System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to select and upload financial report material; means for storing the uploaded financial report material in a server; means for converting the stored financial report material into an image; means for extracting text data from the converted image; means for identifying necessary financial items from the extracted text data and extracting numerical values; and means for inputting the extracted numerical values into a management spreadsheet.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditionally, manually transferring financial reporting data into spreadsheets required a great deal of time and effort, and was prone to human error. Furthermore, when managing large amounts of corporate financial data, the verification process alone required significant resources. This led to issues such as inefficient data processing and difficulty in ensuring accuracy. [Means for solving the problem]

[0005] The present invention relates to a system that automatically analyzes financial reporting documents and transfers their contents to a management spreadsheet. It includes a means for a user to select and upload financial reporting documents, a means for saving the uploaded documents to a server, a means for converting the saved documents into images, a means for extracting text data from the converted images, a means for identifying necessary financial items from the extracted text data and extracting numerical values, and a means for inputting the extracted numerical values ​​into a management spreadsheet. It also includes a means for a user to confirm the accuracy of the extracted numerical values ​​and a means for automatically inputting the data into the management spreadsheet, thereby solving the problems of the related art.

[0006] "Financial reporting materials" are documents prepared by companies and organizations to report on their business and financial status.

[0007] "User" means a person or organization that uses the System to upload financial reporting materials and operate management spreadsheets.

[0008] A "server" is a computer system that stores uploaded financial report materials and performs analytical processing.

[0009] "Converting to image" means converting a document format file such as PDF into an image format.

[0010] "Extracting text data" means extracting text information from financial report documents that have been converted into images.

[0011] "Financial items" are financial-related elements such as revenues, profits, and expenses that are listed in financial reporting documents.

[0012] "Extracting numerical values" means extracting specific numerical values ​​related to financial items from within the text data.

[0013] A "management spreadsheet" is a spreadsheet software used to systematically organize and store data extracted from financial reporting documents.

[0014] "Entering" means entering the extracted numerical data into a management spreadsheet.

[0015] "Confirming" means having the user check the accuracy of the extracted numerical data.

[0016] "Automatically writing data" means that the system autonomously enters data into the management spreadsheet without the need for manual operation. [Brief explanation of the drawings]

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

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

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

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

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

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

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0038] The present invention relates to a system that automatically analyzes financial report documents and transfers the results to a management spreadsheet. The specific processing steps and implementation method of this system are described below.

[0039] System Flow Overview

[0040] 1. User uploads PDF

[0041] Users can select and upload financial report documents (PDF files) using a dedicated web interface. This operation is completed by simply selecting the file in the browser and clicking the upload button.

[0042] 2. Receiving and saving the PDF on the server

[0043] The server receives the uploaded PDF file and saves it in the specified folder on the server. For example, let's say the file name is "financial_report_2023.pdf".

[0044] 3. Parsing the PDF by the server

[0045] The server processes the saved PDF file to convert it into an image format. For example, it uses the "pdf2image" library. The image data is then converted into text data using OCR (Optical Character Recognition) technology. At this stage, all the text information in the PDF is obtained as text data.

[0046] 4. Extracting financial data

[0047] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" is found in the text data, the respective numerical information is extracted.

[0048] 5. Transferring data to a management spreadsheet

[0049] The server automatically writes the extracted data to a managed spreadsheet. To do this, it uses the Google® Sheets API to connect to the spreadsheet and perform data write operations. When new data is added to the spreadsheet, it is integrated into the existing table.

[0050] 6. User Confirmation

[0051] The terminal displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user can review and correct the data as necessary to ensure its accuracy.

[0052] Specific examples

[0053] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. The following information is found in the text data:

[0054] Revenue: 1,500,000

[0055] Profit: 300000

[0056] Expenses: 1200000

[0057] The server will extract these numbers and automatically enter them into a corresponding spreadsheet, which will look something like this:

[0058] | Revenue | Profit | Expenses |

[0059] |---------|--------|----------|

[0060] | 1,500,000 | 300,000 | 1,200,000 |

[0061] The terminal presents this spreadsheet to the user, who then checks the data.

[0062] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, thereby reducing human error and improving business efficiency.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] A user accesses a web interface, selects a PDF file as a financial report document, and clicks an upload button to upload the selected file.

[0066] Step 2:

[0067] The server receives the uploaded PDF file and saves it in the specified folder. For example, it saves it as " / path / to / save / financial_report_2023.pdf".

[0068] Step 3:

[0069] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[0070] Step 4:

[0071] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[0072] Step 5:

[0073] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the acquired text data and extracts the corresponding numerical data. If the text data contains information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000," the respective numerical information will be extracted.

[0074] Step 6:

[0075] The server automatically writes the extracted numerical data to a managed spreadsheet. It connects to the spreadsheet using the Google Sheets API and adds the extracted data as a new row. The new data is then integrated into the spreadsheet.

[0076] Step 7:

[0077] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[0078] Step 8:

[0079] Once users have completed the verification process, the risk of errors and inaccurate data is reduced across the system, helping to maintain accurate financial data.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] Traditional methods for managing financial reporting documents involve a lot of manual data entry, which is prone to errors. Furthermore, there is a lack of automation to efficiently and accurately input large amounts of data into management spreadsheets. This results in a heavy workload and a lack of efficiency and accuracy.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for automatically writing data into the management spreadsheet, and means for saving the data in a specified folder. This enables automatic analysis of financial report documents and efficient data transcription.

[0085] A "user" is an entity that uses the system to upload financial reporting materials and check and correct data.

[0086] "Financial reporting materials" are documents created by companies or organizations to report their financial status, and are usually saved in PDF format.

[0087] "Server" means a computer system responsible for receiving financial reporting materials uploaded by users, storing, analyzing, and transcribing the data into a management spreadsheet.

[0088] "Means for converting to images" refers to technology or software for converting PDF files into still images, such as using the "pdf2image" library.

[0089] "Means for extracting text data" refers to technology or software for extracting text information from image data, such as using "OCR (optical character recognition)" technology.

[0090] "Means for identifying financial items and extracting numerical values" refers to algorithms or techniques for finding specific financial items (e.g., revenue, profit, expense) and their numerical values ​​from the extracted text data.

[0091] A "management spreadsheet" is a spreadsheet software for organizing and storing financial data, such as "Google Sheets."

[0092] The "means for writing data" refers to technology or software for automatically inputting the extracted numerical data into designated cells in the management spreadsheet.

[0093] The "means of saving in a designated folder" refers to a method or technology for saving uploaded financial reporting materials in a specific directory.

[0094] The present invention relates to a system for automatically analyzing financial report materials and transcribing the contents of the analysis into a management spreadsheet. Specific embodiments of the present invention will be described below.

[0095] Overall structure

[0096] This system is composed of users, servers, and terminals, and each part functions in cooperation with the others.

[0097] User uploads PDF

[0098] A user uploads financial report documents (PDF files) using a web interface. Specifically, the user launches a browser, accesses the system's website, clicks the "Choose File" button, selects the PDF they want to upload, and then clicks the "Upload" button to submit it.

[0099] Server processing

[0100] 1. Receiving and saving files:

[0101] The server receives the HTTP request and saves the uploaded PDF file in the specified folder. For example, it is saved with the file name "financial_report_2023.pdf".

[0102] 2. Parse PDF:

[0103] The server uses the "pdf2image" library to convert the saved PDF file into multiple image files. Then, it uses "OCR (Optical Character Recognition)" technology to extract text information from these images. At this stage, all text information in the PDF is obtained as text data.

[0104] 3. Financial Data Extraction:

[0105] The server uses regular expressions to identify specific financial items (e.g., revenue, profit, expenses, etc.) within the text data and extracts the associated numerical data, such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000."

[0106] 4. Post to control spreadsheet:

[0107] The server connects to a managed spreadsheet via the Google Sheets API and automatically populates the appropriate cells with the extracted data, thereby integrating the financial data into the spreadsheet.

[0108] User confirmation

[0109] The device displays the latest management spreadsheet in the browser, allowing the user to check the accuracy of the data. The user can then review and make any necessary corrections to ensure the accuracy of the data.

[0110] Specific examples

[0111] As a concrete example, consider a scenario where a user uploads "Company X's" financial statement for fiscal year 2023, "company_x_2023.pdf." The server receives the file and saves it in a specified folder. The saved PDF file is converted into multiple images, and OCR technology is used to extract the following text data:

[0112] Revenue: 1,500,000

[0113] Profit: 300000

[0114] Expenses: 1200000

[0115] The server identifies these numbers and enters them into a spreadsheet via the Google Sheets API. For example, the administrative spreadsheet might look like this:

[0116] | Revenue | Profit | Expenses |

[0117] |---------|--------|----------|

[0118] | 1,500,000 | 300,000 | 1,200,000 |

[0119] The terminal displays the spreadsheet and the user checks the data to ensure its accuracy.

[0120] Prompt Sentence Examples

[0121] What are the specific steps in a system that parses PDF financial reports and transfers them to a management spreadsheet?

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step-by-step explanation

[0124] Step 1: User uploads PDF

[0125] Input: A user has a PDF file of a financial report.

[0126] Specific behavior: A user launches a browser, accesses the system's website, clicks the "Choose File" button, selects a PDF file, and clicks the "Upload" button.

[0127] Output: The selected PDF file is sent to the server.

[0128] Step 2: The server receives and saves the PDF

[0129] Input: A PDF file submitted by the user.

[0130] Specific operation: The server receives the HTTP request and saves the uploaded PDF file data in the specified folder. For example, it saves it as "financial_report_2023.pdf".

[0131] Output: The PDF file will be saved in the specified folder on the server.

[0132] Step 3: Parse the PDF on the server

[0133] Input: PDF file stored on the server.

[0134] What it does: The server uses the "pdf2image" library to convert the PDF file into multiple image files.

[0135] Output: The PDF file is converted into multiple image files.

[0136] Step 4: OCR analysis of image data

[0137] Input: Multiple image files.

[0138] Specific operation: The server uses an OCR library such as "Tesseract" to extract text data from the image data. At this stage, all character information is obtained as text data.

[0139] Output: Text data extracted from the image.

[0140] Step 5: Extract financial data

[0141] Input: Text data.

[0142] What it does: The server uses regular expressions to identify financial items (e.g., revenue, profit, expense) within the text data and extracts the associated numerical data.

[0143] Output: Identified financial items and their numerical data.

[0144] Step 6: Transfer data to a management spreadsheet

[0145] Input: Identified financial items and their numerical data.

[0146] What happens: The server connects to a managed spreadsheet using the Google Sheets API and automatically populates the appropriate cells with the extracted data.

[0147] Output: New data is posted to the control spreadsheet.

[0148] Step 7: User confirmation

[0149] Input: Data posted to the control spreadsheet.

[0150] Specific behavior: The device displays the spreadsheet in a browser, and the user can verify the accuracy of the data. The user can then review and manually correct any errors if necessary.

[0151] Output: An up-to-date control spreadsheet with all the corrections completed and data accuracy assured.

[0152] Through the above steps, the system achieves automatic analysis of financial reporting materials and efficient data transcription, reducing the user's workload and ensuring data accuracy.

[0153] (Application example 1)

[0154] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0155] Manually analyzing financial reporting documents and entering them into management spreadsheets is time-consuming, labor-intensive, and prone to human error. There is also a lack of convenient ways to quickly check data. This creates a need for systems that significantly improve the efficiency of accounting operations and management decisions. There is a growing need for technology that can analyze and check data in real time using smart devices, especially in brick-and-mortar stores.

[0156] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0157] In this invention, the server includes means for a user to select and upload financial reporting documents, means for saving the uploaded financial reporting documents on the server, means for converting the saved financial reporting documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for taking a picture of the financial reporting documents using smart glasses and analyzing the data in real time, and means for displaying the analyzed data to the user. This automates the analysis and transcription of financial reporting documents, enabling quick and accurate data confirmation.

[0158] A "User" is a person whose role is to use the system to upload and review financial reporting materials.

[0159] A "financial reporting document" is a document that contains important financial information such as revenues, profits, and expenses.

[0160] The "uploading means" is an interface that allows a user to send financial report materials to a server via a browser or the like.

[0161] A "server" is a computer system that receives, stores, and analyzes uploaded financial reporting materials.

[0162] The "means for saving" is a process for saving the uploaded financial report materials in a designated folder on the server.

[0163] "Means for converting to images" refers to the process of converting uploaded financial report documents, such as PDF files, into image format.

[0164] The "means for extracting text data" is a mechanism for obtaining character information from the converted image using OCR technology and converting it into text data.

[0165] "Means for identifying financial items" is the process of finding specific financial information such as revenues, profits, and expenses from the extracted text data.

[0166] The "means for extracting numerical values" is a function for obtaining specific numerical data corresponding to the identified financial items.

[0167] A "management spreadsheet" is an electronic spreadsheet used to compile and centrally manage extracted numerical data.

[0168] "Smart glasses" are wearable devices that have a built-in camera and display, allowing users to view information in real time within their field of vision.

[0169] "Real-time analysis means" refers to the processing power to instantly process financial reporting documents captured using the smart glasses and quickly generate results.

[0170] "Means for displaying to the user" refers to a function for immediately displaying the analyzed data on the display of the smart glasses.

[0171] This invention relates to a system that automatically analyzes financial reporting documents and transcribes the contents into a management spreadsheet, providing a system that allows data to be viewed quickly and accurately in a physical store using smart glasses.

[0172] Hardware and software used

[0173] Smart glasses: with image capture and display capabilities.

[0174] Server: A computer system that stores, analyzes, and processes data.

[0175] Google Sheets API: Used for automated writing to managed spreadsheets.

[0176] OCR software: Tesseract OCR library.

[0177] Image transformation library: OpenCV.

[0178] Authentication Library: ServiceAccountCredentials for OAuth2 authentication.

[0179] Program processing description

[0180] Capture and Upload

[0181] The user wears the smart glasses and holds the financial report document up to the glasses' camera, which captures the image and sends the image data to the server.

[0182] Image analysis and text data extraction

[0183] The server processes the received image data using OpenCV to convert the PDF file format financial report documents into images, and then extracts text data from the images using the Tesseract OCR library.

[0184] Data Identification and Extraction

[0185] The server uses regular expressions to identify important financial items such as "revenue," "profit," and "expenses" from within the text data and extracts the corresponding numerical data.

[0186] Transferring data to a spreadsheet

[0187] The extracted numerical data is automatically written to a managed spreadsheet using the Google Sheets API. During this process, a mechanism is in place to securely access Google Sheets using OAuth2 authentication.

[0188] Review and display data

[0189] The extracted data is displayed in real time on the smart glasses' display, allowing users to immediately check the accuracy of the data, and is automatically transcribed into a spreadsheet so that administrators can correct the data as needed.

[0190] Specific examples

[0191] For example, imagine a retail manager reviewing monthly financial reports. The manager puts on smart glasses and holds up the paper report to the glasses' camera. Within seconds, revenue, profit, and expense data appears on the glasses' display and is automatically posted to Google Sheets. This allows the manager to immediately review the data and improve work efficiency.

[0192] Example prompts for generative AI models

[0193] "Describe a system that uses smart glasses to analyze financial reports in real time and automatically transcribe them into a spreadsheet."

[0194] "How can we develop an application that displays real-time financial data on smart glasses to improve business efficiency?"

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] The user puts on the smart glasses and holds the financial report up to the glasses' camera. The input is the physical financial report, and the output is the image captured by the smart glasses. This step involves the user taking a concrete action to hold the report in place.

[0198] Step 2:

[0199] The smart glasses capture images and send the data to a server. The input is the captured image data, and the output is the image data sent to the server. The specific operations of the smart glasses include activating the camera and transmitting the captured images to the server via wireless communication.

[0200] Step 3:

[0201] The server processes the received image data using OpenCV and converts the financial report documents in PDF file format into images. The input is the image data sent to the server, and the output is the converted image file. This step includes the specific operation of the server applying the image processing algorithm and converting it into PDF format.

[0202] Step 4:

[0203] The server uses the Tesseract OCR library to extract text data from images. The input is image data and the output is text data. The server includes specific operations for recognizing and extracting characters using OCR technology.

[0204] Step 5:

[0205] The server uses regular expressions to identify financial items such as revenue, profit, and expenses from within the text data and extracts the related numerical data. The input is the extracted text data and the output is the identified numerical data. The server uses regular expressions to identify and extract the required data.

[0206] Step 6:

[0207] The server uses the Google Sheets API to automatically write the extracted numerical data to a managed spreadsheet. The input is the extracted numerical data, and the output is an updated spreadsheet. The server accesses Google Sheets and includes specific operations to write data to specified cells.

[0208] Step 7:

[0209] The server displays the analyzed data on the smart glasses display in real time. The input is the updated data in the management spreadsheet, and the output is the data displayed on the smart glasses display. The server transmits the data to the glasses and includes specific actions to provide visual feedback to the user.

[0210] Step 8:

[0211] The user checks the data displayed on the smart glasses display and checks the accuracy of the data as needed. The input is the data displayed on the display, and the output is the user's confirmation. The user's specific actions include checking the displayed figures against actual financial reporting documents.

[0212] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0213] The present invention combines a system that automatically analyzes financial report documents and transcribes them into a management spreadsheet with an emotion engine that recognizes the user's emotions. A specific embodiment of this system is described below.

[0214] System Flow Overview

[0215] 1. User uploads PDF

[0216] Users can select and upload PDF files as financial reporting documents using a dedicated web interface. All they need to do is select the file in their browser and click the upload button.

[0217] 2. Receiving and saving the PDF on the server

[0218] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file name "financial_report_2023.pdf" is saved.

[0219] 3. Parsing the PDF by the server

[0220] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[0221] 4. Extracting financial data

[0222] The server converts each image page into text data using OCR (optical character recognition) technology. Using the "pytesseract" library, all text information in the image is extracted as text data. From the extracted text data, regular expressions are used to identify the necessary financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical data.

[0223] 5. Transferring data to a management spreadsheet

[0224] The server automatically writes the extracted data to a managed spreadsheet, and uses the Google Sheets API to connect to the spreadsheet and add the data as new rows. The new data is then integrated into the spreadsheet.

[0225] 6. User Confirmation

[0226] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[0227] 7. Emotion Recognition with Emotion Engine

[0228] The system is equipped with an emotion engine that recognizes the user's emotions. When the user checks the spreadsheet, the emotion engine analyzes the user's facial expressions and voice in real time to determine their emotions. Based on the analysis results, if the user is feeling stressed or dissatisfied, the system will display appropriate feedback and support messages.

[0229] Specific examples

[0230] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[0231] | Revenue | Profit | Expenses |

[0232] |---------|--------|----------|

[0233] | 1,500,000 | 300,000 | 1,200,000 |

[0234] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[0235] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[0236] The processing flow will be explained below.

[0237] Step 1:

[0238] A user accesses the web interface, selects a PDF file as a financial report document, and clicks the upload button to upload the selected PDF file to the server.

[0239] Step 2:

[0240] The server receives the uploaded PDF file. The received PDF file is saved in the specified folder on the server. For example, the file is saved in " / path / to / save / financial_report_2023.pdf".

[0241] Step 3:

[0242] The server converts the saved PDF file into an image format using the "pdf2image" library, which retrieves each page of the PDF file as an image.

[0243] Step 4:

[0244] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[0245] Step 5:

[0246] The server uses regular expressions to identify the necessary financial items (revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if it finds information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" in the text data, it will pick up the numerical information for each.

[0247] Step 6:

[0248] The server automatically writes the extracted numerical data to a managed spreadsheet, using the Google Sheets API to connect to the spreadsheet and adding the extracted data as a new row to the spreadsheet.

[0249] Step 7:

[0250] The device displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user then views the spreadsheet in a browser to check for inaccuracies.

[0251] Step 8:

[0252] The emotion engine built into the system recognizes the user's emotions in real time by analyzing the user's facial expressions and voice data to determine whether the user is feeling stressed or dissatisfied.

[0253] Step 9:

[0254] The server displays appropriate feedback and support messages to the user based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed on the device.

[0255] Step 10:

[0256] Once the user has completed the verification process, the system records the status and confirms that the entire process is complete. This series of actions ensures the accuracy of the data and reduces the user's psychological burden.

[0257] Specific examples

[0258] A user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it to " / path / to / save / abc_2023.pdf." The server converts the PDF file to an image format and then converts it to text data using OCR technology. From that text data, the server extracts the following information: "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000." The server automatically enters these figures into a management spreadsheet, which is updated as follows:

[0259] | Revenue | Profit | Expenses |

[0260] |---------|--------|----------|

[0261] | 1,500,000 | 300,000 | 1,200,000 |

[0262] The device presents this to the user, who then confirms the accuracy of the data. At that time, the emotion engine analyzes the user's emotions and displays appropriate feedback and support messages. In this example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed.

[0263] Example 2

[0264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0265] Existing financial reporting systems require a lot of work, such as analyzing report documents and transcribing data, and are prone to errors. Furthermore, there is a lack of feedback to users regarding their frustrations and frustrations when using the system. This has led to problems of inefficiency in accounting processes and user fatigue.

[0266] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for a user to select and upload report materials, means for saving the uploaded report materials on the server, means for converting the saved report materials into images, means for extracting text data from the converted images, means for identifying necessary items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management sheet, means for analyzing user emotions, and means for displaying a feedback message based on the emotion analysis. This improves the efficiency of automatic analysis and data transcription of financial report materials, and further reduces the burden on the user by providing appropriate feedback based on the user's emotions.

[0267] "Reporting materials" means documents containing financial or other business data.

[0268] "Uploading means" refers to a function or device for transmitting user-specified report materials to the server.

[0269] "Server" refers to a central computer system that receives uploaded report materials and performs various processes such as analysis and storage.

[0270] "Storing means" refers to a function or device for storing received report materials in a designated folder or data storage.

[0271] "Means for converting to images" refers to software or hardware for converting stored report materials into an image format.

[0272] "Means for extracting text data" refers to OCR (optical character recognition) technology for extracting text information from report materials converted into image format.

[0273] "Means for identifying necessary items and extracting numerical values" refers to a function for identifying specific financial data from the extracted character data using regular expressions or other analytical techniques and extracting the numerical values.

[0274] "Means for inputting data into a management sheet" refers to a function for automatically transferring extracted numerical data into a management spreadsheet or database.

[0275] "Means for analyzing emotions" refers to software or hardware that analyzes a user's emotions in real time based on data such as the user's facial expressions and voice.

[0276] The "means for displaying a feedback message" refers to a function for displaying appropriate messages and support information to the user based on the analyzed emotion data.

[0277] The present invention combines a system that automatically analyzes report materials and transcribes them into a management sheet with an emotion analysis engine that recognizes user emotions. Specific embodiments of the system will be described in detail below.

[0278] System Overview

[0279] Hardware and Software Configuration

[0280] server:

[0281] The server acts as the central processing unit and performs its processing using the following software libraries:

[0282] Convert PDF files to image format using the "pdf2image" library.

[0283] Extract character data from images using the "pytesseract" library.

[0284] Use the "googleapiclient.discovery" library to connect to the Google Sheets API and transfer data to an admin sheet.

[0285] The emotion analysis engine uses Microsoft (registered trademark) Azure (registered trademark) Cognitive Services and Google Cloud Vision API to analyze user emotions in real time.

[0286] Device:

[0287] The terminal is a device that allows the user to interface with the system and is operated through a browser. The terminal is equipped with a camera and microphone, and emotion data is collected through these devices.

[0288] User:

[0289] Users are responsible for uploading documents such as financial reports and verifying the results.

[0290] How it works

[0291] 1. User uploads PDF

[0292] Users access a dedicated interface through a web browser, select a PDF file as a report document, and upload it. For example, a user selects and uploads a file called "abc_2023.pdf."

[0293] 2. Receiving and saving the PDF on the server

[0294] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file is saved as "financial_report_2023.pdf".

[0295] 3. Parsing the PDF by the server

[0296] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is generated as an image and saved as "page1.jpg", "page2.jpg", etc.

[0297] 4. Extracting financial data

[0298] The server converts each image page into text data using OCR (Optical Character Recognition) technology with the "pytesseract" library. From the obtained text data, the necessary financial items (revenue, profit, expenses, etc.) are identified using regular expressions and other analytical methods, and the corresponding numerical data is extracted.

[0299] 5. Transferring data to the management sheet

[0300] The server automatically writes the extracted data to a management spreadsheet using the Google Sheets API. For example, data such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" is added as a new row to the spreadsheet.

[0301] 6. User Confirmation

[0302] The device displays the updated spreadsheet and allows the user to verify the accuracy of the data. The user can then review the spreadsheet and make corrections as necessary.

[0303] 7. Emotion Recognition with Emotion Engine

[0304] The emotion engine analyzes the user's facial expressions and voice in real time. In particular, if the user feels stressed while checking a spreadsheet, the system displays a feedback message such as, "Thank you for your hard work. We recommend that you take a break."

[0305] Specific examples

[0306] For example, consider the case where a user uploads an external report document, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server then automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[0307] | Revenue | Profit | Expenses |

[0308] |---------|--------|----------|

[0309] | 1,500,000 | 300,000 | 1,200,000 |

[0310] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[0311] Prompt Sentence Examples

[0312] "Extract revenue, profit, and expense information from the following PDF document and transcribe it into a spreadsheet. This document is named "abc_2023.pdf."

[0313] In this way, the present invention realizes automatic analysis of report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[0314] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0315] Step 1:

[0316] Users access a dedicated interface through a web browser, select a PDF file as a report document, and click the upload button.

[0317] Input: A PDF file selected by the user (e.g. "abc_2023.pdf")

[0318] Output: PDF file is sent to the server

[0319] Specific behavior: The user accesses "http: / / example.com / upload", clicks the "Choose File" button, selects "abc_2023.pdf", and clicks the "Upload" button.

[0320] Step 2:

[0321] The server receives the uploaded PDF file and saves it in the specified folder.

[0322] Input: Uploaded PDF file

[0323] Output: Saved PDF file (e.g. "financial_report_2023.pdf")

[0324] Specific behavior: The server receives the HTTP request and saves the "abc_2023.pdf" file in the " / uploads" directory as "financial_report_2023.pdf".

[0325] Step 3:

[0326] The server converts the saved PDF file into an image format using the "pdf2image" library.

[0327] Input: Saved PDF file (e.g. "financial_report_2023.pdf")

[0328] Output: Converted image files (e.g. "page1.jpg", "page2.jpg")

[0329] Specific behavior: The server reads the "financial_report_2023.pdf" file and converts each page to a JPEG image using "pdf2image.convert_from_path()".

[0330] Step 4:

[0331] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This operation uses the "pytesseract" library.

[0332] Input: Image files (e.g. "page1.jpg", "page2.jpg")

[0333] Output: Extracted text data

[0334] Specific operation: The server reads the "page1.jpg" and "page2.jpg" files and extracts the text data from each image using "pytesseract.image_to_string()".

[0335] Step 5:

[0336] The server uses regular expressions to identify the necessary items (revenue, profit, expenses, etc.) from the extracted text data and extracts the corresponding numerical data.

[0337] Input: Extracted text data

[0338] Output: Extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000")

[0339] What happens: The server parses the extracted text data and extracts the required financial data using regular expressions (e.g., re.findall(r'Revenue: (\d+)', text)).

[0340] Step 6:

[0341] The server automatically writes the extracted numerical data to a management sheet using the Google Sheets API.

[0342] Input: Extracted numeric data

[0343] Output: Updated control sheet

[0344] What happens: The server connects to the Google Sheets API and adds the extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000") as new rows to the spreadsheet.

[0345] Step 7:

[0346] The terminal displays the updated control sheet, allowing the user to verify the accuracy of the data.

[0347] Input: Updated control sheet

[0348] Output: what the user sees

[0349] Specific operation: The device uses a browser to display the specified Google Sheets sheet, and the user confirms the contents.

[0350] Step 8:

[0351] The emotion engine analyzes the user's facial expressions and voice in real time, and if the user is feeling stressed, the system will display an appropriate feedback message.

[0352] Input: User facial and voice data

[0353] Output: Feedback message

[0354] Specific operation: The emotion engine analyzes data from the device's camera and microphone, and if it determines that the user is feeling stressed, it displays the message "Thank you for your hard work. We recommend that you take a break." on the device.

[0355] (Application example 2)

[0356] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0357] Data management in modern factories is extremely complex, and manual input and verification of financial and production data in particular takes a lot of time and effort. Furthermore, worker stress and fatigue can have a negative impact on productivity. To solve these problems, it is necessary to improve the efficiency of data management work while also providing feedback that takes into account the emotional state of workers.

[0358] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, and means for scanning documents with a camera mounted on a smart device, recognizing the user's emotions in real time with an emotion engine, and providing feedback. This enables the efficiency of data management operations and the reduction of worker stress.

[0359] A "user" is someone who uses the system to upload financial reporting documents and enter data into management spreadsheets.

[0360] A "financial reporting document" is a reporting document that contains financial information such as revenues, profits, and expenses of a company or organization.

[0361] An "uploading means" is a method or device by which a user transmits financial reporting materials to the system.

[0362] "Server" means a central processing unit that stores uploaded financial reporting materials, converts them into images, extracts text data, and transcribes the data into a management spreadsheet.

[0363] "Image conversion means" refers to technology or devices for converting uploaded financial report materials into image format.

[0364] "Text data extraction means" refers to a method or device that uses optical character recognition (OCR) technology to obtain text information from financial reporting documents that have been converted into images.

[0365] The "financial item identification means" refers to a method or device that identifies necessary financial information such as revenue, profit, and expenses from the extracted text data and extracts the numerical values.

[0366] A "control spreadsheet" is an electronic spreadsheet file into which extracted financial data is entered for control purposes.

[0367] A "camera" is a device installed in a smart device that scans documents used by the user.

[0368] An "emotion engine" is software or hardware that analyzes emotions from a user's facial expressions and voice in real time.

[0369] The "feedback providing means" is a method or device that displays an appropriate message based on the user's emotions analyzed by the emotion engine.

[0370] A "smart device" is a portable electronic device equipped with a camera and microphone that can input data and perform emotion analysis.

[0371] This invention is a system for streamlining data management and worker emotion monitoring in factories. The system combines smart devices (smart glasses), a server, a data management spreadsheet, and an emotion engine.

[0372] The system is configured as follows:

[0373] First, the user scans a financial report document using the camera on their smart device, which then captures the document as image data. This image data is then sent to a server via a communication method such as Wi-Fi.

[0374] To analyze the received image data, the server converts it to an image using the pdf2image library, then performs OCR processing using the pytesseract library to extract the text data from the image, and uses regular expressions to identify the required financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical values.

[0375] The extracted numerical data is automatically transferred to a managed spreadsheet using the Google Sheets API, ensuring that the data is always integrated and up-to-date in the managed spreadsheet.

[0376] In addition, the smart device's built-in camera and microphone capture the user's facial expressions and voice, which are then analyzed in real time by the emotion engine. The emotion engine uses EmotionRecognition software to recognize the user's emotional state. If the user is feeling stressed, the system will automatically display a feedback message (e.g., "Thank you for your hard work. We recommend that you take a break.").

[0377] As a practical scenario, we present a specific example of a user scanning financial report documents in a factory. For example, the user uses a smart device to scan the "2023 Production Line Financial Report." This data is sent to a server, where it undergoes OCR processing, extracting figures such as revenue, profit, and expenses. The extracted data is then transcribed into a management spreadsheet and registered as the latest report data. While the user is performing this task, the camera and microphone monitor the user's emotional state and display appropriate feedback messages as needed.

[0378] An example of a prompt for a generative AI model is:

[0379] "You are a factory manager. You use a smart device to scan financial reporting documents. The system analyzes the data and transcribes it into a management spreadsheet, while simultaneously analyzing your facial emotions. If the emotion engine detects that you are nervous while reviewing the documents, what feedback message should be displayed?"

[0380] In this way, the present invention improves the efficiency of data management within a factory and provides business support that takes into account the emotional state of workers.

[0381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0382] Step 1:

[0383] A user scans a financial report document using a camera on a smart device. The input is the financial report document to be scanned, and the output is image data. This image data is temporarily stored in the memory of the smart device.

[0384] Step 2:

[0385] The image data acquired by the device is sent to the server via a communication method such as Wi-Fi. The input is the saved image data, and the output is the image data transferred to the server. After the communication is complete, the device notifies the user of the status of the image data transmission.

[0386] Step 3:

[0387] The server saves the received image data. The input is the image data transferred to the server, and the output is an image file saved in a specified folder on the server. After saving is complete, the server prepares to proceed to the next step.

[0388] Step 4:

[0389] The server converts the received image data to PDF format using the pdf2image library. The input is an image file stored on the server, and the output is a PDF file. The server does this because it treats each page as a separate image, making it easier to convert.

[0390] Step 5:

[0391] The server uses the pytesseract library to perform OCR on images in PDF files. The input is a PDF file, and the output is text data. Through OCR processing, character information in the image is extracted in text format.

[0392] Step 6:

[0393] The server uses regular expressions to identify financial items in the text data. The input is the text data obtained by OCR processing, and the output is the numerical values ​​of the financial items (revenue, profit, expenses, etc.). This process extracts the necessary numerical data from the text.

[0394] Step 7:

[0395] The server uses the Google Sheets API to post the identified financial item values ​​to a management spreadsheet. The input is the financial item values, and the output is an updated management spreadsheet. The server adds the new data to the spreadsheet and saves it.

[0396] Step 8:

[0397] The smart device uses a camera and microphone to capture the user's facial expressions and voice in real time. The input is the user's facial expressions and voice, and the output is the captured data. The data is used for subsequent emotion analysis.

[0398] Step 9:

[0399] The emotion engine uses EmotionRecognition software to analyze the user's emotions from the captured data. The input is the captured facial and voice data, and the output is the emotion analysis result. After the emotion analysis is complete, the system provides appropriate feedback.

[0400] Step 10:

[0401] The device displays an appropriate feedback message based on the emotion analysis results. The input is the emotion analysis results, and the output is the feedback message to be displayed to the user. For example, "Thank you for your hard work. We recommend that you take a break."

[0402] In this way, the system streamlines data management in factory environments and provides feedback that takes into account the emotional state of workers.

[0403] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0404] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0405] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0406] [Second embodiment]

[0407] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0408] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0409] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0410] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0411] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0413] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0414] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0415] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0416] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0417] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0418] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0419] The present invention relates to a system that automatically analyzes financial report documents and transfers the results to a management spreadsheet. The specific processing steps and implementation method of this system are described below.

[0420] System Flow Overview

[0421] 1. User uploads PDF

[0422] Users can select and upload financial report documents (PDF files) using a dedicated web interface. This operation is completed by simply selecting the file in the browser and clicking the upload button.

[0423] 2. Receiving and saving the PDF on the server

[0424] The server receives the uploaded PDF file and saves it in the specified folder on the server. For example, let's say the file name is "financial_report_2023.pdf".

[0425] 3. Parsing the PDF by the server

[0426] The server processes the saved PDF file to convert it into an image format. For example, it uses the "pdf2image" library. The image data is then converted into text data using OCR (Optical Character Recognition) technology. At this stage, all the text information in the PDF is obtained as text data.

[0427] 4. Extracting financial data

[0428] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" is found in the text data, the respective numerical information is extracted.

[0429] 5. Transferring data to a management spreadsheet

[0430] The server automatically writes the extracted data to a managed spreadsheet. To do this, it connects to the spreadsheet using the Google Sheets API and performs write operations. When new data is added to the spreadsheet, it is merged into the existing table.

[0431] 6. User Confirmation

[0432] The terminal displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user can review and correct the data as necessary to ensure its accuracy.

[0433] Specific examples

[0434] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. The following information is found in the text data:

[0435] Revenue: 1,500,000

[0436] Profit: 300000

[0437] Expenses: 1200000

[0438] The server will extract these numbers and automatically enter them into a corresponding spreadsheet, which will look something like this:

[0439] | Revenue | Profit | Expenses |

[0440] |---------|--------|----------|

[0441] | 1,500,000 | 300,000 | 1,200,000 |

[0442] The terminal presents this spreadsheet to the user, who then checks the data.

[0443] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, thereby reducing human error and improving business efficiency.

[0444] The processing flow will be explained below.

[0445] Step 1:

[0446] A user accesses a web interface, selects a PDF file as a financial report document, and clicks an upload button to upload the selected file.

[0447] Step 2:

[0448] The server receives the uploaded PDF file and saves it in the specified folder. For example, it saves it as " / path / to / save / financial_report_2023.pdf".

[0449] Step 3:

[0450] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[0451] Step 4:

[0452] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[0453] Step 5:

[0454] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the acquired text data and extracts the corresponding numerical data. If the text data contains information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000," the respective numerical information will be extracted.

[0455] Step 6:

[0456] The server automatically writes the extracted numerical data to a managed spreadsheet. It connects to the spreadsheet using the Google Sheets API and adds the extracted data as a new row. The new data is then integrated into the spreadsheet.

[0457] Step 7:

[0458] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[0459] Step 8:

[0460] Once users have completed the verification process, the risk of errors and inaccurate data is reduced across the system, helping to maintain accurate financial data.

[0461] Example 1

[0462] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0463] Traditional methods for managing financial reporting documents involve a lot of manual data entry, which is prone to errors. Furthermore, there is a lack of automation to efficiently and accurately input large amounts of data into management spreadsheets. This results in a heavy workload and a lack of efficiency and accuracy.

[0464] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0465] In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for automatically writing data into the management spreadsheet, and means for saving the data in a specified folder. This enables automatic analysis of financial report documents and efficient data transcription.

[0466] A "user" is an entity that uses the system to upload financial reporting materials and check and correct data.

[0467] "Financial reporting materials" are documents created by companies or organizations to report their financial status, and are usually saved in PDF format.

[0468] "Server" means a computer system responsible for receiving financial reporting materials uploaded by users, storing, analyzing, and transcribing the data into a management spreadsheet.

[0469] "Means for converting to images" refers to technology or software for converting PDF files into still images, such as using the "pdf2image" library.

[0470] "Means for extracting text data" refers to technology or software for extracting text information from image data, such as using "OCR (optical character recognition)" technology.

[0471] "Means for identifying financial items and extracting numerical values" refers to algorithms or techniques for finding specific financial items (e.g., revenue, profit, expense) and their numerical values ​​from the extracted text data.

[0472] A "management spreadsheet" is a spreadsheet software for organizing and storing financial data, such as "Google Sheets."

[0473] The "means for writing data" refers to technology or software for automatically inputting the extracted numerical data into designated cells in the management spreadsheet.

[0474] The "means of saving in a designated folder" refers to a method or technology for saving uploaded financial reporting materials in a specific directory.

[0475] The present invention relates to a system for automatically analyzing financial report materials and transcribing the contents of the analysis into a management spreadsheet. Specific embodiments of the present invention will be described below.

[0476] Overall structure

[0477] This system is composed of users, servers, and terminals, and each part functions in cooperation with the others.

[0478] User uploads PDF

[0479] A user uploads financial report documents (PDF files) using a web interface. Specifically, the user launches a browser, accesses the system's website, clicks the "Choose File" button, selects the PDF they want to upload, and then clicks the "Upload" button to submit it.

[0480] Server processing

[0481] 1. Receiving and saving files:

[0482] The server receives the HTTP request and saves the uploaded PDF file in the specified folder. For example, it is saved with the file name "financial_report_2023.pdf".

[0483] 2. Parse PDF:

[0484] The server uses the "pdf2image" library to convert the saved PDF file into multiple image files. Then, it uses "OCR (Optical Character Recognition)" technology to extract text information from these images. At this stage, all text information in the PDF is obtained as text data.

[0485] 3. Financial Data Extraction:

[0486] The server uses regular expressions to identify specific financial items (e.g., revenue, profit, expenses, etc.) within the text data and extracts the associated numerical data, such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000."

[0487] 4. Post to control spreadsheet:

[0488] The server connects to a managed spreadsheet via the Google Sheets API and automatically populates the appropriate cells with the extracted data, thereby integrating the financial data into the spreadsheet.

[0489] User confirmation

[0490] The device displays the latest management spreadsheet in the browser, allowing the user to check the accuracy of the data. The user can then review and make any necessary corrections to ensure the accuracy of the data.

[0491] Specific examples

[0492] As a concrete example, consider a scenario where a user uploads "Company X's" financial statement for fiscal year 2023, "company_x_2023.pdf." The server receives the file and saves it in a specified folder. The saved PDF file is converted into multiple images, and OCR technology is used to extract the following text data:

[0493] Revenue: 1,500,000

[0494] Profit: 300000

[0495] Expenses: 1200000

[0496] The server identifies these numbers and enters them into a spreadsheet via the Google Sheets API. For example, the administrative spreadsheet might look like this:

[0497] | Revenue | Profit | Expenses |

[0498] |---------|--------|----------|

[0499] | 1,500,000 | 300,000 | 1,200,000 |

[0500] The terminal displays the spreadsheet and the user checks the data to ensure its accuracy.

[0501] Prompt Sentence Examples

[0502] What are the specific steps in a system that parses PDF financial reports and transfers them to a management spreadsheet?

[0503] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0504] Step-by-step explanation

[0505] Step 1: User uploads PDF

[0506] Input: A user has a PDF file of a financial report.

[0507] Specific behavior: A user launches a browser, accesses the system's website, clicks the "Choose File" button, selects a PDF file, and clicks the "Upload" button.

[0508] Output: The selected PDF file is sent to the server.

[0509] Step 2: The server receives and saves the PDF

[0510] Input: A PDF file submitted by the user.

[0511] Specific operation: The server receives the HTTP request and saves the uploaded PDF file data in the specified folder. For example, it saves it as "financial_report_2023.pdf".

[0512] Output: The PDF file will be saved in the specified folder on the server.

[0513] Step 3: Parse the PDF on the server

[0514] Input: PDF file stored on the server.

[0515] What it does: The server uses the "pdf2image" library to convert the PDF file into multiple image files.

[0516] Output: The PDF file is converted into multiple image files.

[0517] Step 4: OCR analysis of image data

[0518] Input: Multiple image files.

[0519] Specific operation: The server uses an OCR library such as "Tesseract" to extract text data from the image data. At this stage, all character information is obtained as text data.

[0520] Output: Text data extracted from the image.

[0521] Step 5: Extract financial data

[0522] Input: Text data.

[0523] What it does: The server uses regular expressions to identify financial items (e.g., revenue, profit, expense) within the text data and extracts the associated numerical data.

[0524] Output: Identified financial items and their numerical data.

[0525] Step 6: Transfer data to a management spreadsheet

[0526] Input: Identified financial items and their numerical data.

[0527] What happens: The server connects to a managed spreadsheet using the Google Sheets API and automatically populates the appropriate cells with the extracted data.

[0528] Output: New data is posted to the control spreadsheet.

[0529] Step 7: User confirmation

[0530] Input: Data posted to the control spreadsheet.

[0531] Specific behavior: The device displays the spreadsheet in a browser, and the user can verify the accuracy of the data. The user can then review and manually correct any errors if necessary.

[0532] Output: An up-to-date control spreadsheet with all the corrections completed and data accuracy assured.

[0533] Through the above steps, the system achieves automatic analysis of financial reporting materials and efficient data transcription, reducing the user's workload and ensuring data accuracy.

[0534] (Application example 1)

[0535] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0536] Manually analyzing financial reporting documents and entering them into management spreadsheets is time-consuming, labor-intensive, and prone to human error. There is also a lack of convenient ways to quickly check data. This creates a need for systems that significantly improve the efficiency of accounting operations and management decisions. There is a growing need for technology that can analyze and check data in real time using smart devices, especially in brick-and-mortar stores.

[0537] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0538] In this invention, the server includes means for a user to select and upload financial reporting documents, means for saving the uploaded financial reporting documents on the server, means for converting the saved financial reporting documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for taking a picture of the financial reporting documents using smart glasses and analyzing the data in real time, and means for displaying the analyzed data to the user. This automates the analysis and transcription of financial reporting documents, enabling quick and accurate data confirmation.

[0539] A "User" is a person whose role is to use the system to upload and review financial reporting materials.

[0540] A "financial reporting document" is a document that contains important financial information such as revenues, profits, and expenses.

[0541] The "uploading means" is an interface that allows a user to send financial report materials to a server via a browser or the like.

[0542] A "server" is a computer system that receives, stores, and analyzes uploaded financial reporting materials.

[0543] The "means for saving" is a process for saving the uploaded financial report materials in a designated folder on the server.

[0544] "Means for converting to images" refers to the process of converting uploaded financial report documents, such as PDF files, into image format.

[0545] The "means for extracting text data" is a mechanism for obtaining character information from the converted image using OCR technology and converting it into text data.

[0546] "Means for identifying financial items" is the process of finding specific financial information such as revenues, profits, and expenses from the extracted text data.

[0547] The "means for extracting numerical values" is a function for obtaining specific numerical data corresponding to the identified financial items.

[0548] A "management spreadsheet" is an electronic spreadsheet used to compile and centrally manage extracted numerical data.

[0549] "Smart glasses" are wearable devices that have a built-in camera and display, allowing users to view information in real time within their field of vision.

[0550] "Real-time analysis means" refers to the processing power to instantly process financial reporting documents captured using the smart glasses and quickly generate results.

[0551] "Means for displaying to the user" refers to a function for immediately displaying the analyzed data on the display of the smart glasses.

[0552] This invention relates to a system that automatically analyzes financial reporting documents and transcribes the contents into a management spreadsheet, providing a system that allows data to be viewed quickly and accurately in a physical store using smart glasses.

[0553] Hardware and software used

[0554] Smart glasses: with image capture and display capabilities.

[0555] Server: A computer system that stores, analyzes, and processes data.

[0556] Google Sheets API: Used for automated writing to managed spreadsheets.

[0557] OCR software: Tesseract OCR library.

[0558] Image transformation library: OpenCV.

[0559] Authentication Library: ServiceAccountCredentials for OAuth2 authentication.

[0560] Program processing description

[0561] Capture and Upload

[0562] The user wears the smart glasses and holds the financial report document up to the glasses' camera, which captures the image and sends the image data to the server.

[0563] Image analysis and text data extraction

[0564] The server processes the received image data using OpenCV to convert the PDF file format financial report documents into images, and then extracts text data from the images using the Tesseract OCR library.

[0565] Data Identification and Extraction

[0566] The server uses regular expressions to identify important financial items such as "revenue," "profit," and "expenses" from within the text data and extracts the corresponding numerical data.

[0567] Transferring data to a spreadsheet

[0568] The extracted numerical data is automatically written to a managed spreadsheet using the Google Sheets API. During this process, a mechanism is in place to securely access Google Sheets using OAuth2 authentication.

[0569] Review and display data

[0570] The extracted data is displayed in real time on the smart glasses' display, allowing users to immediately check the accuracy of the data, and is automatically transcribed into a spreadsheet so that administrators can correct the data as needed.

[0571] Specific examples

[0572] For example, imagine a retail manager reviewing monthly financial reports. The manager puts on smart glasses and holds up the paper report to the glasses' camera. Within seconds, revenue, profit, and expense data appears on the glasses' display and is automatically posted to Google Sheets. This allows the manager to immediately review the data and improve work efficiency.

[0573] Example prompts for generative AI models

[0574] "Describe a system that uses smart glasses to analyze financial reports in real time and automatically transcribe them into a spreadsheet."

[0575] "How can we develop an application that displays real-time financial data on smart glasses to improve business efficiency?"

[0576] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0577] Step 1:

[0578] The user puts on the smart glasses and holds the financial report up to the glasses' camera. The input is the physical financial report, and the output is the image captured by the smart glasses. This step involves the user taking a concrete action to hold the report in place.

[0579] Step 2:

[0580] The smart glasses capture images and send the data to a server. The input is the captured image data, and the output is the image data sent to the server. The specific operations of the smart glasses include activating the camera and transmitting the captured images to the server via wireless communication.

[0581] Step 3:

[0582] The server processes the received image data using OpenCV and converts the financial report documents in PDF file format into images. The input is the image data sent to the server, and the output is the converted image file. This step includes the specific operation of the server applying the image processing algorithm and converting it into PDF format.

[0583] Step 4:

[0584] The server uses the Tesseract OCR library to extract text data from images. The input is image data and the output is text data. The server includes specific operations for recognizing and extracting characters using OCR technology.

[0585] Step 5:

[0586] The server uses regular expressions to identify financial items such as revenue, profit, and expenses from within the text data and extracts the related numerical data. The input is the extracted text data and the output is the identified numerical data. The server uses regular expressions to identify and extract the required data.

[0587] Step 6:

[0588] The server uses the Google Sheets API to automatically write the extracted numerical data to a managed spreadsheet. The input is the extracted numerical data, and the output is an updated spreadsheet. The server accesses Google Sheets and includes specific operations to write data to specified cells.

[0589] Step 7:

[0590] The server displays the analyzed data on the smart glasses display in real time. The input is the updated data in the management spreadsheet, and the output is the data displayed on the smart glasses display. The server transmits the data to the glasses and includes specific actions to provide visual feedback to the user.

[0591] Step 8:

[0592] The user checks the data displayed on the smart glasses display and checks the accuracy of the data as needed. The input is the data displayed on the display, and the output is the user's confirmation. The user's specific actions include checking the displayed figures against actual financial reporting documents.

[0593] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0594] The present invention combines a system that automatically analyzes financial report documents and transcribes them into a management spreadsheet with an emotion engine that recognizes the user's emotions. A specific embodiment of this system is described below.

[0595] System Flow Overview

[0596] 1. User uploads PDF

[0597] Users can select and upload PDF files as financial reporting documents using a dedicated web interface. All they need to do is select the file in their browser and click the upload button.

[0598] 2. Receiving and saving the PDF on the server

[0599] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file name "financial_report_2023.pdf" is saved.

[0600] 3. Parsing the PDF by the server

[0601] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[0602] 4. Extracting financial data

[0603] The server converts each image page into text data using OCR (optical character recognition) technology. Using the "pytesseract" library, all text information in the image is extracted as text data. From the extracted text data, regular expressions are used to identify the necessary financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical data.

[0604] 5. Transferring data to a management spreadsheet

[0605] The server automatically writes the extracted data to a managed spreadsheet, and uses the Google Sheets API to connect to the spreadsheet and add the data as new rows. The new data is then integrated into the spreadsheet.

[0606] 6. User Confirmation

[0607] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[0608] 7. Emotion Recognition with Emotion Engine

[0609] The system is equipped with an emotion engine that recognizes the user's emotions. When the user checks the spreadsheet, the emotion engine analyzes the user's facial expressions and voice in real time to determine their emotions. Based on the analysis results, if the user is feeling stressed or dissatisfied, the system will display appropriate feedback and support messages.

[0610] Specific examples

[0611] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[0612] | Revenue | Profit | Expenses |

[0613] |---------|--------|----------|

[0614] | 1,500,000 | 300,000 | 1,200,000 |

[0615] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[0616] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[0617] The processing flow will be explained below.

[0618] Step 1:

[0619] A user accesses the web interface, selects a PDF file as a financial report document, and clicks the upload button to upload the selected PDF file to the server.

[0620] Step 2:

[0621] The server receives the uploaded PDF file. The received PDF file is saved in the specified folder on the server. For example, the file is saved in " / path / to / save / financial_report_2023.pdf".

[0622] Step 3:

[0623] The server converts the saved PDF file into an image format using the "pdf2image" library, which retrieves each page of the PDF file as an image.

[0624] Step 4:

[0625] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[0626] Step 5:

[0627] The server uses regular expressions to identify the necessary financial items (revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if it finds information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" in the text data, it will pick up the numerical information for each.

[0628] Step 6:

[0629] The server automatically writes the extracted numerical data to a managed spreadsheet, using the Google Sheets API to connect to the spreadsheet and adding the extracted data as a new row to the spreadsheet.

[0630] Step 7:

[0631] The device displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user then views the spreadsheet in a browser to check for inaccuracies.

[0632] Step 8:

[0633] The emotion engine built into the system recognizes the user's emotions in real time by analyzing the user's facial expressions and voice data to determine whether the user is feeling stressed or dissatisfied.

[0634] Step 9:

[0635] The server displays appropriate feedback and support messages to the user based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed on the device.

[0636] Step 10:

[0637] Once the user has completed the verification process, the system records the status and confirms that the entire process is complete. This series of actions ensures the accuracy of the data and reduces the user's psychological burden.

[0638] Specific examples

[0639] A user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it to " / path / to / save / abc_2023.pdf." The server converts the PDF file to an image format and then converts it to text data using OCR technology. From that text data, the server extracts the following information: "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000." The server automatically enters these figures into a management spreadsheet, which is updated as follows:

[0640] | Revenue | Profit | Expenses |

[0641] |---------|--------|----------|

[0642] | 1,500,000 | 300,000 | 1,200,000 |

[0643] The device presents this to the user, who then confirms the accuracy of the data. At that time, the emotion engine analyzes the user's emotions and displays appropriate feedback and support messages. In this example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed.

[0644] Example 2

[0645] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0646] Existing financial reporting systems require a lot of work, such as analyzing report documents and transcribing data, and are prone to errors. Furthermore, there is a lack of feedback to users regarding their frustrations and frustrations when using the system. This has led to problems of inefficiency in accounting processes and user fatigue.

[0647] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for a user to select and upload report materials, means for saving the uploaded report materials on the server, means for converting the saved report materials into images, means for extracting text data from the converted images, means for identifying necessary items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management sheet, means for analyzing user emotions, and means for displaying a feedback message based on the emotion analysis. This improves the efficiency of automatic analysis and data transcription of financial report materials, and further reduces the burden on the user by providing appropriate feedback based on the user's emotions.

[0648] "Reporting materials" means documents containing financial or other business data.

[0649] "Uploading means" refers to a function or device for transmitting user-specified report materials to the server.

[0650] "Server" refers to a central computer system that receives uploaded report materials and performs various processes such as analysis and storage.

[0651] "Storing means" refers to a function or device for storing received report materials in a designated folder or data storage.

[0652] "Means for converting to images" refers to software or hardware for converting stored report materials into an image format.

[0653] "Means for extracting text data" refers to OCR (optical character recognition) technology for extracting text information from report materials converted into image format.

[0654] "Means for identifying necessary items and extracting numerical values" refers to a function for identifying specific financial data from the extracted character data using regular expressions or other analytical techniques and extracting the numerical values.

[0655] "Means for inputting data into a management sheet" refers to a function for automatically transferring extracted numerical data into a management spreadsheet or database.

[0656] "Means for analyzing emotions" refers to software or hardware that analyzes a user's emotions in real time based on data such as the user's facial expressions and voice.

[0657] The "means for displaying a feedback message" refers to a function for displaying appropriate messages and support information to the user based on the analyzed emotion data.

[0658] The present invention combines a system that automatically analyzes report materials and transcribes them into a management sheet with an emotion analysis engine that recognizes user emotions. Specific embodiments of the system will be described in detail below.

[0659] System Overview

[0660] Hardware and Software Configuration

[0661] server:

[0662] The server acts as the central processing unit and performs its processing using the following software libraries:

[0663] Convert PDF files to image format using the "pdf2image" library.

[0664] Extract character data from images using the "pytesseract" library.

[0665] Use the "googleapiclient.discovery" library to connect to the Google Sheets API and transfer data to an admin sheet.

[0666] The sentiment analysis engine uses Microsoft Azure Cognitive Services and Google Cloud Vision API to analyze user emotions in real time.

[0667] Device:

[0668] The terminal is a device that allows the user to interface with the system and is operated through a browser. The terminal is equipped with a camera and microphone, and emotion data is collected through these devices.

[0669] User:

[0670] Users are responsible for uploading documents such as financial reports and verifying the results.

[0671] How it works

[0672] 1. User uploads PDF

[0673] Users access a dedicated interface through a web browser, select a PDF file as a report document, and upload it. For example, a user selects and uploads a file called "abc_2023.pdf."

[0674] 2. Receiving and saving the PDF on the server

[0675] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file is saved as "financial_report_2023.pdf".

[0676] 3. Parsing the PDF by the server

[0677] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is generated as an image and saved as "page1.jpg", "page2.jpg", etc.

[0678] 4. Extracting financial data

[0679] The server converts each image page into text data using OCR (Optical Character Recognition) technology with the "pytesseract" library. From the obtained text data, the necessary financial items (revenue, profit, expenses, etc.) are identified using regular expressions and other analytical methods, and the corresponding numerical data is extracted.

[0680] 5. Transferring data to the management sheet

[0681] The server automatically writes the extracted data to a management spreadsheet using the Google Sheets API. For example, data such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" is added as a new row to the spreadsheet.

[0682] 6. User Confirmation

[0683] The device displays the updated spreadsheet and allows the user to verify the accuracy of the data. The user can then review the spreadsheet and make corrections as necessary.

[0684] 7. Emotion Recognition with Emotion Engine

[0685] The emotion engine analyzes the user's facial expressions and voice in real time. In particular, if the user feels stressed while checking a spreadsheet, the system displays a feedback message such as, "Thank you for your hard work. We recommend that you take a break."

[0686] Specific examples

[0687] For example, consider the case where a user uploads an external report document, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server then automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[0688] | Revenue | Profit | Expenses |

[0689] |---------|--------|----------|

[0690] | 1,500,000 | 300,000 | 1,200,000 |

[0691] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[0692] Prompt Sentence Examples

[0693] "Extract revenue, profit, and expense information from the following PDF document and transcribe it into a spreadsheet. This document is named "abc_2023.pdf."

[0694] In this way, the present invention realizes automatic analysis of report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[0695] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0696] Step 1:

[0697] Users access a dedicated interface through a web browser, select a PDF file as a report document, and click the upload button.

[0698] Input: A PDF file selected by the user (e.g. "abc_2023.pdf")

[0699] Output: PDF file is sent to the server

[0700] Specific behavior: The user accesses "http: / / example.com / upload", clicks the "Choose File" button, selects "abc_2023.pdf", and clicks the "Upload" button.

[0701] Step 2:

[0702] The server receives the uploaded PDF file and saves it in the specified folder.

[0703] Input: Uploaded PDF file

[0704] Output: Saved PDF file (e.g. "financial_report_2023.pdf")

[0705] Specific behavior: The server receives the HTTP request and saves the "abc_2023.pdf" file in the " / uploads" directory as "financial_report_2023.pdf".

[0706] Step 3:

[0707] The server converts the saved PDF file into an image format using the "pdf2image" library.

[0708] Input: Saved PDF file (e.g. "financial_report_2023.pdf")

[0709] Output: Converted image files (e.g. "page1.jpg", "page2.jpg")

[0710] Specific behavior: The server reads the "financial_report_2023.pdf" file and converts each page to a JPEG image using "pdf2image.convert_from_path()".

[0711] Step 4:

[0712] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This operation uses the "pytesseract" library.

[0713] Input: Image files (e.g. "page1.jpg", "page2.jpg")

[0714] Output: Extracted text data

[0715] Specific operation: The server reads the "page1.jpg" and "page2.jpg" files and extracts the text data from each image using "pytesseract.image_to_string()".

[0716] Step 5:

[0717] The server uses regular expressions to identify the necessary items (revenue, profit, expenses, etc.) from the extracted text data and extracts the corresponding numerical data.

[0718] Input: Extracted text data

[0719] Output: Extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000")

[0720] What happens: The server parses the extracted text data and extracts the required financial data using regular expressions (e.g., re.findall(r'Revenue: (\d+)', text)).

[0721] Step 6:

[0722] The server automatically writes the extracted numerical data to a management sheet using the Google Sheets API.

[0723] Input: Extracted numeric data

[0724] Output: Updated control sheet

[0725] What happens: The server connects to the Google Sheets API and adds the extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000") as new rows to the spreadsheet.

[0726] Step 7:

[0727] The terminal displays the updated control sheet, allowing the user to verify the accuracy of the data.

[0728] Input: Updated control sheet

[0729] Output: what the user sees

[0730] Specific operation: The device uses a browser to display the specified Google Sheets sheet, and the user confirms the contents.

[0731] Step 8:

[0732] The emotion engine analyzes the user's facial expressions and voice in real time, and if the user is feeling stressed, the system will display an appropriate feedback message.

[0733] Input: User facial and voice data

[0734] Output: Feedback message

[0735] Specific operation: The emotion engine analyzes data from the device's camera and microphone, and if it determines that the user is feeling stressed, it displays the message "Thank you for your hard work. We recommend that you take a break." on the device.

[0736] (Application example 2)

[0737] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0738] Data management in modern factories is extremely complex, and manual input and verification of financial and production data in particular takes a lot of time and effort. Furthermore, worker stress and fatigue can have a negative impact on productivity. To solve these problems, it is necessary to improve the efficiency of data management work while also providing feedback that takes into account the emotional state of workers.

[0739] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, and means for scanning documents with a camera mounted on a smart device, recognizing the user's emotions in real time with an emotion engine, and providing feedback. This enables the efficiency of data management operations and the reduction of worker stress.

[0740] A "user" is someone who uses the system to upload financial reporting documents and enter data into management spreadsheets.

[0741] A "financial reporting document" is a reporting document that contains financial information such as revenues, profits, and expenses of a company or organization.

[0742] An "uploading means" is a method or device by which a user transmits financial reporting materials to the system.

[0743] "Server" means a central processing unit that stores uploaded financial reporting materials, converts them into images, extracts text data, and transcribes the data into a management spreadsheet.

[0744] "Image conversion means" refers to technology or devices for converting uploaded financial report materials into image format.

[0745] "Text data extraction means" refers to a method or device that uses optical character recognition (OCR) technology to obtain text information from financial reporting documents that have been converted into images.

[0746] The "financial item identification means" refers to a method or device that identifies necessary financial information such as revenue, profit, and expenses from the extracted text data and extracts the numerical values.

[0747] A "control spreadsheet" is an electronic spreadsheet file into which extracted financial data is entered for control purposes.

[0748] A "camera" is a device installed in a smart device that scans documents used by the user.

[0749] An "emotion engine" is software or hardware that analyzes emotions from a user's facial expressions and voice in real time.

[0750] The "feedback providing means" is a method or device that displays an appropriate message based on the user's emotions analyzed by the emotion engine.

[0751] A "smart device" is a portable electronic device equipped with a camera and microphone that can input data and perform emotion analysis.

[0752] This invention is a system for streamlining data management and worker emotion monitoring in factories. The system combines smart devices (smart glasses), a server, a data management spreadsheet, and an emotion engine.

[0753] The system is configured as follows:

[0754] First, the user scans a financial report document using the camera on their smart device, which then captures the document as image data. This image data is then sent to a server via a communication method such as Wi-Fi.

[0755] To analyze the received image data, the server converts it to an image using the pdf2image library, then performs OCR processing using the pytesseract library to extract the text data from the image, and uses regular expressions to identify the required financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical values.

[0756] The extracted numerical data is automatically transferred to a managed spreadsheet using the Google Sheets API, ensuring that the data is always integrated and up-to-date in the managed spreadsheet.

[0757] In addition, the smart device's built-in camera and microphone capture the user's facial expressions and voice, which are then analyzed in real time by the emotion engine. The emotion engine uses EmotionRecognition software to recognize the user's emotional state. If the user is feeling stressed, the system will automatically display a feedback message (e.g., "Thank you for your hard work. We recommend that you take a break.").

[0758] As a practical scenario, we present a specific example of a user scanning financial report documents in a factory. For example, the user uses a smart device to scan the "2023 Production Line Financial Report." This data is sent to a server, where it undergoes OCR processing, extracting figures such as revenue, profit, and expenses. The extracted data is then transcribed into a management spreadsheet and registered as the latest report data. While the user is performing this task, the camera and microphone monitor the user's emotional state and display appropriate feedback messages as needed.

[0759] An example of a prompt for a generative AI model is:

[0760] "You are a factory manager. You use a smart device to scan financial reporting documents. The system analyzes the data and transcribes it into a management spreadsheet, while simultaneously analyzing your facial emotions. If the emotion engine detects that you are nervous while reviewing the documents, what feedback message should be displayed?"

[0761] In this way, the present invention improves the efficiency of data management within a factory and provides business support that takes into account the emotional state of workers.

[0762] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0763] Step 1:

[0764] A user scans a financial report document using a camera on a smart device. The input is the financial report document to be scanned, and the output is image data. This image data is temporarily stored in the memory of the smart device.

[0765] Step 2:

[0766] The image data acquired by the device is sent to the server via a communication method such as Wi-Fi. The input is the saved image data, and the output is the image data transferred to the server. After the communication is complete, the device notifies the user of the status of the image data transmission.

[0767] Step 3:

[0768] The server saves the received image data. The input is the image data transferred to the server, and the output is an image file saved in a specified folder on the server. After saving is complete, the server prepares to proceed to the next step.

[0769] Step 4:

[0770] The server converts the received image data to PDF format using the pdf2image library. The input is an image file stored on the server, and the output is a PDF file. The server does this because it treats each page as a separate image, making it easier to convert.

[0771] Step 5:

[0772] The server uses the pytesseract library to perform OCR on images in PDF files. The input is a PDF file, and the output is text data. Through OCR processing, character information in the image is extracted in text format.

[0773] Step 6:

[0774] The server uses regular expressions to identify financial items in the text data. The input is the text data obtained by OCR processing, and the output is the numerical values ​​of the financial items (revenue, profit, expenses, etc.). This process extracts the necessary numerical data from the text.

[0775] Step 7:

[0776] The server uses the Google Sheets API to post the identified financial item values ​​to a management spreadsheet. The input is the financial item values, and the output is an updated management spreadsheet. The server adds the new data to the spreadsheet and saves it.

[0777] Step 8:

[0778] The smart device uses a camera and microphone to capture the user's facial expressions and voice in real time. The input is the user's facial expressions and voice, and the output is the captured data. The data is used for subsequent emotion analysis.

[0779] Step 9:

[0780] The emotion engine uses EmotionRecognition software to analyze the user's emotions from the captured data. The input is the captured facial and voice data, and the output is the emotion analysis result. After the emotion analysis is complete, the system provides appropriate feedback.

[0781] Step 10:

[0782] The device displays an appropriate feedback message based on the emotion analysis results. The input is the emotion analysis results, and the output is the feedback message to be displayed to the user. For example, "Thank you for your hard work. We recommend that you take a break."

[0783] In this way, the system streamlines data management in factory environments and provides feedback that takes into account the emotional state of workers.

[0784] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0785] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0786] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0787] [Third embodiment]

[0788] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0789] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0790] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0791] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0792] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0793] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0794] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0795] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0796] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0797] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0798] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0799] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0800] The present invention relates to a system that automatically analyzes financial report documents and transfers the results to a management spreadsheet. The specific processing steps and implementation method of this system are described below.

[0801] System Flow Overview

[0802] 1. User uploads PDF

[0803] Users can select and upload financial report documents (PDF files) using a dedicated web interface. This operation is completed by simply selecting the file in the browser and clicking the upload button.

[0804] 2. Receiving and saving the PDF on the server

[0805] The server receives the uploaded PDF file and saves it in the specified folder on the server. For example, let's say the file name is "financial_report_2023.pdf".

[0806] 3. Parsing the PDF by the server

[0807] The server processes the saved PDF file to convert it into an image format. For example, it uses the "pdf2image" library. The image data is then converted into text data using OCR (Optical Character Recognition) technology. At this stage, all the text information in the PDF is obtained as text data.

[0808] 4. Extracting financial data

[0809] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" is found in the text data, the respective numerical information is extracted.

[0810] 5. Transferring data to a management spreadsheet

[0811] The server automatically writes the extracted data to a managed spreadsheet. To do this, it connects to the spreadsheet using the Google Sheets API and performs write operations. When new data is added to the spreadsheet, it is merged into the existing table.

[0812] 6. User Confirmation

[0813] The terminal displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user can review and correct the data as necessary to ensure its accuracy.

[0814] Specific examples

[0815] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. The following information is found in the text data:

[0816] Revenue: 1,500,000

[0817] Profit: 300000

[0818] Expenses: 1200000

[0819] The server will extract these numbers and automatically enter them into a corresponding spreadsheet, which will look something like this:

[0820] | Revenue | Profit | Expenses |

[0821] |---------|--------|----------|

[0822] | 1,500,000 | 300,000 | 1,200,000 |

[0823] The terminal presents this spreadsheet to the user, who then checks the data.

[0824] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, thereby reducing human error and improving business efficiency.

[0825] The processing flow will be explained below.

[0826] Step 1:

[0827] A user accesses a web interface, selects a PDF file as a financial report document, and clicks an upload button to upload the selected file.

[0828] Step 2:

[0829] The server receives the uploaded PDF file and saves it in the specified folder. For example, it saves it as " / path / to / save / financial_report_2023.pdf".

[0830] Step 3:

[0831] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[0832] Step 4:

[0833] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[0834] Step 5:

[0835] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the acquired text data and extracts the corresponding numerical data. If the text data contains information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000," the respective numerical information will be extracted.

[0836] Step 6:

[0837] The server automatically writes the extracted numerical data to a managed spreadsheet. It connects to the spreadsheet using the Google Sheets API and adds the extracted data as a new row. The new data is then integrated into the spreadsheet.

[0838] Step 7:

[0839] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[0840] Step 8:

[0841] Once users have completed the verification process, the risk of errors and inaccurate data is reduced across the system, helping to maintain accurate financial data.

[0842] Example 1

[0843] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0844] Traditional methods for managing financial reporting documents involve a lot of manual data entry, which is prone to errors. Furthermore, there is a lack of automation to efficiently and accurately input large amounts of data into management spreadsheets. This results in a heavy workload and a lack of efficiency and accuracy.

[0845] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0846] In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for automatically writing data into the management spreadsheet, and means for saving the data in a specified folder. This enables automatic analysis of financial report documents and efficient data transcription.

[0847] A "user" is an entity that uses the system to upload financial reporting materials and check and correct data.

[0848] "Financial reporting materials" are documents created by companies or organizations to report their financial status, and are usually saved in PDF format.

[0849] "Server" means a computer system responsible for receiving financial reporting materials uploaded by users, storing, analyzing, and transcribing the data into a management spreadsheet.

[0850] "Means for converting to images" refers to technology or software for converting PDF files into still images, such as using the "pdf2image" library.

[0851] "Means for extracting text data" refers to technology or software for extracting text information from image data, such as using "OCR (optical character recognition)" technology.

[0852] "Means for identifying financial items and extracting numerical values" refers to algorithms or techniques for finding specific financial items (e.g., revenue, profit, expense) and their numerical values ​​from the extracted text data.

[0853] A "management spreadsheet" is a spreadsheet software for organizing and storing financial data, such as "Google Sheets."

[0854] The "means for writing data" refers to technology or software for automatically inputting the extracted numerical data into designated cells in the management spreadsheet.

[0855] The "means of saving in a designated folder" refers to a method or technology for saving uploaded financial reporting materials in a specific directory.

[0856] The present invention relates to a system for automatically analyzing financial report materials and transcribing the contents of the analysis into a management spreadsheet. Specific embodiments of the present invention will be described below.

[0857] Overall structure

[0858] This system is composed of users, servers, and terminals, and each part functions in cooperation with the others.

[0859] User uploads PDF

[0860] A user uploads financial report documents (PDF files) using a web interface. Specifically, the user launches a browser, accesses the system's website, clicks the "Choose File" button, selects the PDF they want to upload, and then clicks the "Upload" button to submit it.

[0861] Server processing

[0862] 1. Receiving and saving files:

[0863] The server receives the HTTP request and saves the uploaded PDF file in the specified folder. For example, it is saved with the file name "financial_report_2023.pdf".

[0864] 2. Parse PDF:

[0865] The server uses the "pdf2image" library to convert the saved PDF file into multiple image files. Then, it uses "OCR (Optical Character Recognition)" technology to extract text information from these images. At this stage, all text information in the PDF is obtained as text data.

[0866] 3. Financial Data Extraction:

[0867] The server uses regular expressions to identify specific financial items (e.g., revenue, profit, expenses, etc.) within the text data and extracts the associated numerical data, such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000."

[0868] 4. Post to control spreadsheet:

[0869] The server connects to a managed spreadsheet via the Google Sheets API and automatically populates the appropriate cells with the extracted data, thereby integrating the financial data into the spreadsheet.

[0870] User confirmation

[0871] The device displays the latest management spreadsheet in the browser, allowing the user to check the accuracy of the data. The user can then review and make any necessary corrections to ensure the accuracy of the data.

[0872] Specific examples

[0873] As a concrete example, consider a scenario where a user uploads "Company X's" financial statement for fiscal year 2023, "company_x_2023.pdf." The server receives the file and saves it in a specified folder. The saved PDF file is converted into multiple images, and OCR technology is used to extract the following text data:

[0874] Revenue: 1,500,000

[0875] Profit: 300000

[0876] Expenses: 1200000

[0877] The server identifies these numbers and enters them into a spreadsheet via the Google Sheets API. For example, the administrative spreadsheet might look like this:

[0878] | Revenue | Profit | Expenses |

[0879] |---------|--------|----------|

[0880] | 1,500,000 | 300,000 | 1,200,000 |

[0881] The terminal displays the spreadsheet and the user checks the data to ensure its accuracy.

[0882] Prompt Sentence Examples

[0883] What are the specific steps in a system that parses PDF financial reports and transfers them to a management spreadsheet?

[0884] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0885] Step-by-step explanation

[0886] Step 1: User uploads PDF

[0887] Input: A user has a PDF file of a financial report.

[0888] Specific behavior: A user launches a browser, accesses the system's website, clicks the "Choose File" button, selects a PDF file, and clicks the "Upload" button.

[0889] Output: The selected PDF file is sent to the server.

[0890] Step 2: The server receives and saves the PDF

[0891] Input: A PDF file submitted by the user.

[0892] Specific operation: The server receives the HTTP request and saves the uploaded PDF file data in the specified folder. For example, it saves it as "financial_report_2023.pdf".

[0893] Output: The PDF file will be saved in the specified folder on the server.

[0894] Step 3: Parse the PDF on the server

[0895] Input: PDF file stored on the server.

[0896] What it does: The server uses the "pdf2image" library to convert the PDF file into multiple image files.

[0897] Output: The PDF file is converted into multiple image files.

[0898] Step 4: OCR analysis of image data

[0899] Input: Multiple image files.

[0900] Specific operation: The server uses an OCR library such as "Tesseract" to extract text data from the image data. At this stage, all character information is obtained as text data.

[0901] Output: Text data extracted from the image.

[0902] Step 5: Extract financial data

[0903] Input: Text data.

[0904] What it does: The server uses regular expressions to identify financial items (e.g., revenue, profit, expense) within the text data and extracts the associated numerical data.

[0905] Output: Identified financial items and their numerical data.

[0906] Step 6: Transfer data to a management spreadsheet

[0907] Input: Identified financial items and their numerical data.

[0908] What happens: The server connects to a managed spreadsheet using the Google Sheets API and automatically populates the appropriate cells with the extracted data.

[0909] Output: New data is posted to the control spreadsheet.

[0910] Step 7: User confirmation

[0911] Input: Data posted to the control spreadsheet.

[0912] Specific behavior: The device displays the spreadsheet in a browser, and the user can verify the accuracy of the data. The user can then review and manually correct any errors if necessary.

[0913] Output: An up-to-date control spreadsheet with all the corrections completed and data accuracy assured.

[0914] Through the above steps, the system achieves automatic analysis of financial reporting materials and efficient data transcription, reducing the user's workload and ensuring data accuracy.

[0915] (Application example 1)

[0916] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0917] Manually analyzing financial reporting documents and entering them into management spreadsheets is time-consuming, labor-intensive, and prone to human error. There is also a lack of convenient ways to quickly check data. This creates a need for systems that significantly improve the efficiency of accounting operations and management decisions. There is a growing need for technology that can analyze and check data in real time using smart devices, especially in brick-and-mortar stores.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0919] In this invention, the server includes means for a user to select and upload financial reporting documents, means for saving the uploaded financial reporting documents on the server, means for converting the saved financial reporting documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for taking a picture of the financial reporting documents using smart glasses and analyzing the data in real time, and means for displaying the analyzed data to the user. This automates the analysis and transcription of financial reporting documents, enabling quick and accurate data confirmation.

[0920] A "User" is a person whose role is to use the system to upload and review financial reporting materials.

[0921] A "financial reporting document" is a document that contains important financial information such as revenues, profits, and expenses.

[0922] The "uploading means" is an interface that allows a user to send financial report materials to a server via a browser or the like.

[0923] A "server" is a computer system that receives, stores, and analyzes uploaded financial reporting materials.

[0924] The "means for saving" is a process for saving the uploaded financial report materials in a designated folder on the server.

[0925] "Means for converting to images" refers to the process of converting uploaded financial report documents, such as PDF files, into image format.

[0926] The "means for extracting text data" is a mechanism for obtaining character information from the converted image using OCR technology and converting it into text data.

[0927] "Means for identifying financial items" is the process of finding specific financial information such as revenues, profits, and expenses from the extracted text data.

[0928] The "means for extracting numerical values" is a function for obtaining specific numerical data corresponding to the identified financial items.

[0929] A "management spreadsheet" is an electronic spreadsheet used to compile and centrally manage extracted numerical data.

[0930] "Smart glasses" are wearable devices that have a built-in camera and display, allowing users to view information in real time within their field of vision.

[0931] "Real-time analysis means" refers to the processing power to instantly process financial reporting documents captured using the smart glasses and quickly generate results.

[0932] "Means for displaying to the user" refers to a function for immediately displaying the analyzed data on the display of the smart glasses.

[0933] This invention relates to a system that automatically analyzes financial reporting documents and transcribes the contents into a management spreadsheet, providing a system that allows data to be viewed quickly and accurately in a physical store using smart glasses.

[0934] Hardware and software used

[0935] Smart glasses: with image capture and display capabilities.

[0936] Server: A computer system that stores, analyzes, and processes data.

[0937] Google Sheets API: Used for automated writing to managed spreadsheets.

[0938] OCR software: Tesseract OCR library.

[0939] Image transformation library: OpenCV.

[0940] Authentication Library: ServiceAccountCredentials for OAuth2 authentication.

[0941] Program processing description

[0942] Capture and Upload

[0943] The user wears the smart glasses and holds the financial report document up to the glasses' camera, which captures the image and sends the image data to the server.

[0944] Image analysis and text data extraction

[0945] The server processes the received image data using OpenCV to convert the PDF file format financial report documents into images, and then extracts text data from the images using the Tesseract OCR library.

[0946] Data Identification and Extraction

[0947] The server uses regular expressions to identify important financial items such as "revenue," "profit," and "expenses" from within the text data and extracts the corresponding numerical data.

[0948] Transferring data to a spreadsheet

[0949] The extracted numerical data is automatically written to a managed spreadsheet using the Google Sheets API. During this process, a mechanism is in place to securely access Google Sheets using OAuth2 authentication.

[0950] Review and display data

[0951] The extracted data is displayed in real time on the smart glasses' display, allowing users to immediately check the accuracy of the data, and is automatically transcribed into a spreadsheet so that administrators can correct the data as needed.

[0952] Specific examples

[0953] For example, imagine a retail manager reviewing monthly financial reports. The manager puts on smart glasses and holds up the paper report to the glasses' camera. Within seconds, revenue, profit, and expense data appears on the glasses' display and is automatically posted to Google Sheets. This allows the manager to immediately review the data and improve work efficiency.

[0954] Example prompts for generative AI models

[0955] "Describe a system that uses smart glasses to analyze financial reports in real time and automatically transcribe them into a spreadsheet."

[0956] "How can we develop an application that displays real-time financial data on smart glasses to improve business efficiency?"

[0957] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0958] Step 1:

[0959] The user puts on the smart glasses and holds the financial report up to the glasses' camera. The input is the physical financial report, and the output is the image captured by the smart glasses. This step involves the user taking a concrete action to hold the report in place.

[0960] Step 2:

[0961] The smart glasses capture images and send the data to a server. The input is the captured image data, and the output is the image data sent to the server. The specific operations of the smart glasses include activating the camera and transmitting the captured images to the server via wireless communication.

[0962] Step 3:

[0963] The server processes the received image data using OpenCV and converts the financial report documents in PDF file format into images. The input is the image data sent to the server, and the output is the converted image file. This step includes the specific operation of the server applying the image processing algorithm and converting it into PDF format.

[0964] Step 4:

[0965] The server uses the Tesseract OCR library to extract text data from images. The input is image data and the output is text data. The server includes specific operations for recognizing and extracting characters using OCR technology.

[0966] Step 5:

[0967] The server uses regular expressions to identify financial items such as revenue, profit, and expenses from within the text data and extracts the related numerical data. The input is the extracted text data and the output is the identified numerical data. The server uses regular expressions to identify and extract the required data.

[0968] Step 6:

[0969] The server uses the Google Sheets API to automatically write the extracted numerical data to a managed spreadsheet. The input is the extracted numerical data, and the output is an updated spreadsheet. The server accesses Google Sheets and includes specific operations to write data to specified cells.

[0970] Step 7:

[0971] The server displays the analyzed data on the smart glasses display in real time. The input is the updated data in the management spreadsheet, and the output is the data displayed on the smart glasses display. The server transmits the data to the glasses and includes specific actions to provide visual feedback to the user.

[0972] Step 8:

[0973] The user checks the data displayed on the smart glasses display and checks the accuracy of the data as needed. The input is the data displayed on the display, and the output is the user's confirmation. The user's specific actions include checking the displayed figures against actual financial reporting documents.

[0974] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0975] The present invention combines a system that automatically analyzes financial report documents and transcribes them into a management spreadsheet with an emotion engine that recognizes the user's emotions. A specific embodiment of this system is described below.

[0976] System Flow Overview

[0977] 1. User uploads PDF

[0978] Users can select and upload PDF files as financial reporting documents using a dedicated web interface. All they need to do is select the file in their browser and click the upload button.

[0979] 2. Receiving and saving the PDF on the server

[0980] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file name "financial_report_2023.pdf" is saved.

[0981] 3. Parsing the PDF by the server

[0982] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[0983] 4. Extracting financial data

[0984] The server converts each image page into text data using OCR (optical character recognition) technology. Using the "pytesseract" library, all text information in the image is extracted as text data. From the extracted text data, regular expressions are used to identify the necessary financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical data.

[0985] 5. Transferring data to a management spreadsheet

[0986] The server automatically writes the extracted data to a managed spreadsheet, and uses the Google Sheets API to connect to the spreadsheet and add the data as new rows. The new data is then integrated into the spreadsheet.

[0987] 6. User Confirmation

[0988] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[0989] 7. Emotion Recognition with Emotion Engine

[0990] The system is equipped with an emotion engine that recognizes the user's emotions. When the user checks the spreadsheet, the emotion engine analyzes the user's facial expressions and voice in real time to determine their emotions. Based on the analysis results, if the user is feeling stressed or dissatisfied, the system will display appropriate feedback and support messages.

[0991] Specific examples

[0992] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[0993] | Revenue | Profit | Expenses |

[0994] |---------|--------|----------|

[0995] | 1,500,000 | 300,000 | 1,200,000 |

[0996] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[0997] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[0998] The processing flow will be explained below.

[0999] Step 1:

[1000] A user accesses the web interface, selects a PDF file as a financial report document, and clicks the upload button to upload the selected PDF file to the server.

[1001] Step 2:

[1002] The server receives the uploaded PDF file. The received PDF file is saved in the specified folder on the server. For example, the file is saved in " / path / to / save / financial_report_2023.pdf".

[1003] Step 3:

[1004] The server converts the saved PDF file into an image format using the "pdf2image" library, which retrieves each page of the PDF file as an image.

[1005] Step 4:

[1006] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[1007] Step 5:

[1008] The server uses regular expressions to identify the necessary financial items (revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if it finds information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" in the text data, it will pick up the numerical information for each.

[1009] Step 6:

[1010] The server automatically writes the extracted numerical data to a managed spreadsheet, using the Google Sheets API to connect to the spreadsheet and adding the extracted data as a new row to the spreadsheet.

[1011] Step 7:

[1012] The device displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user then views the spreadsheet in a browser to check for inaccuracies.

[1013] Step 8:

[1014] The emotion engine built into the system recognizes the user's emotions in real time by analyzing the user's facial expressions and voice data to determine whether the user is feeling stressed or dissatisfied.

[1015] Step 9:

[1016] The server displays appropriate feedback and support messages to the user based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed on the device.

[1017] Step 10:

[1018] Once the user has completed the verification process, the system records the status and confirms that the entire process is complete. This series of actions ensures the accuracy of the data and reduces the user's psychological burden.

[1019] Specific examples

[1020] A user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it to " / path / to / save / abc_2023.pdf." The server converts the PDF file to an image format and then converts it to text data using OCR technology. From that text data, the server extracts the following information: "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000." The server automatically enters these figures into a management spreadsheet, which is updated as follows:

[1021] | Revenue | Profit | Expenses |

[1022] |---------|--------|----------|

[1023] | 1,500,000 | 300,000 | 1,200,000 |

[1024] The device presents this to the user, who then confirms the accuracy of the data. At that time, the emotion engine analyzes the user's emotions and displays appropriate feedback and support messages. In this example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed.

[1025] Example 2

[1026] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1027] Existing financial reporting systems require a lot of work, such as analyzing report documents and transcribing data, and are prone to errors. Furthermore, there is a lack of feedback to users regarding their frustrations and frustrations when using the system. This has led to problems of inefficiency in accounting processes and user fatigue.

[1028] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for a user to select and upload report materials, means for saving the uploaded report materials on the server, means for converting the saved report materials into images, means for extracting text data from the converted images, means for identifying necessary items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management sheet, means for analyzing user emotions, and means for displaying a feedback message based on the emotion analysis. This improves the efficiency of automatic analysis and data transcription of financial report materials, and further reduces the burden on the user by providing appropriate feedback based on the user's emotions.

[1029] "Reporting materials" means documents containing financial or other business data.

[1030] "Uploading means" refers to a function or device for transmitting user-specified report materials to the server.

[1031] "Server" refers to a central computer system that receives uploaded report materials and performs various processes such as analysis and storage.

[1032] "Storing means" refers to a function or device for storing received report materials in a designated folder or data storage.

[1033] "Means for converting to images" refers to software or hardware for converting stored report materials into an image format.

[1034] "Means for extracting text data" refers to OCR (optical character recognition) technology for extracting text information from report materials converted into image format.

[1035] "Means for identifying necessary items and extracting numerical values" refers to a function for identifying specific financial data from the extracted character data using regular expressions or other analytical techniques and extracting the numerical values.

[1036] "Means for inputting data into a management sheet" refers to a function for automatically transferring extracted numerical data into a management spreadsheet or database.

[1037] "Means for analyzing emotions" refers to software or hardware that analyzes a user's emotions in real time based on data such as the user's facial expressions and voice.

[1038] The "means for displaying a feedback message" refers to a function for displaying appropriate messages and support information to the user based on the analyzed emotion data.

[1039] The present invention combines a system that automatically analyzes report materials and transcribes them into a management sheet with an emotion analysis engine that recognizes user emotions. Specific embodiments of the system will be described in detail below.

[1040] System Overview

[1041] Hardware and Software Configuration

[1042] server:

[1043] The server acts as the central processing unit and performs its processing using the following software libraries:

[1044] Convert PDF files to image format using the "pdf2image" library.

[1045] Extract character data from images using the "pytesseract" library.

[1046] Use the "googleapiclient.discovery" library to connect to the Google Sheets API and transfer data to an admin sheet.

[1047] The sentiment analysis engine uses Microsoft Azure Cognitive Services and Google Cloud Vision API to analyze user emotions in real time.

[1048] Device:

[1049] The terminal is a device that allows the user to interface with the system and is operated through a browser. The terminal is equipped with a camera and microphone, and emotion data is collected through these devices.

[1050] User:

[1051] Users are responsible for uploading documents such as financial reports and verifying the results.

[1052] How it works

[1053] 1. User uploads PDF

[1054] Users access a dedicated interface through a web browser, select a PDF file as a report document, and upload it. For example, a user selects and uploads a file called "abc_2023.pdf."

[1055] 2. Receiving and saving the PDF on the server

[1056] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file is saved as "financial_report_2023.pdf".

[1057] 3. Parsing the PDF by the server

[1058] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is generated as an image and saved as "page1.jpg", "page2.jpg", etc.

[1059] 4. Extracting financial data

[1060] The server converts each image page into text data using OCR (Optical Character Recognition) technology with the "pytesseract" library. From the obtained text data, the necessary financial items (revenue, profit, expenses, etc.) are identified using regular expressions and other analytical methods, and the corresponding numerical data is extracted.

[1061] 5. Transferring data to the management sheet

[1062] The server automatically writes the extracted data to a management spreadsheet using the Google Sheets API. For example, data such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" is added as a new row to the spreadsheet.

[1063] 6. User Confirmation

[1064] The device displays the updated spreadsheet and allows the user to verify the accuracy of the data. The user can then review the spreadsheet and make corrections as necessary.

[1065] 7. Emotion Recognition with Emotion Engine

[1066] The emotion engine analyzes the user's facial expressions and voice in real time. In particular, if the user feels stressed while checking a spreadsheet, the system displays a feedback message such as, "Thank you for your hard work. We recommend that you take a break."

[1067] Specific examples

[1068] For example, consider the case where a user uploads an external report document, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server then automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[1069] | Revenue | Profit | Expenses |

[1070] |---------|--------|----------|

[1071] | 1,500,000 | 300,000 | 1,200,000 |

[1072] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[1073] Prompt Sentence Examples

[1074] "Extract revenue, profit, and expense information from the following PDF document and transcribe it into a spreadsheet. This document is named "abc_2023.pdf."

[1075] In this way, the present invention realizes automatic analysis of report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[1076] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1077] Step 1:

[1078] Users access a dedicated interface through a web browser, select a PDF file as a report document, and click the upload button.

[1079] Input: A PDF file selected by the user (e.g. "abc_2023.pdf")

[1080] Output: PDF file is sent to the server

[1081] Specific behavior: The user accesses "http: / / example.com / upload", clicks the "Choose File" button, selects "abc_2023.pdf", and clicks the "Upload" button.

[1082] Step 2:

[1083] The server receives the uploaded PDF file and saves it in the specified folder.

[1084] Input: Uploaded PDF file

[1085] Output: Saved PDF file (e.g. "financial_report_2023.pdf")

[1086] Specific behavior: The server receives the HTTP request and saves the "abc_2023.pdf" file in the " / uploads" directory as "financial_report_2023.pdf".

[1087] Step 3:

[1088] The server converts the saved PDF file into an image format using the "pdf2image" library.

[1089] Input: Saved PDF file (e.g. "financial_report_2023.pdf")

[1090] Output: Converted image files (e.g. "page1.jpg", "page2.jpg")

[1091] Specific behavior: The server reads the "financial_report_2023.pdf" file and converts each page to a JPEG image using "pdf2image.convert_from_path()".

[1092] Step 4:

[1093] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This operation uses the "pytesseract" library.

[1094] Input: Image files (e.g. "page1.jpg", "page2.jpg")

[1095] Output: Extracted text data

[1096] Specific operation: The server reads the "page1.jpg" and "page2.jpg" files and extracts the text data from each image using "pytesseract.image_to_string()".

[1097] Step 5:

[1098] The server uses regular expressions to identify the necessary items (revenue, profit, expenses, etc.) from the extracted text data and extracts the corresponding numerical data.

[1099] Input: Extracted text data

[1100] Output: Extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000")

[1101] What happens: The server parses the extracted text data and extracts the required financial data using regular expressions (e.g., re.findall(r'Revenue: (\d+)', text)).

[1102] Step 6:

[1103] The server automatically writes the extracted numerical data to a management sheet using the Google Sheets API.

[1104] Input: Extracted numeric data

[1105] Output: Updated control sheet

[1106] What happens: The server connects to the Google Sheets API and adds the extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000") as new rows to the spreadsheet.

[1107] Step 7:

[1108] The terminal displays the updated control sheet, allowing the user to verify the accuracy of the data.

[1109] Input: Updated control sheet

[1110] Output: what the user sees

[1111] Specific operation: The device uses a browser to display the specified Google Sheets sheet, and the user confirms the contents.

[1112] Step 8:

[1113] The emotion engine analyzes the user's facial expressions and voice in real time, and if the user is feeling stressed, the system will display an appropriate feedback message.

[1114] Input: User facial and voice data

[1115] Output: Feedback message

[1116] Specific operation: The emotion engine analyzes data from the device's camera and microphone, and if it determines that the user is feeling stressed, it displays the message "Thank you for your hard work. We recommend that you take a break." on the device.

[1117] (Application example 2)

[1118] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1119] Data management in modern factories is extremely complex, and manual input and verification of financial and production data in particular takes a lot of time and effort. Furthermore, worker stress and fatigue can have a negative impact on productivity. To solve these problems, it is necessary to improve the efficiency of data management work while also providing feedback that takes into account the emotional state of workers.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, and means for scanning documents with a camera mounted on a smart device, recognizing the user's emotions in real time with an emotion engine, and providing feedback. This enables the efficiency of data management operations and the reduction of worker stress.

[1121] A "user" is someone who uses the system to upload financial reporting documents and enter data into management spreadsheets.

[1122] A "financial reporting document" is a reporting document that contains financial information such as revenues, profits, and expenses of a company or organization.

[1123] An "uploading means" is a method or device by which a user transmits financial reporting materials to the system.

[1124] "Server" means a central processing unit that stores uploaded financial reporting materials, converts them into images, extracts text data, and transcribes the data into a management spreadsheet.

[1125] "Image conversion means" refers to technology or devices for converting uploaded financial report materials into image format.

[1126] "Text data extraction means" refers to a method or device that uses optical character recognition (OCR) technology to obtain text information from financial reporting documents that have been converted into images.

[1127] The "financial item identification means" refers to a method or device that identifies necessary financial information such as revenue, profit, and expenses from the extracted text data and extracts the numerical values.

[1128] A "control spreadsheet" is an electronic spreadsheet file into which extracted financial data is entered for control purposes.

[1129] A "camera" is a device installed in a smart device that scans documents used by the user.

[1130] An "emotion engine" is software or hardware that analyzes emotions from a user's facial expressions and voice in real time.

[1131] The "feedback providing means" is a method or device that displays an appropriate message based on the user's emotions analyzed by the emotion engine.

[1132] A "smart device" is a portable electronic device equipped with a camera and microphone that can input data and perform emotion analysis.

[1133] This invention is a system for streamlining data management and worker emotion monitoring in factories. The system combines smart devices (smart glasses), a server, a data management spreadsheet, and an emotion engine.

[1134] The system is configured as follows:

[1135] First, the user scans a financial report document using the camera on their smart device, which then captures the document as image data. This image data is then sent to a server via a communication method such as Wi-Fi.

[1136] To analyze the received image data, the server converts it to an image using the pdf2image library, then performs OCR processing using the pytesseract library to extract the text data from the image, and uses regular expressions to identify the required financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical values.

[1137] The extracted numerical data is automatically transferred to a managed spreadsheet using the Google Sheets API, ensuring that the data is always integrated and up-to-date in the managed spreadsheet.

[1138] In addition, the smart device's built-in camera and microphone capture the user's facial expressions and voice, which are then analyzed in real time by the emotion engine. The emotion engine uses EmotionRecognition software to recognize the user's emotional state. If the user is feeling stressed, the system will automatically display a feedback message (e.g., "Thank you for your hard work. We recommend that you take a break.").

[1139] As a practical scenario, we present a specific example of a user scanning financial report documents in a factory. For example, the user uses a smart device to scan the "2023 Production Line Financial Report." This data is sent to a server, where it undergoes OCR processing, extracting figures such as revenue, profit, and expenses. The extracted data is then transcribed into a management spreadsheet and registered as the latest report data. While the user is performing this task, the camera and microphone monitor the user's emotional state and display appropriate feedback messages as needed.

[1140] An example of a prompt for a generative AI model is:

[1141] "You are a factory manager. You use a smart device to scan financial reporting documents. The system analyzes the data and transcribes it into a management spreadsheet, while simultaneously analyzing your facial emotions. If the emotion engine detects that you are nervous while reviewing the documents, what feedback message should be displayed?"

[1142] In this way, the present invention improves the efficiency of data management within a factory and provides business support that takes into account the emotional state of workers.

[1143] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1144] Step 1:

[1145] A user scans a financial report document using a camera on a smart device. The input is the financial report document to be scanned, and the output is image data. This image data is temporarily stored in the memory of the smart device.

[1146] Step 2:

[1147] The image data acquired by the device is sent to the server via a communication method such as Wi-Fi. The input is the saved image data, and the output is the image data transferred to the server. After the communication is complete, the device notifies the user of the status of the image data transmission.

[1148] Step 3:

[1149] The server saves the received image data. The input is the image data transferred to the server, and the output is an image file saved in a specified folder on the server. After saving is complete, the server prepares to proceed to the next step.

[1150] Step 4:

[1151] The server converts the received image data to PDF format using the pdf2image library. The input is an image file stored on the server, and the output is a PDF file. The server does this because it treats each page as a separate image, making it easier to convert.

[1152] Step 5:

[1153] The server uses the pytesseract library to perform OCR on images in PDF files. The input is a PDF file, and the output is text data. Through OCR processing, character information in the image is extracted in text format.

[1154] Step 6:

[1155] The server uses regular expressions to identify financial items in the text data. The input is the text data obtained by OCR processing, and the output is the numerical values ​​of the financial items (revenue, profit, expenses, etc.). This process extracts the necessary numerical data from the text.

[1156] Step 7:

[1157] The server uses the Google Sheets API to post the identified financial item values ​​to a management spreadsheet. The input is the financial item values, and the output is an updated management spreadsheet. The server adds the new data to the spreadsheet and saves it.

[1158] Step 8:

[1159] The smart device uses a camera and microphone to capture the user's facial expressions and voice in real time. The input is the user's facial expressions and voice, and the output is the captured data. The data is used for subsequent emotion analysis.

[1160] Step 9:

[1161] The emotion engine uses EmotionRecognition software to analyze the user's emotions from the captured data. The input is the captured facial and voice data, and the output is the emotion analysis result. After the emotion analysis is complete, the system provides appropriate feedback.

[1162] Step 10:

[1163] The device displays an appropriate feedback message based on the emotion analysis results. The input is the emotion analysis results, and the output is the feedback message to be displayed to the user. For example, "Thank you for your hard work. We recommend that you take a break."

[1164] In this way, the system streamlines data management in factory environments and provides feedback that takes into account the emotional state of workers.

[1165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1166] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1167] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1168] [Fourth embodiment]

[1169] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1173] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1176] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1178] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1180] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1181] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1182] The present invention relates to a system that automatically analyzes financial report documents and transfers the results to a management spreadsheet. The specific processing steps and implementation method of this system are described below.

[1183] System Flow Overview

[1184] 1. User uploads PDF

[1185] Users can select and upload financial report documents (PDF files) using a dedicated web interface. This operation is completed by simply selecting the file in the browser and clicking the upload button.

[1186] 2. Receiving and saving the PDF on the server

[1187] The server receives the uploaded PDF file and saves it in the specified folder on the server. For example, let's say the file name is "financial_report_2023.pdf".

[1188] 3. Parsing the PDF by the server

[1189] The server processes the saved PDF file to convert it into an image format. For example, it uses the "pdf2image" library. The image data is then converted into text data using OCR (Optical Character Recognition) technology. At this stage, all the text information in the PDF is obtained as text data.

[1190] 4. Extracting financial data

[1191] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" is found in the text data, the respective numerical information is extracted.

[1192] 5. Transferring data to a management spreadsheet

[1193] The server automatically writes the extracted data to a managed spreadsheet. To do this, it connects to the spreadsheet using the Google Sheets API and performs write operations. When new data is added to the spreadsheet, it is merged into the existing table.

[1194] 6. User Confirmation

[1195] The terminal displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user can review and correct the data as necessary to ensure its accuracy.

[1196] Specific examples

[1197] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. The following information is found in the text data:

[1198] Revenue: 1,500,000

[1199] Profit: 300000

[1200] Expenses: 1200000

[1201] The server will extract these numbers and automatically enter them into a corresponding spreadsheet, which will look something like this:

[1202] | Revenue | Profit | Expenses |

[1203] |---------|--------|----------|

[1204] | 1,500,000 | 300,000 | 1,200,000 |

[1205] The terminal presents this spreadsheet to the user, who then checks the data.

[1206] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, thereby reducing human error and improving business efficiency.

[1207] The processing flow will be explained below.

[1208] Step 1:

[1209] A user accesses a web interface, selects a PDF file as a financial report document, and clicks an upload button to upload the selected file.

[1210] Step 2:

[1211] The server receives the uploaded PDF file and saves it in the specified folder. For example, it saves it as " / path / to / save / financial_report_2023.pdf".

[1212] Step 3:

[1213] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[1214] Step 4:

[1215] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[1216] Step 5:

[1217] The server uses regular expressions to identify the necessary financial items (e.g., revenue, profit, expenses) from the acquired text data and extracts the corresponding numerical data. If the text data contains information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000," the respective numerical information will be extracted.

[1218] Step 6:

[1219] The server automatically writes the extracted numerical data to a managed spreadsheet. It connects to the spreadsheet using the Google Sheets API and adds the extracted data as a new row. The new data is then integrated into the spreadsheet.

[1220] Step 7:

[1221] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[1222] Step 8:

[1223] Once users have completed the verification process, the risk of errors and inaccurate data is reduced across the system, helping to maintain accurate financial data.

[1224] Example 1

[1225] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1226] Traditional methods for managing financial reporting documents involve a lot of manual data entry, which is prone to errors. Furthermore, there is a lack of automation to efficiently and accurately input large amounts of data into management spreadsheets. This results in a heavy workload and a lack of efficiency and accuracy.

[1227] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1228] In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for automatically writing data into the management spreadsheet, and means for saving the data in a specified folder. This enables automatic analysis of financial report documents and efficient data transcription.

[1229] A "user" is an entity that uses the system to upload financial reporting materials and check and correct data.

[1230] "Financial reporting materials" are documents created by companies or organizations to report their financial status, and are usually saved in PDF format.

[1231] "Server" means a computer system responsible for receiving financial reporting materials uploaded by users, storing, analyzing, and transcribing the data into a management spreadsheet.

[1232] "Means for converting to images" refers to technology or software for converting PDF files into still images, such as using the "pdf2image" library.

[1233] "Means for extracting text data" refers to technology or software for extracting text information from image data, such as using "OCR (optical character recognition)" technology.

[1234] "Means for identifying financial items and extracting numerical values" refers to algorithms or techniques for finding specific financial items (e.g., revenue, profit, expense) and their numerical values ​​from the extracted text data.

[1235] A "management spreadsheet" is a spreadsheet software for organizing and storing financial data, such as "Google Sheets."

[1236] The "means for writing data" refers to technology or software for automatically inputting the extracted numerical data into designated cells in the management spreadsheet.

[1237] The "means of saving in a designated folder" refers to a method or technology for saving uploaded financial reporting materials in a specific directory.

[1238] The present invention relates to a system for automatically analyzing financial report materials and transcribing the contents of the analysis into a management spreadsheet. Specific embodiments of the present invention will be described below.

[1239] Overall structure

[1240] This system is composed of users, servers, and terminals, and each part functions in cooperation with the others.

[1241] User uploads PDF

[1242] A user uploads financial report documents (PDF files) using a web interface. Specifically, the user launches a browser, accesses the system's website, clicks the "Choose File" button, selects the PDF they want to upload, and then clicks the "Upload" button to submit it.

[1243] Server processing

[1244] 1. Receiving and saving files:

[1245] The server receives the HTTP request and saves the uploaded PDF file in the specified folder. For example, it is saved with the file name "financial_report_2023.pdf".

[1246] 2. Parse PDF:

[1247] The server uses the "pdf2image" library to convert the saved PDF file into multiple image files. Then, it uses "OCR (Optical Character Recognition)" technology to extract text information from these images. At this stage, all text information in the PDF is obtained as text data.

[1248] 3. Financial Data Extraction:

[1249] The server uses regular expressions to identify specific financial items (e.g., revenue, profit, expenses, etc.) within the text data and extracts the associated numerical data, such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000."

[1250] 4. Post to control spreadsheet:

[1251] The server connects to a managed spreadsheet via the Google Sheets API and automatically populates the appropriate cells with the extracted data, thereby integrating the financial data into the spreadsheet.

[1252] User confirmation

[1253] The device displays the latest management spreadsheet in the browser, allowing the user to check the accuracy of the data. The user can then review and make any necessary corrections to ensure the accuracy of the data.

[1254] Specific examples

[1255] As a concrete example, consider a scenario where a user uploads "Company X's" financial statement for fiscal year 2023, "company_x_2023.pdf." The server receives the file and saves it in a specified folder. The saved PDF file is converted into multiple images, and OCR technology is used to extract the following text data:

[1256] Revenue: 1,500,000

[1257] Profit: 300000

[1258] Expenses: 1200000

[1259] The server identifies these numbers and enters them into a spreadsheet via the Google Sheets API. For example, the administrative spreadsheet might look like this:

[1260] | Revenue | Profit | Expenses |

[1261] |---------|--------|----------|

[1262] | 1,500,000 | 300,000 | 1,200,000 |

[1263] The terminal displays the spreadsheet and the user checks the data to ensure its accuracy.

[1264] Prompt Sentence Examples

[1265] What are the specific steps in a system that parses PDF financial reports and transfers them to a management spreadsheet?

[1266] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1267] Step-by-step explanation

[1268] Step 1: User uploads PDF

[1269] Input: A user has a PDF file of a financial report.

[1270] Specific behavior: A user launches a browser, accesses the system's website, clicks the "Choose File" button, selects a PDF file, and clicks the "Upload" button.

[1271] Output: The selected PDF file is sent to the server.

[1272] Step 2: The server receives and saves the PDF

[1273] Input: A PDF file submitted by the user.

[1274] Specific operation: The server receives the HTTP request and saves the uploaded PDF file data in the specified folder. For example, it saves it as "financial_report_2023.pdf".

[1275] Output: The PDF file will be saved in the specified folder on the server.

[1276] Step 3: Parse the PDF on the server

[1277] Input: PDF file stored on the server.

[1278] What it does: The server uses the "pdf2image" library to convert the PDF file into multiple image files.

[1279] Output: The PDF file is converted into multiple image files.

[1280] Step 4: OCR analysis of image data

[1281] Input: Multiple image files.

[1282] Specific operation: The server uses an OCR library such as "Tesseract" to extract text data from the image data. At this stage, all character information is obtained as text data.

[1283] Output: Text data extracted from the image.

[1284] Step 5: Extract financial data

[1285] Input: Text data.

[1286] What it does: The server uses regular expressions to identify financial items (e.g., revenue, profit, expense) within the text data and extracts the associated numerical data.

[1287] Output: Identified financial items and their numerical data.

[1288] Step 6: Transfer data to a management spreadsheet

[1289] Input: Identified financial items and their numerical data.

[1290] What happens: The server connects to a managed spreadsheet using the Google Sheets API and automatically populates the appropriate cells with the extracted data.

[1291] Output: New data is posted to the control spreadsheet.

[1292] Step 7: User confirmation

[1293] Input: Data posted to the control spreadsheet.

[1294] Specific behavior: The device displays the spreadsheet in a browser, and the user can verify the accuracy of the data. The user can then review and manually correct any errors if necessary.

[1295] Output: An up-to-date control spreadsheet with all the corrections completed and data accuracy assured.

[1296] Through the above steps, the system achieves automatic analysis of financial reporting materials and efficient data transcription, reducing the user's workload and ensuring data accuracy.

[1297] (Application example 1)

[1298] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1299] Manually analyzing financial reporting documents and entering them into management spreadsheets is time-consuming, labor-intensive, and prone to human error. There is also a lack of convenient ways to quickly check data. This creates a need for systems that significantly improve the efficiency of accounting operations and management decisions. There is a growing need for technology that can analyze and check data in real time using smart devices, especially in brick-and-mortar stores.

[1300] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1301] In this invention, the server includes means for a user to select and upload financial reporting documents, means for saving the uploaded financial reporting documents on the server, means for converting the saved financial reporting documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, means for taking a picture of the financial reporting documents using smart glasses and analyzing the data in real time, and means for displaying the analyzed data to the user. This automates the analysis and transcription of financial reporting documents, enabling quick and accurate data confirmation.

[1302] A "User" is a person whose role is to use the system to upload and review financial reporting materials.

[1303] A "financial reporting document" is a document that contains important financial information such as revenues, profits, and expenses.

[1304] The "uploading means" is an interface that allows a user to send financial report materials to a server via a browser or the like.

[1305] A "server" is a computer system that receives, stores, and analyzes uploaded financial reporting materials.

[1306] The "means for saving" is a process for saving the uploaded financial report materials in a designated folder on the server.

[1307] "Means for converting to images" refers to the process of converting uploaded financial report documents, such as PDF files, into image format.

[1308] The "means for extracting text data" is a mechanism for obtaining character information from the converted image using OCR technology and converting it into text data.

[1309] "Means for identifying financial items" is the process of finding specific financial information such as revenues, profits, and expenses from the extracted text data.

[1310] The "means for extracting numerical values" is a function for obtaining specific numerical data corresponding to the identified financial items.

[1311] A "management spreadsheet" is an electronic spreadsheet used to compile and centrally manage extracted numerical data.

[1312] "Smart glasses" are wearable devices that have a built-in camera and display, allowing users to view information in real time within their field of vision.

[1313] "Real-time analysis means" refers to the processing power to instantly process financial reporting documents captured using the smart glasses and quickly generate results.

[1314] "Means for displaying to the user" refers to a function for immediately displaying the analyzed data on the display of the smart glasses.

[1315] This invention relates to a system that automatically analyzes financial reporting documents and transcribes the contents into a management spreadsheet, providing a system that allows data to be viewed quickly and accurately in a physical store using smart glasses.

[1316] Hardware and software used

[1317] Smart glasses: with image capture and display capabilities.

[1318] Server: A computer system that stores, analyzes, and processes data.

[1319] Google Sheets API: Used for automated writing to managed spreadsheets.

[1320] OCR software: Tesseract OCR library.

[1321] Image transformation library: OpenCV.

[1322] Authentication Library: ServiceAccountCredentials for OAuth2 authentication.

[1323] Program processing description

[1324] Capture and Upload

[1325] The user wears the smart glasses and holds the financial report document up to the glasses' camera, which captures the image and sends the image data to the server.

[1326] Image analysis and text data extraction

[1327] The server processes the received image data using OpenCV to convert the PDF file format financial report documents into images, and then extracts text data from the images using the Tesseract OCR library.

[1328] Data Identification and Extraction

[1329] The server uses regular expressions to identify important financial items such as "revenue," "profit," and "expenses" from within the text data and extracts the corresponding numerical data.

[1330] Transferring data to a spreadsheet

[1331] The extracted numerical data is automatically written to a managed spreadsheet using the Google Sheets API. During this process, a mechanism is in place to securely access Google Sheets using OAuth2 authentication.

[1332] Review and display data

[1333] The extracted data is displayed in real time on the smart glasses' display, allowing users to immediately check the accuracy of the data, and is automatically transcribed into a spreadsheet so that administrators can correct the data as needed.

[1334] Specific examples

[1335] For example, imagine a retail manager reviewing monthly financial reports. The manager puts on smart glasses and holds up the paper report to the glasses' camera. Within seconds, revenue, profit, and expense data appears on the glasses' display and is automatically posted to Google Sheets. This allows the manager to immediately review the data and improve work efficiency.

[1336] Example prompts for generative AI models

[1337] "Describe a system that uses smart glasses to analyze financial reports in real time and automatically transcribe them into a spreadsheet."

[1338] "How can we develop an application that displays real-time financial data on smart glasses to improve business efficiency?"

[1339] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1340] Step 1:

[1341] The user puts on the smart glasses and holds the financial report up to the glasses' camera. The input is the physical financial report, and the output is the image captured by the smart glasses. This step involves the user taking a concrete action to hold the report in place.

[1342] Step 2:

[1343] The smart glasses capture images and send the data to a server. The input is the captured image data, and the output is the image data sent to the server. The specific operations of the smart glasses include activating the camera and transmitting the captured images to the server via wireless communication.

[1344] Step 3:

[1345] The server processes the received image data using OpenCV and converts the financial report documents in PDF file format into images. The input is the image data sent to the server, and the output is the converted image file. This step includes the specific operation of the server applying the image processing algorithm and converting it into PDF format.

[1346] Step 4:

[1347] The server uses the Tesseract OCR library to extract text data from images. The input is image data and the output is text data. The server includes specific operations for recognizing and extracting characters using OCR technology.

[1348] Step 5:

[1349] The server uses regular expressions to identify financial items such as revenue, profit, and expenses from within the text data and extracts the related numerical data. The input is the extracted text data and the output is the identified numerical data. The server uses regular expressions to identify and extract the required data.

[1350] Step 6:

[1351] The server uses the Google Sheets API to automatically write the extracted numerical data to a managed spreadsheet. The input is the extracted numerical data, and the output is an updated spreadsheet. The server accesses Google Sheets and includes specific operations to write data to specified cells.

[1352] Step 7:

[1353] The server displays the analyzed data on the smart glasses display in real time. The input is the updated data in the management spreadsheet, and the output is the data displayed on the smart glasses display. The server transmits the data to the glasses and includes specific actions to provide visual feedback to the user.

[1354] Step 8:

[1355] The user checks the data displayed on the smart glasses display and checks the accuracy of the data as needed. The input is the data displayed on the display, and the output is the user's confirmation. The user's specific actions include checking the displayed figures against actual financial reporting documents.

[1356] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1357] The present invention combines a system that automatically analyzes financial report documents and transcribes them into a management spreadsheet with an emotion engine that recognizes the user's emotions. A specific embodiment of this system is described below.

[1358] System Flow Overview

[1359] 1. User uploads PDF

[1360] Users can select and upload PDF files as financial reporting documents using a dedicated web interface. All they need to do is select the file in their browser and click the upload button.

[1361] 2. Receiving and saving the PDF on the server

[1362] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file name "financial_report_2023.pdf" is saved.

[1363] 3. Parsing the PDF by the server

[1364] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is converted to an image.

[1365] 4. Extracting financial data

[1366] The server converts each image page into text data using OCR (optical character recognition) technology. Using the "pytesseract" library, all text information in the image is extracted as text data. From the extracted text data, regular expressions are used to identify the necessary financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical data.

[1367] 5. Transferring data to a management spreadsheet

[1368] The server automatically writes the extracted data to a managed spreadsheet, and uses the Google Sheets API to connect to the spreadsheet and add the data as new rows. The new data is then integrated into the spreadsheet.

[1369] 6. User Confirmation

[1370] The device displays the updated management spreadsheet, allowing the user to verify the accuracy of the data. The user can then review the spreadsheet and make any necessary corrections.

[1371] 7. Emotion Recognition with Emotion Engine

[1372] The system is equipped with an emotion engine that recognizes the user's emotions. When the user checks the spreadsheet, the emotion engine analyzes the user's facial expressions and voice in real time to determine their emotions. Based on the analysis results, if the user is feeling stressed or dissatisfied, the system will display appropriate feedback and support messages.

[1373] Specific examples

[1374] For example, a user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[1375] | Revenue | Profit | Expenses |

[1376] |---------|--------|----------|

[1377] | 1,500,000 | 300,000 | 1,200,000 |

[1378] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[1379] In this way, the present invention realizes automatic analysis of financial report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[1380] The processing flow will be explained below.

[1381] Step 1:

[1382] A user accesses the web interface, selects a PDF file as a financial report document, and clicks the upload button to upload the selected PDF file to the server.

[1383] Step 2:

[1384] The server receives the uploaded PDF file. The received PDF file is saved in the specified folder on the server. For example, the file is saved in " / path / to / save / financial_report_2023.pdf".

[1385] Step 3:

[1386] The server converts the saved PDF file into an image format using the "pdf2image" library, which retrieves each page of the PDF file as an image.

[1387] Step 4:

[1388] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This process uses the "pytesseract" library. All text information in the image is obtained as text data.

[1389] Step 5:

[1390] The server uses regular expressions to identify the necessary financial items (revenue, profit, expenses) from the text data it retrieves, and extracts the corresponding numerical data. For example, if it finds information such as "Revenue: 1,000,000," "Profit: 200,000," and "Expenses: 800,000" in the text data, it will pick up the numerical information for each.

[1391] Step 6:

[1392] The server automatically writes the extracted numerical data to a managed spreadsheet, using the Google Sheets API to connect to the spreadsheet and adding the extracted data as a new row to the spreadsheet.

[1393] Step 7:

[1394] The device displays the updated management spreadsheet and allows the user to verify the accuracy of the data. The user then views the spreadsheet in a browser to check for inaccuracies.

[1395] Step 8:

[1396] The emotion engine built into the system recognizes the user's emotions in real time by analyzing the user's facial expressions and voice data to determine whether the user is feeling stressed or dissatisfied.

[1397] Step 9:

[1398] The server displays appropriate feedback and support messages to the user based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed on the device.

[1399] Step 10:

[1400] Once the user has completed the verification process, the system records the status and confirms that the entire process is complete. This series of actions ensures the accuracy of the data and reduces the user's psychological burden.

[1401] Specific examples

[1402] A user uploads ABC Corporation's financial statement for fiscal year 2023, "abc_2023.pdf." The server receives the file and saves it to " / path / to / save / abc_2023.pdf." The server converts the PDF file to an image format and then converts it to text data using OCR technology. From that text data, the server extracts the following information: "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000." The server automatically enters these figures into a management spreadsheet, which is updated as follows:

[1403] | Revenue | Profit | Expenses |

[1404] |---------|--------|----------|

[1405] | 1,500,000 | 300,000 | 1,200,000 |

[1406] The device presents this to the user, who then confirms the accuracy of the data. At that time, the emotion engine analyzes the user's emotions and displays appropriate feedback and support messages. In this example, if the user is feeling stressed, the message "Thank you for your hard work. We recommend that you take a break" will be displayed.

[1407] Example 2

[1408] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1409] Existing financial reporting systems require a lot of work, such as analyzing report documents and transcribing data, and are prone to errors. Furthermore, there is a lack of feedback to users regarding their frustrations and frustrations when using the system. This has led to problems of inefficiency in accounting processes and user fatigue.

[1410] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for a user to select and upload report materials, means for saving the uploaded report materials on the server, means for converting the saved report materials into images, means for extracting text data from the converted images, means for identifying necessary items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management sheet, means for analyzing user emotions, and means for displaying a feedback message based on the emotion analysis. This improves the efficiency of automatic analysis and data transcription of financial report materials, and further reduces the burden on the user by providing appropriate feedback based on the user's emotions.

[1411] "Reporting materials" means documents containing financial or other business data.

[1412] "Uploading means" refers to a function or device for transmitting user-specified report materials to the server.

[1413] "Server" refers to a central computer system that receives uploaded report materials and performs various processes such as analysis and storage.

[1414] "Storing means" refers to a function or device for storing received report materials in a designated folder or data storage.

[1415] "Means for converting to images" refers to software or hardware for converting stored report materials into an image format.

[1416] "Means for extracting text data" refers to OCR (optical character recognition) technology for extracting text information from report materials converted into image format.

[1417] "Means for identifying necessary items and extracting numerical values" refers to a function for identifying specific financial data from the extracted character data using regular expressions or other analytical techniques and extracting the numerical values.

[1418] "Means for inputting data into a management sheet" refers to a function for automatically transferring extracted numerical data into a management spreadsheet or database.

[1419] "Means for analyzing emotions" refers to software or hardware that analyzes a user's emotions in real time based on data such as the user's facial expressions and voice.

[1420] The "means for displaying a feedback message" refers to a function for displaying appropriate messages and support information to the user based on the analyzed emotion data.

[1421] The present invention combines a system that automatically analyzes report materials and transcribes them into a management sheet with an emotion analysis engine that recognizes user emotions. Specific embodiments of the system will be described in detail below.

[1422] System Overview

[1423] Hardware and Software Configuration

[1424] server:

[1425] The server acts as the central processing unit and performs its processing using the following software libraries:

[1426] Convert PDF files to image format using the "pdf2image" library.

[1427] Extract character data from images using the "pytesseract" library.

[1428] Use the "googleapiclient.discovery" library to connect to the Google Sheets API and transfer data to an admin sheet.

[1429] The sentiment analysis engine uses Microsoft Azure Cognitive Services and Google Cloud Vision API to analyze user emotions in real time.

[1430] Device:

[1431] The terminal is a device that allows the user to interface with the system and is operated through a browser. The terminal is equipped with a camera and microphone, and emotion data is collected through these devices.

[1432] User:

[1433] Users are responsible for uploading documents such as financial reports and verifying the results.

[1434] How it works

[1435] 1. User uploads PDF

[1436] Users access a dedicated interface through a web browser, select a PDF file as a report document, and upload it. For example, a user selects and uploads a file called "abc_2023.pdf."

[1437] 2. Receiving and saving the PDF on the server

[1438] The server receives the uploaded PDF file and saves it in the specified folder. For example, the file is saved as "financial_report_2023.pdf".

[1439] 3. Parsing the PDF by the server

[1440] The server converts the saved PDF file to an image format using the "pdf2image" library. Each page of the PDF file is generated as an image and saved as "page1.jpg", "page2.jpg", etc.

[1441] 4. Extracting financial data

[1442] The server converts each image page into text data using OCR (Optical Character Recognition) technology with the "pytesseract" library. From the obtained text data, the necessary financial items (revenue, profit, expenses, etc.) are identified using regular expressions and other analytical methods, and the corresponding numerical data is extracted.

[1443] 5. Transferring data to the management sheet

[1444] The server automatically writes the extracted data to a management spreadsheet using the Google Sheets API. For example, data such as "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" is added as a new row to the spreadsheet.

[1445] 6. User Confirmation

[1446] The device displays the updated spreadsheet and allows the user to verify the accuracy of the data. The user can then review the spreadsheet and make corrections as necessary.

[1447] 7. Emotion Recognition with Emotion Engine

[1448] The emotion engine analyzes the user's facial expressions and voice in real time. In particular, if the user feels stressed while checking a spreadsheet, the system displays a feedback message such as, "Thank you for your hard work. We recommend that you take a break."

[1449] Specific examples

[1450] For example, consider the case where a user uploads an external report document, "abc_2023.pdf." The server receives the file and saves it in a specified folder. The server converts the saved PDF file into an image, and then converts it into text data using OCR. If the server finds the information "Revenue: 1,500,000," "Profit: 300,000," and "Expenses: 1,200,000" in the text data, it extracts the numerical information for each. The server then automatically enters these numbers into a spreadsheet. The spreadsheet is updated as follows:

[1451] | Revenue | Profit | Expenses |

[1452] |---------|--------|----------|

[1453] | 1,500,000 | 300,000 | 1,200,000 |

[1454] The device presents the spreadsheet to the user, who then checks the data. The emotion engine analyzes the user's emotions, and if the user is feeling stressed, for example, the device displays a message such as, "Thank you for your hard work. We recommend that you take a break."

[1455] Prompt Sentence Examples

[1456] "Extract revenue, profit, and expense information from the following PDF document and transcribe it into a spreadsheet. This document is named "abc_2023.pdf."

[1457] In this way, the present invention realizes automatic analysis of report materials and efficient data transcription, and furthermore, can facilitate operations by taking into account the user's feelings.

[1458] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1459] Step 1:

[1460] Users access a dedicated interface through a web browser, select a PDF file as a report document, and click the upload button.

[1461] Input: A PDF file selected by the user (e.g. "abc_2023.pdf")

[1462] Output: PDF file is sent to the server

[1463] Specific behavior: The user accesses "http: / / example.com / upload", clicks the "Choose File" button, selects "abc_2023.pdf", and clicks the "Upload" button.

[1464] Step 2:

[1465] The server receives the uploaded PDF file and saves it in the specified folder.

[1466] Input: Uploaded PDF file

[1467] Output: Saved PDF file (e.g. "financial_report_2023.pdf")

[1468] Specific behavior: The server receives the HTTP request and saves the "abc_2023.pdf" file in the " / uploads" directory as "financial_report_2023.pdf".

[1469] Step 3:

[1470] The server converts the saved PDF file into an image format using the "pdf2image" library.

[1471] Input: Saved PDF file (e.g. "financial_report_2023.pdf")

[1472] Output: Converted image files (e.g. "page1.jpg", "page2.jpg")

[1473] Specific behavior: The server reads the "financial_report_2023.pdf" file and converts each page to a JPEG image using "pdf2image.convert_from_path()".

[1474] Step 4:

[1475] The server converts each image page into text data using OCR (Optical Character Recognition) technology. This operation uses the "pytesseract" library.

[1476] Input: Image files (e.g. "page1.jpg", "page2.jpg")

[1477] Output: Extracted text data

[1478] Specific operation: The server reads the "page1.jpg" and "page2.jpg" files and extracts the text data from each image using "pytesseract.image_to_string()".

[1479] Step 5:

[1480] The server uses regular expressions to identify the necessary items (revenue, profit, expenses, etc.) from the extracted text data and extracts the corresponding numerical data.

[1481] Input: Extracted text data

[1482] Output: Extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000")

[1483] What happens: The server parses the extracted text data and extracts the required financial data using regular expressions (e.g., re.findall(r'Revenue: (\d+)', text)).

[1484] Step 6:

[1485] The server automatically writes the extracted numerical data to a management sheet using the Google Sheets API.

[1486] Input: Extracted numeric data

[1487] Output: Updated control sheet

[1488] What happens: The server connects to the Google Sheets API and adds the extracted numeric data (e.g. "Revenue: 1500000", "Profit: 300000", "Expenses: 1200000") as new rows to the spreadsheet.

[1489] Step 7:

[1490] The terminal displays the updated control sheet, allowing the user to verify the accuracy of the data.

[1491] Input: Updated control sheet

[1492] Output: what the user sees

[1493] Specific operation: The device uses a browser to display the specified Google Sheets sheet, and the user confirms the contents.

[1494] Step 8:

[1495] The emotion engine analyzes the user's facial expressions and voice in real time, and if the user is feeling stressed, the system will display an appropriate feedback message.

[1496] Input: User facial and voice data

[1497] Output: Feedback message

[1498] Specific operation: The emotion engine analyzes data from the device's camera and microphone, and if it determines that the user is feeling stressed, it displays the message "Thank you for your hard work. We recommend that you take a break." on the device.

[1499] (Application example 2)

[1500] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1501] Data management in modern factories is extremely complex, and manual input and verification of financial and production data in particular takes a lot of time and effort. Furthermore, worker stress and fatigue can have a negative impact on productivity. To solve these problems, it is necessary to improve the efficiency of data management work while also providing feedback that takes into account the emotional state of workers.

[1502] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to select and upload financial report documents, means for saving the uploaded financial report documents on the server, means for converting the saved financial report documents into images, means for extracting text data from the converted images, means for identifying necessary financial items from the extracted text data and extracting numerical values, means for inputting the extracted numerical values ​​into a management spreadsheet, and means for scanning documents with a camera mounted on a smart device, recognizing the user's emotions in real time with an emotion engine, and providing feedback. This enables the efficiency of data management operations and the reduction of worker stress.

[1503] A "user" is someone who uses the system to upload financial reporting documents and enter data into management spreadsheets.

[1504] A "financial reporting document" is a reporting document that contains financial information such as revenues, profits, and expenses of a company or organization.

[1505] An "uploading means" is a method or device by which a user transmits financial reporting materials to the system.

[1506] "Server" means a central processing unit that stores uploaded financial reporting materials, converts them into images, extracts text data, and transcribes the data into a management spreadsheet.

[1507] "Image conversion means" refers to technology or devices for converting uploaded financial report materials into image format.

[1508] "Text data extraction means" refers to a method or device that uses optical character recognition (OCR) technology to obtain text information from financial reporting documents that have been converted into images.

[1509] The "financial item identification means" refers to a method or device that identifies necessary financial information such as revenue, profit, and expenses from the extracted text data and extracts the numerical values.

[1510] A "control spreadsheet" is an electronic spreadsheet file into which extracted financial data is entered for control purposes.

[1511] A "camera" is a device installed in a smart device that scans documents used by the user.

[1512] An "emotion engine" is software or hardware that analyzes emotions from a user's facial expressions and voice in real time.

[1513] The "feedback providing means" is a method or device that displays an appropriate message based on the user's emotions analyzed by the emotion engine.

[1514] A "smart device" is a portable electronic device equipped with a camera and microphone that can input data and perform emotion analysis.

[1515] This invention is a system for streamlining data management and worker emotion monitoring in factories. The system combines smart devices (smart glasses), a server, a data management spreadsheet, and an emotion engine.

[1516] The system is configured as follows:

[1517] First, the user scans a financial report document using the camera on their smart device, which then captures the document as image data. This image data is then sent to a server via a communication method such as Wi-Fi.

[1518] To analyze the received image data, the server converts it to an image using the pdf2image library, then performs OCR processing using the pytesseract library to extract the text data from the image, and uses regular expressions to identify the required financial items (revenue, profit, expenses, etc.) and extract the corresponding numerical values.

[1519] The extracted numerical data is automatically transferred to a managed spreadsheet using the Google Sheets API, ensuring that the data is always integrated and up-to-date in the managed spreadsheet.

[1520] In addition, the smart device's built-in camera and microphone capture the user's facial expressions and voice, which are then analyzed in real time by the emotion engine. The emotion engine uses EmotionRecognition software to recognize the user's emotional state. If the user is feeling stressed, the system will automatically display a feedback message (e.g., "Thank you for your hard work. We recommend that you take a break.").

[1521] As a practical scenario, we present a specific example of a user scanning financial report documents in a factory. For example, the user uses a smart device to scan the "2023 Production Line Financial Report." This data is sent to a server, where it undergoes OCR processing, extracting figures such as revenue, profit, and expenses. The extracted data is then transcribed into a management spreadsheet and registered as the latest report data. While the user is performing this task, the camera and microphone monitor the user's emotional state and display appropriate feedback messages as needed.

[1522] An example of a prompt for a generative AI model is:

[1523] "You are a factory manager. You use a smart device to scan financial reporting documents. The system analyzes the data and transcribes it into a management spreadsheet, while simultaneously analyzing your facial emotions. If the emotion engine detects that you are nervous while reviewing the documents, what feedback message should be displayed?"

[1524] In this way, the present invention improves the efficiency of data management within a factory and provides business support that takes into account the emotional state of workers.

[1525] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1526] Step 1:

[1527] A user scans a financial report document using a camera on a smart device. The input is the financial report document to be scanned, and the output is image data. This image data is temporarily stored in the memory of the smart device.

[1528] Step 2:

[1529] The image data acquired by the device is sent to the server via a communication method such as Wi-Fi. The input is the saved image data, and the output is the image data transferred to the server. After the communication is complete, the device notifies the user of the status of the image data transmission.

[1530] Step 3:

[1531] The server saves the received image data. The input is the image data transferred to the server, and the output is an image file saved in a specified folder on the server. After saving is complete, the server prepares to proceed to the next step.

[1532] Step 4:

[1533] The server converts the received image data to PDF format using the pdf2image library. The input is an image file stored on the server, and the output is a PDF file. The server does this because it treats each page as a separate image, making it easier to convert.

[1534] Step 5:

[1535] The server uses the pytesseract library to perform OCR on images in PDF files. The input is a PDF file, and the output is text data. Through OCR processing, character information in the image is extracted in text format.

[1536] Step 6:

[1537] The server uses regular expressions to identify financial items in the text data. The input is the text data obtained by OCR processing, and the output is the numerical values ​​of the financial items (revenue, profit, expenses, etc.). This process extracts the necessary numerical data from the text.

[1538] Step 7:

[1539] The server uses the Google Sheets API to post the identified financial item values ​​to a management spreadsheet. The input is the financial item values, and the output is an updated management spreadsheet. The server adds the new data to the spreadsheet and saves it.

[1540] Step 8:

[1541] The smart device uses a camera and microphone to capture the user's facial expressions and voice in real time. The input is the user's facial expressions and voice, and the output is the captured data. The data is used for subsequent emotion analysis.

[1542] Step 9:

[1543] The emotion engine uses EmotionRecognition software to analyze the user's emotions from the captured data. The input is the captured facial and voice data, and the output is the emotion analysis result. After the emotion analysis is complete, the system provides appropriate feedback.

[1544] Step 10:

[1545] The device displays an appropriate feedback message based on the emotion analysis results. The input is the emotion analysis results, and the output is the feedback message to be displayed to the user. For example, "Thank you for your hard work. We recommend that you take a break."

[1546] In this way, the system streamlines data management in factory environments and provides feedback that takes into account the emotional state of workers.

[1547] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1548] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1549] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1550] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1551] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1552] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1553] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1554] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1555] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1556] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1557] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1558] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1559] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1560] 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.

[1561] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1562] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1563] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1564] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1565] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1566] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1567] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1568] The following is further disclosed regarding the above embodiment.

[1569] (Claim 1)

[1570] a means for a user to select and upload financial reporting materials;

[1571] a means for storing the uploaded financial reporting materials on a server;

[1572] a means for converting the stored financial reporting materials into images;

[1573] means for extracting text data from the converted image;

[1574] A means for identifying necessary financial items from the extracted text data and extracting numerical values;

[1575] a means for inputting the extracted values ​​into a management spreadsheet;

[1576] A system including:

[1577] (Claim 2)

[1578] 10. The system of claim 1, further comprising means for prompting a user to confirm the accuracy of the extracted numerical value.

[1579] (Claim 3)

[1580] 10. The system of claim 1, further comprising means for automatically writing data to an administrative spreadsheet.

[1581] "Example 1"

[1582] (Claim 1)

[1583] a means for a user to select and upload financial reporting materials;

[1584] a means for storing the uploaded financial reporting materials on a server;

[1585] a means for converting the stored financial reporting materials into images;

[1586] means for extracting text data from the converted image;

[1587] A means for identifying necessary financial items from the extracted text data and extracting numerical values;

[1588] a means for inputting the extracted values ​​into a management spreadsheet;

[1589] A means of automatically writing data into a management spreadsheet;

[1590] A system including:

[1591] (Claim 2)

[1592] 10. The system of claim 1, further comprising means for prompting a user to confirm the accuracy of the extracted numerical value.

[1593] (Claim 3)

[1594] 10. The system of claim 1, further comprising means for saving to a designated folder.

[1595] "Application Example 1"

[1596] (Claim 1)

[1597] a means for a user to select and upload financial reporting materials;

[1598] a means for storing the uploaded financial reporting materials on a server;

[1599] a means for converting the stored financial reporting materials into images;

[1600] means for extracting text data from the converted image;

[1601] A means for identifying necessary financial items from the extracted text data and extracting numerical values;

[1602] a means for inputting the extracted values ​​into a management spreadsheet;

[1603] A method for capturing images of financial reporting documents using smart glasses and analyzing the data in real time;

[1604] means for displaying the parsed data to a user;

[1605] A system including:

[1606] (Claim 2)

[1607] 10. The system of claim 1, further comprising means for prompting a user to confirm the accuracy of the extracted numerical value.

[1608] (Claim 3)

[1609] 10. The system of claim 1, further comprising means for automatically writing data to an administrative spreadsheet.

[1610] "Example 2: Combining Emotion Engines"

[1611] (Claim 1)

[1612] a means for a user to select and upload reporting materials;

[1613] A means for storing the uploaded report materials on a server;

[1614] means for converting the stored report materials into images;

[1615] means for extracting character data from the converted image;

[1616] A means for identifying necessary items from the extracted character data and extracting numerical values;

[1617] A means for inputting the extracted numerical values ​​into a management sheet;

[1618] means for analyzing user emotions;

[1619] means for displaying a feedback message based on sentiment analysis;

[1620] A system including:

[1621] (Claim 2)

[1622] 10. The system of claim 1, further comprising means for prompting a user to confirm the accuracy of the extracted numerical value.

[1623] (Claim 3)

[1624] 10. The system of claim 1, further comprising means for automatically writing data to the management sheet.

[1625] "Application example 2 when combining emotion engines"

[1626] (Claim 1)

[1627] a means for a user to select and upload financial reporting materials;

[1628] a means for storing the uploaded financial reporting materials on a server;

[1629] a means for converting the stored financial reporting materials into images;

[1630] means for extracting text data from the converted image;

[1631] A means for identifying necessary financial items from the extracted text data and extracting numerical values;

[1632] a means for inputting the extracted values ​​into a management spreadsheet;

[1633] A method for scanning documents with a camera installed on a smart device, recognizing the user's emotions in real time with an emotion engine, and providing feedback;

[1634] A system including:

[1635] (Claim 2)

[1636] 10. The system of claim 1, further comprising means for prompting a user to confirm the accuracy of the extracted numerical value.

[1637] (Claim 3)

[1638] 10. The system of claim 1, further comprising means for automatically writing data to an administrative spreadsheet. [Explanation of symbols]

[1639] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to select and upload financial reporting materials; a means for storing the uploaded financial reporting materials on a server; a means for converting the stored financial reporting materials into images; means for extracting text data from the converted image; A means for identifying necessary financial items from the extracted text data and extracting numerical values; a means for inputting the extracted values ​​into a management spreadsheet; A system including:

2. 10. The system of claim 1, further comprising means for prompting a user to confirm the accuracy of the extracted numerical value.

3. 10. The system of claim 1, further comprising means for automatically writing data to a management spreadsheet.

Images