state in real time, means for adjusting the content of the materials based on the emotional information obtained by the emotion recognition means, means for analyzing market research report data and generating a report, and means for sending the generated materials to the user's terminal. This automates the entire material creation process and enables the creation of optimal materials that reflect the user's emotions.

[0998] "User's device" refers to an information processing device such as a computer, smartphone, or tablet used by the user for operation.

[0999] A "PDF file" is an abbreviation for Portable Document Format, a file format for electronically displaying and printing documents.

[1000] "Means of receiving" refers to methods and functions for acquiring data via a network.

[1001] "Text data" refers to digital data expressed as character information.

[1002] "Means of conversion" refers to methods or functions for changing data in one format to another.

[1003] "Means for extracting necessary information" refers to methods or functions for extracting specific information from data.

[1004] "Proposal materials for clients" refer to documents and presentation materials used to make proposals to clients.

[1005] "Translation methods" refer to methods or functions for converting text from one language into another language.

[1006] "Emotion recognition means" refers to methods or functions that use sensors and analytical algorithms to determine the emotional state of a user.

[1007] "Real-time" refers to a state where data acquisition and processing occur instantly without delay.

[1008] "Emotional information" refers to data that represents the emotional state of a user.

[1009] "Means of adjusting content" refers to methods or functions for appropriately changing the content of data according to the situation.

[1010] A "market research report" is a report that compiles statistical information and analytical results regarding a specific market.

[1011] "Means of analyzing data" refers to the methods and functions used to perform analysis on data.

[1012] "Means for generating reports" refers to methods and functions for creating reports based on analysis results and information.

[1013] "Means of transmission" refers to methods or functions for sending data to other devices or systems.

[1014] This invention improves the user's work efficiency by combining an emotion recognition engine with a system that automates everything from content analysis of PDF files to automatic generation of documents, translation, summarization of market research reports, and creation of presentation materials. This system is implemented using the user's terminal, server, and network.

[1015] First, the user operates the device to select a specific PDF file, which is then loaded within the device. At this time, the device obtains the file path and filename of the selected PDF file and sends it to the server. The device also performs emotion recognition, using sensors such as a camera and microphone to capture and analyze the user's facial expressions and voice tone. OpenCV and speech recognition software are used for this emotion analysis.

[1016] The server analyzes the received PDF file and converts its content into text data using a PDF analysis library (e.g., PyPDF2 or PDFMiner). It then extracts the necessary information from the converted text data and generates a proposal document. During this process, it automatically translates the document using a translation API (e.g., Google Translate API) according to the language specified by the user. For example, English documents can be translated into Japanese.

[1017] Based on the user's emotional information obtained through emotion recognition, the content of the proposal document is optimized. For example, if the user is expressing surprise, specific points in the document can be emphasized. This makes the document more effective in communicating with the user.

[1018] The server also analyzes market research data files (e.g., Excel files) and extracts statistical information. The pandas library is used for the analysis, automatically generating visually clear graphs and charts, which are then incorporated into the report. During this process, the presentation method and content of the analysis results are adjusted to reflect the results of the sentiment engine.

[1019] Furthermore, the server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. This process uses the Python python-pptx library, and the generated slides are saved in PowerPoint or PDF format. The content of the slides is also adjusted to reflect the user's emotional state.

[1020] Finally, the generated documents are sent from the server to the user's terminal, which saves the received documents to its local storage and notifies the user, allowing the user to view, edit, and save the documents.

[1021] As a concrete example, the functionality of this system can be applied by inputting the following prompt sentence into the generating AI model.

[1022] Please create your proposal using the following PDF file. The file name is "product_info.pdf". The proposal should be in Japanese and in a format that is easy for the client to understand. Also, if the client's tone of voice is close to one of surprise, please add emphasis to the document.

[1023] Thus, this invention aims to improve the efficiency and accuracy of users' work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials, and by optimizing the content through the combination of sentiment recognition.

[1024] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1025] Step 1:

[1026] The user operates the terminal to select and load a PDF file. The input is the PDF file selected by the user (e.g., "product_info.pdf"). The terminal obtains the path (e.g., "C:\Users\username\Documents\product_info.pdf") and filename of this file. The output is data containing the obtained file path and filename. Specifically, when the user selects a file using a file selection dialog, this information is sent to the terminal.

[1027] Step 2:

[1028] The terminal sends the acquired PDF file to the server. The input is data containing the file path and filename acquired in the previous step. The terminal sends this to the server over the network. The output is the PDF file sent to the server. Specifically, the file data is encoded and sent to the server via an HTTP request.

[1029] Step 3:

[1030] The server parses the received PDF file and converts it into text data. The input is the received PDF file. The server uses a PDF parsing library such as Python's PyPDF2 or PDFMiner to convert the file contents into text data. The output is the converted text data. Specifically, the server reads the PDF file and extracts its contents as text.

[1031] Step 4:

[1032] The server extracts necessary information from the converted text data and generates a proposal document. The input is the extracted text data. The server uses an algorithm to extract the necessary information from the text data and automatically generates a proposal document based on a template. The output is the generated proposal document. Specifically, the algorithm analyzes keywords and important phrases, and the document is constructed based on that.

[1033] Step 5:

[1034] The server translates the proposal document into the specified language as needed. The input consists of the generated proposal document and the target language information. The server uses a translation API, such as the Google Translate API, to translate the document. The output is the translated proposal document. Specifically, the text of the proposal document is sent to the API, and the translated text is received.

[1035] Step 6:

[1036] The device uses its built-in emotion recognition engine to recognize the user's emotions. The input is emotion data such as the user's facial expressions and voice tone. The device uses sensors such as cameras and microphones to capture and analyze this data in real time. The output is the analyzed emotion information. Specifically, the device uses emotion recognition software (e.g., OpenCV, Google Cloud Speech-to-Text API) to identify emotions.

[1037] Step 7:

[1038] The server adjusts the content of the proposal document based on the emotion recognition results. The input is emotion information and the generated proposal document. The server uses an algorithm to adjust the emphasis and expression of the document based on the emotion information. The output is the adjusted proposal document. Specifically, for example, if the user indicates surprise, the formatting is changed so that specific parts of the document are emphasized.

[1039] Step 8:

[1040] The server analyzes market research data and generates the report. The input is a data file for market research (e.g., an Excel file). The server uses the pandas library to analyze the data and extract key statistical information. The output is a market research report containing the statistical information. Specifically, it reads the data file, performs statistical analysis, and generates visual graphs and charts.

[1041] Step 9:

[1042] The server automatically generates presentation slides from market research reports and other relevant materials. The input consists of market research reports and related materials. The server uses the Python python-pptx library to generate and save the slides. The output is the generated presentation slides. Specifically, the server extracts key information and creates slides based on a template.

[1043] Step 10:

[1044] The server sends various generated documents to the user's terminal. Inputs include generated proposal documents, translated documents, revised documents, and presentation slides. The server sends these documents to the user's terminal as a whole. Outputs are the documents saved on the terminal. Specifically, the documents are packaged and sent to the terminal via the network.

[1045] Step 11:

[1046] The terminal saves the received documents and notifies the user. The input is documents sent from the server. The terminal saves these to local storage and notifies the user. The output is the user environment where the notification is received and the documents can be viewed. Specifically, the documents are saved, and a pop-up notification or email notification is sent to the user.

[1047] (Application Example 2)

[1048] 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."

[1049] Traditional systems lacked the ability to automate the entire process, from analyzing PDF file content to automatically generating documents, translating, summarizing market research reports, and creating presentation materials. This resulted in a significant amount of manual work and decreased operational efficiency. Furthermore, the systems failed to consider the emotional state of users, leading to unoptimized proposals and market research reports, making it difficult to provide compelling materials to clients.

[1050] 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.

[1051] In this invention, the server includes means for receiving PDF files from the user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating materials in different languages ​​as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for recognizing the user's emotional state and adjusting the content of the generated materials, and means for being installed on a smartphone. This enables the provision of optimized materials based on the user's emotions and improves work efficiency.

[1052] definition statement

[1053] "User's terminal" refers to an electronic device used for processing, including receiving and sending PDF files.

[1054] "PDF file" is an abbreviation for Portable Document Format, a file format that allows documents to be transferred while maintaining their layout.

[1055] "Text data" refers to text information converted from a PDF file.

[1056] "Extracting necessary information" refers to the process of extracting specific important data from the converted text data.

[1057] A "proposal document for the customer" is a document that is automatically generated based on extracted information and tailored to the customer's needs.

[1058] Translation is the process of converting information into a different language.

[1059] A "market research report" is a document that analyzes and summarizes market trends and statistical data.

[1060] "Analysis" is the process of examining data in detail to understand its patterns and structure.

[1061] "Emotional state" refers to the psychological state of a user, as judged from their facial expressions, tone of voice, and other factors.

[1062] "Adjustment" means optimizing the content of the material based on the perceived emotional state.

[1063] A "smartphone" is a portable electronic device that has communication capabilities and can run a variety of applications.

[1064] "Sending" refers to the act of sending generated materials as data to another device.

[1065] A "system" is a general term for the equipment and software used to combine the above-mentioned methods and execute a series of processes.

[1066] Modes for carrying out the invention

[1067] The present invention is a system implemented using a user's terminal, server, and network. Specific embodiments of this system will be described below.

[1068] 1. Receiving PDF files from the user's device.

[1069] The user's device selects and loads a specific PDF file, obtaining its file path and filename. This device is typically a mobile electronic device such as a smartphone. The device has a means of sending the loaded PDF file to a server; therefore, a network connection is required.

[1070] 2. Content analysis of PDF files

[1071] The server uses a PDF parsing library such as PyPDF2 to analyze the received PDF file. The contents of the PDF file are converted into text data, and the necessary information is extracted from the converted text data. Important keywords and data are extracted during this parsing process.

[1072] 3. Emotion recognition by an emotion engine

[1073] The user's device uses a built-in emotion engine to recognize the user's emotions. This involves using sensors such as cameras and microphones to determine the emotional state from the user's facial expressions and tone of voice while viewing materials. Emotion analysis tools like SentimentAnalyzer are used for emotion recognition.

[1074] 4. Generating and translating proposal materials for clients.

[1075] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API, such as Google Translator, to translate materials in different languages ​​(e.g., English) into a specified language (e.g., Japanese). The generated materials are then adjusted based on the user's emotional state, as determined by an emotion engine, and stored in storage.

[1076] 5. Generating Market Research Reports

[1077] The server analyzes market research data files (e.g., Excel files), extracts key statistical information, and automatically compiles a market research report. During this process, graphs and charts are automatically generated to present the analysis results in a visually easy-to-understand format. Furthermore, the results of the emotion engine are taken into consideration, and the analysis results are adjusted to reflect the user's emotional state.

[1078] 6. Creating presentation materials

[1079] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations. This process also incorporates the results of the emotion engine, adjusting the content to match the user's emotional state.

[1080] 7. Sending to the user's device

[1081] The server sends the generated documents to the user's terminal. The user's terminal saves the received documents and notifies the user, allowing them to view, edit, and save these documents.

[1082] Specific example

[1083] As a concrete example, imagine a user loading a PDF file named "product_introduction.pdf" from their device and sending it to the server. Also, consider a scenario where the user provides emotional input such as, "I'm interested in this product, but I want to know more." In this case, the following prompt message would be used:

[1084] Contents of the PDF file: Text content of product_introduction.pdf

[1085] User's emotional state: Interested in this product, but wants more information.

[1086] Product information generation and translation: Generated product description and translation results

[1087] Market Research Report: Generated Market Research Report

[1088] Presentation materials: Generated presentation materials

[1089] This system allows users to quickly and efficiently create the necessary documents and provides them with documents optimized based on their emotions.

[1090] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1091] Program processing steps

[1092] Step 1:

[1093] Select and load the PDF file on the user's device.

[1094] The user selects a specific PDF file from their device and loads it. The device obtains the file path and filename. This data is sent to the server.

[1095] Input: PDF file

[1096] Output: File path and file name

[1097] Step 2:

[1098] Sending PDF files from the terminal to the server

[1099] The device sends the acquired PDF file to the server. The data is transferred over the network.

[1100] Input: File path and file name

[1101] Output: PDF file sent to the server

[1102] Step 3:

[1103] Content analysis of PDF files on the server

[1104] The server uses the PyPDF2 library to analyze the received PDF file, converting its contents into text data. It then extracts the necessary information from the converted text data.

[1105] Input: PDF file sent to the server

[1106] Output: Text data

[1107] Step 4:

[1108] Emotion recognition by devices

[1109] The device uses a built-in emotion engine to recognize the user's emotions. It uses the camera and microphone to analyze facial expressions and tone of voice while viewing materials to determine the user's emotional state.

[1110] Input: User's facial expressions and voice (sensor data)

[1111] Output: Emotional state

[1112] Step 5:

[1113] Server-based generation and translation of customer proposal documents.

[1114] The server extracts necessary information from the converted text data and generates proposal materials for the customer. It uses the Google Translator API to translate materials in different languages ​​into the specified language. It then uses the results of an emotion engine to refine the content of the materials.

[1115] Input: Text data, emotional state

[1116] Output: Proposal document (translated)

[1117] Step 6:

[1118] Server-based market research report generation

[1119] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. It automatically generates graphs and charts, and optimizes the report content by incorporating the results of the sentiment engine.

[1120] Input: Market research data files, emotional state

[1121] Output: Market research report

[1122] Step 7:

[1123] Server-based creation of presentation materials

[1124] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. It then incorporates the results of the emotion engine and adjusts the content to suit the user.

[1125] Input: Market research report, emotional state

[1126] Output: Presentation slides

[1127] Step 8:

[1128] Sending and notifying the terminal of the generated documents

[1129] The server sends the generated document to the user's terminal, which saves the received document and notifies the user. This allows the user to view, edit, and save the document.

[1130] Input: Generated documents (proposal documents, reports, slides)

[1131] Output: Documents and notifications sent to the user's device.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] [Fourth Embodiment]

[1136] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1137] 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.

[1138] 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).

[1139] 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.

[1140] 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.

[1141] 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).

[1142] 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.

[1143] 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.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] 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.

[1148] 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".

[1149] This invention is a system that automates tasks ranging from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials, in order to improve the work efficiency of users. This system can be implemented using the user's terminal, server, and network.

[1150] 1. Loading and sending PDF files

[1151] The user operates the device and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server.

[1152] 2. Analysis of PDF content

[1153] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[1154] 3. Generation and translation of product materials

[1155] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if the materials include English, a translation API is used to translate them into Japanese, matching the language specified by the user. The translated data is then formatted as a Japanese version of the proposal material and stored in storage.

[1156] 4. Generate market research reports

[1157] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format.

[1158] 5. Creating presentation materials

[1159] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations.

[1160] 6. Sending to the user

[1161] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[1162] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, and integrated analysis of market research reports.

[1163] The following describes the processing flow.

[1164] Step 1:

[1165] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[1166] Step 2:

[1167] The device reads the PDF file and sends it to the server. The server saves the received PDF file to its storage.

[1168] Step 3:

[1169] The server uses a PDF parsing library to convert the contents of the PDF file into text data. This process may also utilize optical character recognition (OCR) technology.

[1170] Step 4:

[1171] The server analyzes the converted text data and extracts the necessary information. For example, it analyzes keywords such as product features, pricing information, and specifications.

[1172] Step 5:

[1173] Based on the information extracted by the server, proposal documents for the customer are generated. The generated documents are formatted according to a predefined template.

[1174] Step 6:

[1175] The server detects whether the text data contains English. If it does, it uses a translation API to translate it into Japanese.

[1176] Step 7:

[1177] The server integrates the translated text data into the Japanese proposal document and reformats it as needed.

[1178] Step 8:

[1179] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts statistical information and trends.

[1180] Step 9:

[1181] The server automatically generates market research reports based on the extracted information. The generated reports automatically include graphs and charts.

[1182] Step 10:

[1183] The server extracts key information from market research reports and other materials and automatically generates presentation slides. The slides use a design optimized for presentations.

[1184] Step 11:

[1185] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[1186] Step 12:

[1187] Users can operate their devices to view, edit, and save various documents they receive. Based on these documents, users can then make proposals to clients.

[1188] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[1189] (Example 1)

[1190] 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".

[1191] In today's business environment, the ability to quickly process large volumes of data and accurately extract and analyze information is essential. However, performing a wide range of tasks individually—such as document content analysis, translation, market research report generation, and visual presentation material creation—is time-consuming and labor-intensive, leading to decreased operational efficiency. Therefore, there is a need for a system that automates these tasks consistently.

[1192] 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.

[1193] In this invention, the server includes means for receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating a proposal document, means for translating the document into different languages ​​as needed, means for formatting and saving the translated document, means for analyzing market research data and extracting important statistical information, means for automatically generating graphs and charts based on the important statistical information, means for extracting important information from market research reports and other materials and generating presentation slides, and means for sending the generated document to the user's terminal. This makes it possible to automate the entire process from document content analysis to document generation, translation, data analysis, and presentation material creation.

[1194] "User's device" refers to electronic devices such as computers and mobile devices used by the user.

[1195] A "server" is a central processing system that receives data transmitted from user terminals via a network and performs analysis and processing on it.

[1196] A "PDF file" is an abbreviation for Portable Document Format, an electronic file format that can save a document's formatting and images as they are.

[1197] "Text data" refers to character information extracted from a PDF file, and is data in a format that can be analyzed and edited.

[1198] "Information extraction" is the process of extracting necessary keywords or specific data related to the content of a document from text data.

[1199] A "proposal document" is a document used to explain or propose a product to a customer.

[1200] "Translation" is the process of converting information written in one language into another language.

[1201] "Different languages" refers to the source and target languages ​​specified by the user, such as English and Japanese.

[1202] "Formatting" refers to the process of formatting translated data or generated documents into a format that is easy to read and understand.

[1203] "Saving" refers to the process of recording data in a file system or database to prevent data loss.

[1204] "Market research data" refers to various types of information and statistical data related to a specific market.

[1205] "Important statistical information" refers to key numerical data and statistics needed for decision-making.

[1206] "Graphs and charts" are visual elements used to visually represent data, and include bar graphs, line graphs, pie charts, and so on.

[1207] A "presentation slide" is a slide-based document used to convey information visually.

[1208] This invention is a system that improves the work efficiency of users, automating the entire process from content analysis of PDF files to automatic generation and translation of documents, summarization of market research reports, and creation of presentation materials. This system can be implemented using the user's terminal, server, and network.

[1209] The user operates the terminal and loads PDF files containing product information and market research reports. The loaded PDF files are then sent from the terminal to the server. For example, a sales representative uploads a PDF file named "Market_Analysis_Report2019.pdf" to the system.

[1210] The server analyzes the received PDF file. This process uses PDF analysis libraries such as "Apache PDFBox" or "PyMuPDF". The analysis library extracts the contents of the PDF file as text data. This extracted text data is used for preprocessing to make it easier to obtain the information needed for the next process. The server extracts and formats the text data from all pages of "Market_Analysis_Report2019.pdf".

[1211] The server extracts necessary information from text data and automatically generates product materials for customer proposals. If the user includes English materials, the server uses translation APIs such as "Google Cloud Translation API" or "Microsoft Azure Translator" to translate the English into Japanese. This translated data is formatted as a Japanese proposal document and stored in cloud storage. The server generates an English proposal document, translates it into Japanese, and stores it in "Amazon S3".

[1212] The server uses analysis libraries such as "pandas" and "openpyxl" to analyze the received market research data files (e.g., Excel files). It extracts important statistical information and compiles it into a report. During this process, it uses libraries such as "Matplotlib" and "Seaborn" to automatically generate graphs and charts to visualize key points, incorporating them into the report in a visually easy-to-understand format. The server analyzes "Market_Analysis_Data2019.xlsx" and generates a market research report in PDF format.

[1213] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. Here, libraries such as "python-pptx" are used to create the slides, which are then saved in PowerPoint or PDF format. These automatically generated slides are later used by the user for presentations. The server generates and saves PowerPoint slides based on market research data and proposal materials.

[1214] The server sends the generated materials to the user's terminal. The terminal saves the received materials to local storage and notifies the user. The server sends the proposal materials, market research report, and presentation slides to the user's terminal, which saves them and notifies the user.

[1215] Example of a prompt:

[1216] "Please use this PDF file to generate a customer proposal document, including a translation from English to Japanese."

[1217] This invention makes it possible to automate the entire process from document content analysis to material generation, translation, data analysis, and presentation material creation. Specifically, it enables users to quickly and efficiently create necessary materials even in busy work environments.

[1218] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1219] Step 1:

[1220] The user selects PDF files containing product information or market research reports from their device and sends them to the server.

[1221] Input: PDF file (e.g., "Market_Analysis_Report2019.pdf")

[1222] Output: PDF file sent to the server

[1223] Specific action: The user selects a PDF file using the terminal's file selection dialog and clicks the "Send" button. The terminal uploads the selected PDF file to the server.

[1224] Step 2:

[1225] The server analyzes the received PDF file and converts it into text data.

[1226] Input: Received PDF file

[1227] Output: Extracted text data

[1228] Specific operation: The server reads the PDF file using the "Apache PDFBox" library. It sequentially analyzes each page, extracts text data, and formats it into a parsable format by removing unnecessary line breaks and spaces.

[1229] Step 3:

[1230] The server extracts the necessary information from the text data and generates the proposal document.

[1231] Input: Parsed text data

[1232] Output: Proposal document

[1233] Specific operation: The server uses NLP technology to extract product names, prices, features, etc., from the analyzed text data, and automatically generates proposal documents by applying them to a template.

[1234] Step 4:

[1235] The server will use a translation API to translate the document into a different language as needed.

[1236] Input: Proposal document, source language, target language

[1237] Output: Translated proposal document

[1238] Specific operation: The server uses either the Google Cloud Translation API or Microsoft Azure Translator to translate the relevant portion of the proposal document into the specified language.

[1239] Step 5:

[1240] The server formats and saves the translated document.

[1241] Input: Translated proposal document

[1242] Output: Saved Japanese version of the proposal document

[1243] Specific operation: The server formats the translated data for readability and saves it to cloud storage. As a specific example, the translated documents are saved to "Amazon S3".

[1244] Step 6:

[1245] The server analyzes market research data, extracts key statistical information, and generates market research reports.

[1246] Input: Market research data file (e.g., Excel file)

[1247] Output: Market research report

[1248] Specific operation: The server uses "pandas" and "openpyxl" to read Excel files and extract important statistical information such as sales figures and growth rates. Next, it automatically generates graphs and charts using "Matplotlib" and "Seaborn" and incorporates them into a report in a visually easy-to-understand format.

[1249] Step 7:

[1250] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides.

[1251] Input: Market research reports, proposal documents, etc.

[1252] Output: Presentation slides

[1253] Specific operation: The server uses "python-pptx" to generate the slide storyline and adds important data and graphs to the slides. The generated slides are saved in PowerPoint and PDF formats.

[1254] Step 8:

[1255] The server sends the generated documents to the user's terminal.

[1256] Input: Various generated documents (proposal documents, market research reports, presentation slides)

[1257] Output: Documents sent to the user's terminal

[1258] Specific operation: The server encodes the data and sends it to the user's terminal as an HTTP response. The terminal saves the received data to local storage and notifies the user.

[1259] (Application Example 1)

[1260] 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".

[1261] Within factories, a wide variety of documents (maintenance procedures, operation manuals, quality control reports, etc.) are used on a daily basis. However, the lack of efficient means to manage these documents and to translate, analyze, and automatically generate them as needed can lead to decreased work efficiency and communication errors among workers. Furthermore, when there are many workers who speak different languages, it becomes difficult to smoothly share work procedures.

[1262] 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.

[1263] In this invention, the server includes means for receiving PDF files from a user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating the materials into a specified language as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for implementing the above functions in a factory document management robot, means for scanning documents used in the factory and sending them to the server, and means for sending scanned documents to the terminals of factory workers and notifying them. This enables consistent management, translation, analysis, and automatic generation of various documents, significantly improving work efficiency within the factory.

[1264] A "user" refers to a person or organization that operates the system and uses a device to receive and send PDF files.

[1265] A "terminal" is an electronic device used to read and send PDF files.

[1266] A "PDF file" is a fixed-layout electronic document format known as Portable Document Format.

[1267] "Text data" refers to the text information extracted from a PDF file.

[1268] "Proposal materials for customers" are documents that are automatically generated by a system and used to make proposals to customers.

[1269] "Translation" is the process of replacing text data from one language to another.

[1270] A "market research report" is a report that analyzes market data and summarizes the research findings.

[1271] A "factory document management robot" is an automated device that has the function of scanning documents used within a factory and sending them to a server.

[1272] "Scanning" is the process of reading a physical document into electronic data.

[1273] "Analysis" refers to the process of extracting necessary information from received data and processing that data.

[1274] A "server" is a computer system that processes and stores received PDF files and other data.

[1275] "Notification" refers to the process of sending generated materials and update information to a device to inform the user.

[1276] The system of the present invention automates the analysis of PDF files, the generation of documents, translation, and the creation of market research reports using the user's terminal, server, and network. The following describes specific embodiments for carrying out the present invention.

[1277] 1. Loading and sending PDF files

[1278] First, the user operates the device to load PDF files containing product information and market research reports. The loaded PDF files are then sent from the device to the server. This loading and transmission process takes place via an internet connection.

[1279] 2. Analysis of PDF content

[1280] The server uses an appropriate PDF parsing library, such as pdftotext, to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then preprocessed for subsequent processing.

[1281] 3. Generation and translation of product materials

[1282] The server extracts the necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API (such as Google Translate) to translate the necessary parts according to the language specified by the user. This automatically generates multilingual materials, which are then stored in storage.

[1283] 4. Generate market research reports

[1284] Furthermore, the server analyzes market research data files (e.g., Excel files) and extracts important statistical information. Based on this, reports, graphs, and charts are automatically generated. In this generation process, libraries such as openpyxl and matplotlib are used to incorporate the data into the reports in a visually easy-to-understand format.

[1285] 5. Creating presentation materials

[1286] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. These slides are created using python-pptx and saved in PowerPoint or PDF format.

[1287] 6. Sending to the user

[1288] The various documents generated by the server are ultimately sent to the user's terminal. The terminal saves the received documents and notifies the user, allowing them to review, edit, and save them.

[1289] Implementation of a document management robot within a factory

[1290] A characteristic application of this invention is its implementation in a robot used to manage documents within a factory. This robot has the function of scanning physical documents and sending them to a server. The scanned documents are sent to the user's terminal or the worker's tablet terminal, allowing for immediate access to the information when needed.

[1291] Specific example

[1292] For example, it is possible to automatically analyze quality control reports within a factory, translate them from English to Japanese, and generate new work procedure manuals. This allows for smooth sharing of work procedures among multilingual workers. Furthermore, the generated documents are automatically sent to terminals held by workers within the factory, significantly improving work efficiency.

[1293] Example of a prompt

[1294] "Please analyze the PDF report, extract the key points, and translate them into Japanese."

[1295] "Please automatically generate Excel reports and presentations based on this data."

[1296] Thus, the invention automates the entire process of analyzing PDF files, generating documents automatically, translating them, and creating reports, thereby significantly improving operational efficiency within the factory.

[1297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1298] Step 1:

[1299] The user operates the device to load PDF files containing product information and market research reports. The input is a PDF file, and the output is the state in which that PDF file is loaded onto the device. Specifically, the user selects the PDF file from the file selection screen on the device and then performs the operation to load it.

[1300] Step 2:

[1301] The device reads a PDF file and sends it to the server. The input is the PDF file read by the device, and the output is the PDF data sent to the server. Specifically, the device uploads the PDF data to the server via the network.

[1302] Step 3:

[1303] The server parses the received PDF file and converts its contents into text data. The input is the PDF data received by the server, and the output is the converted text data. Specifically, the server uses the pdftotext library to convert the PDF file into text format.

[1304] Step 4:

[1305] The server extracts the necessary information from the converted text data and generates a proposal document for the customer. The input is the converted text data, and the output is the proposal document. Specifically, the server uses natural language processing technology to pick out the necessary information from the text data and format it into the proposal document format.

[1306] Step 5:

[1307] The server translates the document into the specified language as needed. The input is the language of the proposal document, and the output is the translated proposal document. Specifically, the server uses the googletrans API to translate the text data into the other language.

[1308] Step 6:

[1309] The server analyzes data from market research reports, extracts key statistical information, and generates a report. The input is a market research data file (e.g., an Excel file), and the output is the generated report. Specifically, the server uses openpyxl to analyze the Excel file and matplotlib to generate graphs and charts.

[1310] Step 7:

[1311] The server automatically generates presentation slides from market research reports and other relevant materials. The input is the market research report and related materials, and the output is the generated presentation slides. Specifically, the server uses the python-pptx library to create the slides and saves them.

[1312] Step 8:

[1313] The server generates various documents and sends them to the user's terminal. The input is the generated documents, and the output is the documents sent to the terminal. Specifically, the server sends the documents to the terminal over the network, and the terminal receives them.

[1314] Step 9:

[1315] The system saves received documents on the user's device and notifies the user. The input is the documents sent to the device, and the output is the received and saved documents. Specifically, the device saves the documents to local storage and displays a notification to the user.

[1316] Step 10:

[1317] A document management robot within the factory scans physical documents and sends them to a server. The input is the physical document, and the output is the scanned data sent to the server. Specifically, the robot reads the document with a scanner and sends the data to the server.

[1318] Step 11:

[1319] The server sends scanned documents to the worker's terminal and notifies them. The input is the scanned data, and the output is the document sent to the worker's terminal. Specifically, the server sends the scanned data to the worker's terminal via the network, and the terminal receives it and notifies the worker.

[1320] 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.

[1321] This invention aims to improve users' work efficiency by integrating an emotion engine into a system that automates everything from content analysis of PDF files to automatic document generation, translation, market research report summarization, and presentation material creation, thereby providing even more optimized materials. This system can be implemented using the user's terminal, server, and network.

[1322] 1. Loading and sending PDF files

[1323] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename. The loaded PDF file is then sent from the device to the server.

[1324] 2. Emotion recognition by an emotion engine

[1325] The device uses a built-in emotion engine to recognize the user's emotions. Sensors such as cameras and microphones are used to determine the user's emotional state from their facial expressions and tone of voice while they are viewing materials.

[1326] 3. Analysis of PDF content

[1327] The server uses an appropriate PDF parsing library to analyze the received PDF file. This parsing library converts the contents of the PDF file into text data. The converted text data is then used as a preprocessing step to extract the necessary information.

[1328] 4. Generation and translation of product materials

[1329] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. At this time, if English materials are included, they are translated into Japanese using a translation API, according to the language specified by the user. The translated data is formatted as a Japanese version of the proposal material and stored in storage. Furthermore, the content of the proposal material is adjusted based on the user's emotional state recognized by the emotion engine.

[1330] 5. Generating Market Research Reports

[1331] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. Based on this, it automatically compiles a report. During this generation process, graphs and charts are automatically generated as needed and incorporated into the report in a visually easy-to-understand format. At this time, the user's emotional state, obtained by the emotion engine, is fed back, and the presentation method and content of the analysis results are adjusted.

[1332] 6. Creating presentation materials

[1333] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for the presentation. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[1334] 7. Sending to the user

[1335] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user, allowing the user to view, edit, and save these documents.

[1336] As described above, this invention aims to improve the efficiency and accuracy of work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials, and further optimizing the content by combining it with an emotion engine. This allows users to quickly and efficiently create necessary documents even in busy work environments. Specifically, it enables the rapid creation of proposal documents based on product materials, automatic translation of English documents, integrated analysis of market research reports, and emotion-based optimization of documents.

[1337] The following describes the processing flow.

[1338] Step 1:

[1339] The user operates the device to select and load a PDF file. The device loads the specified PDF file and retrieves its file path and filename.

[1340] Step 2:

[1341] The device uses a built-in emotion engine to recognize the user's emotions. This includes processing that analyzes the user's facial expressions and voice tone using sensors such as cameras and microphones.

[1342] Step 3:

[1343] The device sends the scanned PDF file and recognized emotion data to the server. The server saves the received PDF file to its storage.

[1344] Step 4:

[1345] The server uses a PDF parsing library to convert the contents of the PDF file into text data. Optical character recognition (OCR) technology may be used in this process.

[1346] Step 5:

[1347] The server analyzes the converted text data and extracts the necessary information. For example, it extracts keywords such as product features, pricing information, and specifications.

[1348] Step 6:

[1349] Based on the information extracted by the server, proposal materials for the customer are generated. This generation process is performed automatically according to a predefined template.

[1350] Step 7:

[1351] The server detects whether the parsed text data contains English. If it does, it uses a translation API to translate it into Japanese.

[1352] Step 8:

[1353] The server integrates the translated text data into the Japanese proposal document and reformats it as needed. In addition, the server adjusts the content of the proposal document based on the user's emotional state. For example, if the user is feeling stressed, the server will adjust the tone of the document to be more calming.

[1354] Step 9:

[1355] The user uploads a market research report data file (e.g., an Excel file). The server analyzes this data file and extracts important statistics and trends.

[1356] Step 10:

[1357] The server automatically generates market research reports based on the extracted information. These reports automatically include graphs and charts, which are also adjusted based on the user's emotional state.

[1358] Step 11:

[1359] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The slides use a design optimized for presentations. This process also takes into account the results of the emotion engine, and the slides are adjusted to reflect the user's emotional state.

[1360] Step 12:

[1361] The server sends various generated documents to the user's terminal. The terminal saves the received documents and notifies the user.

[1362] Step 13:

[1363] The user operates the terminal to view, edit, and save various received documents. Based on these documents, the user can then make proposals to customers.

[1364] The above is a detailed flow of the processing steps in the present invention. This allows users to eliminate a lot of manual work and significantly improve work efficiency.

[1365] (Example 2)

[1366] 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".

[1367] In conventional business support systems, data extraction and translation from PDF files, as well as the generation of market research reports, are separate processes, resulting in significant time and effort for users. Furthermore, these processes fail to consider the user's feelings, making it difficult to provide optimal proposal materials.

[1368] 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 receiving a PDF file from the user's terminal, means for converting the contents of the received PDF file into text data, means for extracting necessary information from the converted text data and generating proposal materials for the customer, means for translating between multiple languages ​​as necessary, means for recognizing the user's emotional state in real time, means for adjusting the content of the materials based on the emotional information obtained by the emotion recognition means, means for analyzing market research report data and generating a report, and means for sending the generated materials to the user's terminal. This automates the entire material creation process and enables the creation of optimal materials that reflect the user's emotions.

[1369] "User's device" refers to an information processing device such as a computer, smartphone, or tablet used by the user for operation.

[1370] A "PDF file" is an abbreviation for Portable Document Format, a file format for electronically displaying and printing documents.

[1371] "Means of receiving" refers to methods and functions for acquiring data via a network.

[1372] "Text data" refers to digital data expressed as character information.

[1373] "Means of conversion" refers to methods or functions for changing data in one format to another.

[1374] "Means for extracting necessary information" refers to methods or functions for extracting specific information from data.

[1375] "Proposal materials for clients" refer to documents and presentation materials used to make proposals to clients.

[1376] "Translation methods" refer to methods or functions for converting text from one language into another language.

[1377] "Emotion recognition means" refers to methods or functions that use sensors and analytical algorithms to determine the emotional state of a user.

[1378] "Real-time" refers to a state where data acquisition and processing occur instantly without delay.

[1379] "Emotional information" refers to data that represents the emotional state of a user.

[1380] "Means of adjusting content" refers to methods or functions for appropriately changing the content of data according to the situation.

[1381] A "market research report" is a report that compiles statistical information and analytical results regarding a specific market.

[1382] "Means of analyzing data" refers to the methods and functions used to perform analysis on data.

[1383] "Means for generating reports" refers to methods and functions for creating reports based on analysis results and information.

[1384] "Means of transmission" refers to methods or functions for sending data to other devices or systems.

[1385] This invention improves the user's work efficiency by combining an emotion recognition engine with a system that automates everything from content analysis of PDF files to automatic generation of documents, translation, summarization of market research reports, and creation of presentation materials. This system is implemented using the user's terminal, server, and network.

[1386] First, the user operates the device to select a specific PDF file, which is then loaded within the device. At this time, the device obtains the file path and filename of the selected PDF file and sends it to the server. The device also performs emotion recognition, using sensors such as a camera and microphone to capture and analyze the user's facial expressions and voice tone. OpenCV and speech recognition software are used for this emotion analysis.

[1387] The server analyzes the received PDF file and converts its content into text data using a PDF analysis library (e.g., PyPDF2 or PDFMiner). It then extracts the necessary information from the converted text data and generates a proposal document. During this process, it automatically translates the document using a translation API (e.g., Google Translate API) according to the language specified by the user. For example, English documents can be translated into Japanese.

[1388] Based on the user's emotional information obtained through emotion recognition, the content of the proposal document is optimized. For example, if the user is expressing surprise, specific points in the document can be emphasized. This makes the document more effective in communicating with the user.

[1389] The server also analyzes market research data files (e.g., Excel files) and extracts statistical information. The pandas library is used for the analysis, automatically generating visually clear graphs and charts, which are then incorporated into the report. During this process, the presentation method and content of the analysis results are adjusted to reflect the results of the sentiment engine.

[1390] Furthermore, the server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. This process uses the Python python-pptx library, and the generated slides are saved in PowerPoint or PDF format. The content of the slides is also adjusted to reflect the user's emotional state.

[1391] Finally, the generated documents are sent from the server to the user's terminal, which saves the received documents to its local storage and notifies the user, allowing the user to view, edit, and save the documents.

[1392] As a concrete example, the functionality of this system can be applied by inputting the following prompt sentence into the generating AI model.

[1393] Please create your proposal using the following PDF file. The file name is "product_info.pdf". The proposal should be in Japanese and in a format that is easy for the client to understand. Also, if the client's tone of voice is close to one of surprise, please add emphasis to the document.

[1394] Thus, this invention aims to improve the efficiency and accuracy of users' work by automating the entire process from reading PDF files to generating documents, translating, analyzing data, and creating presentation materials, and by optimizing the content through the combination of sentiment recognition.

[1395] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1396] Step 1:

[1397] The user operates the terminal to select and load a PDF file. The input is the PDF file selected by the user (e.g., "product_info.pdf"). The terminal obtains the path (e.g., "C:\Users\username\Documents\product_info.pdf") and filename of this file. The output is data containing the obtained file path and filename. Specifically, when the user selects a file using a file selection dialog, this information is sent to the terminal.

[1398] Step 2:

[1399] The terminal sends the acquired PDF file to the server. The input is data containing the file path and filename acquired in the previous step. The terminal sends this to the server over the network. The output is the PDF file sent to the server. Specifically, the file data is encoded and sent to the server via an HTTP request.

[1400] Step 3:

[1401] The server parses the received PDF file and converts it into text data. The input is the received PDF file. The server uses a PDF parsing library such as Python's PyPDF2 or PDFMiner to convert the file contents into text data. The output is the converted text data. Specifically, the server reads the PDF file and extracts its contents as text.

[1402] Step 4:

[1403] The server extracts necessary information from the converted text data and generates a proposal document. The input is the extracted text data. The server uses an algorithm to extract the necessary information from the text data and automatically generates a proposal document based on a template. The output is the generated proposal document. Specifically, the algorithm analyzes keywords and important phrases, and the document is constructed based on that.

[1404] Step 5:

[1405] The server translates the proposal document into the specified language as needed. The input consists of the generated proposal document and the target language information. The server uses a translation API, such as the Google Translate API, to translate the document. The output is the translated proposal document. Specifically, the text of the proposal document is sent to the API, and the translated text is received.

[1406] Step 6:

[1407] The device uses its built-in emotion recognition engine to recognize the user's emotions. The input is emotion data such as the user's facial expressions and voice tone. The device uses sensors such as cameras and microphones to capture and analyze this data in real time. The output is the analyzed emotion information. Specifically, the device uses emotion recognition software (e.g., OpenCV, Google Cloud Speech-to-Text API) to identify emotions.

[1408] Step 7:

[1409] The server adjusts the content of the proposal document based on the emotion recognition results. The input is emotion information and the generated proposal document. The server uses an algorithm to adjust the emphasis and expression of the document based on the emotion information. The output is the adjusted proposal document. Specifically, for example, if the user indicates surprise, the formatting is changed so that specific parts of the document are emphasized.

[1410] Step 8:

[1411] The server analyzes market research data and generates the report. The input is a data file for market research (e.g., an Excel file). The server uses the pandas library to analyze the data and extract key statistical information. The output is a market research report containing the statistical information. Specifically, it reads the data file, performs statistical analysis, and generates visual graphs and charts.

[1412] Step 9:

[1413] The server automatically generates presentation slides from market research reports and other relevant materials. The input consists of market research reports and related materials. The server uses the Python python-pptx library to generate and save the slides. The output is the generated presentation slides. Specifically, the server extracts key information and creates slides based on a template.

[1414] Step 10:

[1415] The server sends various generated documents to the user's terminal. Inputs include generated proposal documents, translated documents, revised documents, and presentation slides. The server sends these documents to the user's terminal as a whole. Outputs are the documents saved on the terminal. Specifically, the documents are packaged and sent to the terminal via the network.

[1416] Step 11:

[1417] The terminal saves the received documents and notifies the user. The input is documents sent from the server. The terminal saves these to local storage and notifies the user. The output is the user environment where the notification is received and the documents can be viewed. Specifically, the documents are saved, and a pop-up notification or email notification is sent to the user.

[1418] (Application Example 2)

[1419] 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".

[1420] Traditional systems lacked the ability to automate the entire process, from analyzing PDF file content to automatically generating documents, translating, summarizing market research reports, and creating presentation materials. This resulted in a significant amount of manual work and decreased operational efficiency. Furthermore, the systems failed to consider the emotional state of users, leading to unoptimized proposals and market research reports, making it difficult to provide compelling materials to clients.

[1421] 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.

[1422] In this invention, the server includes means for receiving PDF files from the user's terminal, means for converting the contents of the received PDF files into text data, means for extracting necessary information from the converted text data and generating proposal materials for customers, means for translating materials in different languages ​​as needed, means for analyzing market research report data and generating reports, means for sending the generated materials to the user's terminal, means for recognizing the user's emotional state and adjusting the content of the generated materials, and means for being installed on a smartphone. This enables the provision of optimized materials based on the user's emotions and improves work efficiency.

[1423] definition statement

[1424] "User's terminal" refers to an electronic device used for processing, including receiving and sending PDF files.

[1425] "PDF file" is an abbreviation for Portable Document Format, a file format that allows documents to be transferred while maintaining their layout.

[1426] "Text data" refers to text information converted from a PDF file.

[1427] "Extracting necessary information" refers to the process of extracting specific important data from the converted text data.

[1428] A "proposal document for the customer" is a document that is automatically generated based on extracted information and tailored to the customer's needs.

[1429] Translation is the process of converting information into a different language.

[1430] A "market research report" is a document that analyzes and summarizes market trends and statistical data.

[1431] "Analysis" is the process of examining data in detail to understand its patterns and structure.

[1432] "Emotional state" refers to the psychological state of a user, as judged from their facial expressions, tone of voice, and other factors.

[1433] "Adjustment" means optimizing the content of the material based on the perceived emotional state.

[1434] A "smartphone" is a portable electronic device that has communication capabilities and can run a variety of applications.

[1435] "Sending" refers to the act of sending generated materials as data to another device.

[1436] A "system" is a general term for the equipment and software used to combine the above-mentioned methods and execute a series of processes.

[1437] Modes for carrying out the invention

[1438] The present invention is a system implemented using a user's terminal, server, and network. Specific embodiments of this system will be described below.

[1439] 1. Receiving PDF files from the user's device.

[1440] The user's device selects and loads a specific PDF file, obtaining its file path and filename. This device is typically a mobile electronic device such as a smartphone. The device has a means of sending the loaded PDF file to a server; therefore, a network connection is required.

[1441] 2. Content analysis of PDF files

[1442] The server uses a PDF parsing library such as PyPDF2 to analyze the received PDF file. The contents of the PDF file are converted into text data, and the necessary information is extracted from the converted text data. Important keywords and data are extracted during this parsing process.

[1443] 3. Emotion recognition by an emotion engine

[1444] The user's device uses a built-in emotion engine to recognize the user's emotions. This involves using sensors such as cameras and microphones to determine the emotional state from the user's facial expressions and tone of voice while viewing materials. Emotion analysis tools like SentimentAnalyzer are used for emotion recognition.

[1445] 4. Generating and translating proposal materials for clients.

[1446] The server extracts necessary information from the converted text data and automatically generates proposal materials for the customer. It uses a translation API, such as Google Translator, to translate materials in different languages ​​(e.g., English) into a specified language (e.g., Japanese). The generated materials are then adjusted based on the user's emotional state, as determined by an emotion engine, and stored in storage.

[1447] 5. Generating Market Research Reports

[1448] The server analyzes market research data files (e.g., Excel files), extracts key statistical information, and automatically compiles a market research report. During this process, graphs and charts are automatically generated to present the analysis results in a visually easy-to-understand format. Furthermore, the results of the emotion engine are taken into consideration, and the analysis results are adjusted to reflect the user's emotional state.

[1449] 6. Creating presentation materials

[1450] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. The generated slides are saved in PowerPoint or PDF format and used for presentations. This process also incorporates the results of the emotion engine, adjusting the content to match the user's emotional state.

[1451] 7. Sending to the user's device

[1452] The server sends the generated documents to the user's terminal. The user's terminal saves the received documents and notifies the user, allowing them to view, edit, and save these documents.

[1453] Specific example

[1454] As a concrete example, imagine a user loading a PDF file named "product_introduction.pdf" from their device and sending it to the server. Also, consider a scenario where the user provides emotional input such as, "I'm interested in this product, but I want to know more." In this case, the following prompt message would be used:

[1455] Contents of the PDF file: Text content of product_introduction.pdf

[1456] User's emotional state: Interested in this product, but wants more information.

[1457] Product information generation and translation: Generated product description and translation results

[1458] Market Research Report: Generated Market Research Report

[1459] Presentation materials: Generated presentation materials

[1460] This system allows users to quickly and efficiently create the necessary documents and provides them with documents optimized based on their emotions.

[1461] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1462] Program processing steps

[1463] Step 1:

[1464] Select and load the PDF file on the user's device.

[1465] The user selects a specific PDF file from their device and loads it. The device obtains the file path and filename. This data is sent to the server.

[1466] Input: PDF file

[1467] Output: File path and file name

[1468] Step 2:

[1469] Sending PDF files from the terminal to the server

[1470] The device sends the acquired PDF file to the server. The data is transferred over the network.

[1471] Input: File path and file name

[1472] Output: PDF file sent to the server

[1473] Step 3:

[1474] Content analysis of PDF files on the server

[1475] The server uses the PyPDF2 library to analyze the received PDF file, converting its contents into text data. It then extracts the necessary information from the converted text data.

[1476] Input: PDF file sent to the server

[1477] Output: Text data

[1478] Step 4:

[1479] Emotion recognition by devices

[1480] The device uses a built-in emotion engine to recognize the user's emotions. It uses the camera and microphone to analyze facial expressions and tone of voice while viewing materials to determine the user's emotional state.

[1481] Input: User's facial expressions and voice (sensor data)

[1482] Output: Emotional state

[1483] Step 5:

[1484] Server-based generation and translation of customer proposal documents.

[1485] The server extracts necessary information from the converted text data and generates proposal materials for the customer. It uses the Google Translator API to translate materials in different languages ​​into the specified language. It then uses the results of an emotion engine to refine the content of the materials.

[1486] Input: Text data, emotional state

[1487] Output: Proposal document (translated)

[1488] Step 6:

[1489] Server-based market research report generation

[1490] The server analyzes market research data files (e.g., Excel files) and extracts key statistical information. It automatically generates graphs and charts, and optimizes the report content by incorporating the results of the sentiment engine.

[1491] Input: Market research data files, emotional state

[1492] Output: Market research report

[1493] Step 7:

[1494] Server-based creation of presentation materials

[1495] The server extracts key information from market research reports and other relevant materials and automatically generates presentation slides. It then incorporates the results of the emotion engine and adjusts the content to suit the user.

[1496] Input: Market research report, emotional state

[1497] Output: Presentation slides

[1498] Step 8:

[1499] Sending and notifying the terminal of the generated documents

[1500] The server sends the generated document to the user's terminal, which saves the received document and notifies the user. This allows the user to view, edit, and save the document.

[1501] Input: Generated documents (proposal documents, reports, slides)

[1502] Output: Documents and notifications sent to the user's device.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] 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.

[1509] 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.

[1510] 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.

[1511] 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."

[1512] 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.

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] 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 as being incorporated by reference.

[1524] The following is further disclosed regarding the embodiments described above.

[1525] (Claim 1)

[1526] A means of receiving PDF files from the user's device,

[1527] A means of converting the contents of a received PDF file into text data,

[1528] A means of extracting necessary information from converted text data and generating proposal materials for customers,

[1529] Means to translate English documents into Japanese as needed,

[1530] A means of analyzing market research report data and generating reports,

[1531] A system that includes means for transmitting generated materials to the user's terminal.

[1532] (Claim 2)

[1533] The system according to claim 1, wherein the user specifies the source of the English document to be translated.

[1534] (Claim 3)

[1535] The system according to claim 1, which includes means for automatically generating graphs and charts when analyzing data from a market research report.

[1536] "Example 1"

[1537] (Claim 1)

[1538] A means of receiving PDF files from the user's device,

[1539] A means of converting the contents of a received PDF file into text data,

[1540] A means for extracting necessary information from converted text data and generating proposal documents,

[1541] Means to translate materials into different languages ​​as needed,

[1542] Means for formatting and preserving translated materials,

[1543] A means of analyzing market research data and extracting important statistical information,

[1544] A method for automatically generating graphs and charts based on important statistical information,

[1545] A means for extracting important information from market research reports and other materials and generating presentation slides,

[1546] A system that includes means for transmitting generated materials to the user's terminal.

[1547] (Claim 2)

[1548] The system according to claim 1, wherein the user specifies the document to be translated.

[1549] (Claim 3)

[1550] The system according to claim 1, which automatically generates graphs and charts when analyzing data from a market research report.

[1551] "Application Example 1"

[1552] (Claim 1)

[1553] A means of receiving PDF files from the user's device,

[1554] A means of converting the contents of a received PDF file into text data,

[1555] A means of extracting necessary information from converted text data and generating proposal materials for customers,

[1556] Means to translate materials into the specified language as needed,

[1557] A means of analyzing market research report data and generating reports,

[1558] A means of sending the generated materials to the user's terminal,

[1559] A means of implementing the above functions in a factory document management robot,

[1560] A means of scanning documents used within a factory and sending them to a server,

[1561] A system that includes means for sending scanned documents to terminals of factory workers and providing notifications.

[1562] (Claim 2)

[1563] The system according to claim 1, wherein the data to be translated is specified by the user.

[1564] (Claim 3)

[1565] The system according to claim 1, which includes means for automatically generating graphs and charts when analyzing data from a market research report.

[1566] "Example 2 of combining an emotion engine"

[1567] (Claim 1)

[1568] A means of receiving PDF files from the user's device,

[1569] A means of converting the contents of a received PDF file into text data,

[1570] A means of extracting necessary information from converted text data and generating proposal materials for customers,

[1571] Means of translating between multiple languages ​​as needed,

[1572] An emotion recognition method for recognizing the emotional state of a user in real time,

[1573] A means of adjusting the content of a document based on emotional information obtained by an emotion recognition means,

[1574] A means of analyzing market research report data and generating reports,

[1575] A system that includes means for transmitting generated materials to the user's terminal.

[1576] (Claim 2)

[1577] The system according to claim 1, wherein the object to be translated is specified by the user.

[1578] (Claim 3)

[1579] The system according to claim 1, which includes means for automatically generating graphs and charts when analyzing data from a market research report.

[1580] "Application example 2 of combining emotional engines"

[1581] (Claim 1)

[1582] A means of receiving PDF files from the user's device,

[1583] A means of converting the contents of a received PDF file into text data,

[1584] A means of extracting necessary information from converted text data and generating proposal materials for customers,

[1585] Means to translate materials into different languages ​​as needed,

[1586] A means of analyzing market research report data and generating reports,

[1587] A means of sending the generated materials to the user's terminal,

[1588] A means of recognizing the emotional state of the user and adjusting the content of the generated materials,

[1589] The means by which it is installed on a smartphone,

[1590] A system that includes this.

[1591] (Claim 2)

[1592] The system according to claim 1, wherein the user specifies the source of the translation of materials in different languages.

[1593] (Claim 3)

[1594] The system according to claim 1, which includes means for automatically generating graphs and charts when analyzing data from a market research report, and means for adjusting the method and content of presenting the analysis results based on sentiment recognition. [Explanation of Symbols]

[1595] 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. A means of receiving PDF files from the user's device, A means of converting the contents of a received PDF file into text data, A means of extracting necessary information from converted text data and generating proposal materials for customers, Means to translate English documents into Japanese as needed, A means of analyzing market research report data and generating reports, A system that includes means for transmitting generated materials to the user's terminal.

2. The system according to claim 1, wherein the user specifies the source of the English document to be translated.

3. The system according to claim 1, which includes means for automatically generating graphs and charts when analyzing data from a market research report.

Images