system

Abstract

We provide the system. [Solution] A means of learning company-specific formats and layouts, A means of automatically collecting and analyzing financial and sales data from diverse data sources, A means of creating documents that reflect the latest information in real time, A means to respond to urgent revision requests 24 / 7, A means of learning from past revision history and reflecting it in document creation, A means of managing tasks and schedules and reducing user stress, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the preparation of corporate financial statements, it is necessary to collect and analyze a wide variety of data, which places a great burden on the preparers. Also, it is necessary to quickly respond to urgent correction requests and sudden change requests, which causes further stress. Furthermore, since the formats and layouts of the documents vary from company to company, processing according to them is required, which is a factor reducing efficiency. Due to these problems, it takes a lot of time to prepare the documents and the workload of the persons in charge is increasing.

Means for Solving the Problems

[0005] This invention provides a system that learns company-specific formats and layouts and automatically collects and analyzes data from diverse data sources. This system autonomously creates financial statements that reflect the latest information in real time and responds to urgent revision requests 24 / 7. Furthermore, it learns past revision history and incorporates it into subsequent document creation, improving revision efficiency. In addition, it reduces workload and stress by managing user tasks and schedules. Therefore, this invention can streamline document creation and significantly reduce the workload of those responsible.

[0006] "Format" refers to the rules and standards that define the appearance and structure of a document or paper, and in particular, it refers to a specific document format used based on company guidelines.

[0007] "Layout" refers to the design process that determines the arrangement and placement of text, graphs, and diagrams within a document or material, and is a structure used to optimize visual presentations.

[0008] A "data source" refers to a source of information that exists in various forms, such as databases, APIs, and spreadsheets, which serve as the starting point for acquiring information.

[0009] "Real-time" refers to processing or responding instantly without delay, representing a state where data collection and processing occur instantaneously.

[0010] "Revision history" refers to a record of changes and revisions made during the document creation process, and is historical data that provides useful information for future document creation.

[0011] A "task" refers to specific tasks or work items related to creating or revising documents, and includes the steps and activities required for each task.

[0012] A "schedule" is a plan that indicates the time or period during which tasks related to the preparation of financial statements are scheduled to be carried out, and refers to a timetable necessary to promote the efficient progress of the work. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Implementing the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This system aims to efficiently create corporate financial statements and reduce the burden on those responsible for preparing them. It consists of three elements: a server, terminals, and users, each fulfilling its respective role to function.

[0035] The server acts as the central processing unit, collecting necessary information from the company's internal databases and external APIs. Using pre-configured access information, the server initiates data collection according to a schedule and analyzes the collected data quickly and accurately. Specifically, it analyzes data trends and patterns using statistical models and machine learning algorithms, and automatically generates reports based on this analysis. These reports are created according to the company's specific format and layout, and can also be customized to meet individual user requirements.

[0036] The terminal functions as a user interface, receiving instructions from the user and providing output from the server to the user. Through the terminal, the user can input necessary corrections in real time, which the server processes immediately and reflects in the document. This allows for quick responses to urgent corrections, reducing stress on the person in charge.

[0037] The user is the primary operator of this system, mainly creating documents and issuing revision requests. Past revision history is recorded by the server and used as learning data for subsequent document creation, enabling the automatic reflection of similar revisions. This feedback loop further improves the efficiency of the document creation process.

[0038] As a concrete example, consider a scenario where a finance officer prepares a quarterly earnings report. The server first collects the latest sales data from the ERP system, automatically calculates key metrics such as year-on-year comparisons, and graphs the trends. When a user requests additional details for a specific expense item via a terminal, the server re-analyzes the relevant data and immediately reflects it in the report. As a result, the completed report is quickly submitted to management.

[0039] In this way, the present invention significantly increases the efficiency of the document creation process and reduces the effort and time of the person in charge, thereby contributing to the improvement of overall company productivity.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server performs an initialization process to establish connections to data sources both inside and outside the enterprise. This process loads the necessary API keys and database credentials, ensuring security and preparing access for future use.

[0043] Step 2:

[0044] The server initiates data collection according to scheduled tasks. Internal data is retrieved from the database via SQL queries, while external data is retrieved by sending HTTP requests via APIs. This allows for real-time information to be obtained.

[0045] Step 3:

[0046] The server cleans the acquired data. Specifically, it imputes missing values ​​in the data using appropriate methods and filters out outliers. It also normalizes the data and converts it into a unified format in preparation for analysis.

[0047] Step 4:

[0048] The server analyzes the cleansed data. Using statistical methods and machine learning, it extracts data trends and key indicators, preparing the information necessary for financial statements. The analysis results are automatically visualized as graphs and tables.

[0049] Step 5:

[0050] Based on the analysis results, the server automatically generates financial statements tailored to the company's specific format and layout. This includes inserting standard phrases and creating presentation slides.

[0051] Step 6:

[0052] Users input necessary correction instructions via their terminals. This allows for flexible responses to urgent correction requests. The server analyzes the corrections and updates the document immediately.

[0053] Step 7:

[0054] The server performs a security check on the completed document to ensure that confidential information is handled appropriately. After this check, the document is provided to the user via the terminal.

[0055] Step 8:

[0056] If revisions are needed based on user-submitted materials and feedback, the server learns from that information and uses it to improve future material creation. This enables a sequential learning function, contributing to increased efficiency in material creation.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In corporate document creation processes, manual information gathering and analysis are time-consuming and labor-intensive, and responding to urgent revision requests can create additional burdens. In particular, creating documents quickly while maintaining data consistency and accuracy is a challenging task. Furthermore, there is a need to build efficient processes that utilize past revision history.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for collecting and analyzing information from diverse information sources, means for performing data analysis using statistical models and machine learning algorithms and automatically generating documents in a company-specific format, and means for receiving user input and reflecting those requests in the documents in real time using an information processing device. This enables rapid and accurate collection and analysis of information, automatic generation of documents, immediate response to user requests, and efficient document creation that leverages past experience.

[0062] An "information processing device" refers to a computer system used for collecting, analyzing, generating, and displaying data.

[0063] "Information sources" refer to various sources, including internal databases and external APIs from which financial and sales data are obtained.

[0064] A "statistical model" refers to an algorithm that mathematically analyzes data patterns and trends, and is useful for prediction and analysis.

[0065] A "machine learning algorithm" is a method for learning from data to make predictions and recognize patterns, and it is a technology that constitutes part of artificial intelligence.

[0066] "Real-time" refers to the characteristic of immediately responding to user requests and inputs and updating documents and information accordingly.

[0067] "Automatic document generation" refers to the process of automatically creating documents on a computer using analysis results, based on pre-set formats and layouts.

[0068] "User input" refers to user interaction, such as making corrections to documents or requesting additional information, via a terminal.

[0069] "Past revision history" refers to a record of revisions made when a document was previously created, and this data is used to improve the accuracy and efficiency of documents in the future by referring to it.

[0070] This invention aims to streamline corporate document creation based on an information processing system. The server, acting as an information processing device, collects necessary information from internal corporate databases and external APIs. This server can utilize common data acquisition protocols, such as SQL databases and RESTful APIs. The server performs data trend analysis and prediction by implementing statistical models and machine learning algorithms using programming languages ​​such as Python and R.

[0071] The server automatically generates documents in a company-specific format based on the analysis results. These documents can be visualized using spreadsheet software such as Microsoft® Excel® or Google® Sheets, and then converted to formats such as PDF for distribution.

[0072] The terminal functions as a user interface, through which users can review and modify documents. The software running on the terminal is typically designed as a web application that can run in a browser, and is built using HTML, CSS, JavaScript (registered trademark), etc. Users can easily make corrections to documents through the interface displayed on the terminal.

[0073] The user instructs the server to start generating data by using a prompt such as, "Please graph the latest sales data and generate a document that comprehensively shows the sales trend." In response to this prompt, the server generates the most suitable document according to the specified requirements and returns the results through the terminal.

[0074] Thus, the present invention is a system that can significantly streamline and improve the accuracy of a company's document creation process through an information processing device, a user interface, and user operation.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server first collects the necessary data from various sources. It receives pre-configured database connection information and external API endpoint information as input. The server retrieves sales and financial data using SQL queries and HTTP requests. As output, it stores this data in internal memory. Specifically, the server starts the data collection process daily at 9:00 AM.

[0078] Step 2:

[0079] The server analyzes the collected data. It uses existing sales and financial data as input. The server applies machine learning algorithms to detect data trends and anomalies. As output, it generates statistical information and graph data as analysis results. Specifically, the server manipulates dataframes using the Pandas library and applies machine learning models using scikit-learn.

[0080] Step 3:

[0081] The server automatically generates documents based on the analysis results. It receives statistical information and graph data as input. The server uses a template engine to create reports according to company-specific formats. The completed documents are saved as output in PDF or spreadsheet format. Specifically, the server constructs documents using LaTeX or Excel templates.

[0082] Step 4:

[0083] The terminal receives completed documents from the server. It receives PDF and spreadsheet files as input. The terminal provides these to the user and displays them on the interface. As output, it generates a screen display for user confirmation. Specifically, the terminal launches a PDF viewer or online spreadsheet so the user can view the documents in a browser.

[0084] Step 5:

[0085] The user reviews the document through their terminal and requests corrections as needed. The system accepts correction requests for specific parts of the document as input. The user enters these as prompts. The correction commands are sent to the server as output. For example, the user might enter a prompt in the form such as, "Please change the graph range of the sales data to year-on-year comparison."

[0086] Step 6:

[0087] The server re-analyzes and updates the data based on the user's correction instructions. It receives correction commands from the user as input. The server re-analyzes the collected data and generates new data. It generates the corrected data as output and sends it to the terminal. Specifically, the server re-executes a Python script to generate updated graphs and tables.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In diverse electronic trading environments, the rapid and accurate collection and analysis of transaction and statistical data is a critical challenge for businesses. In particular, extracting important data from the vast volume of daily transactions and creating real-time reports to aid decision-making places a significant burden on those responsible for data processing. Furthermore, there is a need to respond quickly to urgent revisions after report generation and to expedite information sharing.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for learning the unique structure and layout of a company, means for automatically collecting and analyzing transaction data and statistical data from diverse sources, and means for creating reports that reflect the latest information in real time. This enables the automation of information processing and reduces the burden on users.

[0093] "Methods for learning company-specific structures and layouts" refer to techniques for analyzing the format and layout of documents used by a specific company and understanding and reproducing their characteristics.

[0094] "Means for automatically collecting and analyzing transaction data and statistical data from diverse information sources" refers to technologies equipped with the functionality to acquire necessary data from multiple databases and APIs, and to process and analyze it mechanically.

[0095] "A means of creating reports that reflect the latest information in real time" refers to a technology that processes collected data immediately and outputs the results as a report in an up-to-date state.

[0096] "Means for visualizing information processing results and outputting them as analytical information" refers to technologies for converting extracted analytical data into a format that is easy for users to understand and providing it visually.

[0097] "A means of outputting generated reports in electronic format and facilitating appropriate information sharing" refers to technology that allows created reports to be saved in digital format and quickly shared among relevant parties.

[0098] This invention is a system that enables companies to efficiently collect and analyze transaction data in an electronic transaction environment and generate reports in real time. It mainly consists of the following elements:

[0099] The server functions as a central processing unit and implements algorithms that learn the company's specific structure and layout. This allows it to automatically analyze the format and layout of reports used by the company and collect necessary data from diverse sources. In this process, the server uses APIs to retrieve transaction data and statistical information from external databases. A Python program executes this process, utilizing libraries such as pandas and scikit-learn for data collection and analysis.

[0100] The terminal acts as a user interface, providing users with data output from the server. When users input corrections or additional information through the terminal, the server immediately reflects the changes, and the report is updated. The generated information is visualized using matplotlib and output as a report in PDF format. This allows for quick sharing of information with stakeholders while maintaining consistency.

[0101] Users interact with this system, review trading data as needed, and make decisions based on the analysis results. This process is streamlined using generative AI models, and optimal output is obtained from the system through prompt messages.

[0102] A concrete example is a process where a company analyzes monthly settlement data and outputs the results as a report for use in management meetings. In this case, the user can input prompts into the system such as, "Please generate a report analyzing monthly trends based on the latest settlement data. Specifically, please provide a graph that shows the fluctuations in transaction volume for March in detail and includes predicted trends," and obtain the necessary analysis results.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects transaction data from various sources. It sends requests using API endpoints and database authentication information specified by the user to retrieve transaction data. The input is the API endpoint and authentication information, and the output is transaction data in JSON format.

[0106] Step 2:

[0107] The server analyzes the acquired transaction data. Using the pandas library, it converts the JSON data into a DataFrame, performs data processing such as date formatting, and completes data completion for missing information. The input is transaction data in JSON format, and the output is the processed DataFrame.

[0108] Step 3:

[0109] The server performs trend analysis using statistical models. It leverages the scikit-learn library to build a regression model based on trading data and perform predictions. The input is a DataFrame, and the output is the model's prediction result.

[0110] Step 4:

[0111] The server visualizes the analysis results. Using the matplotlib library, it graphs the trends in trading volume and predictions, enabling visual analysis. The input is the prediction results from the model, and the output is the generated graph.

[0112] Step 5:

[0113] The server outputs the generated graphs and analysis information as a report in PDF format. It uses the FPDF library to automatically generate reports that include visual information. The input is the generated graphs, and the output is a PDF report.

[0114] Step 6:

[0115] The terminal presents the generated report to the user. The user can use this to verify the accuracy of the information and request additional corrections as needed. The input is a report in PDF format, and the output is user review and feedback.

[0116] Step 7:

[0117] The user submits new analysis and modification requests to the system through prompt messages. Based on these requests, the server resumes processing, recollects the necessary data, and updates the report. The input is the user's prompt messages, and the output is the updated analysis results and report.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] This invention incorporates an emotion engine that recognizes user emotions into a system designed to streamline the preparation of corporate financial statements and reduce the burden on employees. This enables the optimization of emotion-based interactions. The system is built around a server, terminals, and users, each playing the following roles.

[0120] The server automatically collects necessary information from the company's internal databases and external data sources, and analyzes the data using advanced machine learning. The analyzed data is automatically generated as documents according to the company's specific format and layout. This document generation process is performed in real time, ensuring that the latest information is included. The server also has the capability to quickly respond to user requests for corrections and update documents rapidly.

[0121] The terminal functions as a user interface, providing users with a means to make corrections and comments on documents. In particular, the emotion engine works in conjunction with the terminal to determine the user's emotions from their facial expressions, tone of voice, and typing speed. For example, if the terminal detects that the user is stressed, it changes the display and provides appropriate messages and guidance to improve the user experience.

[0122] Users are the most important users of the system, primarily responsible for creating and revising documents. Based on feedback from the emotion engine, users can check their own emotional state and take action to improve it. The system also records the user's emotional history and uses this information to improve future processes, thereby increasing the overall efficiency of document creation.

[0123] As a concrete example, when a sales representative prepares an important presentation using financial statements, the server retrieves and analyzes the latest sales data and quickly incorporates it into the presentation. The terminal monitors the user's emotional state in real time, and if it detects that tension is rising during presentation preparation, it displays a message suggesting ways to relax. In this way, the system supports efficient document creation and contributes to reducing the user's psychological burden.

[0124] This invention makes it possible to reduce the time required for document creation, improve accuracy, and support the mental well-being of users.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server initiates the process of collecting necessary information via the company's internal databases or external APIs. During this process, it performs authentication using pre-stored access information and retrieves financial and sales data in real time.

[0128] Step 2:

[0129] The server cleans the collected data. This process removes outliers and handles missing values, formatting the data into an analyzable format. This ensures the accuracy and consistency of the data.

[0130] Step 3:

[0131] The server utilizes machine learning algorithms to analyze the cleaned data. It calculates sales trends and financial indicators, and visualizes these results by creating graphs.

[0132] Step 4:

[0133] The server automatically generates financial statements based on analysis results, adhering to the company's specific format. These statements include text summaries and inserted charts and graphs, and are prepared in a format that can be updated immediately.

[0134] Step 5:

[0135] The device activates an emotion engine to recognize the user's emotions in real time. This recognition utilizes facial expression analysis and speech recognition technology to determine signs of stress and anxiety.

[0136] Step 6:

[0137] The device provides an interface and assistance features that respond to the user's emotional state. For example, if it detects that the user is stressed, it will display calmer colors and messages that encourage relaxation.

[0138] Step 7:

[0139] Users review the document content, taking into account feedback from their devices, and enter correction instructions as needed. These instructions are immediately sent to the server and reflected in the document.

[0140] Step 8:

[0141] The server performs a security check on the completed documents to ensure that confidential information is handled appropriately. Once the documents are deemed secure, they are provided to the user via the terminal.

[0142] Step 9:

[0143] Users review the final document and share it with their superiors and relevant parties as needed. The emotional data recorded by the emotion engine at this stage is then used to improve future document creation.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] In modern business operations, there is a demand for rapid and accurate document creation, which increases the burden on employees in the process. Furthermore, there are often urgent revision requests and the need to integrate diverse data sources. On the other hand, the psychological stress experienced by employees impacts the quality and efficiency of the documents, making stress reduction a crucial issue. Moreover, traditional systems that proceed without considering the emotional state of employees risk reducing user convenience and satisfaction.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data from diverse information sources, and means for creating documents that reflect the latest information in real time. This improves the efficiency of document creation and reduces the burden on the person in charge. Furthermore, it incorporates means for understanding the user's emotional state in conjunction with the information processing means, and dynamically adjusts the interface based on the user's emotional state, thereby simultaneously reducing psychological stress and improving convenience.

[0149] "Company-specific format and layout" refers to the layout and design characteristics of documents used by a particular company, and means creating materials based on those characteristics.

[0150] "Diverse information sources" refers to various sources of information, including not only a company's internal database, but also external online databases and public records.

[0151] "Economic data" refers to all kinds of numerical information related to a company's financial situation and performance, including information such as sales and profits.

[0152] "Real-time updates" means quickly obtaining the latest data up to the time the document was created and immediately reflecting it in the document.

[0153] "Information processing means" refers to technologies and methods that use computers and servers to collect and analyze data and automatically perform the necessary processing.

[0154] "Understanding the user's emotional state" refers to recognizing the user's emotions by inferring them from facial expressions, tone of voice, typing speed, etc.

[0155] "Dynamic interface adjustment" means automatically changing the computer screen and operating environment according to the user's emotional state to provide the optimal user experience.

[0156] "Reducing psychological burden" refers to minimizing the stress associated with document creation and work, enabling users to continue working comfortably.

[0157] This invention provides a system that automates the creation of corporate financial statements and also offers user interaction that takes user emotions into consideration. This system is composed of a server, terminals, and users.

[0158] The server retrieves necessary economic data from the company's internal databases and external information sources. This includes data retrieval via APIs and the use of data crawling techniques. The retrieved information is analyzed using machine learning algorithms. Specifically, predictive models and clustering techniques are utilized to perform sales forecasts and trend analysis. Based on the results of this analysis, documents are automatically generated in a format and layout specific to the company. The server uses a template engine (e.g., Jinja2) to dynamically embed these documents into constituent elements.

[0159] The terminal functions as a user interface, instantly presenting materials to the user. Through this interface, the user can review the materials and input any necessary revisions. The terminal incorporates an emotion engine using open-source libraries (e.g., OpenCV and DeepFace) that analyzes the user's facial expressions, voice tone, and input speed to determine their emotional state. Based on this feedback, the system dynamically adjusts the interface according to the user's emotional state. For example, if the system detects that the user is tense, it displays a message suggesting ways to relax.

[0160] Through this system, users can create and revise documents, understand their own emotional state by utilizing feedback from the emotion engine, and take action to improve as needed. The system also records the user's emotional history, which serves as data to reduce stress and increase efficiency in future document creation processes.

[0161] As a concrete example, consider a scenario where a sales representative is preparing financial statements for a large-scale presentation. In this case, the server automatically retrieves the latest sales data and uses an AI model to forecast sales for the next quarter. The terminal then presents the user with a draft version of the document generated based on this information. If the user enters a comment such as "Adjust the plan based on the sales forecast," the system reflects this in real time. Simultaneously, an emotion engine detects fatigue from the user's facial expressions and displays a message such as "Please take a short break," thereby reducing the user's psychological burden.

[0162] An example of a prompt would be, "Describe a system that streamlines the creation of corporate financial statements and optimizes interactions based on user sentiment." This prompt helps to deepen understanding of how to create documents using generative AI models and how the engine works.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server collects necessary economic data from the company's internal databases and external sources. Specifically, it uses APIs to retrieve the latest sales data and market trends and stores them in the database. Inputs include API endpoints and data queries, and output is the retrieved raw data. This data is used for subsequent analysis.

[0166] Step 2:

[0167] The server applies machine learning algorithms to the collected raw data for analysis. For example, it calculates sales forecasts for the next quarter using a predictive model and performs trend analysis. The input is the raw data obtained in step 1, and the output is the analyzed results, such as sales forecasts and market trend information. This allows valuable insights to be extracted from the data.

[0168] Step 3:

[0169] The server automatically generates financial statements in a company-specific format and layout based on the analysis results. It utilizes a template engine to dynamically embed the analysis data into the documents. The input is the analysis data obtained in step 2, and the output is the completed financial statements. These documents are immediately ready to be provided to the user.

[0170] Step 4:

[0171] The terminal displays a preview of the generated document to the user. The user can review the document through the terminal and enter revision requests and feedback in the comments section. Input is the user's actions, and output is the document the user reviews and any revisions made. This process allows the user to confirm the user experience.

[0172] Step 5:

[0173] The device analyzes the user's emotional state using an emotion engine. This includes facial recognition using the camera and microphone, as well as voice analysis. The input is the user's visual and auditory information, and the output is the analyzed emotional state, such as "tension" or "fatigue." Based on this, the interface is adjusted to display appropriate feedback to the user.

[0174] Step 6:

[0175] Users continue their work based on document creation and emotional feedback. They perform a final review of the document through their device and make final adjustments as needed. Input consists of system feedback and user judgment, while output is the revised, final version of the document. This process allows users to create documents efficiently and without stress.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] Document creation processes in business operations are complex and time-consuming, and responding to urgent revision requests and information updates is particularly burdensome. Furthermore, the mental burden and stress associated with these tasks can negatively impact work efficiency. Therefore, there is a need to build systems that reflect information in real time and optimize user interaction based on user emotions.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data and sales information from diverse sources, and means for creating documents that reflect the latest information in real time. This makes it possible to improve the efficiency of document creation, reduce the burden on users, and support their mental well-being in the workplace.

[0181] "Company-specific formats and layouts" refer to the unique layouts and formats used by each company, which the system learns and utilizes in document creation.

[0182] "Diverse information sources" refers to various types of data sources, such as internal company databases, publicly available external information, and databases of other companies.

[0183] "Economic data" refers to financial information such as a company's financial status, market trends, and sales data.

[0184] "Sales information" refers to information related to sales activities, such as sales analysis, customer information, and marketing data.

[0185] "Documents that reflect the latest information in real time" refers to the process of generating documents that are instantly updated based on the most recent data.

[0186] "Always ready to respond to urgent change requests" means being able to quickly respond to sudden requests for document changes at any time.

[0187] "Past revision history" refers to a record of previous document modifications, which is data used for future document creation.

[0188] "Task and schedule management" refers to methods of supporting efficient work by continuously monitoring the progress and schedule of tasks.

[0189] "Recognizing user emotions" is a process of analyzing the user's facial expressions, tone of voice, input speed, etc., to evaluate their emotional state.

[0190] The system for realizing this invention primarily consists of three elements: a server, a terminal, and a user. The server learns the company's specific format and arrangement, and automatically collects and analyzes economic data and sales information from diverse sources. This process utilizes a database management system and machine learning libraries. Specific software used for database management includes "MySQL®" and "PostgreSQL," while machine learning tools such as "TENSORFLOW®" and "PyTorch" may be used. The server updates information in real time and can automatically generate documents based on the most up-to-date information.

[0191] The terminal provides users with a means to access the system through a user interface and make necessary changes or review documents. The terminal works in conjunction with an emotion analysis engine to evaluate the user's emotional state. Common PCs and smart devices are used as hardware, and "EmotionAPI" and "FaceAPI" are employed for emotion recognition. This emotional data is used to optimize the process and reduce the user's mental burden.

[0192] For example, when a sales representative creates a presentation document for a client, the server collects and analyzes the latest economic data in real time and automatically generates the document based on that data. When a user feels stressed, the device suggests ways to relax.

[0193] In developing systems that utilize generative AI models, an example of a prompt might be: "Design an algorithm that senses the tension level of staff interacting with customers in real time and displays stress-relieving advice on the smart glasses' display." By using this prompt, it is possible to generate an algorithm that responds appropriately according to the user's state.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The server collects necessary economic and sales data from the company's database. It takes the company's database connection information as input and executes SQL queries to extract data such as specified financial reports and sales records. This data is the raw material for use in the next analysis step.

[0197] Step 2:

[0198] The server analyzes the collected data using machine learning libraries. Specifically, it uses TensorFlow and PyTorch to model historical data and different patterns, and generates real-time predictions. It converts the input data into features, uses the prediction model to calculate future trends, and provides the output to the document generation step.

[0199] Step 3:

[0200] The server automatically generates documents based on analysis results and company-specific formats. Using document templates and analysis results as input, the program places the latest data in the appropriate locations within the document and outputs the completed document. The generated documents are designed to reflect the latest information while maintaining consistency.

[0201] Step 4:

[0202] The terminal displays the generated document to the user and provides an interface for editing or modifying it. It receives feedback and modification instructions from the user and sends them to the server. As a result, the document content is modified and a new version is saved.

[0203] Step 5:

[0204] The device analyzes the user's emotional state in real time. The emotion analysis engine uses EmotionAPI and FaceAPI, receiving data such as the user's facial expressions and voice tone as input. Based on this data, it evaluates the user's tension and stress levels and displays advice on the screen according to the results.

[0205] Step 6:

[0206] Users receive emotional feedback from their devices and use it to improve their work. Suggestions and guidelines for stress reduction are displayed on the device as output, and by immediately implementing these, users can create more effective documents.

[0207] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0208] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0209] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0213] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0214] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0215] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0216] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0217] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0218] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0219] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0220] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0221] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0222] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0223] This system aims to efficiently create corporate financial statements and reduce the burden on those responsible for preparing them. It consists of three elements: a server, terminals, and users, each fulfilling its respective role to function.

[0224] The server acts as the central processing unit, collecting necessary information from the company's internal databases and external APIs. Using pre-configured access information, the server initiates data collection according to a schedule and analyzes the collected data quickly and accurately. Specifically, it analyzes data trends and patterns using statistical models and machine learning algorithms, and automatically generates reports based on this analysis. These reports are created according to the company's specific format and layout, and can also be customized to meet individual user requirements.

[0225] The terminal functions as a user interface, receiving instructions from the user and providing output from the server to the user. Through the terminal, the user can input necessary corrections in real time, which the server processes immediately and reflects in the document. This allows for quick responses to urgent corrections, reducing stress on the person in charge.

[0226] The user is the primary operator of this system, mainly creating documents and issuing revision requests. Past revision history is recorded by the server and used as learning data for subsequent document creation, enabling the automatic reflection of similar revisions. This feedback loop further improves the efficiency of the document creation process.

[0227] As a concrete example, consider a scenario where a finance officer prepares a quarterly earnings report. The server first collects the latest sales data from the ERP system, automatically calculates key metrics such as year-on-year comparisons, and graphs the trends. When a user requests additional details for a specific expense item via a terminal, the server re-analyzes the relevant data and immediately reflects it in the report. As a result, the completed report is quickly submitted to management.

[0228] In this way, the present invention significantly increases the efficiency of the document creation process and reduces the effort and time of the person in charge, thereby contributing to the improvement of overall company productivity.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server performs an initialization process to establish connections to data sources both inside and outside the enterprise. This process loads the necessary API keys and database credentials, ensuring security and preparing access for future use.

[0232] Step 2:

[0233] The server initiates data collection according to scheduled tasks. Internal data is retrieved from the database via SQL queries, while external data is retrieved by sending HTTP requests via APIs. This allows for real-time information to be obtained.

[0234] Step 3:

[0235] The server cleans the acquired data. Specifically, it imputes missing values ​​in the data using appropriate methods and filters out outliers. It also normalizes the data and converts it into a unified format in preparation for analysis.

[0236] Step 4:

[0237] The server analyzes the cleansed data. Using statistical methods and machine learning, it extracts data trends and key indicators, preparing the information necessary for financial statements. The analysis results are automatically visualized as graphs and tables.

[0238] Step 5:

[0239] Based on the analysis results, the server automatically generates financial statements tailored to the company's specific format and layout. This includes inserting standard phrases and creating presentation slides.

[0240] Step 6:

[0241] Users input necessary correction instructions via their terminals. This allows for flexible responses to urgent correction requests. The server analyzes the corrections and updates the document immediately.

[0242] Step 7:

[0243] The server performs a security check on the completed document to ensure that confidential information is handled appropriately. After this check, the document is provided to the user via the terminal.

[0244] Step 8:

[0245] If revisions are needed based on user-submitted materials and feedback, the server learns from that information and uses it to improve future material creation. This enables a sequential learning function, contributing to increased efficiency in material creation.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] In corporate document creation processes, manual information gathering and analysis are time-consuming and labor-intensive, and responding to urgent revision requests can create additional burdens. In particular, creating documents quickly while maintaining data consistency and accuracy is a challenging task. Furthermore, there is a need to build efficient processes that utilize past revision history.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes means for collecting and analyzing information from diverse information sources, means for performing data analysis using statistical models and machine learning algorithms and automatically generating documents in a company-specific format, and means for receiving user input and reflecting those requests in the documents in real time using an information processing device. This enables rapid and accurate collection and analysis of information, automatic generation of documents, immediate response to user requests, and efficient document creation that leverages past experience.

[0251] An "information processing device" refers to a computer system used for collecting, analyzing, generating, and displaying data.

[0252] "Information sources" refer to various sources, including internal databases and external APIs from which financial and sales data are obtained.

[0253] A "statistical model" refers to an algorithm that mathematically analyzes data patterns and trends, and is useful for prediction and analysis.

[0254] A "machine learning algorithm" is a method for learning from data to make predictions and recognize patterns, and it is a technology that constitutes part of artificial intelligence.

[0255] "Real-time" refers to the characteristic of immediately responding to user requests and inputs and updating documents and information accordingly.

[0256] "Automatic document generation" refers to the process of automatically creating documents on a computer using analysis results, based on pre-set formats and layouts.

[0257] "User input" refers to user interaction, such as making corrections to documents or requesting additional information, via a terminal.

[0258] "Past revision history" refers to a record of revisions made when a document was previously created, and this data is used to improve the accuracy and efficiency of documents in the future by referring to it.

[0259] This invention aims to streamline corporate document creation based on an information processing system. The server, acting as an information processing device, collects necessary information from internal corporate databases and external APIs. This server can utilize common data acquisition protocols, such as SQL databases and RESTful APIs. The server performs data trend analysis and prediction by implementing statistical models and machine learning algorithms using programming languages ​​such as Python and R.

[0260] The server automatically generates documents in a company-specific format based on the analysis results. These documents can be visualized using spreadsheet software such as Microsoft Excel or Google Sheets, and then converted to a format such as PDF for distribution.

[0261] The terminal functions as a user interface, through which users can review and modify documents. The software running on the terminal is typically designed as a web application that can run in a browser, and is built using HTML, CSS, JavaScript, etc. Users can easily make corrections to documents through the interface displayed on the terminal.

[0262] The user instructs the server to start generating data by using a prompt such as, "Please graph the latest sales data and generate a document that comprehensively shows the sales trend." In response to this prompt, the server generates the most suitable document according to the specified requirements and returns the results through the terminal.

[0263] Thus, the present invention is a system that can significantly streamline and improve the accuracy of a company's document creation process through an information processing device, a user interface, and user operation.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The server first collects the necessary data from various sources. It receives pre-configured database connection information and external API endpoint information as input. The server retrieves sales and financial data using SQL queries and HTTP requests. As output, it stores this data in internal memory. Specifically, the server starts the data collection process daily at 9:00 AM.

[0267] Step 2:

[0268] The server analyzes the collected data. It uses existing sales and financial data as input. The server applies machine learning algorithms to detect data trends and anomalies. As output, it generates statistical information and graph data as analysis results. Specifically, the server manipulates dataframes using the Pandas library and applies machine learning models using scikit-learn.

[0269] Step 3:

[0270] The server automatically generates documents based on the analysis results. It receives statistical information and graph data as input. The server uses a template engine to create reports according to company-specific formats. The completed documents are saved as output in PDF or spreadsheet format. Specifically, the server constructs documents using LaTeX or Excel templates.

[0271] Step 4:

[0272] The terminal receives completed documents from the server. It receives PDF and spreadsheet files as input. The terminal provides these to the user and displays them on the interface. As output, it generates a screen display for user confirmation. Specifically, the terminal launches a PDF viewer or online spreadsheet so the user can view the documents in a browser.

[0273] Step 5:

[0274] The user reviews the document through their terminal and requests corrections as needed. The system accepts correction requests for specific parts of the document as input. The user enters these as prompts. The correction commands are sent to the server as output. For example, the user might enter a prompt in the form such as, "Please change the graph range of the sales data to year-on-year comparison."

[0275] Step 6:

[0276] The server re-analyzes and updates the data based on the user's correction instructions. It receives correction commands from the user as input. The server re-analyzes the collected data and generates new data. It generates the corrected data as output and sends it to the terminal. Specifically, the server re-executes a Python script to generate updated graphs and tables.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] In various electronic trading environments, quickly and accurately collecting and analyzing transaction and statistical data is an important issue for enterprises. In particular, extracting important data from the huge number of daily transactions and creating reports that contribute to real-time decision-making places a heavy burden on those responsible for data processing. In addition, responding to urgent corrections after report generation and accelerating information sharing are also required.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for learning the unique structure and arrangement of an enterprise, means for automatically collecting and analyzing transaction data and statistical data from various information sources, and means for creating a report that reflects the latest information in real time. As a result, automation of information processing and reduction of the burden on users become possible.

[0282] The "means for learning the unique structure and arrangement of an enterprise" is a technology for analyzing the format and layout of materials used by a specific enterprise and grasping and reproducing its characteristics.

[0283] The "means for automatically collecting and analyzing transaction data and statistical data from various information sources" is a technology equipped with a function of acquiring necessary data from a plurality of databases and APIs and mechanically processing and analyzing this data.

[0284] The "means for creating a report that reflects the latest information in real time" is a technology for immediately processing information using the collected data and outputting the result as a report in the latest state.

[0285] The "means for visualizing information processing results and outputting them as analysis information" is a technology for converting the extracted analysis data into a format that is easy for users to understand and providing it visually.

[0286] The means of "outputting the generated report in electronic form and promoting appropriate information sharing" is a technology that saves the created report in digital form and enables it to be promptly distributed among relevant parties.

[0287] This invention is a system for enterprises to efficiently collect, analyze transaction data, and generate reports in real time in an electronic trading environment. It is mainly composed of the following elements.

[0288] The server functions as a central processing unit and implements an algorithm for learning the unique structure and layout of the enterprise. Thereby, it automatically analyzes the format and layout of the reports used by the enterprise and collects the necessary data from various information sources. At this time, the server uses APIs to obtain transaction data and statistical information from external databases. A program using Python executes this, and libraries such as pandas and scikit-learn are utilized for data collection and analysis.

[0289] The terminal serves as a user interface and provides the data output from the server to the user. When the user inputs modified or additional information through the terminal, the server immediately reflects it and the report is updated. The generated information is visualized using matplotlib and output as a PDF report. Thereby, while maintaining the consistency of the information, it can be quickly shared with relevant parties.

[0290] The user operates this system, reviews transaction data as needed, and makes decisions based on the analysis results. This process is optimized using a generative AI model, and the optimal output from the system can be obtained through prompt sentences.

[0291] A concrete example is a process where a company analyzes monthly settlement data and outputs the results as a report for use in management meetings. In this case, the user can input prompts into the system such as, "Please generate a report analyzing monthly trends based on the latest settlement data. Specifically, please provide a graph that shows the fluctuations in transaction volume for March in detail and includes predicted trends," and obtain the necessary analysis results.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The server collects transaction data from various sources. It sends requests using API endpoints and database authentication information specified by the user to retrieve transaction data. The input is the API endpoint and authentication information, and the output is transaction data in JSON format.

[0295] Step 2:

[0296] The server analyzes the acquired transaction data. Using the pandas library, it converts the JSON data into a DataFrame, performs data processing such as date formatting, and completes data completion for missing information. The input is transaction data in JSON format, and the output is the processed DataFrame.

[0297] Step 3:

[0298] The server performs trend analysis using statistical models. It leverages the scikit-learn library to build a regression model based on trading data and perform predictions. The input is a DataFrame, and the output is the model's prediction result.

[0299] Step 4:

[0300] The server visualizes the analysis results. Using the matplotlib library, it graphs the trends in trading volume and prediction trends, enabling visual analysis. The input is the prediction result by the model, and the output is the generated graph.

[0301] Step 5:

[0302] The server outputs the generated graph and analysis information as a report in PDF format. Using the FPDF library, it automatically generates a report containing visual information. The input is the generated graph, and the output is the report in PDF format.

[0303] Step 6:

[0304] The terminal presents the generated report to the user. The user can use this to confirm the accuracy of the information and request additional corrections if necessary. The input is the report in PDF format, and the output is the confirmation and feedback by the user.

[0305] Step 7:

[0306] The user makes new analysis and correction requests to the system through the prompt text. Based on this request, the server resumes processing and performs re-collection of necessary data and update of the report. The input is the prompt text by the user, and the output is the updated analysis results and report.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0308] The present invention incorporates an emotion engine for recognizing the user's emotion into a system for streamlining the preparation of corporate financial statements and reducing the burden on the responsible personnel. Thereby, optimization of emotion-based interactions is realized. This system is constructed around a server, a terminal, and a user, and each plays the following roles.

[0309] The server automatically collects necessary information from the company's internal databases and external data sources, and analyzes the data using advanced machine learning. The analyzed data is automatically generated as documents according to the company's specific format and layout. This document generation process is performed in real time, ensuring that the latest information is included. The server also has the capability to quickly respond to user requests for corrections and update documents rapidly.

[0310] The terminal functions as a user interface, providing users with a means to make corrections and comments on documents. In particular, the emotion engine works in conjunction with the terminal to determine the user's emotions from their facial expressions, tone of voice, and typing speed. For example, if the terminal detects that the user is stressed, it changes the display and provides appropriate messages and guidance to improve the user experience.

[0311] Users are the most important users of the system, primarily responsible for creating and revising documents. Based on feedback from the emotion engine, users can check their own emotional state and take action to improve it. The system also records the user's emotional history and uses this information to improve future processes, thereby increasing the overall efficiency of document creation.

[0312] As a concrete example, when a sales representative prepares an important presentation using financial statements, the server retrieves and analyzes the latest sales data and quickly incorporates it into the presentation. The terminal monitors the user's emotional state in real time, and if it detects that tension is rising during presentation preparation, it displays a message suggesting ways to relax. In this way, the system supports efficient document creation and contributes to reducing the user's psychological burden.

[0313] This invention makes it possible to reduce the time required for document creation, improve accuracy, and support the mental well-being of users.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The server initiates the process of collecting necessary information via the company's internal databases or external APIs. During this process, it performs authentication using pre-stored access information and retrieves financial and sales data in real time.

[0317] Step 2:

[0318] The server cleans the collected data. This process removes outliers and handles missing values, formatting the data into an analyzable format. This ensures the accuracy and consistency of the data.

[0319] Step 3:

[0320] The server utilizes machine learning algorithms to analyze the cleaned data. It calculates sales trends and financial indicators, and visualizes these results by creating graphs.

[0321] Step 4:

[0322] The server automatically generates financial statements based on analysis results, adhering to the company's specific format. These statements include text summaries and inserted charts and graphs, and are prepared in a format that can be updated immediately.

[0323] Step 5:

[0324] The device activates an emotion engine to recognize the user's emotions in real time. This recognition utilizes facial expression analysis and speech recognition technology to determine signs of stress and anxiety.

[0325] Step 6:

[0326] The device provides an interface and assistance features that respond to the user's emotional state. For example, if it detects that the user is stressed, it will display calmer colors and messages that encourage relaxation.

[0327] Step 7:

[0328] Users review the document content, taking into account feedback from their devices, and enter correction instructions as needed. These instructions are immediately sent to the server and reflected in the document.

[0329] Step 8:

[0330] The server performs a security check on the completed documents to ensure that confidential information is handled appropriately. Once the documents are deemed secure, they are provided to the user via the terminal.

[0331] Step 9:

[0332] Users review the final document and share it with their superiors and relevant parties as needed. The emotional data recorded by the emotion engine at this stage is then used to improve future document creation.

[0333] (Example 2)

[0334] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0335] In modern business operations, there is a demand for rapid and accurate document creation, which increases the burden on employees in the process. Furthermore, there are often urgent revision requests and the need to integrate diverse data sources. On the other hand, the psychological stress experienced by employees impacts the quality and efficiency of the documents, making stress reduction a crucial issue. Moreover, traditional systems that proceed without considering the emotional state of employees risk reducing user convenience and satisfaction.

[0336] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0337] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data from diverse information sources, and means for creating documents that reflect the latest information in real time. This improves the efficiency of document creation and reduces the burden on the person in charge. Furthermore, it incorporates means for understanding the user's emotional state in conjunction with the information processing means, and dynamically adjusts the interface based on the user's emotional state, thereby simultaneously reducing psychological stress and improving convenience.

[0338] "Company-specific format and layout" refers to the layout and design characteristics of documents used by a particular company, and means creating materials based on those characteristics.

[0339] "Diverse information sources" refers to various sources of information, including not only a company's internal database, but also external online databases and public records.

[0340] "Economic data" refers to all kinds of numerical information related to a company's financial situation and performance, including information such as sales and profits.

[0341] "Real-time updates" means quickly obtaining the latest data up to the time the document was created and immediately reflecting it in the document.

[0342] "Information processing means" refers to technologies and methods that use computers and servers to collect and analyze data and automatically perform the necessary processing.

[0343] "Understanding the user's emotional state" refers to recognizing the user's emotions by inferring them from facial expressions, tone of voice, typing speed, etc.

[0344] "Dynamic interface adjustment" means automatically changing the computer screen and operating environment according to the user's emotional state to provide the optimal user experience.

[0345] "Reducing psychological burden" refers to minimizing the stress associated with document creation and work, enabling users to continue working comfortably.

[0346] This invention provides a system that automates the creation of corporate financial statements and also offers user interaction that takes user emotions into consideration. This system is composed of a server, terminals, and users.

[0347] The server retrieves necessary economic data from the company's internal databases and external information sources. This includes data retrieval via APIs and the use of data crawling techniques. The retrieved information is analyzed using machine learning algorithms. Specifically, predictive models and clustering techniques are utilized to perform sales forecasts and trend analysis. Based on the results of this analysis, documents are automatically generated in a format and layout specific to the company. The server uses a template engine (e.g., Jinja2) to dynamically embed these documents into constituent elements.

[0348] The terminal functions as a user interface, instantly presenting materials to the user. Through this interface, the user can review the materials and input any necessary revisions. The terminal incorporates an emotion engine using open-source libraries (e.g., OpenCV and DeepFace) that analyzes the user's facial expressions, voice tone, and input speed to determine their emotional state. Based on this feedback, the system dynamically adjusts the interface according to the user's emotional state. For example, if the system detects that the user is tense, it displays a message suggesting ways to relax.

[0349] Through this system, users can create and revise documents, understand their own emotional state by utilizing feedback from the emotion engine, and take action to improve as needed. The system also records the user's emotional history, which serves as data to reduce stress and increase efficiency in future document creation processes.

[0350] As a concrete example, consider a scenario where a sales representative is preparing financial statements for a large-scale presentation. In this case, the server automatically retrieves the latest sales data and uses an AI model to forecast sales for the next quarter. The terminal then presents the user with a draft version of the document generated based on this information. If the user enters a comment such as "Adjust the plan based on the sales forecast," the system reflects this in real time. Simultaneously, an emotion engine detects fatigue from the user's facial expressions and displays a message such as "Please take a short break," thereby reducing the user's psychological burden.

[0351] An example of a prompt would be, "Describe a system that streamlines the creation of corporate financial statements and optimizes interactions based on user sentiment." This prompt helps to deepen understanding of how to create documents using generative AI models and how the engine works.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The server collects necessary economic data from the company's internal databases and external sources. Specifically, it uses APIs to retrieve the latest sales data and market trends and stores them in the database. Inputs include API endpoints and data queries, and output is the retrieved raw data. This data is used for subsequent analysis.

[0355] Step 2:

[0356] The server applies machine learning algorithms to the collected raw data for analysis. For example, it calculates sales forecasts for the next quarter using a predictive model and performs trend analysis. The input is the raw data obtained in step 1, and the output is the analyzed results, such as sales forecasts and market trend information. This allows valuable insights to be extracted from the data.

[0357] Step 3:

[0358] The server automatically generates financial statements in a company-specific format and layout based on the analysis results. It utilizes a template engine to dynamically embed the analysis data into the documents. The input is the analysis data obtained in step 2, and the output is the completed financial statements. These documents are immediately ready to be provided to the user.

[0359] Step 4:

[0360] The terminal displays a preview of the generated document to the user. The user can review the document through the terminal and enter revision requests and feedback in the comments section. Input is the user's actions, and output is the document the user reviews and any revisions made. This process allows the user to confirm the user experience.

[0361] Step 5:

[0362] The device analyzes the user's emotional state using an emotion engine. This includes facial recognition using the camera and microphone, as well as voice analysis. The input is the user's visual and auditory information, and the output is the analyzed emotional state, such as "tension" or "fatigue." Based on this, the interface is adjusted to display appropriate feedback to the user.

[0363] Step 6:

[0364] Users continue their work based on document creation and emotional feedback. They perform a final review of the document through their device and make final adjustments as needed. Input consists of system feedback and user judgment, while output is the revised, final version of the document. This process allows users to create documents efficiently and without stress.

[0365] (Application Example 2)

[0366] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0367] Document creation processes in business operations are complex and time-consuming, and responding to urgent revision requests and information updates is particularly burdensome. Furthermore, the mental burden and stress associated with these tasks can negatively impact work efficiency. Therefore, there is a need to build systems that reflect information in real time and optimize user interaction based on user emotions.

[0368] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0369] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data and sales information from diverse sources, and means for creating documents that reflect the latest information in real time. This makes it possible to improve the efficiency of document creation, reduce the burden on users, and support their mental well-being in the workplace.

[0370] "Company-specific formats and layouts" refer to the unique layouts and formats used by each company, which the system learns and utilizes in document creation.

[0371] "Diverse information sources" refers to various types of data sources, such as internal company databases, publicly available external information, and databases of other companies.

[0372] "Economic data" refers to financial information such as a company's financial status, market trends, and sales data.

[0373] "Sales information" refers to information related to sales activities, such as sales analysis, customer information, and marketing data.

[0374] "Documents that reflect the latest information in real time" refers to the process of generating documents that are instantly updated based on the most recent data.

[0375] "Always ready to respond to urgent change requests" means being able to quickly respond to sudden requests for document changes at any time.

[0376] "Past revision history" refers to a record of previous document modifications, which is data used for future document creation.

[0377] "Task and schedule management" refers to methods of supporting efficient work by continuously monitoring the progress and schedule of tasks.

[0378] "Recognizing user emotions" is a process of analyzing the user's facial expressions, tone of voice, input speed, etc., to evaluate their emotional state.

[0379] The system for realizing this invention primarily consists of three elements: a server, a terminal, and a user. The server learns the company's specific format and arrangement, and automatically collects and analyzes economic data and sales information from diverse sources. This process utilizes database management systems and machine learning libraries. Specific software used for database management might include MySQL or PostgreSQL, while machine learning might utilize TensorFlow or PyTorch. The server updates information in real time and can automatically generate documents based on the most up-to-date information.

[0380] The terminal provides users with a means to access the system through a user interface and make necessary changes or review documents. The terminal works in conjunction with an emotion analysis engine to evaluate the user's emotional state. Common PCs and smart devices are used as hardware, and "EmotionAPI" and "FaceAPI" are employed for emotion recognition. This emotional data is used to optimize the process and reduce the user's mental burden.

[0381] For example, when a sales representative creates a presentation document for a client, the server collects and analyzes the latest economic data in real time and automatically generates the document based on that data. When a user feels stressed, the device suggests ways to relax.

[0382] In developing systems that utilize generative AI models, an example of a prompt might be: "Design an algorithm that senses the tension level of staff interacting with customers in real time and displays stress-relieving advice on the smart glasses' display." By using this prompt, it is possible to generate an algorithm that responds appropriately according to the user's state.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] The server collects necessary economic and sales data from the company's database. It takes the company's database connection information as input and executes SQL queries to extract data such as specified financial reports and sales records. This data is the raw material for use in the next analysis step.

[0386] Step 2:

[0387] The server analyzes the collected data using machine learning libraries. Specifically, it uses TensorFlow and PyTorch to model historical data and different patterns, and generates real-time predictions. It converts the input data into features, uses the prediction model to calculate future trends, and provides the output to the document generation step.

[0388] Step 3:

[0389] The server automatically generates documents based on analysis results and company-specific formats. Using document templates and analysis results as input, the program places the latest data in the appropriate locations within the document and outputs the completed document. The generated documents are designed to reflect the latest information while maintaining consistency.

[0390] Step 4:

[0391] The terminal displays the generated document to the user and provides an interface for editing or modifying it. It receives feedback and modification instructions from the user and sends them to the server. As a result, the document content is modified and a new version is saved.

[0392] Step 5:

[0393] The device analyzes the user's emotional state in real time. The emotion analysis engine uses EmotionAPI and FaceAPI, receiving data such as the user's facial expressions and voice tone as input. Based on this data, it evaluates the user's tension and stress levels and displays advice on the screen according to the results.

[0394] Step 6:

[0395] Users receive emotional feedback from their devices and use it to improve their work. Suggestions and guidelines for stress reduction are displayed on the device as output, and by immediately implementing these, users can create more effective documents.

[0396] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0397] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0398] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0402] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0403] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0404] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0405] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0406] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0407] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0408] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0409] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0410] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0411] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0412] This system aims to efficiently create corporate financial statements and reduce the burden on those responsible for preparing them. It consists of three elements: a server, terminals, and users, each fulfilling its respective role to function.

[0413] The server acts as the central processing unit, collecting necessary information from the company's internal databases and external APIs. Using pre-configured access information, the server initiates data collection according to a schedule and analyzes the collected data quickly and accurately. Specifically, it analyzes data trends and patterns using statistical models and machine learning algorithms, and automatically generates reports based on this analysis. These reports are created according to the company's specific format and layout, and can also be customized to meet individual user requirements.

[0414] The terminal functions as a user interface, receiving instructions from the user and providing output from the server to the user. Through the terminal, the user can input necessary corrections in real time, which the server processes immediately and reflects in the document. This allows for quick responses to urgent corrections, reducing stress on the person in charge.

[0415] The user is the primary operator of this system, mainly creating documents and issuing revision requests. Past revision history is recorded by the server and used as learning data for subsequent document creation, enabling the automatic reflection of similar revisions. This feedback loop further improves the efficiency of the document creation process.

[0416] As a concrete example, consider a scenario where a finance officer prepares a quarterly earnings report. The server first collects the latest sales data from the ERP system, automatically calculates key metrics such as year-on-year comparisons, and graphs the trends. When a user requests additional details for a specific expense item via a terminal, the server re-analyzes the relevant data and immediately reflects it in the report. As a result, the completed report is quickly submitted to management.

[0417] In this way, the present invention significantly increases the efficiency of the document creation process and reduces the effort and time of the person in charge, thereby contributing to the improvement of overall company productivity.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] The server performs an initialization process to establish connections to data sources both inside and outside the enterprise. This process loads the necessary API keys and database credentials, ensuring security and preparing access for future use.

[0421] Step 2:

[0422] The server initiates data collection according to scheduled tasks. Internal data is retrieved from the database via SQL queries, while external data is retrieved by sending HTTP requests via APIs. This allows for real-time information to be obtained.

[0423] Step 3:

[0424] The server cleans the acquired data. Specifically, it imputes missing values ​​in the data using appropriate methods and filters out outliers. It also normalizes the data and converts it into a unified format in preparation for analysis.

[0425] Step 4:

[0426] The server analyzes the cleansed data. Using statistical methods and machine learning, it extracts data trends and key indicators, preparing the information necessary for financial statements. The analysis results are automatically visualized as graphs and tables.

[0427] Step 5:

[0428] Based on the analysis results, the server automatically generates financial statements tailored to the company's specific format and layout. This includes inserting standard phrases and creating presentation slides.

[0429] Step 6:

[0430] Users input necessary correction instructions via their terminals. This allows for flexible responses to urgent correction requests. The server analyzes the corrections and updates the document immediately.

[0431] Step 7:

[0432] The server performs a security check on the completed document to ensure that confidential information is handled appropriately. After this check, the document is provided to the user via the terminal.

[0433] Step 8:

[0434] If revisions are needed based on user-submitted materials and feedback, the server learns from that information and uses it to improve future material creation. This enables a sequential learning function, contributing to increased efficiency in material creation.

[0435] (Example 1)

[0436] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] In corporate document creation processes, manual information gathering and analysis are time-consuming and labor-intensive, and responding to urgent revision requests can create additional burdens. In particular, creating documents quickly while maintaining data consistency and accuracy is a challenging task. Furthermore, there is a need to build efficient processes that utilize past revision history.

[0438] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0439] In this invention, the server includes means for collecting and analyzing information from diverse information sources, means for performing data analysis using statistical models and machine learning algorithms and automatically generating documents in a company-specific format, and means for receiving user input and reflecting those requests in the documents in real time using an information processing device. This enables rapid and accurate collection and analysis of information, automatic generation of documents, immediate response to user requests, and efficient document creation that leverages past experience.

[0440] An "information processing device" refers to a computer system used for collecting, analyzing, generating, and displaying data.

[0441] "Information sources" refer to various sources, including internal databases and external APIs from which financial and sales data are obtained.

[0442] A "statistical model" refers to an algorithm that mathematically analyzes data patterns and trends, and is useful for prediction and analysis.

[0443] A "machine learning algorithm" is a method for learning from data to make predictions and recognize patterns, and it is a technology that constitutes part of artificial intelligence.

[0444] "Real-time" refers to the characteristic of immediately responding to user requests and inputs and updating documents and information accordingly.

[0445] "Automatic document generation" refers to the process of automatically creating documents on a computer using analysis results, based on pre-set formats and layouts.

[0446] "User input" refers to user interaction, such as making corrections to documents or requesting additional information, via a terminal.

[0447] "Past revision history" refers to a record of revisions made when a document was previously created, and this data is used to improve the accuracy and efficiency of documents in the future by referring to it.

[0448] This invention aims to streamline corporate document creation based on an information processing system. The server, acting as an information processing device, collects necessary information from internal corporate databases and external APIs. This server can utilize common data acquisition protocols, such as SQL databases and RESTful APIs. The server performs data trend analysis and prediction by implementing statistical models and machine learning algorithms using programming languages ​​such as Python and R.

[0449] The server automatically generates documents in a company-specific format based on the analysis results. These documents can be visualized using spreadsheet software such as Microsoft Excel or Google Sheets, and then converted to a format such as PDF for distribution.

[0450] The terminal functions as a user interface, through which users can review and modify documents. The software running on the terminal is typically designed as a web application that can run in a browser, and is built using HTML, CSS, JavaScript, etc. Users can easily make corrections to documents through the interface displayed on the terminal.

[0451] The user instructs the server to start generating data by using a prompt such as, "Please graph the latest sales data and generate a document that comprehensively shows the sales trend." In response to this prompt, the server generates the most suitable document according to the specified requirements and returns the results through the terminal.

[0452] Thus, the present invention is a system that can significantly streamline and improve the accuracy of a company's document creation process through an information processing device, a user interface, and user operation.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The server first collects the necessary data from various sources. It receives pre-configured database connection information and external API endpoint information as input. The server retrieves sales and financial data using SQL queries and HTTP requests. As output, it stores this data in internal memory. Specifically, the server starts the data collection process daily at 9:00 AM.

[0456] Step 2:

[0457] The server analyzes the collected data. It uses existing sales and financial data as input. The server applies machine learning algorithms to detect data trends and anomalies. As output, it generates statistical information and graph data as analysis results. Specifically, the server manipulates dataframes using the Pandas library and applies machine learning models using scikit-learn.

[0458] Step 3:

[0459] The server automatically generates documents based on the analysis results. It receives statistical information and graph data as input. The server uses a template engine to create reports according to company-specific formats. The completed documents are saved as output in PDF or spreadsheet format. Specifically, the server constructs documents using LaTeX or Excel templates.

[0460] Step 4:

[0461] The terminal receives completed documents from the server. It receives PDF and spreadsheet files as input. The terminal provides these to the user and displays them on the interface. As output, it generates a screen display for user confirmation. Specifically, the terminal launches a PDF viewer or online spreadsheet so the user can view the documents in a browser.

[0462] Step 5:

[0463] The user reviews the document through their terminal and requests corrections as needed. The system accepts correction requests for specific parts of the document as input. The user enters these as prompts. The correction commands are sent to the server as output. For example, the user might enter a prompt in the form such as, "Please change the graph range of the sales data to year-on-year comparison."

[0464] Step 6:

[0465] The server re-analyzes and updates the data based on the user's correction instructions. It receives correction commands from the user as input. The server re-analyzes the collected data and generates new data. It generates the corrected data as output and sends it to the terminal. Specifically, the server re-executes a Python script to generate updated graphs and tables.

[0466] (Application Example 1)

[0467] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0468] In diverse electronic trading environments, the rapid and accurate collection and analysis of transaction and statistical data is a critical challenge for businesses. In particular, extracting important data from the vast volume of daily transactions and creating real-time reports to aid decision-making places a significant burden on those responsible for data processing. Furthermore, there is a need to respond quickly to urgent revisions after report generation and to expedite information sharing.

[0469] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0470] In this invention, the server includes means for learning the unique structure and layout of a company, means for automatically collecting and analyzing transaction data and statistical data from diverse sources, and means for creating reports that reflect the latest information in real time. This enables the automation of information processing and reduces the burden on users.

[0471] "Methods for learning company-specific structures and layouts" refer to techniques for analyzing the format and layout of documents used by a specific company and understanding and reproducing their characteristics.

[0472] "Means for automatically collecting and analyzing transaction data and statistical data from diverse information sources" refers to technologies equipped with the functionality to acquire necessary data from multiple databases and APIs, and to process and analyze it mechanically.

[0473] "A means of creating reports that reflect the latest information in real time" refers to a technology that processes collected data immediately and outputs the results as a report in an up-to-date state.

[0474] "Means for visualizing information processing results and outputting them as analytical information" refers to technologies for converting extracted analytical data into a format that is easy for users to understand and providing it visually.

[0475] "A means of outputting generated reports in electronic format and facilitating appropriate information sharing" refers to technology that allows created reports to be saved in digital format and quickly shared among relevant parties.

[0476] This invention is a system that enables companies to efficiently collect and analyze transaction data in an electronic transaction environment and generate reports in real time. It mainly consists of the following elements:

[0477] The server functions as a central processing unit and implements algorithms that learn the company's specific structure and layout. This allows it to automatically analyze the format and layout of reports used by the company and collect necessary data from diverse sources. In this process, the server uses APIs to retrieve transaction data and statistical information from external databases. A Python program executes this process, utilizing libraries such as pandas and scikit-learn for data collection and analysis.

[0478] The terminal acts as a user interface, providing users with data output from the server. When users input corrections or additional information through the terminal, the server immediately reflects the changes, and the report is updated. The generated information is visualized using matplotlib and output as a report in PDF format. This allows for quick sharing of information with stakeholders while maintaining consistency.

[0479] Users interact with this system, review trading data as needed, and make decisions based on the analysis results. This process is streamlined using generative AI models, and optimal output is obtained from the system through prompt messages.

[0480] A concrete example is a process where a company analyzes monthly settlement data and outputs the results as a report for use in management meetings. In this case, the user can input prompts into the system such as, "Please generate a report analyzing monthly trends based on the latest settlement data. Specifically, please provide a graph that shows the fluctuations in transaction volume for March in detail and includes predicted trends," and obtain the necessary analysis results.

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The server collects transaction data from various sources. It sends requests using API endpoints and database authentication information specified by the user to retrieve transaction data. The input is the API endpoint and authentication information, and the output is transaction data in JSON format.

[0484] Step 2:

[0485] The server analyzes the acquired transaction data. Using the pandas library, it converts the JSON data into a DataFrame, performs data processing such as date formatting, and completes data completion for missing information. The input is transaction data in JSON format, and the output is the processed DataFrame.

[0486] Step 3:

[0487] The server performs trend analysis using statistical models. It leverages the scikit-learn library to build a regression model based on trading data and perform predictions. The input is a DataFrame, and the output is the model's prediction result.

[0488] Step 4:

[0489] The server visualizes the analysis results. Using the matplotlib library, it graphs the trends in trading volume and predictions, enabling visual analysis. The input is the prediction results from the model, and the output is the generated graph.

[0490] Step 5:

[0491] The server outputs the generated graphs and analysis information as a report in PDF format. It uses the FPDF library to automatically generate reports that include visual information. The input is the generated graphs, and the output is a PDF report.

[0492] Step 6:

[0493] The terminal presents the generated report to the user. The user can use this to verify the accuracy of the information and request additional corrections as needed. The input is a report in PDF format, and the output is user review and feedback.

[0494] Step 7:

[0495] The user submits new analysis and modification requests to the system through prompt messages. Based on these requests, the server resumes processing, recollects the necessary data, and updates the report. The input is the user's prompt messages, and the output is the updated analysis results and report.

[0496] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0497] This invention incorporates an emotion engine that recognizes user emotions into a system designed to streamline the preparation of corporate financial statements and reduce the burden on employees. This enables the optimization of emotion-based interactions. The system is built around a server, terminals, and users, each playing the following roles.

[0498] The server automatically collects necessary information from the company's internal databases and external data sources, and analyzes the data using advanced machine learning. The analyzed data is automatically generated as documents according to the company's specific format and layout. This document generation process is performed in real time, ensuring that the latest information is included. The server also has the capability to quickly respond to user requests for corrections and update documents rapidly.

[0499] The terminal functions as a user interface, providing users with a means to make corrections and comments on documents. In particular, the emotion engine works in conjunction with the terminal to determine the user's emotions from their facial expressions, tone of voice, and typing speed. For example, if the terminal detects that the user is stressed, it changes the display and provides appropriate messages and guidance to improve the user experience.

[0500] Users are the most important users of the system, primarily responsible for creating and revising documents. Based on feedback from the emotion engine, users can check their own emotional state and take action to improve it. The system also records the user's emotional history and uses this information to improve future processes, thereby increasing the overall efficiency of document creation.

[0501] As a concrete example, when a sales representative prepares an important presentation using financial statements, the server retrieves and analyzes the latest sales data and quickly incorporates it into the presentation. The terminal monitors the user's emotional state in real time, and if it detects that tension is rising during presentation preparation, it displays a message suggesting ways to relax. In this way, the system supports efficient document creation and contributes to reducing the user's psychological burden.

[0502] This invention makes it possible to reduce the time required for document creation, improve accuracy, and support the mental well-being of users.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] The server initiates the process of collecting necessary information via the company's internal databases or external APIs. During this process, it performs authentication using pre-stored access information and retrieves financial and sales data in real time.

[0506] Step 2:

[0507] The server cleans the collected data. This process removes outliers and handles missing values, formatting the data into an analyzable format. This ensures the accuracy and consistency of the data.

[0508] Step 3:

[0509] The server utilizes machine learning algorithms to analyze the cleaned data. It calculates sales trends and financial indicators, and visualizes these results by creating graphs.

[0510] Step 4:

[0511] The server automatically generates financial statements based on analysis results, adhering to the company's specific format. These statements include text summaries and inserted charts and graphs, and are prepared in a format that can be updated immediately.

[0512] Step 5:

[0513] The device activates an emotion engine to recognize the user's emotions in real time. This recognition utilizes facial expression analysis and speech recognition technology to determine signs of stress and anxiety.

[0514] Step 6:

[0515] The device provides an interface and assistance features that respond to the user's emotional state. For example, if it detects that the user is stressed, it will display calmer colors and messages that encourage relaxation.

[0516] Step 7:

[0517] Users review the document content, taking into account feedback from their devices, and enter correction instructions as needed. These instructions are immediately sent to the server and reflected in the document.

[0518] Step 8:

[0519] The server performs a security check on the completed documents to ensure that confidential information is handled appropriately. Once the documents are deemed secure, they are provided to the user via the terminal.

[0520] Step 9:

[0521] Users review the final document and share it with their superiors and relevant parties as needed. The emotional data recorded by the emotion engine at this stage is then used to improve future document creation.

[0522] (Example 2)

[0523] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] In modern business operations, there is a demand for rapid and accurate document creation, which increases the burden on employees in the process. Furthermore, there are often urgent revision requests and the need to integrate diverse data sources. On the other hand, the psychological stress experienced by employees impacts the quality and efficiency of the documents, making stress reduction a crucial issue. Moreover, traditional systems that proceed without considering the emotional state of employees risk reducing user convenience and satisfaction.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0526] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data from diverse information sources, and means for creating documents that reflect the latest information in real time. This improves the efficiency of document creation and reduces the burden on the person in charge. Furthermore, it incorporates means for understanding the user's emotional state in conjunction with the information processing means, and dynamically adjusts the interface based on the user's emotional state, thereby simultaneously reducing psychological stress and improving convenience.

[0527] "Company-specific format and layout" refers to the layout and design characteristics of documents used by a particular company, and means creating materials based on those characteristics.

[0528] "Diverse information sources" refers to various sources of information, including not only a company's internal database, but also external online databases and public records.

[0529] "Economic data" refers to all kinds of numerical information related to a company's financial situation and performance, including information such as sales and profits.

[0530] "Real-time updates" means quickly obtaining the latest data up to the time the document was created and immediately reflecting it in the document.

[0531] "Information processing means" refers to technologies and methods that use computers and servers to collect and analyze data and automatically perform the necessary processing.

[0532] "Understanding the user's emotional state" refers to recognizing the user's emotions by inferring them from facial expressions, tone of voice, typing speed, etc.

[0533] "Dynamic interface adjustment" means automatically changing the computer screen and operating environment according to the user's emotional state to provide the optimal user experience.

[0534] "Reducing psychological burden" refers to minimizing the stress associated with document creation and work, enabling users to continue working comfortably.

[0535] This invention provides a system that automates the creation of corporate financial statements and also offers user interaction that takes user emotions into consideration. This system is composed of a server, terminals, and users.

[0536] The server retrieves necessary economic data from the company's internal databases and external information sources. This includes data retrieval via APIs and the use of data crawling techniques. The retrieved information is analyzed using machine learning algorithms. Specifically, predictive models and clustering techniques are utilized to perform sales forecasts and trend analysis. Based on the results of this analysis, documents are automatically generated in a format and layout specific to the company. The server uses a template engine (e.g., Jinja2) to dynamically embed these documents into constituent elements.

[0537] The terminal functions as a user interface, instantly presenting materials to the user. Through this interface, the user can review the materials and input any necessary revisions. The terminal incorporates an emotion engine using open-source libraries (e.g., OpenCV and DeepFace) that analyzes the user's facial expressions, voice tone, and input speed to determine their emotional state. Based on this feedback, the system dynamically adjusts the interface according to the user's emotional state. For example, if the system detects that the user is tense, it displays a message suggesting ways to relax.

[0538] Through this system, users can create and revise documents, understand their own emotional state by utilizing feedback from the emotion engine, and take action to improve as needed. The system also records the user's emotional history, which serves as data to reduce stress and increase efficiency in future document creation processes.

[0539] As a concrete example, consider a scenario where a sales representative is preparing financial statements for a large-scale presentation. In this case, the server automatically retrieves the latest sales data and uses an AI model to forecast sales for the next quarter. The terminal then presents the user with a draft version of the document generated based on this information. If the user enters a comment such as "Adjust the plan based on the sales forecast," the system reflects this in real time. Simultaneously, an emotion engine detects fatigue from the user's facial expressions and displays a message such as "Please take a short break," thereby reducing the user's psychological burden.

[0540] An example of a prompt would be, "Describe a system that streamlines the creation of corporate financial statements and optimizes interactions based on user sentiment." This prompt helps to deepen understanding of how to create documents using generative AI models and how the engine works.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The server collects necessary economic data from the company's internal databases and external sources. Specifically, it uses APIs to retrieve the latest sales data and market trends and stores them in the database. Inputs include API endpoints and data queries, and output is the retrieved raw data. This data is used for subsequent analysis.

[0544] Step 2:

[0545] The server applies machine learning algorithms to the collected raw data for analysis. For example, it calculates sales forecasts for the next quarter using a predictive model and performs trend analysis. The input is the raw data obtained in step 1, and the output is the analyzed results, such as sales forecasts and market trend information. This allows valuable insights to be extracted from the data.

[0546] Step 3:

[0547] The server automatically generates financial statements in a company-specific format and layout based on the analysis results. It utilizes a template engine to dynamically embed the analysis data into the documents. The input is the analysis data obtained in step 2, and the output is the completed financial statements. These documents are immediately ready to be provided to the user.

[0548] Step 4:

[0549] The terminal displays a preview of the generated document to the user. The user can review the document through the terminal and enter revision requests and feedback in the comments section. Input is the user's actions, and output is the document the user reviews and any revisions made. This process allows the user to confirm the user experience.

[0550] Step 5:

[0551] The device analyzes the user's emotional state using an emotion engine. This includes facial recognition using the camera and microphone, as well as voice analysis. The input is the user's visual and auditory information, and the output is the analyzed emotional state, such as "tension" or "fatigue." Based on this, the interface is adjusted to display appropriate feedback to the user.

[0552] Step 6:

[0553] Users continue their work based on document creation and emotional feedback. They perform a final review of the document through their device and make final adjustments as needed. Input consists of system feedback and user judgment, while output is the revised, final version of the document. This process allows users to create documents efficiently and without stress.

[0554] (Application Example 2)

[0555] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0556] Document creation processes in business operations are complex and time-consuming, and responding to urgent revision requests and information updates is particularly burdensome. Furthermore, the mental burden and stress associated with these tasks can negatively impact work efficiency. Therefore, there is a need to build systems that reflect information in real time and optimize user interaction based on user emotions.

[0557] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0558] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data and sales information from diverse sources, and means for creating documents that reflect the latest information in real time. This makes it possible to improve the efficiency of document creation, reduce the burden on users, and support their mental well-being in the workplace.

[0559] "Company-specific formats and layouts" refer to the unique layouts and formats used by each company, which the system learns and utilizes in document creation.

[0560] "Diverse information sources" refers to various types of data sources, such as internal company databases, publicly available external information, and databases of other companies.

[0561] "Economic data" refers to financial information such as a company's financial status, market trends, and sales data.

[0562] "Sales information" refers to information related to sales activities, such as sales analysis, customer information, and marketing data.

[0563] "Documents that reflect the latest information in real time" refers to the process of generating documents that are instantly updated based on the most recent data.

[0564] "Always ready to respond to urgent change requests" means being able to quickly respond to sudden requests for document changes at any time.

[0565] "Past revision history" refers to a record of previous document modifications, which is data used for future document creation.

[0566] "Task and schedule management" refers to methods of supporting efficient work by continuously monitoring the progress and schedule of tasks.

[0567] "Recognizing user emotions" is a process of analyzing the user's facial expressions, tone of voice, input speed, etc., to evaluate their emotional state.

[0568] The system for realizing this invention primarily consists of three elements: a server, a terminal, and a user. The server learns the company's specific format and arrangement, and automatically collects and analyzes economic data and sales information from diverse sources. This process utilizes database management systems and machine learning libraries. Specific software used for database management might include MySQL or PostgreSQL, while machine learning might utilize TensorFlow or PyTorch. The server updates information in real time and can automatically generate documents based on the most up-to-date information.

[0569] The terminal provides users with a means to access the system through a user interface and make necessary changes or review documents. The terminal works in conjunction with an emotion analysis engine to evaluate the user's emotional state. Common PCs and smart devices are used as hardware, and "EmotionAPI" and "FaceAPI" are employed for emotion recognition. This emotional data is used to optimize the process and reduce the user's mental burden.

[0570] For example, when a sales representative creates a presentation document for a client, the server collects and analyzes the latest economic data in real time and automatically generates the document based on that data. When a user feels stressed, the device suggests ways to relax.

[0571] In developing systems that utilize generative AI models, an example of a prompt might be: "Design an algorithm that senses the tension level of staff interacting with customers in real time and displays stress-relieving advice on the smart glasses' display." By using this prompt, it is possible to generate an algorithm that responds appropriately according to the user's state.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] The server collects necessary economic and sales data from the company's database. It takes the company's database connection information as input and executes SQL queries to extract data such as specified financial reports and sales records. This data is the raw material for use in the next analysis step.

[0575] Step 2:

[0576] The server analyzes the collected data using machine learning libraries. Specifically, it uses TensorFlow and PyTorch to model historical data and different patterns, and generates real-time predictions. It converts the input data into features, uses the prediction model to calculate future trends, and provides the output to the document generation step.

[0577] Step 3:

[0578] The server automatically generates documents based on analysis results and company-specific formats. Using document templates and analysis results as input, the program places the latest data in the appropriate locations within the document and outputs the completed document. The generated documents are designed to reflect the latest information while maintaining consistency.

[0579] Step 4:

[0580] The terminal displays the generated document to the user and provides an interface for editing or modifying it. It receives feedback and modification instructions from the user and sends them to the server. As a result, the document content is modified and a new version is saved.

[0581] Step 5:

[0582] The device analyzes the user's emotional state in real time. The emotion analysis engine uses EmotionAPI and FaceAPI, receiving data such as the user's facial expressions and voice tone as input. Based on this data, it evaluates the user's tension and stress levels and displays advice on the screen according to the results.

[0583] Step 6:

[0584] Users receive emotional feedback from their devices and use it to improve their work. Suggestions and guidelines for stress reduction are displayed on the device as output, and by immediately implementing these, users can create more effective documents.

[0585] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0596] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0598] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0599] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] This system aims to efficiently create corporate financial statements and reduce the burden on those responsible for preparing them. It consists of three elements: a server, terminals, and users, each fulfilling its respective role to function.

[0603] The server acts as the central processing unit, collecting necessary information from the company's internal databases and external APIs. Using pre-configured access information, the server initiates data collection according to a schedule and analyzes the collected data quickly and accurately. Specifically, it analyzes data trends and patterns using statistical models and machine learning algorithms, and automatically generates reports based on this analysis. These reports are created according to the company's specific format and layout, and can also be customized to meet individual user requirements.

[0604] The terminal functions as a user interface, receiving instructions from the user and providing output from the server to the user. Through the terminal, the user can input necessary corrections in real time, which the server processes immediately and reflects in the document. This allows for quick responses to urgent corrections, reducing stress on the person in charge.

[0605] The user is the primary operator of this system, mainly creating documents and issuing revision requests. Past revision history is recorded by the server and used as learning data for subsequent document creation, enabling the automatic reflection of similar revisions. This feedback loop further improves the efficiency of the document creation process.

[0606] As a concrete example, consider a scenario where a finance officer prepares a quarterly earnings report. The server first collects the latest sales data from the ERP system, automatically calculates key metrics such as year-on-year comparisons, and graphs the trends. When a user requests additional details for a specific expense item via a terminal, the server re-analyzes the relevant data and immediately reflects it in the report. As a result, the completed report is quickly submitted to management.

[0607] In this way, the present invention significantly increases the efficiency of the document creation process and reduces the effort and time of the person in charge, thereby contributing to the improvement of overall company productivity.

[0608] The following describes the processing flow.

[0609] Step 1:

[0610] The server performs an initialization process to establish connections to data sources both inside and outside the enterprise. This process loads the necessary API keys and database credentials, ensuring security and preparing access for future use.

[0611] Step 2:

[0612] The server initiates data collection according to scheduled tasks. Internal data is retrieved from the database via SQL queries, while external data is retrieved by sending HTTP requests via APIs. This allows for real-time information to be obtained.

[0613] Step 3:

[0614] The server cleans the acquired data. Specifically, it imputes missing values ​​in the data using appropriate methods and filters out outliers. It also normalizes the data and converts it into a unified format in preparation for analysis.

[0615] Step 4:

[0616] The server analyzes the cleansed data. Using statistical methods and machine learning, it extracts data trends and key indicators, preparing the information necessary for financial statements. The analysis results are automatically visualized as graphs and tables.

[0617] Step 5:

[0618] Based on the analysis results, the server automatically generates financial statements tailored to the company's specific format and layout. This includes inserting standard phrases and creating presentation slides.

[0619] Step 6:

[0620] Users input necessary correction instructions via their terminals. This allows for flexible responses to urgent correction requests. The server analyzes the corrections and updates the document immediately.

[0621] Step 7:

[0622] The server performs a security check on the completed document to ensure that confidential information is handled appropriately. After this check, the document is provided to the user via the terminal.

[0623] Step 8:

[0624] If revisions are needed based on user-submitted materials and feedback, the server learns from that information and uses it to improve future material creation. This enables a sequential learning function, contributing to increased efficiency in material creation.

[0625] (Example 1)

[0626] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] In corporate document creation processes, manual information gathering and analysis are time-consuming and labor-intensive, and responding to urgent revision requests can create additional burdens. In particular, creating documents quickly while maintaining data consistency and accuracy is a challenging task. Furthermore, there is a need to build efficient processes that utilize past revision history.

[0628] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0629] In this invention, the server includes means for collecting and analyzing information from diverse information sources, means for performing data analysis using statistical models and machine learning algorithms and automatically generating documents in a company-specific format, and means for receiving user input and reflecting those requests in the documents in real time using an information processing device. This enables rapid and accurate collection and analysis of information, automatic generation of documents, immediate response to user requests, and efficient document creation that leverages past experience.

[0630] An "information processing device" refers to a computer system used for collecting, analyzing, generating, and displaying data.

[0631] "Information sources" refer to various sources, including internal databases and external APIs from which financial and sales data are obtained.

[0632] A "statistical model" refers to an algorithm that mathematically analyzes data patterns and trends, and is useful for prediction and analysis.

[0633] A "machine learning algorithm" is a method for learning from data to make predictions and recognize patterns, and it is a technology that constitutes part of artificial intelligence.

[0634] "Real-time" refers to the characteristic of immediately responding to user requests and inputs and updating documents and information accordingly.

[0635] "Automatic document generation" refers to the process of automatically creating documents on a computer using analysis results, based on pre-set formats and layouts.

[0636] "User input" refers to user interaction, such as making corrections to documents or requesting additional information, via a terminal.

[0637] "Past revision history" refers to a record of revisions made when a document was previously created, and this data is used to improve the accuracy and efficiency of documents in the future by referring to it.

[0638] This invention aims to streamline corporate document creation based on an information processing system. The server, acting as an information processing device, collects necessary information from internal corporate databases and external APIs. This server can utilize common data acquisition protocols, such as SQL databases and RESTful APIs. The server performs data trend analysis and prediction by implementing statistical models and machine learning algorithms using programming languages ​​such as Python and R.

[0639] The server automatically generates documents in a company-specific format based on the analysis results. These documents can be visualized using spreadsheet software such as Microsoft Excel or Google Sheets, and then converted to a format such as PDF for distribution.

[0640] The terminal functions as a user interface, through which users can review and modify documents. The software running on the terminal is typically designed as a web application that can run in a browser, and is built using HTML, CSS, JavaScript, etc. Users can easily make corrections to documents through the interface displayed on the terminal.

[0641] The user instructs the server to start generating data by using a prompt such as, "Please graph the latest sales data and generate a document that comprehensively shows the sales trend." In response to this prompt, the server generates the most suitable document according to the specified requirements and returns the results through the terminal.

[0642] Thus, the present invention is a system that can significantly streamline and improve the accuracy of a company's document creation process through an information processing device, a user interface, and user operation.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] The server first collects the necessary data from various sources. It receives pre-configured database connection information and external API endpoint information as input. The server retrieves sales and financial data using SQL queries and HTTP requests. As output, it stores this data in internal memory. Specifically, the server starts the data collection process daily at 9:00 AM.

[0646] Step 2:

[0647] The server analyzes the collected data. It uses existing sales and financial data as input. The server applies machine learning algorithms to detect data trends and anomalies. As output, it generates statistical information and graph data as analysis results. Specifically, the server manipulates dataframes using the Pandas library and applies machine learning models using scikit-learn.

[0648] Step 3:

[0649] The server automatically generates documents based on the analysis results. It receives statistical information and graph data as input. The server uses a template engine to create reports according to company-specific formats. The completed documents are saved as output in PDF or spreadsheet format. Specifically, the server constructs documents using LaTeX or Excel templates.

[0650] Step 4:

[0651] The terminal receives completed documents from the server. It receives PDF and spreadsheet files as input. The terminal provides these to the user and displays them on the interface. As output, it generates a screen display for user confirmation. Specifically, the terminal launches a PDF viewer or online spreadsheet so the user can view the documents in a browser.

[0652] Step 5:

[0653] The user reviews the document through their terminal and requests corrections as needed. The system accepts correction requests for specific parts of the document as input. The user enters these as prompts. The correction commands are sent to the server as output. For example, the user might enter a prompt in the form such as, "Please change the graph range of the sales data to year-on-year comparison."

[0654] Step 6:

[0655] The server re-analyzes and updates the data based on the user's correction instructions. It receives correction commands from the user as input. The server re-analyzes the collected data and generates new data. It generates the corrected data as output and sends it to the terminal. Specifically, the server re-executes a Python script to generate updated graphs and tables.

[0656] (Application Example 1)

[0657] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] In diverse electronic trading environments, the rapid and accurate collection and analysis of transaction and statistical data is a critical challenge for businesses. In particular, extracting important data from the vast volume of daily transactions and creating real-time reports to aid decision-making places a significant burden on those responsible for data processing. Furthermore, there is a need to respond quickly to urgent revisions after report generation and to expedite information sharing.

[0659] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0660] In this invention, the server includes means for learning the unique structure and layout of a company, means for automatically collecting and analyzing transaction data and statistical data from diverse sources, and means for creating reports that reflect the latest information in real time. This enables the automation of information processing and reduces the burden on users.

[0661] "Methods for learning company-specific structures and layouts" refer to techniques for analyzing the format and layout of documents used by a specific company and understanding and reproducing their characteristics.

[0662] "Means for automatically collecting and analyzing transaction data and statistical data from diverse information sources" refers to technologies equipped with the functionality to acquire necessary data from multiple databases and APIs, and to process and analyze it mechanically.

[0663] "A means of creating reports that reflect the latest information in real time" refers to a technology that processes collected data immediately and outputs the results as a report in an up-to-date state.

[0664] "Means for visualizing information processing results and outputting them as analytical information" refers to technologies for converting extracted analytical data into a format that is easy for users to understand and providing it visually.

[0665] "A means of outputting generated reports in electronic format and facilitating appropriate information sharing" refers to technology that allows created reports to be saved in digital format and quickly shared among relevant parties.

[0666] This invention is a system that enables companies to efficiently collect and analyze transaction data in an electronic transaction environment and generate reports in real time. It mainly consists of the following elements:

[0667] The server functions as a central processing unit and implements algorithms that learn the company's specific structure and layout. This allows it to automatically analyze the format and layout of reports used by the company and collect necessary data from diverse sources. In this process, the server uses APIs to retrieve transaction data and statistical information from external databases. A Python program executes this process, utilizing libraries such as pandas and scikit-learn for data collection and analysis.

[0668] The terminal acts as a user interface, providing users with data output from the server. When users input corrections or additional information through the terminal, the server immediately reflects the changes, and the report is updated. The generated information is visualized using matplotlib and output as a report in PDF format. This allows for quick sharing of information with stakeholders while maintaining consistency.

[0669] Users interact with this system, review trading data as needed, and make decisions based on the analysis results. This process is streamlined using generative AI models, and optimal output is obtained from the system through prompt messages.

[0670] A concrete example is a process where a company analyzes monthly settlement data and outputs the results as a report for use in management meetings. In this case, the user can input prompts into the system such as, "Please generate a report analyzing monthly trends based on the latest settlement data. Specifically, please provide a graph that shows the fluctuations in transaction volume for March in detail and includes predicted trends," and obtain the necessary analysis results.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The server collects transaction data from various sources. It sends requests using API endpoints and database authentication information specified by the user to retrieve transaction data. The input is the API endpoint and authentication information, and the output is transaction data in JSON format.

[0674] Step 2:

[0675] The server analyzes the acquired transaction data. Using the pandas library, it converts the JSON data into a DataFrame, performs data processing such as date formatting, and completes data completion for missing information. The input is transaction data in JSON format, and the output is the processed DataFrame.

[0676] Step 3:

[0677] The server performs trend analysis using statistical models. It leverages the scikit-learn library to build a regression model based on trading data and perform predictions. The input is a DataFrame, and the output is the model's prediction result.

[0678] Step 4:

[0679] The server visualizes the analysis results. Using the matplotlib library, it graphs the trends in trading volume and predictions, enabling visual analysis. The input is the prediction results from the model, and the output is the generated graph.

[0680] Step 5:

[0681] The server outputs the generated graphs and analysis information as a report in PDF format. It uses the FPDF library to automatically generate reports that include visual information. The input is the generated graphs, and the output is a PDF report.

[0682] Step 6:

[0683] The terminal presents the generated report to the user. The user can use this to verify the accuracy of the information and request additional corrections as needed. The input is a report in PDF format, and the output is user review and feedback.

[0684] Step 7:

[0685] The user submits new analysis and modification requests to the system through prompt messages. Based on these requests, the server resumes processing, recollects the necessary data, and updates the report. The input is the user's prompt messages, and the output is the updated analysis results and report.

[0686] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0687] This invention incorporates an emotion engine that recognizes user emotions into a system designed to streamline the preparation of corporate financial statements and reduce the burden on employees. This enables the optimization of emotion-based interactions. The system is built around a server, terminals, and users, each playing the following roles.

[0688] The server automatically collects necessary information from the company's internal databases and external data sources, and analyzes the data using advanced machine learning. The analyzed data is automatically generated as documents according to the company's specific format and layout. This document generation process is performed in real time, ensuring that the latest information is included. The server also has the capability to quickly respond to user requests for corrections and update documents rapidly.

[0689] The terminal functions as a user interface, providing users with a means to make corrections and comments on documents. In particular, the emotion engine works in conjunction with the terminal to determine the user's emotions from their facial expressions, tone of voice, and typing speed. For example, if the terminal detects that the user is stressed, it changes the display and provides appropriate messages and guidance to improve the user experience.

[0690] Users are the most important users of the system, primarily responsible for creating and revising documents. Based on feedback from the emotion engine, users can check their own emotional state and take action to improve it. The system also records the user's emotional history and uses this information to improve future processes, thereby increasing the overall efficiency of document creation.

[0691] As a concrete example, when a sales representative prepares an important presentation using financial statements, the server retrieves and analyzes the latest sales data and quickly incorporates it into the presentation. The terminal monitors the user's emotional state in real time, and if it detects that tension is rising during presentation preparation, it displays a message suggesting ways to relax. In this way, the system supports efficient document creation and contributes to reducing the user's psychological burden.

[0692] This invention makes it possible to reduce the time required for document creation, improve accuracy, and support the mental well-being of users.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The server initiates the process of collecting necessary information via the company's internal databases or external APIs. During this process, it performs authentication using pre-stored access information and retrieves financial and sales data in real time.

[0696] Step 2:

[0697] The server cleans the collected data. This process removes outliers and handles missing values, formatting the data into an analyzable format. This ensures the accuracy and consistency of the data.

[0698] Step 3:

[0699] The server utilizes machine learning algorithms to analyze the cleaned data. It calculates sales trends and financial indicators, and visualizes these results by creating graphs.

[0700] Step 4:

[0701] The server automatically generates financial statements based on analysis results, adhering to the company's specific format. These statements include text summaries and inserted charts and graphs, and are prepared in a format that can be updated immediately.

[0702] Step 5:

[0703] The device activates an emotion engine to recognize the user's emotions in real time. This recognition utilizes facial expression analysis and speech recognition technology to determine signs of stress and anxiety.

[0704] Step 6:

[0705] The device provides an interface and assistance features that respond to the user's emotional state. For example, if it detects that the user is stressed, it will display calmer colors and messages that encourage relaxation.

[0706] Step 7:

[0707] Users review the document content, taking into account feedback from their devices, and enter correction instructions as needed. These instructions are immediately sent to the server and reflected in the document.

[0708] Step 8:

[0709] The server performs a security check on the completed documents to ensure that confidential information is handled appropriately. Once the documents are deemed secure, they are provided to the user via the terminal.

[0710] Step 9:

[0711] Users review the final document and share it with their superiors and relevant parties as needed. The emotional data recorded by the emotion engine at this stage is then used to improve future document creation.

[0712] (Example 2)

[0713] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0714] In modern business operations, there is a demand for rapid and accurate document creation, which increases the burden on employees in the process. Furthermore, there are often urgent revision requests and the need to integrate diverse data sources. On the other hand, the psychological stress experienced by employees impacts the quality and efficiency of the documents, making stress reduction a crucial issue. Moreover, traditional systems that proceed without considering the emotional state of employees risk reducing user convenience and satisfaction.

[0715] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0716] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data from diverse information sources, and means for creating documents that reflect the latest information in real time. This improves the efficiency of document creation and reduces the burden on the person in charge. Furthermore, it incorporates means for understanding the user's emotional state in conjunction with the information processing means, and dynamically adjusts the interface based on the user's emotional state, thereby simultaneously reducing psychological stress and improving convenience.

[0717] "Company-specific format and layout" refers to the layout and design characteristics of documents used by a particular company, and means creating materials based on those characteristics.

[0718] "Diverse information sources" refers to various sources of information, including not only a company's internal database, but also external online databases and public records.

[0719] "Economic data" refers to all kinds of numerical information related to a company's financial situation and performance, including information such as sales and profits.

[0720] "Real-time updates" means quickly obtaining the latest data up to the time the document was created and immediately reflecting it in the document.

[0721] "Information processing means" refers to technologies and methods that use computers and servers to collect and analyze data and automatically perform the necessary processing.

[0722] "Understanding the user's emotional state" refers to recognizing the user's emotions by inferring them from facial expressions, tone of voice, typing speed, etc.

[0723] "Dynamic interface adjustment" means automatically changing the computer screen and operating environment according to the user's emotional state to provide the optimal user experience.

[0724] "Reducing psychological burden" refers to minimizing the stress associated with document creation and work, enabling users to continue working comfortably.

[0725] This invention provides a system that automates the creation of corporate financial statements and also offers user interaction that takes user emotions into consideration. This system is composed of a server, terminals, and users.

[0726] The server retrieves necessary economic data from the company's internal databases and external information sources. This includes data retrieval via APIs and the use of data crawling techniques. The retrieved information is analyzed using machine learning algorithms. Specifically, predictive models and clustering techniques are utilized to perform sales forecasts and trend analysis. Based on the results of this analysis, documents are automatically generated in a format and layout specific to the company. The server uses a template engine (e.g., Jinja2) to dynamically embed these documents into constituent elements.

[0727] The terminal functions as a user interface, instantly presenting materials to the user. Through this interface, the user can review the materials and input any necessary revisions. The terminal incorporates an emotion engine using open-source libraries (e.g., OpenCV and DeepFace) that analyzes the user's facial expressions, voice tone, and input speed to determine their emotional state. Based on this feedback, the system dynamically adjusts the interface according to the user's emotional state. For example, if the system detects that the user is tense, it displays a message suggesting ways to relax.

[0728] Through this system, users can create and revise documents, understand their own emotional state by utilizing feedback from the emotion engine, and take action to improve as needed. The system also records the user's emotional history, which serves as data to reduce stress and increase efficiency in future document creation processes.

[0729] As a concrete example, consider a scenario where a sales representative is preparing financial statements for a large-scale presentation. In this case, the server automatically retrieves the latest sales data and uses an AI model to forecast sales for the next quarter. The terminal then presents the user with a draft version of the document generated based on this information. If the user enters a comment such as "Adjust the plan based on the sales forecast," the system reflects this in real time. Simultaneously, an emotion engine detects fatigue from the user's facial expressions and displays a message such as "Please take a short break," thereby reducing the user's psychological burden.

[0730] An example of a prompt would be, "Describe a system that streamlines the creation of corporate financial statements and optimizes interactions based on user sentiment." This prompt helps to deepen understanding of how to create documents using generative AI models and how the engine works.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] The server collects necessary economic data from the company's internal databases and external sources. Specifically, it uses APIs to retrieve the latest sales data and market trends and stores them in the database. Inputs include API endpoints and data queries, and output is the retrieved raw data. This data is used for subsequent analysis.

[0734] Step 2:

[0735] The server applies machine learning algorithms to the collected raw data for analysis. For example, it calculates sales forecasts for the next quarter using a predictive model and performs trend analysis. The input is the raw data obtained in step 1, and the output is the analyzed results, such as sales forecasts and market trend information. This allows valuable insights to be extracted from the data.

[0736] Step 3:

[0737] The server automatically generates financial statements in a company-specific format and layout based on the analysis results. It utilizes a template engine to dynamically embed the analysis data into the documents. The input is the analysis data obtained in step 2, and the output is the completed financial statements. These documents are immediately ready to be provided to the user.

[0738] Step 4:

[0739] The terminal displays a preview of the generated document to the user. The user can review the document through the terminal and enter revision requests and feedback in the comments section. Input is the user's actions, and output is the document the user reviews and any revisions made. This process allows the user to confirm the user experience.

[0740] Step 5:

[0741] The device analyzes the user's emotional state using an emotion engine. This includes facial recognition using the camera and microphone, as well as voice analysis. The input is the user's visual and auditory information, and the output is the analyzed emotional state, such as "tension" or "fatigue." Based on this, the interface is adjusted to display appropriate feedback to the user.

[0742] Step 6:

[0743] Users continue their work based on document creation and emotional feedback. They perform a final review of the document through their device and make final adjustments as needed. Input consists of system feedback and user judgment, while output is the revised, final version of the document. This process allows users to create documents efficiently and without stress.

[0744] (Application Example 2)

[0745] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] Document creation processes in business operations are complex and time-consuming, and responding to urgent revision requests and information updates is particularly burdensome. Furthermore, the mental burden and stress associated with these tasks can negatively impact work efficiency. Therefore, there is a need to build systems that reflect information in real time and optimize user interaction based on user emotions.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0748] In this invention, the server includes means for learning the specific format and configuration of a company, means for automatically collecting and analyzing economic data and sales information from diverse sources, and means for creating documents that reflect the latest information in real time. This makes it possible to improve the efficiency of document creation, reduce the burden on users, and support their mental well-being in the workplace.

[0749] "Company-specific formats and layouts" refer to the unique layouts and formats used by each company, which the system learns and utilizes in document creation.

[0750] "Diverse information sources" refers to various types of data sources, such as internal company databases, publicly available external information, and databases of other companies.

[0751] "Economic data" refers to financial information such as a company's financial status, market trends, and sales data.

[0752] "Sales information" refers to information related to sales activities, such as sales analysis, customer information, and marketing data.

[0753] "Documents that reflect the latest information in real time" refers to the process of generating documents that are instantly updated based on the most recent data.

[0754] "Always ready to respond to urgent change requests" means being able to quickly respond to sudden requests for document changes at any time.

[0755] "Past revision history" refers to a record of previous document modifications, which is data used for future document creation.

[0756] "Task and schedule management" refers to methods of supporting efficient work by continuously monitoring the progress and schedule of tasks.

[0757] "Recognizing user emotions" is a process of analyzing the user's facial expressions, tone of voice, input speed, etc., to evaluate their emotional state.

[0758] The system for realizing this invention primarily consists of three elements: a server, a terminal, and a user. The server learns the company's specific format and arrangement, and automatically collects and analyzes economic data and sales information from diverse sources. This process utilizes database management systems and machine learning libraries. Specific software used for database management might include MySQL or PostgreSQL, while machine learning might utilize TensorFlow or PyTorch. The server updates information in real time and can automatically generate documents based on the most up-to-date information.

[0759] The terminal provides users with a means to access the system through a user interface and make necessary changes or review documents. The terminal works in conjunction with an emotion analysis engine to evaluate the user's emotional state. Common PCs and smart devices are used as hardware, and "EmotionAPI" and "FaceAPI" are employed for emotion recognition. This emotional data is used to optimize the process and reduce the user's mental burden.

[0760] For example, when a sales representative creates a presentation document for a client, the server collects and analyzes the latest economic data in real time and automatically generates the document based on that data. When a user feels stressed, the device suggests ways to relax.

[0761] In developing systems that utilize generative AI models, an example of a prompt might be: "Design an algorithm that senses the tension level of staff interacting with customers in real time and displays stress-relieving advice on the smart glasses' display." By using this prompt, it is possible to generate an algorithm that responds appropriately according to the user's state.

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The server collects necessary economic and sales data from the company's database. It takes the company's database connection information as input and executes SQL queries to extract data such as specified financial reports and sales records. This data is the raw material for use in the next analysis step.

[0765] Step 2:

[0766] The server analyzes the collected data using machine learning libraries. Specifically, it uses TensorFlow and PyTorch to model historical data and different patterns, and generates real-time predictions. It converts the input data into features, uses the prediction model to calculate future trends, and provides the output to the document generation step.

[0767] Step 3:

[0768] The server automatically generates documents based on analysis results and company-specific formats. Using document templates and analysis results as input, the program places the latest data in the appropriate locations within the document and outputs the completed document. The generated documents are designed to reflect the latest information while maintaining consistency.

[0769] Step 4:

[0770] The terminal displays the generated document to the user and provides an interface for editing or modifying it. It receives feedback and modification instructions from the user and sends them to the server. As a result, the document content is modified and a new version is saved.

[0771] Step 5:

[0772] The device analyzes the user's emotional state in real time. The emotion analysis engine uses EmotionAPI and FaceAPI, receiving data such as the user's facial expressions and voice tone as input. Based on this data, it evaluates the user's tension and stress levels and displays advice on the screen according to the results.

[0773] Step 6:

[0774] Users receive emotional feedback from their devices and use it to improve their work. Suggestions and guidelines for stress reduction are displayed on the device as output, and by immediately implementing these, users can create more effective documents.

[0775] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0776] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0777] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0778] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0779] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0780] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0781] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0782] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0783] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0784] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0785] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0786] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0787] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0788] 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.

[0789] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0790] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0791] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0792] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0793] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0794] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0795] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0796] The following is further disclosed regarding the embodiments described above.

[0797] (Claim 1)

[0798] A means of learning company-specific formats and layouts,

[0799] A means of automatically collecting and analyzing financial and sales data from diverse data sources,

[0800] A means of creating documents that reflect the latest information in real time,

[0801] A means to respond to urgent revision requests 24 / 7,

[0802] A means of learning from past revision history and reflecting it in document creation,

[0803] A means of managing tasks and schedules and reducing user stress,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, which automatically generates presentation materials in real time and changes according to the content of speech.

[0807] (Claim 3)

[0808] The system according to claim 1, comprising means for security checks and consistency verification of documents.

[0809] "Example 1"

[0810] (Claim 1)

[0811] In an information processing device, means for collecting and analyzing information from various information sources,

[0812] In information processing equipment, a means of automatically generating documents in a company-specific format by performing data analysis using statistical models and machine learning algorithms,

[0813] A means of receiving user input and reflecting that request in the document in real time using an information processing device,

[0814] A means of improving efficiency by recording past revisions and using them for future document generation,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, which reanalyzes information based on user instructions and immediately updates the data.

[0818] (Claim 3)

[0819] The system according to claim 1, comprising means for verifying the structure and consistency of the generated materials.

[0820] "Application Example 1"

[0821] (Claim 1)

[0822] A means of learning the unique structure and layout of a company,

[0823] A means of automatically collecting and analyzing transaction data and statistical data from diverse sources,

[0824] A means of creating reports that reflect the latest information in real time,

[0825] A means to respond to urgent revision requests 24 / 7,

[0826] A means of learning from past revision history and reflecting it in document creation,

[0827] A means of visualizing the results of information processing and outputting them as analytical information,

[0828] A means to output the generated report in electronic format and promote appropriate information sharing,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, which automatically generates presentation materials in real time and changes according to the content of speech.

[0832] (Claim 3)

[0833] The system according to claim 1, comprising means for security checks and consistency verification of documents.

[0834] "Example 2 of combining an emotion engine"

[0835] (Claim 1)

[0836] A means of learning company-specific formats and arrangements,

[0837] A means of automatically collecting and analyzing economic data from diverse sources,

[0838] A means of creating documents that reflect the latest information in real time,

[0839] A means to constantly respond to urgent revision requests,

[0840] A method for learning from past revision history and using it to create documents,

[0841] A means of understanding the emotional state of users in conjunction with information processing tools,

[0842] A means of dynamically adjusting the interface based on the user's emotional state,

[0843] A means of managing tasks and plans and reducing the psychological burden on users,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, which automatically generates presentation materials in real time according to the content of speech and takes into account changes in the user's emotions.

[0847] (Claim 3)

[0848] The system according to claim 1, comprising means for inspecting the safety and integrity of materials.

[0849] "Application example 2 when combining with an emotional engine"

[0850] (Claim 1)

[0851] A means of learning company-specific formats and arrangements,

[0852] A means of automatically collecting and analyzing economic data and business information from diverse sources,

[0853] A means of creating documents that reflect the latest information in real time,

[0854] A means to constantly respond to sudden change requests,

[0855] A means of learning from past change history and reflecting it in document creation,

[0856] A means of managing tasks and schedules and reducing the burden on users,

[0857] A means of recognizing user emotions and optimizing operations based on them,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which automatically generates presentation documents in real time and changes according to the content of speech and the user's emotional state.

[0861] (Claim 3)

[0862] The system according to claim 1, comprising means for verifying the security and consistency of documents. [Explanation of symbols]

[0863] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of learning company-specific formats and layouts, A means of automatically collecting and analyzing financial and sales data from diverse data sources, A means of creating documents that reflect the latest information in real time, A means to respond to urgent revision requests 24 / 7, A means of learning from past revision history and reflecting it in document creation, A means of managing tasks and schedules and reducing user stress, A system that includes this.

2. The system according to claim 1, which automatically generates presentation materials in real time and changes according to the content of speech.

3. The system according to claim 1, comprising means for security checks and consistency verification of documents.

Images