System

Abstract

A system is provided.SOLUTION: The system includes a data collection means, a data analysis means, a material creation means, and a material sharing means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Preparing for financial reports and management meetings at companies requires collecting, analyzing, and formatting a large amount of data, consuming a huge amount of time and resources. Traditional methods have the drawback of making document design and layout subjective, making it difficult to maintain consistent quality. Additionally, creating documents tailored to the interests of each position and department requires a significant amount of effort. This creates challenges that hinder timely and effective decision-making. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system that includes a data collection means, a data analysis means, a document creation means, and a document sharing means. The system of the present invention automatically collects necessary data from a company's accounting software and other data sources, and the data analysis means analyzes the data to extract key figures and policies. Furthermore, it uses generative AI to automatically generate presentations and financial report materials, providing them in an optimized format based on each recipient's position and interests. This significantly reduces the effort required for document creation and enables the efficient generation of high-quality materials. Furthermore, materials can be quickly shared via email or cloud storage, allowing them to be instantly delivered to each team and position.

[0006] "Data collection tools" are the ability to access a company's accounting software and other data sources to obtain the required data.

[0007] "Data analysis means" is a function that analyzes collected data, extracts important figures and policies, and classifies them according to position and interest.

[0008] "Document creation means" is a function that uses generative AI to automatically generate presentations and financial report materials based on the results of data analysis.

[0009] "Document sharing means" is a function for effectively sharing created documents with each team and position.

[0010] "Generative AI" is a system that uses artificial intelligence technology to automatically generate materials based on input text and data.

[0011] A "presentation" is a document used to share and explain information using visual content and slides.

[0012] "Financial reporting materials" are documents that provide an overview of a company's financial reports and business status, prepared after the end of the accounting period.

[0013] A "job title" refers to a specific position or function within a company, relating to the authority and responsibilities a person holds.

[0014] "Interest" refers to the interest or importance that a particular position or department has in particular information or data.

[0015] "Real-time" means reflecting current events and situations immediately. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention is a system that collects data from various corporate data sources and automatically creates presentations and financial report materials using generative AI. This system mainly involves the server, terminals, and users, each of which functions as follows:

[0038] Data collection

[0039] The server accesses the company's accounting software and other data sources to collect the necessary data. The server uses API calls and database connections to retrieve real-time financial information and stores it in storage. At this stage, the collected data reflects the most up-to-date information.

[0040] Data analysis

[0041] The device reads the collected data from the server and performs data analysis. The device uses a data analysis algorithm to extract important figures and policies, and classifies them according to job title and interests. This allows the specific information needed by each job title to be extracted and organized.

[0042] Document creation

[0043] The server uses generation AI to automatically generate presentations and financial report materials based on the analysis results. The generation AI creates professionally designed materials based on the input text and data. The generated materials are provided in a format optimized for each position and department.

[0044] Sharing and reviewing materials

[0045] Users can review documents created on their devices and edit them as needed. They can also quickly share the reviewed documents with their respective teams and positions via email or cloud storage. This ensures that documents are provided in a timely manner, enabling the rapid communication of information needed for decision-making.

[0046] Specific examples

[0047] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[0048] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers, such as increases or decreases in revenue or expense trends.

[0049] The server uses generative AI to create presentations based on this analysis, such as slides focused on revenue and profit for executives and slides with detailed expense analysis for finance.

[0050] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0051] The processing flow will be explained below.

[0052] Step 1: Data collection

[0053] The server accesses the company's accounting software and other data sources to collect the required data.

[0054] The server makes API calls and database connections to retrieve real-time financial information.

[0055] The collected data is stored in the server's storage and is constantly updated with the latest information.

[0056] Step 2: Save your data

[0057] The server stores the collected data in an appropriate format.

[0058] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[0059] Back up your data to protect against loss or damage.

[0060] Step 3: Data analysis

[0061] The device reads the stored data from the server.

[0062] The device uses data analysis algorithms to extract key figures and trends.

[0063] For example, income fluctuations, profit trends, and expense details are extracted.

[0064] Step 4: Classify the data

[0065] The device categorizes the analysis results according to job title and interests.

[0066] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[0067] A customized dataset is generated for each role.

[0068] Step 5: Prepare materials

[0069] The server uses generative AI to create materials based on the classified data.

[0070] Presentations and financial report materials are automatically generated.

[0071] Each slide contains an appropriate title, graph, and text description.

[0072] Step 6: Applying the design to the material

[0073] Apply professional design to server-generated materials.

[0074] Design templates are used to create consistent, visually appealing materials.

[0075] If necessary, custom designs based on company brand guidelines can also be applied.

[0076] Step 7: Review and edit your materials

[0077] The user checks the materials created on the terminal.

[0078] The user checks whether the content is complete and whether the design is appropriate.

[0079] If necessary, the user edits the document and reflects the corrections.

[0080] Step 8: Share your materials

[0081] Documents that users have reviewed and edited are shared with each team and position.

[0082] Materials will be distributed via email and cloud storage.

[0083] Recipients can receive the information they need in the format that is most convenient for them.

[0084] These are the specific processing steps from data collection to sharing of materials.

[0085] Example 1

[0086] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0087] In traditional corporate activities, collecting and analyzing various data, and creating and sharing reports was a time-consuming and labor-intensive process. In particular, the process of collecting and analyzing data, creating reports, and quickly sharing them with relevant parties was fragmented, making it difficult to provide information in a timely manner. Furthermore, while there was a need to provide information in a format optimized for different positions and departments, there was a lack of efficient ways to achieve this.

[0088] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0089] In this invention, the server includes a data collection means, a data preprocessing means, a data analysis means, an automatic document generation means, a document confirmation means, and a document sharing means. This allows the server to automatically acquire necessary data from a company's accounting system or other data sources, cleanse and preprocess the data, analyze it, automatically create report documents based on the results using a generative AI model, and promptly share the documents with relevant parties after the user has confirmed and corrected them.

[0090] "Data collection tools" are functions for accessing a company's accounting system or other data sources and automatically obtaining the required data.

[0091] "Data preprocessing means" refers to a function for processing acquired data into a format suitable for analysis, such as filling in missing values, deleting invalid data, and standardizing date and time formats.

[0092] "Data analysis tools" are functions for analyzing pre-processed data, extracting important figures and trends, and classifying them according to job title and interests.

[0093] "Automatic document generation means" is a function that uses a generative AI model to automatically create presentations and financial report materials based on the results of data analysis.

[0094] The "material confirmation means" is a function that allows the user to use a terminal to check the automatically generated materials and correct them as necessary.

[0095] "Document sharing means" is a function that allows confirmed and revised documents to be quickly shared with relevant parties via email or cloud storage.

[0096] A "generative AI model" is an artificial intelligence model that generates text from an input prompt sentence based on natural language processing.

[0097] A "prompt" is a text sentence that contains instructions or questions for the generative AI model when generating materials.

[0098] This invention is a system that uses data obtained from a company's accounting system and other data sources to automatically create presentations and financial report materials using a generative AI model. This system involves the involvement of a server, a terminal, and a user, each of which functions as follows:

[0099] Data collection

[0100] The server accesses the company's accounting software and other data sources to collect the necessary data. Specifically, the server uses API calls and database connections to obtain real-time financial information and saves it in storage. For example, the server may obtain the latest data on income, expenses, profits, etc. through the APIs of Xero or QuickBooks. The obtained data is then saved in a database (e.g., PostgreSQL) on the server.

[0101] Data Preprocessing

[0102] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[0103] Data analysis

[0104] The terminal retrieves preprocessed data from the server and analyzes it using algorithms. Specifically, the terminal uses Python's Pandas and NumPy to analyze data, performing trend analysis of income and expenses, outlier detection, and sales analysis by category. For example, the terminal processes data retrieved from the server using Python, extracts important numerical values ​​in array format, and classifies them by department.

[0105] Automatic document generation

[0106] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The generative AI model uses, for example, OpenAI's GPT-4 API. The server generates prompt text and sends it to the generative AI model to automatically create high-quality materials. Examples of prompt text include the following:

[0107] Example prompt sentence:

[0108] "Prepare the monthly financial report for July 2023. Create slides for the management team based on the following data."

[0109] Revenue: $100,000

[0110] Expenditure: $60,000

[0111] Profit: $40,000

[0112] Increase or decrease in main revenue source: Customer A +10%, Customer B -5%

[0113] The generated materials are converted into presentation format using the Google Slides API.

[0114] Check and share materials

[0115] Users can use their devices to review the automatically generated materials and make corrections as necessary. Editing can be done directly in a browser using tools like Google Slides. The corrected materials can then be quickly shared with relevant parties via email or cloud storage (e.g., Google Drive or Dropbox). For example, a user can correct an error in "Sales" on Google Slides, save the revised slide to Google Drive, and email a link to their team.

[0116] This system enables companies to quickly and accurately prepare financial reporting documents based on the latest financial information and provide information to all stakeholders in a timely manner.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] The server accesses the company's accounting system and other data sources to collect the required data. To do this, the server retrieves real-time data using API calls or database connections. For example, the server sends a request to the accounting system's API endpoint using authentication information to retrieve data such as income, expenses, and profits. The retrieved data is then stored in a database (e.g., PostgreSQL) on the server.

[0120] Input: Accounting system credentials and API endpoint

[0121] Output: The financial data obtained (income, expenses, profit, etc.)

[0122] Step 2:

[0123] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[0124] Input: Raw data collected

[0125] Output: A cleansed dataset

[0126] Step 3:

[0127] The terminal retrieves preprocessed data from the server and performs analysis using algorithms, such as Pandas and NumPy in Python, to analyze income and expenditure trends, detect outliers, and analyze sales by category.

[0128] Input: Cleansed dataset

[0129] Output: Analyzed data (e.g., trend analysis results, outlier detection results)

[0130] Step 4:

[0131] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The server generates prompts and sends them to a generative AI model such as GPT-4 to create professionally designed materials. For example, the server could generate a prompt such as, "Prepare monthly financial report materials for July 2023. Create slides for management based on the following data," with detailed data attached.

[0132] Input: Parsed data and prompt statement

[0133] Output: Auto-generated presentation materials

[0134] Step 5:

[0135] Users can use their devices to review the automatically generated materials and make corrections as necessary. Users can also edit the materials directly in their browsers using tools such as Google Slides.

[0136] Input: Auto-generated material

[0137] Output: Corrected and verified material

[0138] Step 6:

[0139] Users can quickly share revised documents with relevant parties via email or cloud storage. For example, users can save revised documents to Google Drive and email the link to their team. This ensures that documents are provided in a timely manner and that the information needed for decision-making can be communicated quickly.

[0140] Input: Corrected and verified material

[0141] Output: Materials shared with stakeholders

[0142] (Application example 1)

[0143] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0144] In today's brick-and-mortar stores, managing sales and inventory data is complicated, making it difficult to grasp the status of store operations in real time. Furthermore, providing information at the right time and creating materials to quickly formulate management strategies requires a lot of effort. There is also a lack of effective tools that allow staff to instantly grasp the status of store operations. There is a need for a system that can solve these issues and streamline store operations.

[0145] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0146] In this invention, the server includes a data collection means, a data analysis means, a document creation means, a document sharing means, a sales data collection means, an inventory data collection means, a report generation means using a generative AI model, and a smart device display means. This enables sales data and inventory data from a physical store to be collected and analyzed in real time, and management reports to be automatically generated using the generative AI model. Furthermore, this allows staff to instantly check the management status using smart devices such as smartphones and smart glasses, providing a system that supports quick and accurate decision-making.

[0147] A "data collection tool" is a device or function that accesses a company's accounting software or other data sources and automatically collects the required data.

[0148] "Data analysis means" refers to a device or function that analyzes collected data, extracts important figures and policies, and classifies them according to position or interest.

[0149] "Document creation means" refers to a device or function that uses a generative AI model to automatically create presentations or financial report materials based on the results of data analysis.

[0150] The "material sharing means" is a device or function that allows the generated materials to be quickly shared using email or cloud storage.

[0151] "Sales data collection means" refers to a device or function for collecting sales data from physical stores.

[0152] "Inventory data collection means" refers to a device or function for collecting inventory data from a physical store.

[0153] A "report generation means using a generative AI model" is a device or function that automatically generates professionally designed reports using a generative AI model based on collected and analyzed data.

[0154] A "smart device display means" is a device or function that displays reports generated by a device such as a smartphone or smart glasses in real time.

[0155] This invention is a system that collects and analyzes sales data and inventory data in a physical store, and automatically creates and displays operational reports using a generative AI model. Specific embodiments for implementing this invention are described below.

[0156] System Configuration

[0157] The system consists of a server, terminals, and users. The server collects data, analyzes data, creates documents, and shares documents, while the terminals display data and check and edit documents. Users are store staff and managers.

[0158] Hardware and Software

[0159] Hardware

[0160] Server: Has the computing power to collect data, analyze, and prepare documentation.

[0161] Terminal: A smart device such as a smartphone or smart glasses.

[0162] Storage: A storage device for storing collected data.

[0163] software

[0164] FastAPI: A web framework for data collection and API provisioning.

[0165] Pandas: A library for analyzing and manipulating data.

[0166] GPT-4: A generative AI that generates reports in natural language.

[0167] SQLite: A database management system that persists data.

[0168] Processing flow

[0169] 1. Data Collection

[0170] The server collects sales data and inventory data from physical stores in CSV file format, etc. For example, it retrieves data from POS systems and inventory management systems via API at regular intervals and stores it in an SQLite database.

[0171] 2. Data Analysis

[0172] After the server collects the data, the terminal uses Pandas to aggregate and analyze the data. The main steps are to aggregate total revenue from sales data and remaining quantities from inventory data, and to extract important trends and outliers.

[0173] 3. Document Creation

[0174] The server uses GPT-4 to generate a report on operational operations based on the analysis results. The report includes suggestions for business strategies and improvements to inventory management. For example, the following prompt sentence is input to GPT-4 to generate a report:

[0175] Based on the sales and inventory data below, please prepare a report on next week's operating policy.

[0176] Sales Data:

[0177] Product A, 500, 300, 200

[0178] Product B, 700, 500, 100

[0179] Inventory Data:

[0180] Product A, 200, 150, 50

[0181] Product B, 300, 250, 50

[0182] The report should focus on sales trends and future marketing strategies for Product A in particular.

[0183] 4. Display and review of materials

[0184] The terminal displays the generated report on a smartphone or smart glasses. Based on this, users can instantly check business improvement measures and sales promotion measures and edit the materials as needed. The edited materials are shared again by the server and quickly notified to all staff.

[0185] This system will significantly improve the operational efficiency of physical stores, enabling increased sales and optimized inventory management.

[0186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0187] Step 1:

[0188] The server collects sales and inventory data from physical stores. This process involves accessing POS systems and inventory management systems using APIs to periodically retrieve the necessary data. The input includes sales and inventory data retrieved by API calls, and the output is the data saved in CSV format or in a database.

[0189] Step 2:

[0190] The terminal reads the collected sales and inventory data from the database and aggregates and analyzes it using the Pandas library. The input includes sales and inventory data, and the output is analysis results showing total sales, inventory status, trends, and outliers. For example, it can detect whether sales of a particular product are increasing rapidly or whether there is a risk of inventory shortage.

[0191] Step 3:

[0192] The server receives the analysis results and generates an operational report using the generative AI model GPT-4. The input includes the analysis results and a prompt, and the output is a detailed operational report. An example of a prompt is, "Based on the sales and inventory data below, please prepare a report on next week's operational policy. Please focus on the sales trend of product A and future marketing strategies."

[0193] Step 4:

[0194] The server sends the generated operation report to the terminal so that the user can view it on their smartphone or smart glasses. The input includes the generated operation report, and the output is that the report is displayed on the user's smart device. This allows the user to grasp the store's operation status in real time and take appropriate measures.

[0195] Step 5:

[0196] The user checks the operation report displayed on the terminal and edits it as necessary. The input includes the generated operation report, and the output is a report that has been modified and optimized by the user. By making modifications, the user can reflect more specific operation policies and improvement measures in the report.

[0197] Step 6:

[0198] The server then shares the revised operation report with all staff again. The input includes the operation report revised by the user, and the output is a report distributed to all staff via email or cloud storage. This allows all staff to share the latest operation information and work together in a unified manner to manage the store.

[0199] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0200] This invention is a system that combines data collection means, data analysis means, document creation means, document sharing means, and an emotion engine that recognizes user emotions. This system collects corporate data and uses generative AI to automatically create and share presentations and financial report materials that correspond to the user's emotions. The following explains in detail the roles played by the server, terminal, and user in each step.

[0201] Data collection

[0202] The server accesses the company's accounting software and other data sources to gather the necessary data, then makes API calls and database connections to retrieve real-time financial information and stores it in storage, ensuring the most up-to-date data is always available.

[0203] Data analysis

[0204] The device reads the stored data from the server and uses data analysis algorithms to extract key figures and trends, categorizing them according to job title and interest – for example, data focused on revenue and profit for management, while data including expense details for the finance department.

[0205] Collecting Emotional Data

[0206] The device uses an emotion engine to collect user emotional data. The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The emotional data is analyzed in real time and reflected in the creation of documents.

[0207] Document creation

[0208] The server uses AI to automatically generate presentations and financial report materials based on analysis results and emotional data. For example, if the user is feeling stressed, the content will be simplified and the tone will be calmed. The generated materials will include appropriate titles, graphs, and text descriptions, and will have a professional design.

[0209] Review and edit materials

[0210] Checks documents created by users on their devices and edits them as necessary. Checks not only the content of the documents but also any design or structure defects and makes appropriate corrections.

[0211] Sharing materials

[0212] Documents that users have reviewed and edited can be shared with each team or role. Documents can be quickly distributed via email or cloud storage. Recipients can receive the information they need in the format that is easiest for them to understand.

[0213] Specific examples

[0214] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[0215] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers from the data, such as increases or decreases in revenue or expense trends.

[0216] The device also uses an emotion engine to collect the user's emotion data. As the user creates materials, the emotion engine analyzes the user's facial expressions and tone of voice in real time to collect emotion data.

[0217] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it might create slides focused on revenue and profit for executives, or slides with detailed expense analysis for finance. If the user is feeling stressed or overwhelmed, it will adjust the slide content to be more concise and easy to understand.

[0218] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0219] As a form for implementing the invention, this system works in cooperation with servers, terminals, and users to automate the entire process from data collection to document creation and sharing, enabling efficient and high-quality document creation.

[0220] The processing flow will be explained below.

[0221] Step 1: Data collection

[0222] The server accesses the company's accounting software and other data sources to collect the required data.

[0223] The server retrieves real-time financial information using API calls and database connections.

[0224] The collected data is stored in the server's storage and is constantly updated with the latest information.

[0225] Step 2: Save your data

[0226] The server stores the collected data in an appropriate format.

[0227] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[0228] Back up your data to protect against loss or damage.

[0229] Step 3: Data analysis

[0230] The device reads the stored data from the server.

[0231] The device uses data analysis algorithms to extract key figures and trends.

[0232] For example, income fluctuations, profit trends, and expense details are extracted.

[0233] Step 4: Classify the data

[0234] The device categorizes the analysis results according to job title and interests.

[0235] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[0236] A customized dataset is generated for each role.

[0237] Step 5: Collecting emotion data

[0238] The terminal uses an emotion engine to collect emotion data of the user.

[0239] The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice.

[0240] Emotional data is analyzed in real time and reflected when creating documents.

[0241] Step 6: Prepare materials

[0242] The server uses generative AI to create materials based on the analysis results and emotional data.

[0243] For example, if a user is feeling stressed, the content will be made simpler and the tone will be calmer.

[0244] Presentations and financial reports are automatically generated with appropriate titles, graphs, and text descriptions.

[0245] Step 7: Applying the design to the material

[0246] Apply professional design to server-generated materials.

[0247] Design templates are used to create consistent, visually appealing materials.

[0248] If necessary, custom designs based on company brand guidelines can also be applied.

[0249] Step 8: Review and edit your materials

[0250] The user checks the materials created on the terminal.

[0251] The user checks whether the content is complete and whether the design is appropriate.

[0252] If necessary, the user edits the document and reflects the corrections.

[0253] Step 9: Share your materials

[0254] Documents that users have reviewed and edited are shared with each team and position.

[0255] Materials will be distributed via email and cloud storage.

[0256] Recipients can receive the information they need in the format that is most convenient for them.

[0257] Example 2

[0258] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0259] In modern companies, the processes from data collection and analysis to document creation and sharing are extremely diverse, and there is a need to quickly create and share high-quality documents efficiently. However, in existing systems, these processes are often carried out separately, making it difficult to create documents that take emotional data into account. Furthermore, creating documents without reflecting user emotions can result in the content being difficult to understand or the information not being properly conveyed, which can reduce the speed and accuracy of decision-making.

[0260] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0261] In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a document creation means, and a document sharing means. This makes it possible to collect necessary data from corporate information systems and other information sources, analyze the data, and classify it according to roles and interests within the organization. The emotion recognition means collects user emotion data, and a generative AI model is used to automatically create and share documents according to the user's emotions, thereby achieving efficient and high-quality document creation.

[0262] "Data collection methods" are the methods used to obtain the required data from the company's information systems and other sources.

[0263] "Data analysis tools" are tools for analyzing collected data and classifying it according to roles and interests within the organization.

[0264] The "emotion recognition means" is a means for detecting emotions from the user's facial expressions and tone of voice, and analyzing this emotional data.

[0265] "Document creation means" refers to a means for automatically creating documents using a generative AI model based on data analysis results and emotion data.

[0266] "Document sharing means" refers to a means for sharing generated documents with teams and positions within an organization via email or cloud storage.

[0267] A "generative AI model" is an artificial intelligence model that performs natural language processing based on given data and user emotional data to generate appropriate sentences and materials.

[0268] A "prompt sentence" is a sentence that specifically inputs instructions or content for creating specific materials to a generative AI model.

[0269] This invention is a system for collecting corporate data, analyzing the data, recognizing emotions, and automatically creating and sharing documents based on the data. This system mainly consists of three entities: a server, a terminal, and a user.

[0270] Server Functionality Description

[0271] Data collection methods

[0272] The server accesses the company's information systems and other sources to gather the necessary data. Specifically, the server makes API calls and database connections. For example, it retrieves financial information such as income, expenses, and profits from accounting software. The specific software used generally includes an "information systems API" or "database management system."

[0273] Document creation method

[0274] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results and emotional data. For example, a "generative AI model" is used for the generative AI. Based on the emotional data, the corresponding tone and content are adjusted.

[0275] Device function description

[0276] Data Analysis Methods

[0277] The device reads the stored data from the server and uses data analysis algorithms, often using the Python pandas library, to extract key figures and trends. The data is then categorized by job title and formatted appropriately for management, finance, etc.

[0278] emotion recognition means

[0279] The device collects user emotional data using an emotion recognition engine. The emotion recognition engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. Specific technologies include "image recognition API" and "voice analysis technology."

[0280] User Roles

[0281] Review and edit materials

[0282] The user reviews the materials created on their device and edits them as necessary. Microsoft PowerPoint and Google Slides are the software typically used. The content and design are checked for flaws, and the optimal materials are completed.

[0283] Material sharing method

[0284] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. Specific sharing methods include email systems and cloud storage services.

[0285] Specific examples

[0286] For example, when creating a company's monthly financial report, the server automatically collects data such as the current month's income, expenses, and profits from the accounting software. The collected data is stored on the server and kept up to date. The device then reads the data from the server's database and analyzes it using Python's pandas library. Key figures such as increases or decreases in income and expense trends are extracted. Additionally, as the user creates the report, an emotion recognition engine collects emotional data in real time.

[0287] The server sends the following prompt to the AI ​​model: "Please create a monthly financial report document. Use data on income, expenses, and profits, and adjust it to an easy-to-understand and concise format, taking into account user sentiment data. Please create slides for management and the finance department." The AI ​​then generates the appropriate document.

[0288] Users can open the generated document in Microsoft PowerPoint, check the titles and graphs, and correct any inappropriate parts. Once the document has been corrected, it is uploaded to Google Drive and a shared link is generated. By sharing this link with internal management and the finance department, information can be shared quickly and accurately.

[0289] In this way, the invention automates data management and document creation processes within a company, enabling efficient and high-quality document creation.

[0290] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0291] Step 1: Data collection

[0292] The server accesses the company's information systems and other information sources to collect the necessary data. The server makes API calls and connects to databases to obtain financial information such as income, expenses, and profits in real time. Specifically, the server sends requests to the "company's accounting data API" and stores the collected data in an internal database. The input is the API request, and the output is the database where the financial information is stored.

[0293] Specific actions

[0294] The server sends an API request to the " / v1 / data / financials" endpoint to retrieve income, expense, and profit data in JSON format, and stores the data in the "financial_data" table in a MySQL database.

[0295] Step 2: Data analysis

[0296] The terminal retrieves data stored in the database from the server and analyzes it. The terminal uses Python's pandas library to read the data and extract key figures and trends. The results of this data analysis are categorized by job position. The input is financial data retrieved from the database, and the output is the analytical results, such as figures and trends.

[0297] Specific actions

[0298] The terminal executes the SQL query "SELECT FROM financial_data WHERE date = '2023-10-31'" and converts the retrieved data into a pandas data frame. It then extracts trends in revenue and expenses and categorizes them into data for management and data for the finance department.

[0299] Step 3: Collecting emotion data

[0300] The device uses an emotion recognition engine to collect user emotion data. The device uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The input is the user's facial expression images and voice data, and the output is emotion data.

[0301] Specific actions

[0302] The device sends the user's facial image to the Google Cloud Vision API and receives the analysis results. It also sends the user's voice data to the IBM Watson Tone Analyzer and obtains the tone analysis results.

[0303] Step 4: Prepare materials

[0304] The server automatically generates presentation materials using a generative AI model. The server sends prompts to the generative AI model based on the data analysis results and emotional data. The inputs are the data analysis results and emotional data, and the output is the generated presentation materials.

[0305] Specific actions

[0306] The server sends the generation AI a prompt saying, "Please create a monthly financial report. Use income, expense, and profit data, and adjust it into an easy-to-understand and concise format, taking into account the user's emotional data." The server then receives the presentation (pptx format) generated by the AI.

[0307] Step 5: Review and edit your materials

[0308] The user reviews and edits the materials created on the device. The user opens the materials using Microsoft PowerPoint or Google Slides and edits the content and design. The input is the generated presentation materials, and the output is the final materials that have been reviewed and edited.

[0309] Specific actions

[0310] A user opens a pptx file in Microsoft PowerPoint, checks the slide titles and charts, corrects any inappropriate content, and adds new data and charts as needed.

[0311] Step 6: Share your materials

[0312] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. The input is the reviewed and edited final document, and the output is a shared link or email.

[0313] Specific actions

[0314] The user uploads the completed document to Google Drive, generates a shared link, and emails the link to the company's management and finance department with a message saying, "Please see the monthly financial report document at the link below."

[0315] (Application example 2)

[0316] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0317] In today's business environment, store managers need to efficiently analyze sales data and customer feedback and create optimal presentation materials based on that data. These materials also need to be tailored to take into account the manager's emotions and stress levels, but this is difficult to achieve. Furthermore, there is a lack of a way to quickly and efficiently share the created materials. An integrated system is needed to solve these challenges and streamline management tasks.

[0318] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, a material creation means, a material sharing means, an emotion recognition means, and a means for the material creation means to automatically generate materials corresponding to the user's emotions based on the data collected by the data collection means and the emotion data collected by the emotion recognition means. This enables a manager of a physical store to automatically generate presentation materials that match the manager's emotions and stress level based on sales data and customer feedback, and quickly share them.

[0319] "Data collection means" refers to the means for obtaining necessary information from external data sources.

[0320] "Data analysis means" refers to the means of analyzing collected data and classifying its contents according to job title and interest.

[0321] The "material creation means" is a means for generating presentation materials and reports based on data and emotion data.

[0322] The "material sharing means" is a means for quickly distributing the created materials to each of the parties involved.

[0323] The "emotion recognition means" is a means for detecting the user's emotions from facial expressions, tone of voice, etc.

[0324] The "server" is a computer system for executing the data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[0325] An "integrated system" is a system in which multiple means work together to automate the entire process, from data collection to document creation and sharing.

[0326] "Presentation materials" are materials that include slides, graphs, tables, etc., to visually present the contents of a report.

[0327] "Emotion data" is information that indicates the user's emotional state, and is data obtained from facial expressions and tone of voice.

[0328] This invention is a system that improves the efficiency of store management and automatically generates and shares presentation materials according to the manager's stress level and emotions. This system is composed of a combination of data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[0329] Data collection

[0330] The server uses data collection methods to access the organization's trading software and other data sources to collect the required data such as sales data, customer feedback, etc. The data is retrieved in real time through API calls and database connections and stored in the server's storage.

[0331] Data analysis

[0332] The device reads the data stored on the server and analyzes it using data analysis tools. This analysis includes categorizing it according to job titles, such as management or finance. For example, key figures and trends can be extracted from sales data to identify points that managers should pay attention to.

[0333] Collecting Emotional Data

[0334] The device uses an emotion recognition method to collect emotional data from the administrator. The emotion recognition method involves capturing the administrator's facial expressions with a camera and using an expression recognition algorithm. Audio can also be captured and emotions can be analyzed from the tone of voice. The emotional data collected in this way is analyzed in real time and reflected in the creation of documents.

[0335] Document creation

[0336] The server uses generative AI to automatically generate presentation materials based on data analysis results and emotional data. If the administrator is feeling stressed or anxious, the generated materials are adjusted to be concise and easy to understand. The generated materials include appropriate titles, graphs, and text explanations, and have a professional design.

[0337] Review and edit materials

[0338] Users can use their devices to check the documents they have created and edit them as necessary. They can check for defects in not only the content of the documents but also the design and structure, and make appropriate corrections.

[0339] Sharing materials

[0340] Users can quickly share documents they have reviewed and edited with each team and position. Documents are efficiently distributed via email or cloud storage, allowing recipients to receive the information they need in the format that is easiest for them to understand.

[0341] Specific examples

[0342] For example, consider the creation of monthly review materials for a physical store. The server automatically collects one month's worth of sales data and customer feedback from the organization's trading software. This data is updated in real time and stored on the server.

[0343] The device then reads the data stored on the server and runs it through data analysis algorithms to extract key figures, trends and outliers, including trends in revenue growth and customer satisfaction.

[0344] The terminal also uses an emotion recognition means to collect emotional data of the administrator. During the process of the administrator creating the materials, the emotion recognition means analyzes the administrator's facial expressions and tone of voice in real time to collect emotional data.

[0345] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it creates slides for management that focus on sales and customer satisfaction, while for finance it creates slides with detailed expense analysis. If a manager is feeling stressed or overwhelmed, it adjusts the slide content to be more concise and easy to understand.

[0346] Finally, managers review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0347] Prompt Sentence Examples

[0348] "Generate monthly review materials based on the latest sales data and customer feedback, taking into account store manager sentiment. If managers are feeling stressed, simplify the content and adapt it to an easy-to-understand format."

[0349] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0350] Step 1:

[0351] Data collection

[0352] The server uses data collection methods to collect sales data and customer feedback from the organization's trading software and other data sources, specifically through API calls and database connections, and stores the data in the server's storage in real time.

[0353] The input data is the API key and connection information for the trading software, and the output data is collected sales data and customer feedback.

[0354] Step 2:

[0355] Data analysis

[0356] The device reads the stored data from the server and analyzes it using data analytics. Specifically, it runs sales data and customer feedback through analytical algorithms to extract key figures and trends. It also categorizes the information needed according to job title and interests.

[0357] Input data is sales data and customer feedback obtained from the server, and output data is analyzed key figures, trends and classified information.

[0358] Step 3:

[0359] Collecting Emotional Data

[0360] The device collects the administrator's emotional data using an emotion recognition means. Specifically, it captures the administrator's facial expressions with a camera and analyzes their emotions using an expression recognition algorithm. It also performs voice analysis to determine emotions from the tone of voice.

[0361] The input data is camera images and audio data, and the output data is analyzed emotion data.

[0362] Step 4:

[0363] Document creation

[0364] The server uses a generative AI model to automatically generate presentation materials based on data analysis results and emotion data. Specifically, the server inputs prompts into the generative AI to generate the content of the materials, creating materials that include appropriate titles, graphs, and text descriptions.

[0365] The input data are the analysis results (main figures, trends) and emotion data, and the output data are the generated presentation materials.

[0366] Step 5:

[0367] Review and edit materials

[0368] The user uses the terminal to check the created materials and edit them as necessary, specifically checking the content, design, and structure of the materials and making appropriate corrections.

[0369] The input data is the generated presentation material, and the output data is the revised version of the material.

[0370] Step 6:

[0371] Sharing materials

[0372] Users can quickly share the documents they have reviewed and edited with each team or position by distributing them via email or cloud storage.

[0373] The input data is the revised document, and the output data is the document shared with all parties involved.

[0374] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0375] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0376] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0377] [Second embodiment]

[0378] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0379] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0380] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0381] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0382] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0383] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0384] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0385] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0386] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0387] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0388] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0389] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0390] This invention is a system that collects data from various corporate data sources and automatically creates presentations and financial report materials using generative AI. This system mainly involves the server, terminals, and users, each of which functions as follows:

[0391] Data collection

[0392] The server accesses the company's accounting software and other data sources to collect the necessary data. The server uses API calls and database connections to retrieve real-time financial information and stores it in storage. At this stage, the collected data reflects the most up-to-date information.

[0393] Data analysis

[0394] The device reads the collected data from the server and performs data analysis. The device uses a data analysis algorithm to extract important figures and policies, and classifies them according to job title and interests. This allows the specific information needed by each job title to be extracted and organized.

[0395] Document creation

[0396] The server uses generation AI to automatically generate presentations and financial report materials based on the analysis results. The generation AI creates professionally designed materials based on the input text and data. The generated materials are provided in a format optimized for each position and department.

[0397] Sharing and reviewing materials

[0398] Users can review documents created on their devices and edit them as needed. They can also quickly share the reviewed documents with their respective teams and positions via email or cloud storage. This ensures that documents are provided in a timely manner, enabling the rapid communication of information needed for decision-making.

[0399] Specific examples

[0400] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[0401] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers, such as increases or decreases in revenue or expense trends.

[0402] The server uses generative AI to create presentations based on this analysis, such as slides focused on revenue and profit for executives and slides with detailed expense analysis for finance.

[0403] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0404] The processing flow will be explained below.

[0405] Step 1: Data collection

[0406] The server accesses the company's accounting software and other data sources to collect the required data.

[0407] The server makes API calls and database connections to retrieve real-time financial information.

[0408] The collected data is stored in the server's storage and is constantly updated with the latest information.

[0409] Step 2: Save your data

[0410] The server stores the collected data in an appropriate format.

[0411] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[0412] Back up your data to protect against loss or damage.

[0413] Step 3: Data analysis

[0414] The device reads the stored data from the server.

[0415] The device uses data analysis algorithms to extract key figures and trends.

[0416] For example, income fluctuations, profit trends, and expense details are extracted.

[0417] Step 4: Classify the data

[0418] The device categorizes the analysis results according to job title and interests.

[0419] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[0420] A customized dataset is generated for each role.

[0421] Step 5: Prepare materials

[0422] The server uses generative AI to create materials based on the classified data.

[0423] Presentations and financial report materials are automatically generated.

[0424] Each slide contains an appropriate title, graph, and text description.

[0425] Step 6: Applying the design to the material

[0426] Apply professional design to server-generated materials.

[0427] Design templates are used to create consistent, visually appealing materials.

[0428] If necessary, custom designs based on company brand guidelines can also be applied.

[0429] Step 7: Review and edit your materials

[0430] The user checks the materials created on the terminal.

[0431] The user checks whether the content is complete and whether the design is appropriate.

[0432] If necessary, the user edits the document and reflects the corrections.

[0433] Step 8: Share your materials

[0434] Documents that users have reviewed and edited are shared with each team and position.

[0435] Materials will be distributed via email and cloud storage.

[0436] Recipients can receive the information they need in the format that is most convenient for them.

[0437] These are the specific processing steps from data collection to sharing of materials.

[0438] Example 1

[0439] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0440] In traditional corporate activities, collecting and analyzing various data, and creating and sharing reports was a time-consuming and labor-intensive process. In particular, the process of collecting and analyzing data, creating reports, and quickly sharing them with relevant parties was fragmented, making it difficult to provide information in a timely manner. Furthermore, while there was a need to provide information in a format optimized for different positions and departments, there was a lack of efficient ways to achieve this.

[0441] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0442] In this invention, the server includes a data collection means, a data preprocessing means, a data analysis means, an automatic document generation means, a document confirmation means, and a document sharing means. This allows the server to automatically acquire necessary data from a company's accounting system or other data sources, cleanse and preprocess the data, analyze it, automatically create report documents based on the results using a generative AI model, and promptly share the documents with relevant parties after the user has confirmed and corrected them.

[0443] "Data collection tools" are functions for accessing a company's accounting system or other data sources and automatically obtaining the required data.

[0444] "Data preprocessing means" refers to a function for processing acquired data into a format suitable for analysis, such as filling in missing values, deleting invalid data, and standardizing date and time formats.

[0445] "Data analysis tools" are functions for analyzing pre-processed data, extracting important figures and trends, and classifying them according to job title and interests.

[0446] "Automatic document generation means" is a function that uses a generative AI model to automatically create presentations and financial report materials based on the results of data analysis.

[0447] The "material confirmation means" is a function that allows the user to use a terminal to check the automatically generated materials and correct them as necessary.

[0448] "Document sharing means" is a function that allows confirmed and revised documents to be quickly shared with relevant parties via email or cloud storage.

[0449] A "generative AI model" is an artificial intelligence model that generates text from an input prompt sentence based on natural language processing.

[0450] A "prompt" is a text sentence that contains instructions or questions for the generative AI model when generating materials.

[0451] This invention is a system that uses data obtained from a company's accounting system and other data sources to automatically create presentations and financial report materials using a generative AI model. This system involves the involvement of a server, a terminal, and a user, each of which functions as follows:

[0452] Data collection

[0453] The server accesses the company's accounting software and other data sources to collect the necessary data. Specifically, the server uses API calls and database connections to obtain real-time financial information and saves it in storage. For example, the server may obtain the latest data on income, expenses, profits, etc. through the APIs of Xero or QuickBooks. The obtained data is then saved in a database (e.g., PostgreSQL) on the server.

[0454] Data Preprocessing

[0455] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[0456] Data analysis

[0457] The terminal retrieves preprocessed data from the server and analyzes it using algorithms. Specifically, the terminal uses Python's Pandas and NumPy to analyze data, performing trend analysis of income and expenses, outlier detection, and sales analysis by category. For example, the terminal processes data retrieved from the server using Python, extracts important numerical values ​​in array format, and classifies them by department.

[0458] Automatic document generation

[0459] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The generative AI model uses, for example, OpenAI's GPT-4 API. The server generates prompt text and sends it to the generative AI model to automatically create high-quality materials. Examples of prompt text include the following:

[0460] Example prompt sentence:

[0461] "Prepare the monthly financial report for July 2023. Create slides for the management team based on the following data."

[0462] Revenue: $100,000

[0463] Expenditure: $60,000

[0464] Profit: $40,000

[0465] Increase or decrease in main revenue source: Customer A +10%, Customer B -5%

[0466] The generated materials are converted into presentation format using the Google Slides API.

[0467] Check and share materials

[0468] Users can use their devices to review the automatically generated materials and make corrections as necessary. Editing can be done directly in a browser using tools like Google Slides. The corrected materials can then be quickly shared with relevant parties via email or cloud storage (e.g., Google Drive or Dropbox). For example, a user can correct an error in "Sales" on Google Slides, save the revised slide to Google Drive, and email a link to their team.

[0469] This system enables companies to quickly and accurately prepare financial reporting documents based on the latest financial information and provide information to all stakeholders in a timely manner.

[0470] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0471] Step 1:

[0472] The server accesses the company's accounting system and other data sources to collect the required data. To do this, the server retrieves real-time data using API calls or database connections. For example, the server sends a request to the accounting system's API endpoint using authentication information to retrieve data such as income, expenses, and profits. The retrieved data is then stored in a database (e.g., PostgreSQL) on the server.

[0473] Input: Accounting system credentials and API endpoint

[0474] Output: The financial data obtained (income, expenses, profit, etc.)

[0475] Step 2:

[0476] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[0477] Input: Raw data collected

[0478] Output: A cleansed dataset

[0479] Step 3:

[0480] The terminal retrieves preprocessed data from the server and performs analysis using algorithms, such as Pandas and NumPy in Python, to analyze income and expenditure trends, detect outliers, and analyze sales by category.

[0481] Input: Cleansed dataset

[0482] Output: Analyzed data (e.g., trend analysis results, outlier detection results)

[0483] Step 4:

[0484] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The server generates prompts and sends them to a generative AI model such as GPT-4 to create professionally designed materials. For example, the server could generate a prompt such as, "Prepare monthly financial report materials for July 2023. Create slides for management based on the following data," with detailed data attached.

[0485] Input: Parsed data and prompt statement

[0486] Output: Auto-generated presentation materials

[0487] Step 5:

[0488] Users can use their devices to review the automatically generated materials and make corrections as necessary. Users can also edit the materials directly in their browsers using tools such as Google Slides.

[0489] Input: Auto-generated material

[0490] Output: Corrected and verified material

[0491] Step 6:

[0492] Users can quickly share revised documents with relevant parties via email or cloud storage. For example, users can save revised documents to Google Drive and email the link to their team. This ensures that documents are provided in a timely manner and that the information needed for decision-making can be communicated quickly.

[0493] Input: Corrected and verified material

[0494] Output: Materials shared with stakeholders

[0495] (Application example 1)

[0496] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0497] In today's brick-and-mortar stores, managing sales and inventory data is complicated, making it difficult to grasp the status of store operations in real time. Furthermore, providing information at the right time and creating materials to quickly formulate management strategies requires a lot of effort. There is also a lack of effective tools that allow staff to instantly grasp the status of store operations. There is a need for a system that can solve these issues and streamline store operations.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0499] In this invention, the server includes a data collection means, a data analysis means, a document creation means, a document sharing means, a sales data collection means, an inventory data collection means, a report generation means using a generative AI model, and a smart device display means. This enables sales data and inventory data from a physical store to be collected and analyzed in real time, and management reports to be automatically generated using the generative AI model. Furthermore, this allows staff to instantly check the management status using smart devices such as smartphones and smart glasses, providing a system that supports quick and accurate decision-making.

[0500] A "data collection tool" is a device or function that accesses a company's accounting software or other data sources and automatically collects the required data.

[0501] "Data analysis means" refers to a device or function that analyzes collected data, extracts important figures and policies, and classifies them according to position or interest.

[0502] "Document creation means" refers to a device or function that uses a generative AI model to automatically create presentations or financial report materials based on the results of data analysis.

[0503] The "material sharing means" is a device or function that allows the generated materials to be quickly shared using email or cloud storage.

[0504] "Sales data collection means" refers to a device or function for collecting sales data from physical stores.

[0505] "Inventory data collection means" refers to a device or function for collecting inventory data from a physical store.

[0506] A "report generation means using a generative AI model" is a device or function that automatically generates professionally designed reports using a generative AI model based on collected and analyzed data.

[0507] A "smart device display means" is a device or function that displays reports generated by a device such as a smartphone or smart glasses in real time.

[0508] This invention is a system that collects and analyzes sales data and inventory data in a physical store, and automatically creates and displays operational reports using a generative AI model. Specific embodiments for implementing this invention are described below.

[0509] System Configuration

[0510] The system consists of a server, terminals, and users. The server collects data, analyzes data, creates documents, and shares documents, while the terminals display data and check and edit documents. Users are store staff and managers.

[0511] Hardware and Software

[0512] Hardware

[0513] Server: Has the computing power to collect data, analyze, and prepare documentation.

[0514] Terminal: A smart device such as a smartphone or smart glasses.

[0515] Storage: A storage device for storing collected data.

[0516] software

[0517] FastAPI: A web framework for data collection and API provisioning.

[0518] Pandas: A library for analyzing and manipulating data.

[0519] GPT-4: A generative AI that generates reports in natural language.

[0520] SQLite: A database management system that persists data.

[0521] Processing flow

[0522] 1. Data Collection

[0523] The server collects sales data and inventory data from physical stores in CSV file format, etc. For example, it retrieves data from POS systems and inventory management systems via API at regular intervals and stores it in an SQLite database.

[0524] 2. Data Analysis

[0525] After the server collects the data, the terminal uses Pandas to aggregate and analyze the data. The main steps are to aggregate total revenue from sales data and remaining quantities from inventory data, and to extract important trends and outliers.

[0526] 3. Document Creation

[0527] The server uses GPT-4 to generate a report on operational operations based on the analysis results. The report includes suggestions for business strategies and improvements to inventory management. For example, the following prompt sentence is input to GPT-4 to generate a report:

[0528] Based on the sales and inventory data below, please prepare a report on next week's operating policy.

[0529] Sales Data:

[0530] Product A, 500, 300, 200

[0531] Product B, 700, 500, 100

[0532] Inventory Data:

[0533] Product A, 200, 150, 50

[0534] Product B, 300, 250, 50

[0535] The report should focus on sales trends and future marketing strategies for Product A in particular.

[0536] 4. Display and review of materials

[0537] The terminal displays the generated report on a smartphone or smart glasses. Based on this, users can instantly check business improvement measures and sales promotion measures and edit the materials as needed. The edited materials are shared again by the server and quickly notified to all staff.

[0538] This system will significantly improve the operational efficiency of physical stores, enabling increased sales and optimized inventory management.

[0539] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0540] Step 1:

[0541] The server collects sales and inventory data from physical stores. This process involves accessing POS systems and inventory management systems using APIs to periodically retrieve the necessary data. The input includes sales and inventory data retrieved by API calls, and the output is the data saved in CSV format or in a database.

[0542] Step 2:

[0543] The terminal reads the collected sales and inventory data from the database and aggregates and analyzes it using the Pandas library. The input includes sales and inventory data, and the output is analysis results showing total sales, inventory status, trends, and outliers. For example, it can detect whether sales of a particular product are increasing rapidly or whether there is a risk of inventory shortage.

[0544] Step 3:

[0545] The server receives the analysis results and generates an operational report using the generative AI model GPT-4. The input includes the analysis results and a prompt, and the output is a detailed operational report. An example of a prompt is, "Based on the sales and inventory data below, please prepare a report on next week's operational policy. Please focus on the sales trend of product A and future marketing strategies."

[0546] Step 4:

[0547] The server sends the generated operation report to the terminal so that the user can view it on their smartphone or smart glasses. The input includes the generated operation report, and the output is that the report is displayed on the user's smart device. This allows the user to grasp the store's operation status in real time and take appropriate measures.

[0548] Step 5:

[0549] The user checks the operation report displayed on the terminal and edits it as necessary. The input includes the generated operation report, and the output is a report that has been modified and optimized by the user. By making modifications, the user can reflect more specific operation policies and improvement measures in the report.

[0550] Step 6:

[0551] The server then shares the revised operation report with all staff again. The input includes the operation report revised by the user, and the output is a report distributed to all staff via email or cloud storage. This allows all staff to share the latest operation information and work together in a unified manner to manage the store.

[0552] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0553] This invention is a system that combines data collection means, data analysis means, document creation means, document sharing means, and an emotion engine that recognizes user emotions. This system collects corporate data and uses generative AI to automatically create and share presentations and financial report materials that correspond to the user's emotions. The following explains in detail the roles played by the server, terminal, and user in each step.

[0554] Data collection

[0555] The server accesses the company's accounting software and other data sources to gather the necessary data, then makes API calls and database connections to retrieve real-time financial information and stores it in storage, ensuring the most up-to-date data is always available.

[0556] Data analysis

[0557] The device reads the stored data from the server and uses data analysis algorithms to extract key figures and trends, categorizing them according to job title and interest – for example, data focused on revenue and profit for management, while data including expense details for the finance department.

[0558] Collecting Emotional Data

[0559] The device uses an emotion engine to collect user emotional data. The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The emotional data is analyzed in real time and reflected in the creation of documents.

[0560] Document creation

[0561] The server uses AI to automatically generate presentations and financial report materials based on analysis results and emotional data. For example, if the user is feeling stressed, the content will be simplified and the tone will be calmed. The generated materials will include appropriate titles, graphs, and text descriptions, and will have a professional design.

[0562] Review and edit materials

[0563] Checks documents created by users on their devices and edits them as necessary. Checks not only the content of the documents but also any design or structure defects and makes appropriate corrections.

[0564] Sharing materials

[0565] Documents that users have reviewed and edited can be shared with each team or role. Documents can be quickly distributed via email or cloud storage. Recipients can receive the information they need in the format that is easiest for them to understand.

[0566] Specific examples

[0567] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[0568] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers from the data, such as increases or decreases in revenue or expense trends.

[0569] The device also uses an emotion engine to collect the user's emotion data. As the user creates materials, the emotion engine analyzes the user's facial expressions and tone of voice in real time to collect emotion data.

[0570] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it might create slides focused on revenue and profit for executives, or slides with detailed expense analysis for finance. If the user is feeling stressed or overwhelmed, it will adjust the slide content to be more concise and easy to understand.

[0571] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0572] As a form for implementing the invention, this system works in cooperation with servers, terminals, and users to automate the entire process from data collection to document creation and sharing, enabling efficient and high-quality document creation.

[0573] The processing flow will be explained below.

[0574] Step 1: Data collection

[0575] The server accesses the company's accounting software and other data sources to collect the required data.

[0576] The server retrieves real-time financial information using API calls and database connections.

[0577] The collected data is stored in the server's storage and is constantly updated with the latest information.

[0578] Step 2: Save your data

[0579] The server stores the collected data in an appropriate format.

[0580] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[0581] Back up your data to protect against loss or damage.

[0582] Step 3: Data analysis

[0583] The device reads the stored data from the server.

[0584] The device uses data analysis algorithms to extract key figures and trends.

[0585] For example, income fluctuations, profit trends, and expense details are extracted.

[0586] Step 4: Classify the data

[0587] The device categorizes the analysis results according to job title and interests.

[0588] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[0589] A customized dataset is generated for each role.

[0590] Step 5: Collecting emotion data

[0591] The terminal uses an emotion engine to collect emotion data of the user.

[0592] The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice.

[0593] Emotional data is analyzed in real time and reflected when creating documents.

[0594] Step 6: Prepare materials

[0595] The server uses generative AI to create materials based on the analysis results and emotional data.

[0596] For example, if a user is feeling stressed, the content will be made simpler and the tone will be calmer.

[0597] Presentations and financial reports are automatically generated with appropriate titles, graphs, and text descriptions.

[0598] Step 7: Applying the design to the material

[0599] Apply professional design to server-generated materials.

[0600] Design templates are used to create consistent, visually appealing materials.

[0601] If necessary, custom designs based on company brand guidelines can also be applied.

[0602] Step 8: Review and edit your materials

[0603] The user checks the materials created on the terminal.

[0604] The user checks whether the content is complete and whether the design is appropriate.

[0605] If necessary, the user edits the document and reflects the corrections.

[0606] Step 9: Share your materials

[0607] Documents that users have reviewed and edited are shared with each team and position.

[0608] Materials will be distributed via email and cloud storage.

[0609] Recipients can receive the information they need in the format that is most convenient for them.

[0610] Example 2

[0611] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0612] In modern companies, the processes from data collection and analysis to document creation and sharing are extremely diverse, and there is a need to quickly create and share high-quality documents efficiently. However, in existing systems, these processes are often carried out separately, making it difficult to create documents that take emotional data into account. Furthermore, creating documents without reflecting user emotions can result in the content being difficult to understand or the information not being properly conveyed, which can reduce the speed and accuracy of decision-making.

[0613] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0614] In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a document creation means, and a document sharing means. This makes it possible to collect necessary data from corporate information systems and other information sources, analyze the data, and classify it according to roles and interests within the organization. The emotion recognition means collects user emotion data, and a generative AI model is used to automatically create and share documents according to the user's emotions, thereby achieving efficient and high-quality document creation.

[0615] "Data collection methods" are the methods used to obtain the required data from the company's information systems and other sources.

[0616] "Data analysis tools" are tools for analyzing collected data and classifying it according to roles and interests within the organization.

[0617] The "emotion recognition means" is a means for detecting emotions from the user's facial expressions and tone of voice, and analyzing this emotional data.

[0618] "Document creation means" refers to a means for automatically creating documents using a generative AI model based on data analysis results and emotion data.

[0619] "Document sharing means" refers to a means for sharing generated documents with teams and positions within an organization via email or cloud storage.

[0620] A "generative AI model" is an artificial intelligence model that performs natural language processing based on given data and user emotional data to generate appropriate sentences and materials.

[0621] A "prompt sentence" is a sentence that specifically inputs instructions or content for creating specific materials to a generative AI model.

[0622] This invention is a system for collecting corporate data, analyzing the data, recognizing emotions, and automatically creating and sharing documents based on the data. This system mainly consists of three entities: a server, a terminal, and a user.

[0623] Server Functionality Description

[0624] Data collection methods

[0625] The server accesses the company's information systems and other sources to gather the necessary data. Specifically, the server makes API calls and database connections. For example, it retrieves financial information such as income, expenses, and profits from accounting software. The specific software used generally includes an "information systems API" or "database management system."

[0626] Document creation method

[0627] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results and emotional data. For example, a "generative AI model" is used for the generative AI. Based on the emotional data, the corresponding tone and content are adjusted.

[0628] Device function description

[0629] Data Analysis Methods

[0630] The device reads the stored data from the server and uses data analysis algorithms, often using the Python pandas library, to extract key figures and trends. The data is then categorized by job title and formatted appropriately for management, finance, etc.

[0631] emotion recognition means

[0632] The device collects user emotional data using an emotion recognition engine. The emotion recognition engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. Specific technologies include "image recognition API" and "voice analysis technology."

[0633] User Roles

[0634] Review and edit materials

[0635] The user reviews the materials created on their device and edits them as necessary. Microsoft PowerPoint and Google Slides are the software typically used. The content and design are checked for flaws, and the optimal materials are completed.

[0636] Material sharing method

[0637] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. Specific sharing methods include email systems and cloud storage services.

[0638] Specific examples

[0639] For example, when creating a company's monthly financial report, the server automatically collects data such as the current month's income, expenses, and profits from the accounting software. The collected data is stored on the server and kept up to date. The device then reads the data from the server's database and analyzes it using Python's pandas library. Key figures such as increases or decreases in income and expense trends are extracted. Additionally, as the user creates the report, an emotion recognition engine collects emotional data in real time.

[0640] The server sends the following prompt to the AI ​​model: "Please create a monthly financial report document. Use data on income, expenses, and profits, and adjust it to an easy-to-understand and concise format, taking into account user sentiment data. Please create slides for management and the finance department." The AI ​​then generates the appropriate document.

[0641] Users can open the generated document in Microsoft PowerPoint, check the titles and graphs, and correct any inappropriate parts. Once the document has been corrected, it is uploaded to Google Drive and a shared link is generated. By sharing this link with internal management and the finance department, information can be shared quickly and accurately.

[0642] In this way, the invention automates data management and document creation processes within a company, enabling efficient and high-quality document creation.

[0643] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0644] Step 1: Data collection

[0645] The server accesses the company's information systems and other information sources to collect the necessary data. The server makes API calls and connects to databases to obtain financial information such as income, expenses, and profits in real time. Specifically, the server sends requests to the "company's accounting data API" and stores the collected data in an internal database. The input is the API request, and the output is the database where the financial information is stored.

[0646] Specific actions

[0647] The server sends an API request to the " / v1 / data / financials" endpoint to retrieve income, expense, and profit data in JSON format, and stores the data in the "financial_data" table in a MySQL database.

[0648] Step 2: Data analysis

[0649] The terminal retrieves data stored in the database from the server and analyzes it. The terminal uses Python's pandas library to read the data and extract key figures and trends. The results of this data analysis are categorized by job position. The input is financial data retrieved from the database, and the output is the analytical results, such as figures and trends.

[0650] Specific actions

[0651] The terminal executes the SQL query "SELECT FROM financial_data WHERE date = '2023-10-31'" and converts the retrieved data into a pandas data frame. It then extracts trends in revenue and expenses and categorizes them into data for management and data for the finance department.

[0652] Step 3: Collecting emotion data

[0653] The device uses an emotion recognition engine to collect user emotion data. The device uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The input is the user's facial expression images and voice data, and the output is emotion data.

[0654] Specific actions

[0655] The device sends the user's facial image to the Google Cloud Vision API and receives the analysis results. It also sends the user's voice data to the IBM Watson Tone Analyzer and obtains the tone analysis results.

[0656] Step 4: Prepare materials

[0657] The server automatically generates presentation materials using a generative AI model. The server sends prompts to the generative AI model based on the data analysis results and emotional data. The inputs are the data analysis results and emotional data, and the output is the generated presentation materials.

[0658] Specific actions

[0659] The server sends the generation AI a prompt saying, "Please create a monthly financial report. Use income, expense, and profit data, and adjust it into an easy-to-understand and concise format, taking into account the user's emotional data." The server then receives the presentation (pptx format) generated by the AI.

[0660] Step 5: Review and edit your materials

[0661] The user reviews and edits the materials created on the device. The user opens the materials using Microsoft PowerPoint or Google Slides and edits the content and design. The input is the generated presentation materials, and the output is the final materials that have been reviewed and edited.

[0662] Specific actions

[0663] A user opens a pptx file in Microsoft PowerPoint, checks the slide titles and charts, corrects any inappropriate content, and adds new data and charts as needed.

[0664] Step 6: Share your materials

[0665] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. The input is the reviewed and edited final document, and the output is a shared link or email.

[0666] Specific actions

[0667] The user uploads the completed document to Google Drive, generates a shared link, and emails the link to the company's management and finance department with a message saying, "Please see the monthly financial report document at the link below."

[0668] (Application example 2)

[0669] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0670] In today's business environment, store managers need to efficiently analyze sales data and customer feedback and create optimal presentation materials based on that data. These materials also need to be tailored to take into account the manager's emotions and stress levels, but this is difficult to achieve. Furthermore, there is a lack of a way to quickly and efficiently share the created materials. An integrated system is needed to solve these challenges and streamline management tasks.

[0671] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, a material creation means, a material sharing means, an emotion recognition means, and a means for the material creation means to automatically generate materials corresponding to the user's emotions based on the data collected by the data collection means and the emotion data collected by the emotion recognition means. This enables a manager of a physical store to automatically generate presentation materials that match the manager's emotions and stress level based on sales data and customer feedback, and quickly share them.

[0672] "Data collection means" refers to the means for obtaining necessary information from external data sources.

[0673] "Data analysis means" refers to the means of analyzing collected data and classifying its contents according to job title and interest.

[0674] The "material creation means" is a means for generating presentation materials and reports based on data and emotion data.

[0675] The "material sharing means" is a means for quickly distributing the created materials to each of the parties involved.

[0676] The "emotion recognition means" is a means for detecting the user's emotions from facial expressions, tone of voice, etc.

[0677] The "server" is a computer system for executing the data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[0678] An "integrated system" is a system in which multiple means work together to automate the entire process, from data collection to document creation and sharing.

[0679] "Presentation materials" are materials that include slides, graphs, tables, etc., to visually present the contents of a report.

[0680] "Emotion data" is information that indicates the user's emotional state, and is data obtained from facial expressions and tone of voice.

[0681] This invention is a system that improves the efficiency of store management and automatically generates and shares presentation materials according to the manager's stress level and emotions. This system is composed of a combination of data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[0682] Data collection

[0683] The server uses data collection methods to access the organization's trading software and other data sources to collect the required data such as sales data, customer feedback, etc. The data is retrieved in real time through API calls and database connections and stored in the server's storage.

[0684] Data analysis

[0685] The device reads the data stored on the server and analyzes it using data analysis tools. This analysis includes categorizing it according to job titles, such as management or finance. For example, key figures and trends can be extracted from sales data to identify points that managers should pay attention to.

[0686] Collecting Emotional Data

[0687] The device uses an emotion recognition method to collect emotional data from the administrator. The emotion recognition method involves capturing the administrator's facial expressions with a camera and using an expression recognition algorithm. Audio can also be captured and emotions can be analyzed from the tone of voice. The emotional data collected in this way is analyzed in real time and reflected in the creation of documents.

[0688] Document creation

[0689] The server uses generative AI to automatically generate presentation materials based on data analysis results and emotional data. If the administrator is feeling stressed or anxious, the generated materials are adjusted to be concise and easy to understand. The generated materials include appropriate titles, graphs, and text explanations, and have a professional design.

[0690] Review and edit materials

[0691] Users can use their devices to check the documents they have created and edit them as necessary. They can check for defects in not only the content of the documents but also the design and structure, and make appropriate corrections.

[0692] Sharing materials

[0693] Users can quickly share documents they have reviewed and edited with each team and position. Documents are efficiently distributed via email or cloud storage, allowing recipients to receive the information they need in the format that is easiest for them to understand.

[0694] Specific examples

[0695] For example, consider the creation of monthly review materials for a physical store. The server automatically collects one month's worth of sales data and customer feedback from the organization's trading software. This data is updated in real time and stored on the server.

[0696] The device then reads the data stored on the server and runs it through data analysis algorithms to extract key figures, trends and outliers, including trends in revenue growth and customer satisfaction.

[0697] The terminal also uses an emotion recognition means to collect emotional data of the administrator. During the process of the administrator creating the materials, the emotion recognition means analyzes the administrator's facial expressions and tone of voice in real time to collect emotional data.

[0698] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it creates slides for management that focus on sales and customer satisfaction, while for finance it creates slides with detailed expense analysis. If a manager is feeling stressed or overwhelmed, it adjusts the slide content to be more concise and easy to understand.

[0699] Finally, managers review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0700] Prompt Sentence Examples

[0701] "Generate monthly review materials based on the latest sales data and customer feedback, taking into account store manager sentiment. If managers are feeling stressed, simplify the content and adapt it to an easy-to-understand format."

[0702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0703] Step 1:

[0704] Data collection

[0705] The server uses data collection methods to collect sales data and customer feedback from the organization's trading software and other data sources, specifically through API calls and database connections, and stores the data in the server's storage in real time.

[0706] The input data is the API key and connection information for the trading software, and the output data is collected sales data and customer feedback.

[0707] Step 2:

[0708] Data analysis

[0709] The device reads the stored data from the server and analyzes it using data analytics. Specifically, it runs sales data and customer feedback through analytical algorithms to extract key figures and trends. It also categorizes the information needed according to job title and interests.

[0710] Input data is sales data and customer feedback obtained from the server, and output data is analyzed key figures, trends and classified information.

[0711] Step 3:

[0712] Collecting Emotional Data

[0713] The device collects the administrator's emotional data using an emotion recognition means. Specifically, it captures the administrator's facial expressions with a camera and analyzes their emotions using an expression recognition algorithm. It also performs voice analysis to determine emotions from the tone of voice.

[0714] The input data is camera images and audio data, and the output data is analyzed emotion data.

[0715] Step 4:

[0716] Document creation

[0717] The server uses a generative AI model to automatically generate presentation materials based on data analysis results and emotion data. Specifically, the server inputs prompts into the generative AI to generate the content of the materials, creating materials that include appropriate titles, graphs, and text descriptions.

[0718] The input data are the analysis results (main figures, trends) and emotion data, and the output data are the generated presentation materials.

[0719] Step 5:

[0720] Review and edit materials

[0721] The user uses the terminal to check the created materials and edit them as necessary, specifically checking the content, design, and structure of the materials and making appropriate corrections.

[0722] The input data is the generated presentation material, and the output data is the revised version of the material.

[0723] Step 6:

[0724] Sharing materials

[0725] Users can quickly share the documents they have reviewed and edited with each team or position by distributing them via email or cloud storage.

[0726] The input data is the revised document, and the output data is the document shared with all parties involved.

[0727] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0728] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0729] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0730] [Third embodiment]

[0731] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0732] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0733] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0734] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0735] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0736] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0737] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0738] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0739] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0740] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0741] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0742] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0743] This invention is a system that collects data from various corporate data sources and automatically creates presentations and financial report materials using generative AI. This system mainly involves the server, terminals, and users, each of which functions as follows:

[0744] Data collection

[0745] The server accesses the company's accounting software and other data sources to collect the necessary data. The server uses API calls and database connections to retrieve real-time financial information and stores it in storage. At this stage, the collected data reflects the most up-to-date information.

[0746] Data analysis

[0747] The device reads the collected data from the server and performs data analysis. The device uses a data analysis algorithm to extract important figures and policies, and classifies them according to job title and interests. This allows the specific information needed by each job title to be extracted and organized.

[0748] Document creation

[0749] The server uses generation AI to automatically generate presentations and financial report materials based on the analysis results. The generation AI creates professionally designed materials based on the input text and data. The generated materials are provided in a format optimized for each position and department.

[0750] Sharing and reviewing materials

[0751] Users can review documents created on their devices and edit them as needed. They can also quickly share the reviewed documents with their respective teams and positions via email or cloud storage. This ensures that documents are provided in a timely manner, enabling the rapid communication of information needed for decision-making.

[0752] Specific examples

[0753] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[0754] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers, such as increases or decreases in revenue or expense trends.

[0755] The server uses generative AI to create presentations based on this analysis, such as slides focused on revenue and profit for executives and slides with detailed expense analysis for finance.

[0756] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0757] The processing flow will be explained below.

[0758] Step 1: Data collection

[0759] The server accesses the company's accounting software and other data sources to collect the required data.

[0760] The server makes API calls and database connections to retrieve real-time financial information.

[0761] The collected data is stored in the server's storage and is constantly updated with the latest information.

[0762] Step 2: Save your data

[0763] The server stores the collected data in an appropriate format.

[0764] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[0765] Back up your data to protect against loss or damage.

[0766] Step 3: Data analysis

[0767] The device reads the stored data from the server.

[0768] The device uses data analysis algorithms to extract key figures and trends.

[0769] For example, income fluctuations, profit trends, and expense details are extracted.

[0770] Step 4: Classify the data

[0771] The device categorizes the analysis results according to job title and interests.

[0772] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[0773] A customized dataset is generated for each role.

[0774] Step 5: Prepare materials

[0775] The server uses generative AI to create materials based on the classified data.

[0776] Presentations and financial report materials are automatically generated.

[0777] Each slide contains an appropriate title, graph, and text description.

[0778] Step 6: Applying the design to the material

[0779] Apply professional design to server-generated materials.

[0780] Design templates are used to create consistent, visually appealing materials.

[0781] If necessary, custom designs based on company brand guidelines can also be applied.

[0782] Step 7: Review and edit your materials

[0783] The user checks the materials created on the terminal.

[0784] The user checks whether the content is complete and whether the design is appropriate.

[0785] If necessary, the user edits the document and reflects the corrections.

[0786] Step 8: Share your materials

[0787] Documents that users have reviewed and edited are shared with each team and position.

[0788] Materials will be distributed via email and cloud storage.

[0789] Recipients can receive the information they need in the format that is most convenient for them.

[0790] These are the specific processing steps from data collection to sharing of materials.

[0791] Example 1

[0792] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0793] In traditional corporate activities, collecting and analyzing various data, and creating and sharing reports was a time-consuming and labor-intensive process. In particular, the process of collecting and analyzing data, creating reports, and quickly sharing them with relevant parties was fragmented, making it difficult to provide information in a timely manner. Furthermore, while there was a need to provide information in a format optimized for different positions and departments, there was a lack of efficient ways to achieve this.

[0794] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0795] In this invention, the server includes a data collection means, a data preprocessing means, a data analysis means, an automatic document generation means, a document confirmation means, and a document sharing means. This allows the server to automatically acquire necessary data from a company's accounting system or other data sources, cleanse and preprocess the data, analyze it, automatically create report documents based on the results using a generative AI model, and promptly share the documents with relevant parties after the user has confirmed and corrected them.

[0796] "Data collection tools" are functions for accessing a company's accounting system or other data sources and automatically obtaining the required data.

[0797] "Data preprocessing means" refers to a function for processing acquired data into a format suitable for analysis, such as filling in missing values, deleting invalid data, and standardizing date and time formats.

[0798] "Data analysis tools" are functions for analyzing pre-processed data, extracting important figures and trends, and classifying them according to job title and interests.

[0799] "Automatic document generation means" is a function that uses a generative AI model to automatically create presentations and financial report materials based on the results of data analysis.

[0800] The "material confirmation means" is a function that allows the user to use a terminal to check the automatically generated materials and correct them as necessary.

[0801] "Document sharing means" is a function that allows confirmed and revised documents to be quickly shared with relevant parties via email or cloud storage.

[0802] A "generative AI model" is an artificial intelligence model that generates text from an input prompt sentence based on natural language processing.

[0803] A "prompt" is a text sentence that contains instructions or questions for the generative AI model when generating materials.

[0804] This invention is a system that uses data obtained from a company's accounting system and other data sources to automatically create presentations and financial report materials using a generative AI model. This system involves the involvement of a server, a terminal, and a user, each of which functions as follows:

[0805] Data collection

[0806] The server accesses the company's accounting software and other data sources to collect the necessary data. Specifically, the server uses API calls and database connections to obtain real-time financial information and saves it in storage. For example, the server may obtain the latest data on income, expenses, profits, etc. through the APIs of Xero or QuickBooks. The obtained data is then saved in a database (e.g., PostgreSQL) on the server.

[0807] Data Preprocessing

[0808] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[0809] Data analysis

[0810] The terminal retrieves preprocessed data from the server and analyzes it using algorithms. Specifically, the terminal uses Python's Pandas and NumPy to analyze data, performing trend analysis of income and expenses, outlier detection, and sales analysis by category. For example, the terminal processes data retrieved from the server using Python, extracts important numerical values ​​in array format, and classifies them by department.

[0811] Automatic document generation

[0812] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The generative AI model uses, for example, OpenAI's GPT-4 API. The server generates prompt text and sends it to the generative AI model to automatically create high-quality materials. Examples of prompt text include the following:

[0813] Example prompt sentence:

[0814] "Prepare the monthly financial report for July 2023. Create slides for the management team based on the following data."

[0815] Revenue: $100,000

[0816] Expenditure: $60,000

[0817] Profit: $40,000

[0818] Increase or decrease in main revenue source: Customer A +10%, Customer B -5%

[0819] The generated materials are converted into presentation format using the Google Slides API.

[0820] Check and share materials

[0821] Users can use their devices to review the automatically generated materials and make corrections as necessary. Editing can be done directly in a browser using tools like Google Slides. The corrected materials can then be quickly shared with relevant parties via email or cloud storage (e.g., Google Drive or Dropbox). For example, a user can correct an error in "Sales" on Google Slides, save the revised slide to Google Drive, and email a link to their team.

[0822] This system enables companies to quickly and accurately prepare financial reporting documents based on the latest financial information and provide information to all stakeholders in a timely manner.

[0823] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0824] Step 1:

[0825] The server accesses the company's accounting system and other data sources to collect the required data. To do this, the server retrieves real-time data using API calls or database connections. For example, the server sends a request to the accounting system's API endpoint using authentication information to retrieve data such as income, expenses, and profits. The retrieved data is then stored in a database (e.g., PostgreSQL) on the server.

[0826] Input: Accounting system credentials and API endpoint

[0827] Output: The financial data obtained (income, expenses, profit, etc.)

[0828] Step 2:

[0829] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[0830] Input: Raw data collected

[0831] Output: A cleansed dataset

[0832] Step 3:

[0833] The terminal retrieves preprocessed data from the server and performs analysis using algorithms, such as Pandas and NumPy in Python, to analyze income and expenditure trends, detect outliers, and analyze sales by category.

[0834] Input: Cleansed dataset

[0835] Output: Analyzed data (e.g., trend analysis results, outlier detection results)

[0836] Step 4:

[0837] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The server generates prompts and sends them to a generative AI model such as GPT-4 to create professionally designed materials. For example, the server could generate a prompt such as, "Prepare monthly financial report materials for July 2023. Create slides for management based on the following data," with detailed data attached.

[0838] Input: Parsed data and prompt statement

[0839] Output: Auto-generated presentation materials

[0840] Step 5:

[0841] Users can use their devices to review the automatically generated materials and make corrections as necessary. Users can also edit the materials directly in their browsers using tools such as Google Slides.

[0842] Input: Auto-generated material

[0843] Output: Corrected and verified material

[0844] Step 6:

[0845] Users can quickly share revised documents with relevant parties via email or cloud storage. For example, users can save revised documents to Google Drive and email the link to their team. This ensures that documents are provided in a timely manner and that the information needed for decision-making can be communicated quickly.

[0846] Input: Corrected and verified material

[0847] Output: Materials shared with stakeholders

[0848] (Application example 1)

[0849] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0850] In today's brick-and-mortar stores, managing sales and inventory data is complicated, making it difficult to grasp the status of store operations in real time. Furthermore, providing information at the right time and creating materials to quickly formulate management strategies requires a lot of effort. There is also a lack of effective tools that allow staff to instantly grasp the status of store operations. There is a need for a system that can solve these issues and streamline store operations.

[0851] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0852] In this invention, the server includes a data collection means, a data analysis means, a document creation means, a document sharing means, a sales data collection means, an inventory data collection means, a report generation means using a generative AI model, and a smart device display means. This enables sales data and inventory data from a physical store to be collected and analyzed in real time, and management reports to be automatically generated using the generative AI model. Furthermore, this allows staff to instantly check the management status using smart devices such as smartphones and smart glasses, providing a system that supports quick and accurate decision-making.

[0853] A "data collection tool" is a device or function that accesses a company's accounting software or other data sources and automatically collects the required data.

[0854] "Data analysis means" refers to a device or function that analyzes collected data, extracts important figures and policies, and classifies them according to position or interest.

[0855] "Document creation means" refers to a device or function that uses a generative AI model to automatically create presentations or financial report materials based on the results of data analysis.

[0856] The "material sharing means" is a device or function that allows the generated materials to be quickly shared using email or cloud storage.

[0857] "Sales data collection means" refers to a device or function for collecting sales data from physical stores.

[0858] "Inventory data collection means" refers to a device or function for collecting inventory data from a physical store.

[0859] A "report generation means using a generative AI model" is a device or function that automatically generates professionally designed reports using a generative AI model based on collected and analyzed data.

[0860] A "smart device display means" is a device or function that displays reports generated by a device such as a smartphone or smart glasses in real time.

[0861] This invention is a system that collects and analyzes sales data and inventory data in a physical store, and automatically creates and displays operational reports using a generative AI model. Specific embodiments for implementing this invention are described below.

[0862] System Configuration

[0863] The system consists of a server, terminals, and users. The server collects data, analyzes data, creates documents, and shares documents, while the terminals display data and check and edit documents. Users are store staff and managers.

[0864] Hardware and Software

[0865] Hardware

[0866] Server: Has the computing power to collect data, analyze, and prepare documentation.

[0867] Terminal: A smart device such as a smartphone or smart glasses.

[0868] Storage: A storage device for storing collected data.

[0869] software

[0870] FastAPI: A web framework for data collection and API provisioning.

[0871] Pandas: A library for analyzing and manipulating data.

[0872] GPT-4: A generative AI that generates reports in natural language.

[0873] SQLite: A database management system that persists data.

[0874] Processing flow

[0875] 1. Data Collection

[0876] The server collects sales data and inventory data from physical stores in CSV file format, etc. For example, it retrieves data from POS systems and inventory management systems via API at regular intervals and stores it in an SQLite database.

[0877] 2. Data Analysis

[0878] After the server collects the data, the terminal uses Pandas to aggregate and analyze the data. The main steps are to aggregate total revenue from sales data and remaining quantities from inventory data, and to extract important trends and outliers.

[0879] 3. Document Creation

[0880] The server uses GPT-4 to generate a report on operational operations based on the analysis results. The report includes suggestions for business strategies and improvements to inventory management. For example, the following prompt sentence is input to GPT-4 to generate a report:

[0881] Based on the sales and inventory data below, please prepare a report on next week's operating policy.

[0882] Sales Data:

[0883] Product A, 500, 300, 200

[0884] Product B, 700, 500, 100

[0885] Inventory Data:

[0886] Product A, 200, 150, 50

[0887] Product B, 300, 250, 50

[0888] The report should focus on sales trends and future marketing strategies for Product A in particular.

[0889] 4. Display and review of materials

[0890] The terminal displays the generated report on a smartphone or smart glasses. Based on this, users can instantly check business improvement measures and sales promotion measures and edit the materials as needed. The edited materials are shared again by the server and quickly notified to all staff.

[0891] This system will significantly improve the operational efficiency of physical stores, enabling increased sales and optimized inventory management.

[0892] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0893] Step 1:

[0894] The server collects sales and inventory data from physical stores. This process involves accessing POS systems and inventory management systems using APIs to periodically retrieve the necessary data. The input includes sales and inventory data retrieved by API calls, and the output is the data saved in CSV format or in a database.

[0895] Step 2:

[0896] The terminal reads the collected sales and inventory data from the database and aggregates and analyzes it using the Pandas library. The input includes sales and inventory data, and the output is analysis results showing total sales, inventory status, trends, and outliers. For example, it can detect whether sales of a particular product are increasing rapidly or whether there is a risk of inventory shortage.

[0897] Step 3:

[0898] The server receives the analysis results and generates an operational report using the generative AI model GPT-4. The input includes the analysis results and a prompt, and the output is a detailed operational report. An example of a prompt is, "Based on the sales and inventory data below, please prepare a report on next week's operational policy. Please focus on the sales trend of product A and future marketing strategies."

[0899] Step 4:

[0900] The server sends the generated operation report to the terminal so that the user can view it on their smartphone or smart glasses. The input includes the generated operation report, and the output is that the report is displayed on the user's smart device. This allows the user to grasp the store's operation status in real time and take appropriate measures.

[0901] Step 5:

[0902] The user checks the operation report displayed on the terminal and edits it as necessary. The input includes the generated operation report, and the output is a report that has been modified and optimized by the user. By making modifications, the user can reflect more specific operation policies and improvement measures in the report.

[0903] Step 6:

[0904] The server then shares the revised operation report with all staff again. The input includes the operation report revised by the user, and the output is a report distributed to all staff via email or cloud storage. This allows all staff to share the latest operation information and work together in a unified manner to manage the store.

[0905] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0906] This invention is a system that combines data collection means, data analysis means, document creation means, document sharing means, and an emotion engine that recognizes user emotions. This system collects corporate data and uses generative AI to automatically create and share presentations and financial report materials that correspond to the user's emotions. The following explains in detail the roles played by the server, terminal, and user in each step.

[0907] Data collection

[0908] The server accesses the company's accounting software and other data sources to gather the necessary data, then makes API calls and database connections to retrieve real-time financial information and stores it in storage, ensuring the most up-to-date data is always available.

[0909] Data analysis

[0910] The device reads the stored data from the server and uses data analysis algorithms to extract key figures and trends, categorizing them according to job title and interest – for example, data focused on revenue and profit for management, while data including expense details for the finance department.

[0911] Collecting Emotional Data

[0912] The device uses an emotion engine to collect user emotional data. The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The emotional data is analyzed in real time and reflected in the creation of documents.

[0913] Document creation

[0914] The server uses AI to automatically generate presentations and financial report materials based on analysis results and emotional data. For example, if the user is feeling stressed, the content will be simplified and the tone will be calmed. The generated materials will include appropriate titles, graphs, and text descriptions, and will have a professional design.

[0915] Review and edit materials

[0916] Checks documents created by users on their devices and edits them as necessary. Checks not only the content of the documents but also any design or structure defects and makes appropriate corrections.

[0917] Sharing materials

[0918] Documents that users have reviewed and edited can be shared with each team or role. Documents can be quickly distributed via email or cloud storage. Recipients can receive the information they need in the format that is easiest for them to understand.

[0919] Specific examples

[0920] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[0921] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers from the data, such as increases or decreases in revenue or expense trends.

[0922] The device also uses an emotion engine to collect the user's emotion data. As the user creates materials, the emotion engine analyzes the user's facial expressions and tone of voice in real time to collect emotion data.

[0923] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it might create slides focused on revenue and profit for executives, or slides with detailed expense analysis for finance. If the user is feeling stressed or overwhelmed, it will adjust the slide content to be more concise and easy to understand.

[0924] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[0925] As a form for implementing the invention, this system works in cooperation with servers, terminals, and users to automate the entire process from data collection to document creation and sharing, enabling efficient and high-quality document creation.

[0926] The processing flow will be explained below.

[0927] Step 1: Data collection

[0928] The server accesses the company's accounting software and other data sources to collect the required data.

[0929] The server retrieves real-time financial information using API calls and database connections.

[0930] The collected data is stored in the server's storage and is constantly updated with the latest information.

[0931] Step 2: Save your data

[0932] The server stores the collected data in an appropriate format.

[0933] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[0934] Back up your data to protect against loss or damage.

[0935] Step 3: Data analysis

[0936] The device reads the stored data from the server.

[0937] The device uses data analysis algorithms to extract key figures and trends.

[0938] For example, income fluctuations, profit trends, and expense details are extracted.

[0939] Step 4: Classify the data

[0940] The device categorizes the analysis results according to job title and interests.

[0941] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[0942] A customized dataset is generated for each role.

[0943] Step 5: Collecting emotion data

[0944] The terminal uses an emotion engine to collect emotion data of the user.

[0945] The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice.

[0946] Emotional data is analyzed in real time and reflected when creating documents.

[0947] Step 6: Prepare materials

[0948] The server uses generative AI to create materials based on the analysis results and emotional data.

[0949] For example, if a user is feeling stressed, the content will be made simpler and the tone will be calmer.

[0950] Presentations and financial reports are automatically generated with appropriate titles, graphs, and text descriptions.

[0951] Step 7: Applying the design to the material

[0952] Apply professional design to server-generated materials.

[0953] Design templates are used to create consistent, visually appealing materials.

[0954] If necessary, custom designs based on company brand guidelines can also be applied.

[0955] Step 8: Review and edit your materials

[0956] The user checks the materials created on the terminal.

[0957] The user checks whether the content is complete and whether the design is appropriate.

[0958] If necessary, the user edits the document and reflects the corrections.

[0959] Step 9: Share your materials

[0960] Documents that users have reviewed and edited are shared with each team and position.

[0961] Materials will be distributed via email and cloud storage.

[0962] Recipients can receive the information they need in the format that is most convenient for them.

[0963] Example 2

[0964] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0965] In modern companies, the processes from data collection and analysis to document creation and sharing are extremely diverse, and there is a need to quickly create and share high-quality documents efficiently. However, in existing systems, these processes are often carried out separately, making it difficult to create documents that take emotional data into account. Furthermore, creating documents without reflecting user emotions can result in the content being difficult to understand or the information not being properly conveyed, which can reduce the speed and accuracy of decision-making.

[0966] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0967] In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a document creation means, and a document sharing means. This makes it possible to collect necessary data from corporate information systems and other information sources, analyze the data, and classify it according to roles and interests within the organization. The emotion recognition means collects user emotion data, and a generative AI model is used to automatically create and share documents according to the user's emotions, thereby achieving efficient and high-quality document creation.

[0968] "Data collection methods" are the methods used to obtain the required data from the company's information systems and other sources.

[0969] "Data analysis tools" are tools for analyzing collected data and classifying it according to roles and interests within the organization.

[0970] The "emotion recognition means" is a means for detecting emotions from the user's facial expressions and tone of voice, and analyzing this emotional data.

[0971] "Document creation means" refers to a means for automatically creating documents using a generative AI model based on data analysis results and emotion data.

[0972] "Document sharing means" refers to a means for sharing generated documents with teams and positions within an organization via email or cloud storage.

[0973] A "generative AI model" is an artificial intelligence model that performs natural language processing based on given data and user emotional data to generate appropriate sentences and materials.

[0974] A "prompt sentence" is a sentence that specifically inputs instructions or content for creating specific materials to a generative AI model.

[0975] This invention is a system for collecting corporate data, analyzing the data, recognizing emotions, and automatically creating and sharing documents based on the data. This system mainly consists of three entities: a server, a terminal, and a user.

[0976] Server Functionality Description

[0977] Data collection methods

[0978] The server accesses the company's information systems and other sources to gather the necessary data. Specifically, the server makes API calls and database connections. For example, it retrieves financial information such as income, expenses, and profits from accounting software. The specific software used generally includes an "information systems API" or "database management system."

[0979] Document creation method

[0980] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results and emotional data. For example, a "generative AI model" is used for the generative AI. Based on the emotional data, the corresponding tone and content are adjusted.

[0981] Device function description

[0982] Data Analysis Methods

[0983] The device reads the stored data from the server and uses data analysis algorithms, often using the Python pandas library, to extract key figures and trends. The data is then categorized by job title and formatted appropriately for management, finance, etc.

[0984] emotion recognition means

[0985] The device collects user emotional data using an emotion recognition engine. The emotion recognition engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. Specific technologies include "image recognition API" and "voice analysis technology."

[0986] User Roles

[0987] Review and edit materials

[0988] The user reviews the materials created on their device and edits them as necessary. Microsoft PowerPoint and Google Slides are the software typically used. The content and design are checked for flaws, and the optimal materials are completed.

[0989] Material sharing method

[0990] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. Specific sharing methods include email systems and cloud storage services.

[0991] Specific examples

[0992] For example, when creating a company's monthly financial report, the server automatically collects data such as the current month's income, expenses, and profits from the accounting software. The collected data is stored on the server and kept up to date. The device then reads the data from the server's database and analyzes it using Python's pandas library. Key figures such as increases or decreases in income and expense trends are extracted. Additionally, as the user creates the report, an emotion recognition engine collects emotional data in real time.

[0993] The server sends the following prompt to the AI ​​model: "Please create a monthly financial report document. Use data on income, expenses, and profits, and adjust it to an easy-to-understand and concise format, taking into account user sentiment data. Please create slides for management and the finance department." The AI ​​then generates the appropriate document.

[0994] Users can open the generated document in Microsoft PowerPoint, check the titles and graphs, and correct any inappropriate parts. Once the document has been corrected, it is uploaded to Google Drive and a shared link is generated. By sharing this link with internal management and the finance department, information can be shared quickly and accurately.

[0995] In this way, the invention automates data management and document creation processes within a company, enabling efficient and high-quality document creation.

[0996] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0997] Step 1: Data collection

[0998] The server accesses the company's information systems and other information sources to collect the necessary data. The server makes API calls and connects to databases to obtain financial information such as income, expenses, and profits in real time. Specifically, the server sends requests to the "company's accounting data API" and stores the collected data in an internal database. The input is the API request, and the output is the database where the financial information is stored.

[0999] Specific actions

[1000] The server sends an API request to the " / v1 / data / financials" endpoint to retrieve income, expense, and profit data in JSON format, and stores the data in the "financial_data" table in a MySQL database.

[1001] Step 2: Data analysis

[1002] The terminal retrieves data stored in the database from the server and analyzes it. The terminal uses Python's pandas library to read the data and extract key figures and trends. The results of this data analysis are categorized by job position. The input is financial data retrieved from the database, and the output is the analytical results, such as figures and trends.

[1003] Specific actions

[1004] The terminal executes the SQL query "SELECT FROM financial_data WHERE date = '2023-10-31'" and converts the retrieved data into a pandas data frame. It then extracts trends in revenue and expenses and categorizes them into data for management and data for the finance department.

[1005] Step 3: Collecting emotion data

[1006] The device uses an emotion recognition engine to collect user emotion data. The device uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The input is the user's facial expression images and voice data, and the output is emotion data.

[1007] Specific actions

[1008] The device sends the user's facial image to the Google Cloud Vision API and receives the analysis results. It also sends the user's voice data to the IBM Watson Tone Analyzer and obtains the tone analysis results.

[1009] Step 4: Prepare materials

[1010] The server automatically generates presentation materials using a generative AI model. The server sends prompts to the generative AI model based on the data analysis results and emotional data. The inputs are the data analysis results and emotional data, and the output is the generated presentation materials.

[1011] Specific actions

[1012] The server sends the generation AI a prompt saying, "Please create a monthly financial report. Use income, expense, and profit data, and adjust it into an easy-to-understand and concise format, taking into account the user's emotional data." The server then receives the presentation (pptx format) generated by the AI.

[1013] Step 5: Review and edit your materials

[1014] The user reviews and edits the materials created on the device. The user opens the materials using Microsoft PowerPoint or Google Slides and edits the content and design. The input is the generated presentation materials, and the output is the final materials that have been reviewed and edited.

[1015] Specific actions

[1016] A user opens a pptx file in Microsoft PowerPoint, checks the slide titles and charts, corrects any inappropriate content, and adds new data and charts as needed.

[1017] Step 6: Share your materials

[1018] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. The input is the reviewed and edited final document, and the output is a shared link or email.

[1019] Specific actions

[1020] The user uploads the completed document to Google Drive, generates a shared link, and emails the link to the company's management and finance department with a message saying, "Please see the monthly financial report document at the link below."

[1021] (Application example 2)

[1022] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1023] In today's business environment, store managers need to efficiently analyze sales data and customer feedback and create optimal presentation materials based on that data. These materials also need to be tailored to take into account the manager's emotions and stress levels, but this is difficult to achieve. Furthermore, there is a lack of a way to quickly and efficiently share the created materials. An integrated system is needed to solve these challenges and streamline management tasks.

[1024] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, a material creation means, a material sharing means, an emotion recognition means, and a means for the material creation means to automatically generate materials corresponding to the user's emotions based on the data collected by the data collection means and the emotion data collected by the emotion recognition means. This enables a manager of a physical store to automatically generate presentation materials that match the manager's emotions and stress level based on sales data and customer feedback, and quickly share them.

[1025] "Data collection means" refers to the means for obtaining necessary information from external data sources.

[1026] "Data analysis means" refers to the means of analyzing collected data and classifying its contents according to job title and interest.

[1027] The "material creation means" is a means for generating presentation materials and reports based on data and emotion data.

[1028] The "material sharing means" is a means for quickly distributing the created materials to each of the parties involved.

[1029] The "emotion recognition means" is a means for detecting the user's emotions from facial expressions, tone of voice, etc.

[1030] The "server" is a computer system for executing the data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[1031] An "integrated system" is a system in which multiple means work together to automate the entire process, from data collection to document creation and sharing.

[1032] "Presentation materials" are materials that include slides, graphs, tables, etc., to visually present the contents of a report.

[1033] "Emotion data" is information that indicates the user's emotional state, and is data obtained from facial expressions and tone of voice.

[1034] This invention is a system that improves the efficiency of store management and automatically generates and shares presentation materials according to the manager's stress level and emotions. This system is composed of a combination of data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[1035] Data collection

[1036] The server uses data collection methods to access the organization's trading software and other data sources to collect the required data such as sales data, customer feedback, etc. The data is retrieved in real time through API calls and database connections and stored in the server's storage.

[1037] Data analysis

[1038] The device reads the data stored on the server and analyzes it using data analysis tools. This analysis includes categorizing it according to job titles, such as management or finance. For example, key figures and trends can be extracted from sales data to identify points that managers should pay attention to.

[1039] Collecting Emotional Data

[1040] The device uses an emotion recognition method to collect emotional data from the administrator. The emotion recognition method involves capturing the administrator's facial expressions with a camera and using an expression recognition algorithm. Audio can also be captured and emotions can be analyzed from the tone of voice. The emotional data collected in this way is analyzed in real time and reflected in the creation of documents.

[1041] Document creation

[1042] The server uses generative AI to automatically generate presentation materials based on data analysis results and emotional data. If the administrator is feeling stressed or anxious, the generated materials are adjusted to be concise and easy to understand. The generated materials include appropriate titles, graphs, and text explanations, and have a professional design.

[1043] Review and edit materials

[1044] Users can use their devices to check the documents they have created and edit them as necessary. They can check for defects in not only the content of the documents but also the design and structure, and make appropriate corrections.

[1045] Sharing materials

[1046] Users can quickly share documents they have reviewed and edited with each team and position. Documents are efficiently distributed via email or cloud storage, allowing recipients to receive the information they need in the format that is easiest for them to understand.

[1047] Specific examples

[1048] For example, consider the creation of monthly review materials for a physical store. The server automatically collects one month's worth of sales data and customer feedback from the organization's trading software. This data is updated in real time and stored on the server.

[1049] The device then reads the data stored on the server and runs it through data analysis algorithms to extract key figures, trends and outliers, including trends in revenue growth and customer satisfaction.

[1050] The terminal also uses an emotion recognition means to collect emotional data of the administrator. During the process of the administrator creating the materials, the emotion recognition means analyzes the administrator's facial expressions and tone of voice in real time to collect emotional data.

[1051] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it creates slides for management that focus on sales and customer satisfaction, while for finance it creates slides with detailed expense analysis. If a manager is feeling stressed or overwhelmed, it adjusts the slide content to be more concise and easy to understand.

[1052] Finally, managers review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[1053] Prompt Sentence Examples

[1054] "Generate monthly review materials based on the latest sales data and customer feedback, taking into account store manager sentiment. If managers are feeling stressed, simplify the content and adapt it to an easy-to-understand format."

[1055] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1056] Step 1:

[1057] Data collection

[1058] The server uses data collection methods to collect sales data and customer feedback from the organization's trading software and other data sources, specifically through API calls and database connections, and stores the data in the server's storage in real time.

[1059] The input data is the API key and connection information for the trading software, and the output data is collected sales data and customer feedback.

[1060] Step 2:

[1061] Data analysis

[1062] The device reads the stored data from the server and analyzes it using data analytics. Specifically, it runs sales data and customer feedback through analytical algorithms to extract key figures and trends. It also categorizes the information needed according to job title and interests.

[1063] Input data is sales data and customer feedback obtained from the server, and output data is analyzed key figures, trends and classified information.

[1064] Step 3:

[1065] Collecting Emotional Data

[1066] The device collects the administrator's emotional data using an emotion recognition means. Specifically, it captures the administrator's facial expressions with a camera and analyzes their emotions using an expression recognition algorithm. It also performs voice analysis to determine emotions from the tone of voice.

[1067] The input data is camera images and audio data, and the output data is analyzed emotion data.

[1068] Step 4:

[1069] Document creation

[1070] The server uses a generative AI model to automatically generate presentation materials based on data analysis results and emotion data. Specifically, the server inputs prompts into the generative AI to generate the content of the materials, creating materials that include appropriate titles, graphs, and text descriptions.

[1071] The input data are the analysis results (main figures, trends) and emotion data, and the output data are the generated presentation materials.

[1072] Step 5:

[1073] Review and edit materials

[1074] The user uses the terminal to check the created materials and edit them as necessary, specifically checking the content, design, and structure of the materials and making appropriate corrections.

[1075] The input data is the generated presentation material, and the output data is the revised version of the material.

[1076] Step 6:

[1077] Sharing materials

[1078] Users can quickly share the documents they have reviewed and edited with each team or position by distributing them via email or cloud storage.

[1079] The input data is the revised document, and the output data is the document shared with all parties involved.

[1080] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1082] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1083] [Fourth embodiment]

[1084] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1085] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1086] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1087] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1088] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1089] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1090] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1091] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1092] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1093] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1095] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1096] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1097] This invention is a system that collects data from various corporate data sources and automatically creates presentations and financial report materials using generative AI. This system mainly involves the server, terminals, and users, each of which functions as follows:

[1098] Data collection

[1099] The server accesses the company's accounting software and other data sources to collect the necessary data. The server uses API calls and database connections to retrieve real-time financial information and stores it in storage. At this stage, the collected data reflects the most up-to-date information.

[1100] Data analysis

[1101] The device reads the collected data from the server and performs data analysis. The device uses a data analysis algorithm to extract important figures and policies, and classifies them according to job title and interests. This allows the specific information needed by each job title to be extracted and organized.

[1102] Document creation

[1103] The server uses generation AI to automatically generate presentations and financial report materials based on the analysis results. The generation AI creates professionally designed materials based on the input text and data. The generated materials are provided in a format optimized for each position and department.

[1104] Sharing and reviewing materials

[1105] Users can review documents created on their devices and edit them as needed. They can also quickly share the reviewed documents with their respective teams and positions via email or cloud storage. This ensures that documents are provided in a timely manner, enabling the rapid communication of information needed for decision-making.

[1106] Specific examples

[1107] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[1108] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers, such as increases or decreases in revenue or expense trends.

[1109] The server uses generative AI to create presentations based on this analysis, such as slides focused on revenue and profit for executives and slides with detailed expense analysis for finance.

[1110] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[1111] The processing flow will be explained below.

[1112] Step 1: Data collection

[1113] The server accesses the company's accounting software and other data sources to collect the required data.

[1114] The server makes API calls and database connections to retrieve real-time financial information.

[1115] The collected data is stored in the server's storage and is constantly updated with the latest information.

[1116] Step 2: Save your data

[1117] The server stores the collected data in an appropriate format.

[1118] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[1119] Back up your data to protect against loss or damage.

[1120] Step 3: Data analysis

[1121] The device reads the stored data from the server.

[1122] The device uses data analysis algorithms to extract key figures and trends.

[1123] For example, income fluctuations, profit trends, and expense details are extracted.

[1124] Step 4: Classify the data

[1125] The device categorizes the analysis results according to job title and interests.

[1126] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[1127] A customized dataset is generated for each role.

[1128] Step 5: Prepare materials

[1129] The server uses generative AI to create materials based on the classified data.

[1130] Presentations and financial report materials are automatically generated.

[1131] Each slide contains an appropriate title, graph, and text description.

[1132] Step 6: Applying the design to the material

[1133] Apply professional design to server-generated materials.

[1134] Design templates are used to create consistent, visually appealing materials.

[1135] If necessary, custom designs based on company brand guidelines can also be applied.

[1136] Step 7: Review and edit your materials

[1137] The user checks the materials created on the terminal.

[1138] The user checks whether the content is complete and whether the design is appropriate.

[1139] If necessary, the user edits the document and reflects the corrections.

[1140] Step 8: Share your materials

[1141] Documents that users have reviewed and edited are shared with each team and position.

[1142] Materials will be distributed via email and cloud storage.

[1143] Recipients can receive the information they need in the format that is most convenient for them.

[1144] These are the specific processing steps from data collection to sharing of materials.

[1145] Example 1

[1146] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1147] In traditional corporate activities, collecting and analyzing various data, and creating and sharing reports was a time-consuming and labor-intensive process. In particular, the process of collecting and analyzing data, creating reports, and quickly sharing them with relevant parties was fragmented, making it difficult to provide information in a timely manner. Furthermore, while there was a need to provide information in a format optimized for different positions and departments, there was a lack of efficient ways to achieve this.

[1148] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1149] In this invention, the server includes a data collection means, a data preprocessing means, a data analysis means, an automatic document generation means, a document confirmation means, and a document sharing means. This allows the server to automatically acquire necessary data from a company's accounting system or other data sources, cleanse and preprocess the data, analyze it, automatically create report documents based on the results using a generative AI model, and promptly share the documents with relevant parties after the user has confirmed and corrected them.

[1150] "Data collection tools" are functions for accessing a company's accounting system or other data sources and automatically obtaining the required data.

[1151] "Data preprocessing means" refers to a function for processing acquired data into a format suitable for analysis, such as filling in missing values, deleting invalid data, and standardizing date and time formats.

[1152] "Data analysis tools" are functions for analyzing pre-processed data, extracting important figures and trends, and classifying them according to job title and interests.

[1153] "Automatic document generation means" is a function that uses a generative AI model to automatically create presentations and financial report materials based on the results of data analysis.

[1154] The "material confirmation means" is a function that allows the user to use a terminal to check the automatically generated materials and correct them as necessary.

[1155] "Document sharing means" is a function that allows confirmed and revised documents to be quickly shared with relevant parties via email or cloud storage.

[1156] A "generative AI model" is an artificial intelligence model that generates text from an input prompt sentence based on natural language processing.

[1157] A "prompt" is a text sentence that contains instructions or questions for the generative AI model when generating materials.

[1158] This invention is a system that uses data obtained from a company's accounting system and other data sources to automatically create presentations and financial report materials using a generative AI model. This system involves the involvement of a server, a terminal, and a user, each of which functions as follows:

[1159] Data collection

[1160] The server accesses the company's accounting software and other data sources to collect the necessary data. Specifically, the server uses API calls and database connections to obtain real-time financial information and saves it in storage. For example, the server may obtain the latest data on income, expenses, profits, etc. through the APIs of Xero or QuickBooks. The obtained data is then saved in a database (e.g., PostgreSQL) on the server.

[1161] Data Preprocessing

[1162] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[1163] Data analysis

[1164] The terminal retrieves preprocessed data from the server and analyzes it using algorithms. Specifically, the terminal uses Python's Pandas and NumPy to analyze data, performing trend analysis of income and expenses, outlier detection, and sales analysis by category. For example, the terminal processes data retrieved from the server using Python, extracts important numerical values ​​in array format, and classifies them by department.

[1165] Automatic document generation

[1166] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The generative AI model uses, for example, OpenAI's GPT-4 API. The server generates prompt text and sends it to the generative AI model to automatically create high-quality materials. Examples of prompt text include the following:

[1167] Example prompt sentence:

[1168] "Prepare the monthly financial report for July 2023. Create slides for the management team based on the following data."

[1169] Revenue: $100,000

[1170] Expenditure: $60,000

[1171] Profit: $40,000

[1172] Increase or decrease in main revenue source: Customer A +10%, Customer B -5%

[1173] The generated materials are converted into presentation format using the Google Slides API.

[1174] Check and share materials

[1175] Users can use their devices to review the automatically generated materials and make corrections as necessary. Editing can be done directly in a browser using tools like Google Slides. The corrected materials can then be quickly shared with relevant parties via email or cloud storage (e.g., Google Drive or Dropbox). For example, a user can correct an error in "Sales" on Google Slides, save the revised slide to Google Drive, and email a link to their team.

[1176] This system enables companies to quickly and accurately prepare financial reporting documents based on the latest financial information and provide information to all stakeholders in a timely manner.

[1177] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1178] Step 1:

[1179] The server accesses the company's accounting system and other data sources to collect the required data. To do this, the server retrieves real-time data using API calls or database connections. For example, the server sends a request to the accounting system's API endpoint using authentication information to retrieve data such as income, expenses, and profits. The retrieved data is then stored in a database (e.g., PostgreSQL) on the server.

[1180] Input: Accounting system credentials and API endpoint

[1181] Output: The financial data obtained (income, expenses, profit, etc.)

[1182] Step 2:

[1183] The server cleanses the acquired data and processes it into a format suitable for analysis. The server uses Python's Pandas library to read the data and performs cleansing processes such as filling in missing values ​​and deleting invalid data. It also performs conversion processes to unify date and time formats and ensure consistency of categories.

[1184] Input: Raw data collected

[1185] Output: A cleansed dataset

[1186] Step 3:

[1187] The terminal retrieves preprocessed data from the server and performs analysis using algorithms, such as Pandas and NumPy in Python, to analyze income and expenditure trends, detect outliers, and analyze sales by category.

[1188] Input: Cleansed dataset

[1189] Output: Analyzed data (e.g., trend analysis results, outlier detection results)

[1190] Step 4:

[1191] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results. The server generates prompts and sends them to a generative AI model such as GPT-4 to create professionally designed materials. For example, the server could generate a prompt such as, "Prepare monthly financial report materials for July 2023. Create slides for management based on the following data," with detailed data attached.

[1192] Input: Parsed data and prompt statement

[1193] Output: Auto-generated presentation materials

[1194] Step 5:

[1195] Users can use their devices to review the automatically generated materials and make corrections as necessary. Users can also edit the materials directly in their browsers using tools such as Google Slides.

[1196] Input: Auto-generated material

[1197] Output: Corrected and verified material

[1198] Step 6:

[1199] Users can quickly share revised documents with relevant parties via email or cloud storage. For example, users can save revised documents to Google Drive and email the link to their team. This ensures that documents are provided in a timely manner and that the information needed for decision-making can be communicated quickly.

[1200] Input: Corrected and verified material

[1201] Output: Materials shared with stakeholders

[1202] (Application example 1)

[1203] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1204] In today's brick-and-mortar stores, managing sales and inventory data is complicated, making it difficult to grasp the status of store operations in real time. Furthermore, providing information at the right time and creating materials to quickly formulate management strategies requires a lot of effort. There is also a lack of effective tools that allow staff to instantly grasp the status of store operations. There is a need for a system that can solve these issues and streamline store operations.

[1205] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1206] In this invention, the server includes a data collection means, a data analysis means, a document creation means, a document sharing means, a sales data collection means, an inventory data collection means, a report generation means using a generative AI model, and a smart device display means. This enables sales data and inventory data from a physical store to be collected and analyzed in real time, and management reports to be automatically generated using the generative AI model. Furthermore, this allows staff to instantly check the management status using smart devices such as smartphones and smart glasses, providing a system that supports quick and accurate decision-making.

[1207] A "data collection tool" is a device or function that accesses a company's accounting software or other data sources and automatically collects the required data.

[1208] "Data analysis means" refers to a device or function that analyzes collected data, extracts important figures and policies, and classifies them according to position or interest.

[1209] "Document creation means" refers to a device or function that uses a generative AI model to automatically create presentations or financial report materials based on the results of data analysis.

[1210] The "material sharing means" is a device or function that allows the generated materials to be quickly shared using email or cloud storage.

[1211] "Sales data collection means" refers to a device or function for collecting sales data from physical stores.

[1212] "Inventory data collection means" refers to a device or function for collecting inventory data from a physical store.

[1213] A "report generation means using a generative AI model" is a device or function that automatically generates professionally designed reports using a generative AI model based on collected and analyzed data.

[1214] A "smart device display means" is a device or function that displays reports generated by a device such as a smartphone or smart glasses in real time.

[1215] This invention is a system that collects and analyzes sales data and inventory data in a physical store, and automatically creates and displays operational reports using a generative AI model. Specific embodiments for implementing this invention are described below.

[1216] System Configuration

[1217] The system consists of a server, terminals, and users. The server collects data, analyzes data, creates documents, and shares documents, while the terminals display data and check and edit documents. Users are store staff and managers.

[1218] Hardware and Software

[1219] Hardware

[1220] Server: Has the computing power to collect data, analyze, and prepare documentation.

[1221] Terminal: A smart device such as a smartphone or smart glasses.

[1222] Storage: A storage device for storing collected data.

[1223] software

[1224] FastAPI: A web framework for data collection and API provisioning.

[1225] Pandas: A library for analyzing and manipulating data.

[1226] GPT-4: A generative AI that generates reports in natural language.

[1227] SQLite: A database management system that persists data.

[1228] Processing flow

[1229] 1. Data Collection

[1230] The server collects sales data and inventory data from physical stores in CSV file format, etc. For example, it retrieves data from POS systems and inventory management systems via API at regular intervals and stores it in an SQLite database.

[1231] 2. Data Analysis

[1232] After the server collects the data, the terminal uses Pandas to aggregate and analyze the data. The main steps are to aggregate total revenue from sales data and remaining quantities from inventory data, and to extract important trends and outliers.

[1233] 3. Document Creation

[1234] The server uses GPT-4 to generate a report on operational operations based on the analysis results. The report includes suggestions for business strategies and improvements to inventory management. For example, the following prompt sentence is input to GPT-4 to generate a report:

[1235] Based on the sales and inventory data below, please prepare a report on next week's operating policy.

[1236] Sales Data:

[1237] Product A, 500, 300, 200

[1238] Product B, 700, 500, 100

[1239] Inventory Data:

[1240] Product A, 200, 150, 50

[1241] Product B, 300, 250, 50

[1242] The report should focus on sales trends and future marketing strategies for Product A in particular.

[1243] 4. Display and review of materials

[1244] The terminal displays the generated report on a smartphone or smart glasses. Based on this, users can instantly check business improvement measures and sales promotion measures and edit the materials as needed. The edited materials are shared again by the server and quickly notified to all staff.

[1245] This system will significantly improve the operational efficiency of physical stores, enabling increased sales and optimized inventory management.

[1246] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1247] Step 1:

[1248] The server collects sales and inventory data from physical stores. This process involves accessing POS systems and inventory management systems using APIs to periodically retrieve the necessary data. The input includes sales and inventory data retrieved by API calls, and the output is the data saved in CSV format or in a database.

[1249] Step 2:

[1250] The terminal reads the collected sales and inventory data from the database and aggregates and analyzes it using the Pandas library. The input includes sales and inventory data, and the output is analysis results showing total sales, inventory status, trends, and outliers. For example, it can detect whether sales of a particular product are increasing rapidly or whether there is a risk of inventory shortage.

[1251] Step 3:

[1252] The server receives the analysis results and generates an operational report using the generative AI model GPT-4. The input includes the analysis results and a prompt, and the output is a detailed operational report. An example of a prompt is, "Based on the sales and inventory data below, please prepare a report on next week's operational policy. Please focus on the sales trend of product A and future marketing strategies."

[1253] Step 4:

[1254] The server sends the generated operation report to the terminal so that the user can view it on their smartphone or smart glasses. The input includes the generated operation report, and the output is that the report is displayed on the user's smart device. This allows the user to grasp the store's operation status in real time and take appropriate measures.

[1255] Step 5:

[1256] The user checks the operation report displayed on the terminal and edits it as necessary. The input includes the generated operation report, and the output is a report that has been modified and optimized by the user. By making modifications, the user can reflect more specific operation policies and improvement measures in the report.

[1257] Step 6:

[1258] The server then shares the revised operation report with all staff again. The input includes the operation report revised by the user, and the output is a report distributed to all staff via email or cloud storage. This allows all staff to share the latest operation information and work together in a unified manner to manage the store.

[1259] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1260] This invention is a system that combines data collection means, data analysis means, document creation means, document sharing means, and an emotion engine that recognizes user emotions. This system collects corporate data and uses generative AI to automatically create and share presentations and financial report materials that correspond to the user's emotions. The following explains in detail the roles played by the server, terminal, and user in each step.

[1261] Data collection

[1262] The server accesses the company's accounting software and other data sources to gather the necessary data, then makes API calls and database connections to retrieve real-time financial information and stores it in storage, ensuring the most up-to-date data is always available.

[1263] Data analysis

[1264] The device reads the stored data from the server and uses data analysis algorithms to extract key figures and trends, categorizing them according to job title and interest – for example, data focused on revenue and profit for management, while data including expense details for the finance department.

[1265] Collecting Emotional Data

[1266] The device uses an emotion engine to collect user emotional data. The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The emotional data is analyzed in real time and reflected in the creation of documents.

[1267] Document creation

[1268] The server uses AI to automatically generate presentations and financial report materials based on analysis results and emotional data. For example, if the user is feeling stressed, the content will be simplified and the tone will be calmed. The generated materials will include appropriate titles, graphs, and text descriptions, and will have a professional design.

[1269] Review and edit materials

[1270] Checks documents created by users on their devices and edits them as necessary. Checks not only the content of the documents but also any design or structure defects and makes appropriate corrections.

[1271] Sharing materials

[1272] Documents that users have reviewed and edited can be shared with each team or role. Documents can be quickly distributed via email or cloud storage. Recipients can receive the information they need in the format that is easiest for them to understand.

[1273] Specific examples

[1274] For example, let's consider the case where a company is preparing monthly financial statements. At the end of the month, the server automatically collects data such as income, expenses, and profits for that month from the company's accounting software. The collected data is stored on the server and updated in real time.

[1275] The device then reads the data stored on the server and runs it through a data analysis algorithm, which extracts key figures, trends, and outliers from the data, such as increases or decreases in revenue or expense trends.

[1276] The device also uses an emotion engine to collect the user's emotion data. As the user creates materials, the emotion engine analyzes the user's facial expressions and tone of voice in real time to collect emotion data.

[1277] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it might create slides focused on revenue and profit for executives, or slides with detailed expense analysis for finance. If the user is feeling stressed or overwhelmed, it will adjust the slide content to be more concise and easy to understand.

[1278] Finally, users review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[1279] As a form for implementing the invention, this system works in cooperation with servers, terminals, and users to automate the entire process from data collection to document creation and sharing, enabling efficient and high-quality document creation.

[1280] The processing flow will be explained below.

[1281] Step 1: Data collection

[1282] The server accesses the company's accounting software and other data sources to collect the required data.

[1283] The server retrieves real-time financial information using API calls and database connections.

[1284] The collected data is stored in the server's storage and is constantly updated with the latest information.

[1285] Step 2: Save your data

[1286] The server stores the collected data in an appropriate format.

[1287] The data is saved in a file format that corresponds to its content, such as JSON or CSV.

[1288] Back up your data to protect against loss or damage.

[1289] Step 3: Data analysis

[1290] The device reads the stored data from the server.

[1291] The device uses data analysis algorithms to extract key figures and trends.

[1292] For example, income fluctuations, profit trends, and expense details are extracted.

[1293] Step 4: Classify the data

[1294] The device categorizes the analysis results according to job title and interests.

[1295] For management, it focuses on revenue and profits, while for finance, it sifts through data that includes expense details.

[1296] A customized dataset is generated for each role.

[1297] Step 5: Collecting emotion data

[1298] The terminal uses an emotion engine to collect emotion data of the user.

[1299] The emotion engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice.

[1300] Emotional data is analyzed in real time and reflected when creating documents.

[1301] Step 6: Prepare materials

[1302] The server uses generative AI to create materials based on the analysis results and emotional data.

[1303] For example, if a user is feeling stressed, the content will be made simpler and the tone will be calmer.

[1304] Presentations and financial reports are automatically generated with appropriate titles, graphs, and text descriptions.

[1305] Step 7: Applying the design to the material

[1306] Apply professional design to server-generated materials.

[1307] Design templates are used to create consistent, visually appealing materials.

[1308] If necessary, custom designs based on company brand guidelines can also be applied.

[1309] Step 8: Review and edit your materials

[1310] The user checks the materials created on the terminal.

[1311] The user checks whether the content is complete and whether the design is appropriate.

[1312] If necessary, the user edits the document and reflects the corrections.

[1313] Step 9: Share your materials

[1314] Documents that users have reviewed and edited are shared with each team and position.

[1315] Materials will be distributed via email and cloud storage.

[1316] Recipients can receive the information they need in the format that is most convenient for them.

[1317] Example 2

[1318] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1319] In modern companies, the processes from data collection and analysis to document creation and sharing are extremely diverse, and there is a need to quickly create and share high-quality documents efficiently. However, in existing systems, these processes are often carried out separately, making it difficult to create documents that take emotional data into account. Furthermore, creating documents without reflecting user emotions can result in the content being difficult to understand or the information not being properly conveyed, which can reduce the speed and accuracy of decision-making.

[1320] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1321] In this invention, the server includes a data collection means, a data analysis means, an emotion recognition means, a document creation means, and a document sharing means. This makes it possible to collect necessary data from corporate information systems and other information sources, analyze the data, and classify it according to roles and interests within the organization. The emotion recognition means collects user emotion data, and a generative AI model is used to automatically create and share documents according to the user's emotions, thereby achieving efficient and high-quality document creation.

[1322] "Data collection methods" are the methods used to obtain the required data from the company's information systems and other sources.

[1323] "Data analysis tools" are tools for analyzing collected data and classifying it according to roles and interests within the organization.

[1324] The "emotion recognition means" is a means for detecting emotions from the user's facial expressions and tone of voice, and analyzing this emotional data.

[1325] "Document creation means" refers to a means for automatically creating documents using a generative AI model based on data analysis results and emotion data.

[1326] "Document sharing means" refers to a means for sharing generated documents with teams and positions within an organization via email or cloud storage.

[1327] A "generative AI model" is an artificial intelligence model that performs natural language processing based on given data and user emotional data to generate appropriate sentences and materials.

[1328] A "prompt sentence" is a sentence that specifically inputs instructions or content for creating specific materials to a generative AI model.

[1329] This invention is a system for collecting corporate data, analyzing the data, recognizing emotions, and automatically creating and sharing documents based on the data. This system mainly consists of three entities: a server, a terminal, and a user.

[1330] Server Functionality Description

[1331] Data collection methods

[1332] The server accesses the company's information systems and other sources to gather the necessary data. Specifically, the server makes API calls and database connections. For example, it retrieves financial information such as income, expenses, and profits from accounting software. The specific software used generally includes an "information systems API" or "database management system."

[1333] Document creation method

[1334] The server uses a generative AI model to automatically generate presentations and financial report materials based on the analysis results and emotional data. For example, a "generative AI model" is used for the generative AI. Based on the emotional data, the corresponding tone and content are adjusted.

[1335] Device function description

[1336] Data Analysis Methods

[1337] The device reads the stored data from the server and uses data analysis algorithms, often using the Python pandas library, to extract key figures and trends. The data is then categorized by job title and formatted appropriately for management, finance, etc.

[1338] emotion recognition means

[1339] The device collects user emotional data using an emotion recognition engine. The emotion recognition engine uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. Specific technologies include "image recognition API" and "voice analysis technology."

[1340] User Roles

[1341] Review and edit materials

[1342] The user reviews the materials created on their device and edits them as necessary. Microsoft PowerPoint and Google Slides are the software typically used. The content and design are checked for flaws, and the optimal materials are completed.

[1343] Material sharing method

[1344] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. Specific sharing methods include email systems and cloud storage services.

[1345] Specific examples

[1346] For example, when creating a company's monthly financial report, the server automatically collects data such as the current month's income, expenses, and profits from the accounting software. The collected data is stored on the server and kept up to date. The device then reads the data from the server's database and analyzes it using Python's pandas library. Key figures such as increases or decreases in income and expense trends are extracted. Additionally, as the user creates the report, an emotion recognition engine collects emotional data in real time.

[1347] The server sends the following prompt to the AI ​​model: "Please create a monthly financial report document. Use data on income, expenses, and profits, and adjust it to an easy-to-understand and concise format, taking into account user sentiment data. Please create slides for management and the finance department." The AI ​​then generates the appropriate document.

[1348] Users can open the generated document in Microsoft PowerPoint, check the titles and graphs, and correct any inappropriate parts. Once the document has been corrected, it is uploaded to Google Drive and a shared link is generated. By sharing this link with internal management and the finance department, information can be shared quickly and accurately.

[1349] In this way, the invention automates data management and document creation processes within a company, enabling efficient and high-quality document creation.

[1350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1351] Step 1: Data collection

[1352] The server accesses the company's information systems and other information sources to collect the necessary data. The server makes API calls and connects to databases to obtain financial information such as income, expenses, and profits in real time. Specifically, the server sends requests to the "company's accounting data API" and stores the collected data in an internal database. The input is the API request, and the output is the database where the financial information is stored.

[1353] Specific actions

[1354] The server sends an API request to the " / v1 / data / financials" endpoint to retrieve income, expense, and profit data in JSON format, and stores the data in the "financial_data" table in a MySQL database.

[1355] Step 2: Data analysis

[1356] The terminal retrieves data stored in the database from the server and analyzes it. The terminal uses Python's pandas library to read the data and extract key figures and trends. The results of this data analysis are categorized by job position. The input is financial data retrieved from the database, and the output is the analytical results, such as figures and trends.

[1357] Specific actions

[1358] The terminal executes the SQL query "SELECT FROM financial_data WHERE date = '2023-10-31'" and converts the retrieved data into a pandas data frame. It then extracts trends in revenue and expenses and categorizes them into data for management and data for the finance department.

[1359] Step 3: Collecting emotion data

[1360] The device uses an emotion recognition engine to collect user emotion data. The device uses image recognition and voice analysis technology to detect emotions from the user's facial expressions and tone of voice. The input is the user's facial expression images and voice data, and the output is emotion data.

[1361] Specific actions

[1362] The device sends the user's facial image to the Google Cloud Vision API and receives the analysis results. It also sends the user's voice data to the IBM Watson Tone Analyzer and obtains the tone analysis results.

[1363] Step 4: Prepare materials

[1364] The server automatically generates presentation materials using a generative AI model. The server sends prompts to the generative AI model based on the data analysis results and emotional data. The inputs are the data analysis results and emotional data, and the output is the generated presentation materials.

[1365] Specific actions

[1366] The server sends the generation AI a prompt saying, "Please create a monthly financial report. Use income, expense, and profit data, and adjust it into an easy-to-understand and concise format, taking into account the user's emotional data." The server then receives the presentation (pptx format) generated by the AI.

[1367] Step 5: Review and edit your materials

[1368] The user reviews and edits the materials created on the device. The user opens the materials using Microsoft PowerPoint or Google Slides and edits the content and design. The input is the generated presentation materials, and the output is the final materials that have been reviewed and edited.

[1369] Specific actions

[1370] A user opens a pptx file in Microsoft PowerPoint, checks the slide titles and charts, corrects any inappropriate content, and adds new data and charts as needed.

[1371] Step 6: Share your materials

[1372] Documents that users have reviewed and edited are shared with each team and position. Documents are quickly distributed via email or cloud storage. The input is the reviewed and edited final document, and the output is a shared link or email.

[1373] Specific actions

[1374] The user uploads the completed document to Google Drive, generates a shared link, and emails the link to the company's management and finance department with a message saying, "Please see the monthly financial report document at the link below."

[1375] (Application example 2)

[1376] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1377] In today's business environment, store managers need to efficiently analyze sales data and customer feedback and create optimal presentation materials based on that data. These materials also need to be tailored to take into account the manager's emotions and stress levels, but this is difficult to achieve. Furthermore, there is a lack of a way to quickly and efficiently share the created materials. An integrated system is needed to solve these challenges and streamline management tasks.

[1378] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a data collection means, a data analysis means, a material creation means, a material sharing means, an emotion recognition means, and a means for the material creation means to automatically generate materials corresponding to the user's emotions based on the data collected by the data collection means and the emotion data collected by the emotion recognition means. This enables a manager of a physical store to automatically generate presentation materials that match the manager's emotions and stress level based on sales data and customer feedback, and quickly share them.

[1379] "Data collection means" refers to the means for obtaining necessary information from external data sources.

[1380] "Data analysis means" refers to the means of analyzing collected data and classifying its contents according to job title and interest.

[1381] The "material creation means" is a means for generating presentation materials and reports based on data and emotion data.

[1382] The "material sharing means" is a means for quickly distributing the created materials to each of the parties involved.

[1383] The "emotion recognition means" is a means for detecting the user's emotions from facial expressions, tone of voice, etc.

[1384] The "server" is a computer system for executing the data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[1385] An "integrated system" is a system in which multiple means work together to automate the entire process, from data collection to document creation and sharing.

[1386] "Presentation materials" are materials that include slides, graphs, tables, etc., to visually present the contents of a report.

[1387] "Emotion data" is information that indicates the user's emotional state, and is data obtained from facial expressions and tone of voice.

[1388] This invention is a system that improves the efficiency of store management and automatically generates and shares presentation materials according to the manager's stress level and emotions. This system is composed of a combination of data collection means, data analysis means, material creation means, material sharing means, and emotion recognition means.

[1389] Data collection

[1390] The server uses data collection methods to access the organization's trading software and other data sources to collect the required data such as sales data, customer feedback, etc. The data is retrieved in real time through API calls and database connections and stored in the server's storage.

[1391] Data analysis

[1392] The device reads the data stored on the server and analyzes it using data analysis tools. This analysis includes categorizing it according to job titles, such as management or finance. For example, key figures and trends can be extracted from sales data to identify points that managers should pay attention to.

[1393] Collecting Emotional Data

[1394] The device uses an emotion recognition method to collect emotional data from the administrator. The emotion recognition method involves capturing the administrator's facial expressions with a camera and using an expression recognition algorithm. Audio can also be captured and emotions can be analyzed from the tone of voice. The emotional data collected in this way is analyzed in real time and reflected in the creation of documents.

[1395] Document creation

[1396] The server uses generative AI to automatically generate presentation materials based on data analysis results and emotional data. If the administrator is feeling stressed or anxious, the generated materials are adjusted to be concise and easy to understand. The generated materials include appropriate titles, graphs, and text explanations, and have a professional design.

[1397] Review and edit materials

[1398] Users can use their devices to check the documents they have created and edit them as necessary. They can check for defects in not only the content of the documents but also the design and structure, and make appropriate corrections.

[1399] Sharing materials

[1400] Users can quickly share documents they have reviewed and edited with each team and position. Documents are efficiently distributed via email or cloud storage, allowing recipients to receive the information they need in the format that is easiest for them to understand.

[1401] Specific examples

[1402] For example, consider the creation of monthly review materials for a physical store. The server automatically collects one month's worth of sales data and customer feedback from the organization's trading software. This data is updated in real time and stored on the server.

[1403] The device then reads the data stored on the server and runs it through data analysis algorithms to extract key figures, trends and outliers, including trends in revenue growth and customer satisfaction.

[1404] The terminal also uses an emotion recognition means to collect emotional data of the administrator. During the process of the administrator creating the materials, the emotion recognition means analyzes the administrator's facial expressions and tone of voice in real time to collect emotional data.

[1405] The server uses generative AI to create presentations based on this analysis and emotional data. For example, it creates slides for management that focus on sales and customer satisfaction, while for finance it creates slides with detailed expense analysis. If a manager is feeling stressed or overwhelmed, it adjusts the slide content to be more concise and easy to understand.

[1406] Finally, managers review these documents and make any necessary revisions. Once the review is complete, the documents are quickly shared with each team and role via email or cloud storage. This allows each role to receive the necessary information in a format that is easiest for them to understand, improving the speed and accuracy of decision-making.

[1407] Prompt Sentence Examples

[1408] "Generate monthly review materials based on the latest sales data and customer feedback, taking into account store manager sentiment. If managers are feeling stressed, simplify the content and adapt it to an easy-to-understand format."

[1409] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1410] Step 1:

[1411] Data collection

[1412] The server uses data collection methods to collect sales data and customer feedback from the organization's trading software and other data sources, specifically through API calls and database connections, and stores the data in the server's storage in real time.

[1413] The input data is the API key and connection information for the trading software, and the output data is collected sales data and customer feedback.

[1414] Step 2:

[1415] Data analysis

[1416] The device reads the stored data from the server and analyzes it using data analytics. Specifically, it runs sales data and customer feedback through analytical algorithms to extract key figures and trends. It also categorizes the information needed according to job title and interests.

[1417] Input data is sales data and customer feedback obtained from the server, and output data is analyzed key figures, trends and classified information.

[1418] Step 3:

[1419] Collecting Emotional Data

[1420] The device collects the administrator's emotional data using an emotion recognition means. Specifically, it captures the administrator's facial expressions with a camera and analyzes their emotions using an expression recognition algorithm. It also performs voice analysis to determine emotions from the tone of voice.

[1421] The input data is camera images and audio data, and the output data is analyzed emotion data.

[1422] Step 4:

[1423] Document creation

[1424] The server uses a generative AI model to automatically generate presentation materials based on data analysis results and emotion data. Specifically, the server inputs prompts into the generative AI to generate the content of the materials, creating materials that include appropriate titles, graphs, and text descriptions.

[1425] The input data are the analysis results (main figures, trends) and emotion data, and the output data are the generated presentation materials.

[1426] Step 5:

[1427] Review and edit materials

[1428] The user uses the terminal to check the created materials and edit them as necessary, specifically checking the content, design, and structure of the materials and making appropriate corrections.

[1429] The input data is the generated presentation material, and the output data is the revised version of the material.

[1430] Step 6:

[1431] Sharing materials

[1432] Users can quickly share the documents they have reviewed and edited with each team or position by distributing them via email or cloud storage.

[1433] The input data is the revised document, and the output data is the document shared with all parties involved.

[1434] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1435] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1436] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1437] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1438] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1439] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1440] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1441] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1442] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1443] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1444] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1445] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1446] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1447] 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.

[1448] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1449] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1450] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1451] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1452] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1453] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1454] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1455] The following is further disclosed regarding the above embodiment.

[1456] (Claim 1)

[1457] data collection means;

[1458] Data analysis means;

[1459] Document creation means,

[1460] A means of sharing materials;

[1461] A system including:

[1462] (Claim 2)

[1463] 10. The system of claim 1, wherein the data collection means accesses a company's accounting software or other data sources to collect the required data.

[1464] (Claim 3)

[1465] 10. The system of claim 1, wherein the data analysis means analyzes the collected data and categorizes it according to job title or interest.

[1466] (Claim 4)

[1467] The system of claim 1, wherein the document creation means generates presentations and financial report materials based on the data classified using the generation AI.

[1468] (Claim 5)

[1469] 2. The system according to claim 1, wherein the document sharing means shares documents optimized for each team or position.

[1470] (Claim 6)

[1471] 10. The system of claim 1, wherein the material creation means automatically generates materials, the materials having a professional design.

[1472] (Claim 7)

[1473] 10. The system of claim 1, wherein the data analysis means uses a data analysis algorithm to extract significant figures and principles from the data.

[1474] (Claim 8)

[1475] 2. The system according to claim 1, wherein the material sharing means shares the generated material by email or cloud storage.

[1476] (Claim 9)

[1477] 10. The system of claim 1, wherein the data collection means obtains data that is updated in real time.

[1478] (Claim 10)

[1479] 10. The system of claim 1, wherein the material creation means creates materials in slide format and uses design templates.

[1480] "Example 1"

[1481] (Claim 1)

[1482] data collection means;

[1483] data preprocessing means;

[1484] Data analysis means;

[1485] An automatic document generation means;

[1486] Document verification means;

[1487] A means of sharing materials;

[1488] A system including:

[1489] (Claim 2)

[1490] 10. The system of claim 1, wherein the data collection means accesses a company's accounting system or other data sources to obtain the required data.

[1491] (Claim 3)

[1492] 10. The system of claim 1, wherein the data analysis means analyzes the collected data and categorizes it according to job title or interest.

[1493] (Claim 4)

[1494] The system of claim 1, wherein the automatic document generation means uses a generative AI model to automatically create documents based on the results of the analysis.

[1495] (Claim 5)

[1496] 5. The system of claim 4, wherein the automatic document generation means uses prompt sentences to create presentation or financial report documents using a generative AI model.

[1497] (Claim 6)

[1498] 2. The system according to claim 1, wherein the material checking means checks the material created by the user using the terminal and corrects it if necessary.

[1499] (Claim 7)

[1500] 7. The system of claim 6, wherein the document sharing means quickly shares the modified document via email or cloud storage.

[1501] "Application Example 1"

[1502] (Claim 1)

[1503] data collection means;

[1504] Data analysis means;

[1505] Document creation means,

[1506] A means of sharing materials;

[1507] a sales data collection means;

[1508] inventory data collection means;

[1509] A means of generating reports using generative AI models;

[1510] a smart device display means;

[1511] A system including:

[1512] (Claim 2)

[1513] 10. The system of claim 1, wherein the data collection means accesses a company's accounting software or other data sources to collect the required data.

[1514] (Claim 3)

[1515] 10. The system of claim 1, wherein the data analysis means analyzes the collected data and categorizes it according to job title or interest.

[1516] "Example 2: Combining Emotion Engines"

[1517] (Claim 1)

[1518] data collection means;

[1519] Data analysis means;

[1520] An emotion recognition means;

[1521] Document creation means,

[1522] A means of sharing materials;

[1523] A system including:

[1524] (Claim 2)

[1525] 10. The system of claim 1, wherein the data collection means accesses the company's information systems and other sources to collect the required data.

[1526] (Claim 3)

[1527] 10. The system of claim 1, wherein the data analysis means analyzes and categorizes the collected data according to organizational role or interest.

[1528] (Claim 4)

[1529] 2. The system according to claim 1, wherein the emotion recognition means detects emotions from the user's facial expressions and tone of voice, and uses the emotion data in creating materials.

[1530] (Claim 5)

[1531] 2. The system of claim 1, wherein the material creation means uses a generative AI model to automatically create materials based on data analysis results and sentiment data.

[1532] (Claim 6)

[1533] The system according to claim 1, wherein the document sharing means shares the generated documents with teams or positions using email or cloud storage.

[1534] "Application example 2 when combining emotion engines"

[1535] (Claim 1)

[1536] data collection means;

[1537] Data analysis means;

[1538] Document creation means,

[1539] A means of sharing materials;

[1540] An emotion recognition means;

[1541] a means for automatically generating materials according to the user's emotions by a material creation means based on the data collected by the data collection means and the emotion data collected by the emotion recognition means;

[1542] A system including:

[1543] (Claim 2)

[1544] 10. The system of claim 1, wherein the data collection means accesses the organization's trading software and other data sources to collect the required data.

[1545] (Claim 3)

[1546] 10. The system of claim 1, wherein the data analysis means analyzes the collected data and categorizes it according to job title or interest. [Explanation of symbols]

[1547] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. data collection means; Data analysis means; Document creation means, A means of sharing materials; A system including:

2. 10. The system of claim 1, wherein the data collection means accesses a company's accounting software or other data sources to collect the required data.

3. 2. The system of claim 1, wherein the data analysis means analyzes the collected data and categorizes it according to job title or interest.

4. The system according to claim 1, wherein the document creation means creates presentations and financial report materials based on the data classified using the generation AI.

5. The system according to claim 1 , wherein the document sharing means shares documents optimized for each team or position.

6. 2. The system of claim 1, wherein the material creation means automatically generates materials, the materials having a professional design.

7. 10. The system of claim 1, wherein the data analysis means uses a data analysis algorithm to extract significant values ​​and principles from the data.

8. The system according to claim 1 , wherein the material sharing means shares the generated material by using email or cloud storage.

9. 10. The system of claim 1, wherein the data collection means obtains data that is updated in real time.

10. 2. The system according to claim 1, wherein the material creation means creates materials in a slide format and uses design templates.

Images