System

Abstract

A system is provided.SOLUTION: The system includes a data-collecting means, a data-analyzing means, an accounting-data real-time acquiring means, a generation and AI means, and a presentation-material preparing 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] Companies face the challenge of quickly and efficiently compiling financial and performance data for fiscal year-end and management meetings. The process of collecting data from multiple sources, manually analyzing and integrating it, and creating presentation materials in the appropriate format is extremely labor-intensive, making it difficult to ensure the quality of the materials and ensure timely decision-making. The goal of this invention is to automate these processes, enabling the efficient and rapid creation of high-quality materials. [Means for solving the problem]

[0005] The present invention proposes a system that includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, and a presentation material creation means, which automatically creates presentation materials and financial statements using corporate data.

[0006] First, data collection methods are used to collect data from multiple company data sources and store the data in a data warehouse. Next, data analysis methods are used to identify important indicators and trends from the collected data. Finally, real-time accounting data acquisition methods are used to acquire financial data in real time from accounting software and integrate it into the data warehouse.

[0007] The generation AI means analyzes the data, extracts important information, and generates appropriate graphs, charts, and tables. Finally, the presentation creation means optimizes the presentation layout and design based on the generated data, automatically creating the final presentation materials. This reduces the effort and time required for document creation and enables advanced analysis and visualization, professional design, high accuracy, and timely decision-making.

[0008] A "data collection means" is a means having a function for automatically collecting necessary data from multiple data sources of a company.

[0009] "Data analytics tools" are tools and algorithms used to analyze collected data and identify key indicators and trends.

[0010] "Means for acquiring accounting data in real time" refers to a means for acquiring financial data in real time from accounting software and incorporating it into the system.

[0011] "Generative AI methods" are methods that use artificial intelligence techniques to analyze collected data, extract key information, and automatically generate appropriate graphs, charts, and tables.

[0012] The "presentation material creation means" is a means for optimizing the layout and design of the presentation based on the generated data and automatically creating the final presentation material. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

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

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

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

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0028] 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.

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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, and a means for creating presentation materials.

[0035] Data collection

[0036] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[0037] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[0038] Data analysis

[0039] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[0040] Real-time acquisition of accounting data

[0041] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[0042] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[0043] Creating presentation materials using generative AI

[0044] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[0045] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0046] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0047] Specific examples

[0048] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0049] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0050] As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated. The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[0051] In this way, companies can obtain high-quality presentation materials quickly and efficiently, significantly reducing the time and effort required to create them and enabling timely decision-making.

[0052] The processing flow will be explained below.

[0053] Data collection and analysis process steps

[0054] Step 1: Configure the Data Source

[0055] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[0056] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[0057] Step 2: Collect data

[0058] The server periodically accesses the data source set by the user and collects the required data.

[0059] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[0060] Step 3: Store the data

[0061] The server stores the collected data in a data warehouse.

[0062] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[0063] Step 4: Analyze the data

[0064] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[0065] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[0066] Processing steps for real-time accounting data capture

[0067] Step 1: Set up your accounting software

[0068] The user sets up collaboration with accounting software (for example, general accounting software).

[0069] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[0070] Step 2: Obtaining accounting data

[0071] The server retrieves real-time financial data from the accounting software via API.

[0072] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[0073] Step 3: Store and update data

[0074] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[0075] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[0076] Processing steps for creating presentation materials using generative AI

[0077] Step 1: Request for materials

[0078] The user inputs the period and target for which the presentation materials are needed.

[0079] How it works: You specify the requirements for the materials on your device and send them to the server.

[0080] Step 2: Data acquisition and analysis

[0081] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[0082] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[0083] Step 3: Extracting and visualizing information

[0084] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[0085] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[0086] Step 4: Optimize your presentation

[0087] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[0088] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[0089] Step 5: View the material

[0090] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[0091] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[0092] Example 1

[0093] 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."

[0094] Modern companies need to collect information from a variety of data sources, analyze it, and use it to make decisions. However, the processes of collecting and analyzing data, acquiring accounting data in real time, and creating presentation materials are complex and require time and resources. Therefore, there is a need for a method to streamline these processes and quickly create high-quality presentation materials.

[0095] 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.

[0096] In this invention, the server includes a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generative AI means, a means for creating presentation materials, a means for a user to use a terminal to input authentication information for a data source and send it to the server, a means for the server to periodically access the data source and store the data in a data retention system, a means for the server to analyze the data acquired from the data retention system using an analysis tool, a means for a user to use a terminal to set up a connection with accounting software and send it to the server, a means for the server to acquire accounting data in real time using the accounting software's API and store it in the data retention system, a means for a user to use a terminal to input requirements for creating presentation materials and send it to the server, and a means for the server to acquire data from the data retention system based on a specified period, analyze it using a generative AI model, and generate presentation materials. This enables companies to efficiently collect and analyze information from multiple data sources, acquire the latest accounting data in real time, and automatically generate high-quality presentation materials quickly.

[0097] The "data collection means" is a function that periodically accesses multiple data sources set by the user and acquires the necessary data.

[0098] "Data analysis means" is a function that analyzes collected data using analytical tools and algorithms to extract important indicators and trends.

[0099] "Means for acquiring accounting data in real time" refers to a function that acquires accounting data in real time using the accounting software's API and stores it in a data retention system.

[0100] "Generative AI means" is a function that uses a generative AI model to analyze collected and analyzed data, extract important information, and generate output such as presentation materials.

[0101] The "presentation material creation means" is a function that generates appropriate graphs, charts, and tables based on the analysis results of the generation AI means, and optimizes the layout and design of presentation materials.

[0102] "Means for entering authentication information for a data source using a terminal and sending it to a server" refers to the process in which a user uses a terminal to enter authentication information and connection settings for accessing a data source and sends them to a server.

[0103] "Means for the server to periodically access the data source and store the data in the data retention system" refers to the process by which the server accesses the data source at scheduled intervals and securely stores the retrieved data in the data retention system.

[0104] "Means by which the server analyzes data obtained from the data retention system using an analysis tool" refers to the process by which the server obtains data from the data retention system and analyzes it using an analysis tool (e.g., Python's Pandas or Scikit-Learn).

[0105] "Means by which a user uses a terminal to set up a connection with accounting software and send it to a server" refers to the process by which a user uses a terminal to enter the API key and connection information for the accounting software and send it to a server.

[0106] "Means by which the server obtains accounting data in real time using the accounting software's API and stores it in the data retention system" refers to the process by which the server obtains data from the accounting software in real time through the API and stores it in the data retention system.

[0107] "Means for a user to use a terminal to input the requirements for creating presentation materials and send them to a server" refers to the process by which a user inputs the period and target required for creating presentation materials through a terminal and sends that information to a server.

[0108] "Means in which the server obtains data from the data retention system based on a specified period, analyzes it using a generative AI model, and generates presentation materials" refers to the process in which the server obtains data for a specified period from the data retention system, analyzes it using a generative AI model, and generates presentation materials based on the results.

[0109] The present invention is a system that combines multiple functions to enable companies to efficiently collect and analyze data, obtain the latest accounting data in real time, and automatically generate high-quality presentation materials based on that data.

[0110] First, the user uses the terminal to configure the company's data sources, such as ERP systems (e.g., SAP, Oracle) and CRM systems (e.g., Salesforce, HubSpot). The user enters the authentication information and connection settings for these data sources on the terminal and sends them to the server. The server then stores the received authentication information and connection settings in its database.

[0111] The server periodically accesses the data source and collects the required data using a data collection script that runs at scheduled intervals, for example using a cron job, and stores the collected data in a data warehouse (e.g., Amazon Redshift, Google BigQuery).

[0112] The server then analyzes the collected data using a data analysis methodology, using tools such as Python's Pandas library and Scikit-Learn, to extract key metrics and trends. The results of this analysis are passed to a generative AI methodology.

[0113] The user also uses the device to set up the connection with the accounting software. Specifically, they enter the accounting software's API key and connection information, and send it to the server. The server retrieves accounting data in real time via the API and stores it in a data warehouse. This process ensures that the company's financial information is always kept up to date.

[0114] Furthermore, the user specifies the period and target for which the presentation materials need to be created through the terminal. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means. The generative AI means uses a generative AI model such as GPT-4, which performs analysis by inputting a prompt statement. An example of a prompt statement is "Create a performance report for Q3 2023."

[0115] The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables. Finally, the presentation material creation means optimizes the layout and design of the presentation to create high-quality presentation materials containing the necessary information. These materials are then displayed to the user via their device.

[0116] For example, if a company were to create a "2023 Q3 performance report," the user would configure the system to collect data from the ERP system and CRM, and also configure integration settings to obtain financial data from accounting software in real time. When the user inputs the requirements for creating the "2023 Q3 performance report" into the system, the server retrieves the data for the relevant period from the data warehouse, and the generation AI means analyzes it. As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated, and the presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via their device.

[0117] This system allows companies to quickly and efficiently obtain high-quality presentation materials, significantly reducing the time and effort required to create them and enabling timely decision-making.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] Step Name: Configure Data Source

[0121] The user uses the terminal to enter authentication information for data sources such as the company's ERP system or CRM. Specifically, they enter authentication information for each system (user name, password, API key, etc.) and set up the connection. The entered information is sent to the server via the terminal. The server stores the received authentication information and connection settings in a database.

[0122] Input: ERP system or CRM system credentials, connection settings

[0123] Data processing: converting credentials and settings into the appropriate format

[0124] Output: Store in database

[0125] Specific operation: User enters authentication information on the device → Data is sent from the device to the server → Server receives the data → Data is stored in the database

[0126] Step 2:

[0127] Step Name: Data Collection

[0128] The server periodically runs a data collection script, accessing data sources and collecting the necessary data. The script is scheduled using a cron job. Specifically, it retrieves sales and customer data from ERP and CRM systems. The collected data is then stored in a data warehouse.

[0129] Input: Data source credentials and access settings

[0130] Data processing: Acquiring data from data sources and converting data formats

[0131] Output: Data storage in a data warehouse

[0132] Specific operation: The server periodically executes the script → accesses the data source → collects data → stores it in the data warehouse

[0133] Step 3:

[0134] Step Name: Data Analysis

[0135] The server retrieves the collected data from the data storage system and performs analysis using analytical tools. Statistical analysis and machine learning algorithms are applied using Python's Pandas library and Scikit-Learn. Key indicators and trends are extracted as a result of the analysis and then fed into the generative AI method.

[0136] Input: Data stored in a data warehouse

[0137] Data processing: Data analysis, extraction of indicators and trends

[0138] Output: Analysis results

[0139] Specific operations: The server acquires data → Analyzes the data using an analysis tool → Extracts important indicators and trends → Stores the analysis results

[0140] Step 4:

[0141] Step Name: Real-time accounting data acquisition

[0142] The user enters the API key and connection information for the accounting software using a terminal and sends it to the server, which retrieves accounting data (e.g., sales, expenses, profits, etc.) in real time via the API and stores it in a data warehouse.

[0143] Input: Accounting software API key, connection information

[0144] Data processing: Acquiring accounting data through API

[0145] Output: Data storage in a data warehouse

[0146] Specific operation: User enters API key on device → Data is sent to server → Server retrieves data via API → Stores in data warehouse

[0147] Step 5:

[0148] Step Name: Generate presentation materials

[0149] The user uses the device to specify the period and subject for which the presentation material is to be created. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means (e.g., GPT-4 generative AI model). Graphs, charts, and tables are generated as analysis results. Finally, the presentation material creation means automatically creates high-quality presentation materials based on this data and displays them to the user via the device.

[0150] Input: Presentation material creation period, target information

[0151] Data processing: Data acquisition, analysis using generative AI models, and document creation

[0152] Output: Presentation materials

[0153] Specific operation: User inputs requirements on the device → Server acquires data → Analyzes using the generation AI means → Document creation means generates presentation materials → Display on the device

[0154] (Application example 1)

[0155] 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."

[0156] In order for companies to carry out marketing activities and decision-making quickly and effectively, they need to analyze and visualize data collected from various data sources in real time. However, since collecting and analyzing data and creating presentation materials requires a lot of time and effort, a system is needed to streamline these processes.

[0157] 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.

[0158] In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, a visual data generation means, and an automatic report generation means, which enables a company to analyze data collected from multiple data sources in real time and automatically generate high-quality presentation materials and reports using the generation AI.

[0159] A "data collection tool" is a tool for periodically collecting data from multiple data sources within an enterprise.

[0160] "Data analysis tools" are tools used to analyze collected data and identify important indicators and trends.

[0161] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time in conjunction with accounting software.

[0162] "Generative AI means" refers to artificial intelligence means for automatically generating important information based on analysis results.

[0163] The "presentation material creation means" is a means for optimizing the layout and design of the presentation and creating materials based on the data generated by the generation AI means.

[0164] The "visual data generation means" is a means for generating visual data such as graphs and charts based on the analysis results.

[0165] The "automatic report generation means" is a means for automatically creating a report by combining the generated visual data and analysis results.

[0166] "Data storage" refers to a storage device for safely and efficiently storing collected data.

[0167] A system embodying this invention operates by combining the following means. First, a data collection means periodically collects data from multiple data sources of a company, such as an ERP system or CRM. A user uses a terminal to enter the necessary authentication information and connection settings and transmits them to a server. The server accesses the data sources set by the user and stores the collected data in a data storage.

[0168] The data analysis means then analyzes the collected data. The server uses data analysis tools and algorithms to identify important indicators and trends. The analysis results are used for subsequent data processing by the generative AI means.

[0169] The server uses a real-time accounting data acquisition method to connect with the company's accounting software, acquires accounting data in real time via API, and stores this data in data storage, ensuring that the company's financial information is always kept up to date.

[0170] The user specifies the period and subject for which presentation materials are to be created. This information is sent to the server via the device. The server retrieves data corresponding to the specified period from data storage and analyzes the data using the generation AI means. The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0171] Furthermore, the visual data generation means generates visual data based on the analysis results. The generated data is used to create professional presentation materials and reports. The presentation material creation means combines the generated visual data and analysis results to automatically create presentation materials taking into consideration the optimal layout and design.

[0172] As a specific example, consider the case where a company wants to create an "Entertainment Report based on viewing data and advertising effectiveness data for Q3 2023." The user configures the system to collect viewing data from an ERP system or CRM, and also configures integration to obtain advertising data from accounting software in real time. When the user inputs the requirements for report creation into the system, the server retrieves the data for the relevant period from the data storage, analyzes it, and generates visual data. Finally, a report is automatically created based on the generated data.

[0173] The generative AI model uses an advanced generative AI such as GPT-3, and an example prompt based on the analysis results is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create presentation materials including graphs of viewing trends and advertising effectiveness." This allows for the rapid generation of visually easy-to-understand reports and presentation materials, which can be useful for corporate marketing activities and decision-making.

[0174] The hardware used includes servers, user devices, data storage, etc. The software used includes API clients, data collection scripts, data analysis tools such as Python's Pandas and Scikit-learn, and generative AI tools (such as OpenAI's GPT-3), which automate each processing step and enable efficient data analysis and information generation.

[0175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0176] Step 1:

[0177] The user enters authentication information and connection settings using a terminal and sends them to the server. This input data includes the necessary authentication information to access data sources such as ERP systems and CRM. Upon receiving this authentication information, the server configures the settings for periodic access.

[0178] Step 2:

[0179] The server periodically accesses the data sources configured by the user and collects the required data. At this stage, the server connects to each data source using an API client, organizes the collected data, and stores it in data storage. The input is data from the data source, and the output is storage in the data storage.

[0180] Step 3:

[0181] The server analyzes the collected data using data analysis tools such as Python's Pandas and Scikit-learn to extract trends and patterns. In this step, the input is the data stored in the data storage, and the output is the indicators and trends that are the analysis results.

[0182] Step 4:

[0183] The server uses a real-time accounting data acquisition method to link with the company's accounting software and acquires accounting data in real time via API. This data is also stored in the data storage. The input is accounting data from the accounting software, and the output is storage in the data storage.

[0184] Step 5:

[0185] The user specifies the period and subject required for creating presentation materials and sends this information to the server via the terminal. The input is the information about the period and subject from the user, and the output is transmission to the server.

[0186] Step 6:

[0187] The server retrieves data corresponding to the specified period from data storage and analyzes the data using generative AI means. Using a generative AI model (e.g., GPT-3), it automatically generates important information based on the analysis results. The input is the data for the specified period, and the output is text data as the analysis results.

[0188] Step 7:

[0189] The server uses the visual data generation means to generate visual data such as appropriate graphs and charts based on the generated text data. The input is the text data as the analysis result, and the output is the visual data.

[0190] Step 8:

[0191] The server combines the visual data generated using the presentation material creation tool with the analysis results, and automatically creates presentation materials taking into consideration the optimal layout and design. The input is the visual data and the analysis results, and the output is the completed presentation materials.

[0192] Step 9:

[0193] The user can check the final generated presentation materials and reports through the terminal and download them as necessary. The input is the completed presentation materials, and the output is the materials provided to the user. As a specific example, the prompt statement used is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create a presentation material that includes graphs of viewing trends and advertising effectiveness."

[0194] 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.

[0195] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[0196] Data collection

[0197] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[0198] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[0199] Data analysis

[0200] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[0201] Real-time acquisition of accounting data

[0202] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[0203] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[0204] Creating presentation materials using generative AI

[0205] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[0206] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0207] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0208] Introducing the Emotion Engine

[0209] When a user uses the system, the emotion engine automatically recognizes the user's emotions, using facial recognition technology and voice analysis to identify the user's current emotional state.

[0210] The server receives the data from the emotion engine and selects a data visualization method based on the user's emotional state, thereby generating graphs and charts in a format that best suits the user's emotions.

[0211] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[0212] Specific examples

[0213] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0214] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0215] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[0216] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[0217] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[0218] The processing flow will be explained below.

[0219] Data collection and analysis process steps

[0220] Step 1: Configure the Data Source

[0221] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[0222] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[0223] Step 2: Collect data

[0224] The server periodically accesses the data source set by the user and collects the required data.

[0225] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[0226] Step 3: Store the data

[0227] The server stores the collected data in a data warehouse.

[0228] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[0229] Step 4: Analyze the data

[0230] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[0231] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[0232] Processing steps for real-time accounting data capture

[0233] Step 1: Set up your accounting software

[0234] The user sets up collaboration with accounting software (for example, general accounting software).

[0235] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[0236] Step 2: Obtaining accounting data

[0237] The server retrieves real-time financial data from the accounting software via API.

[0238] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[0239] Step 3: Store and update data

[0240] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[0241] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[0242] Processing steps for creating presentation materials using generative AI

[0243] Step 1: Request for materials

[0244] The user inputs the period and target for which the presentation materials are needed.

[0245] How it works: You specify the requirements for the materials on your device and send them to the server.

[0246] Step 2: Data acquisition and analysis

[0247] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[0248] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[0249] Step 3: Extracting and visualizing information

[0250] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[0251] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[0252] Step 4: Optimize your presentation

[0253] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[0254] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[0255] Step 5: View the material

[0256] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[0257] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[0258] Steps to implement the Emotion Engine

[0259] Step 1: Recognizing user emotions

[0260] When a user uses the system, the emotion engine automatically recognizes the user's emotions.

[0261] How it works: Uses the device's built-in camera and microphone to analyze the user's facial expressions and tone of voice to determine their emotional state.

[0262] Step 2: Processing the emotion data

[0263] The server receives the emotional data from the emotion engine and selects a data visualization means based on the user's emotional state.

[0264] What it does: Analyzes the data received from the emotion engine to identify the user's emotional state (e.g., happy, sad, stressed).

[0265] Step 3: Sentiment-based graph generation

[0266] The generative AI means generates appropriate graphs, charts, and tables based on the user's emotions.

[0267] Action: If users are experiencing stress, choose visualizations that are simple and easy to understand at a glance and customize your presentation.

[0268] Step 4: Adjust your design based on emotion

[0269] The server optimizes the layout and design of presentation materials based on the output of the emotion engine.

[0270] Behavior: If the user is calm, create a presentation with complex graphs and detailed data. Conversely, if the user is anxious, choose a simple, easy-to-understand design.

[0271] Specific examples

[0272] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0273] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0274] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[0275] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[0276] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[0277] Example 2

[0278] 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."

[0279] In conventional systems, the processes of data collection, data analysis, accounting data acquisition, and presentation material creation were all performed separately, which required time and effort to integrate them.In addition, there was also the issue of not being able to optimize data display and presentation materials based on the user's emotional state, which resulted in a lack of improvement in the user experience.

[0280] 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. In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, and an emotion engine. This makes it possible to collect data from multiple data sources of a company, acquire accounting data in real time, analyze the data through the generation AI means, and create optimal presentation materials according to the user's emotional state.

[0281] The "data collection means" is a means having a function of acquiring necessary data from multiple data sources and transmitting it to a server.

[0282] "Data analytics tools" are tools and algorithms used to analyze collected data and identify key indicators and trends.

[0283] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time using the API of accounting software and storing it in a data warehouse.

[0284] "Generative AI methods" are methods that extract important information and generate appropriate graphs, charts, and tables based on collected and analyzed data.

[0285] The "presentation material creation means" is a means for automatically creating presentation materials by optimizing the layout and design of the presentation based on the generated data.

[0286] An "emotion engine" is a means including facial recognition technology and voice analysis technology for recognizing a user's emotions.

[0287] A "data warehouse" is a data storage system that centrally stores collected data and allows for efficient subsequent data analysis and use.

[0288] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[0289] Data collection methods

[0290] Users configure the system with multiple corporate data sources (e.g., ERP systems, CRM) by using a terminal to enter the necessary authentication information and connection settings, which are then sent to the server, including API keys, usernames, and passwords.

[0291] The server periodically accesses the data sources configured by the user and sends API requests to collect the required data, which is then stored in the data warehouse.

[0292] Data Analysis Methods

[0293] The server analyzes the data stored in the data warehouse using data analysis tools and algorithms, such as Python's Pandas and Scikit-learn, to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI tools.

[0294] Real-time acquisition of accounting data

[0295] The user sets up the connection with the accounting software by entering the API key and connection information on the device and sending it to the server.

[0296] The server accesses the accounting software's API in real time to retrieve new accounting data, which is then stored in a data warehouse, ensuring that the company's financial information is always up-to-date.

[0297] Generation AI means

[0298] The user specifies the period and target for which presentation materials are to be created, and this information is sent to the server via the terminal.

[0299] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI methods (e.g., GPT model, BERT model), which extract important information based on the analysis results and generate appropriate graphs, charts, and tables.

[0300] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0301] Emotion Engine

[0302] When users use the system, their emotions are automatically recognized by the emotion engine, which uses facial recognition technology and voice analysis.

[0303] The server receives the data from the emotion engine, identifies the user's emotional state, and selects a data visualization method based on the identified emotional state to generate an optimal graph or chart.

[0304] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[0305] Specific examples

[0306] For example, when a company prepares its "2023 Q3 Performance Report," it follows these steps:

[0307] Users configure the system to collect data from ERP systems and CRMs, and then set up integrations to retrieve financial data from accounting software in real time.

[0308] When a user inputs the requirements for creating a "2023 Q3 performance report" into the system, the server retrieves data for that period from the data warehouse. The generation AI means generates analysis results such as a line graph of sales trends or a pie chart of customer distribution. At this time, the emotion engine recognizes the user's emotions, and if the user is calm, for example, a complex graph containing detailed data will be selected. Conversely, if the user is stressed, a simple visualization means that can be understood at a glance will be selected.

[0309] The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[0310] Prompt Sentence Examples

[0311] Here are some example prompts to input to the generative AI model:

[0312] "Collect data from your ERP system and CRM, and use data from your accounting software for Q3 2023 to generate performance reports optimized based on user sentiment."

[0313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0314] Step 1:

[0315] The user enters the connection information for the company's data source (e.g., ERP system, CRM) into the device, enters authentication information, and then sends this to the server. The server accesses the data source based on the entered authentication information and establishes a connection using an API key, username, and password. Based on this input, preparations are made to collect the necessary data. The output is a database that stores the authentication information and connection information.

[0316] Step 2:

[0317] The server periodically sends API requests to the data source set by the user to collect the necessary data. The collected data is stored in a data warehouse after filtering out duplicate data and standardizing the data format. The input is the sent API request, and the output is the retrieved data.

[0318] Step 3:

[0319] The user uses a terminal to enter the API key and connection information for the accounting software and sends it to the server. The server uses this information to establish a connection with the accounting software and prepares to obtain accounting data in real time. The input is the API key and connection information, and the output is the status that the connection is ready.

[0320] Step 4:

[0321] The server accesses the accounting software's API in real time to periodically retrieve new accounting data. The retrieved accounting data is stored in a data warehouse. The input is the API request, and the output is the retrieved accounting data.

[0322] Step 5:

[0323] The server periodically checks the data stored in the data warehouse and, if new data is available, invokes data analysis tools such as Python's Pandas and Scikit-learn to identify important indicators and trends. The input is the new data, and the output is the analysis results.

[0324] Step 6:

[0325] The user inputs the requirements for creating presentation materials (e.g., creation period, target) into the terminal and sends them to the server. The server retrieves the necessary data based on the user's requirements from the data warehouse and activates the generation AI means. The input is the requirement information, and the output is data based on the requirements.

[0326] Step 7:

[0327] The server uses generative AI methods (e.g., GPT model, BERT model) to analyze the data and extract key information. Based on this, it generates appropriate graphs, charts, and tables. The input is the data based on the requirements, and the output is the analyzed information and visualization data.

[0328] Step 8:

[0329] The presentation creation tool optimizes the layout and design of the presentation based on the generated information. Finally, the presentation material is created and displayed to the user via a terminal. The input is visualization data, and the output is the completed presentation material.

[0330] Step 9:

[0331] When using the system, the user completes the settings for facial recognition and voice analysis, which allows the emotion engine to recognize the user's emotions. The input is the necessary hardware permission settings, and the output is the completion of emotion recognition preparation.

[0332] Step 10:

[0333] The emotion engine recognizes the user's emotional state and sends the data to the server. The server selects a data visualization method based on the emotional state and generates graphs and charts that best suit the user's emotions. The input is the user's emotional data, and the output is visualization data that corresponds to the emotional state.

[0334] Step 11:

[0335] The generative AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected. The input is visualization data corresponding to the user's emotional state, and the output is presentation materials optimized for that emotion.

[0336] This series of processes not only enables companies to quickly and efficiently create high-quality presentation materials, but also enables them to provide optimal information according to the user's emotional state.

[0337] (Application example 2)

[0338] 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."

[0339] The purpose of this invention is to improve the efficiency of data collection and analysis in production facilities and to improve production planning and reduce stress for operators and managers by generating optimal presentation materials based on human emotions. Specifically, the purpose is to provide a system that monitors and adjusts production efficiency in real time, visualizes data, and generates presentation materials that are tailored to human emotional states.

[0340] 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 means for acquiring accounting data in real time, a generation AI means, a presentation material creation means, an emotion recognition means, a means for collecting operational status and quality data of automated equipment in a facility, and a means for analyzing and optimizing production efficiency in real time. This enables real-time collection and analysis of production data in a facility, and further enables the generation AI and emotion engine to automatically generate optimal presentation materials that take into account the user's emotions.

[0341] "Data collection means" refers to devices or software used to collect operational status and quality data from automated equipment within a facility.

[0342] "Data analytics tools" are algorithms and tools used to analyze collected data and identify key indicators and trends.

[0343] "Means for obtaining accounting data in real time" refers to an API or interface for obtaining financial data in real time from an external accounting system.

[0344] A "generative AI means" is a system that uses artificial intelligence to analyze data and automatically generate the necessary information.

[0345] The "presentation material creation means" is software for automatically creating visually easy-to-understand presentation materials based on the analyzed data.

[0346] An "emotion recognition means" is an engine or algorithm that analyzes emotions from the user's face and voice and identifies their emotional state.

[0347] The "means for analyzing and optimizing production efficiency in real time" is a system that uses data collected within the facility to continuously analyze the efficiency of the production line and generate optimal production plans and maintenance schedules.

[0348] The present invention provides a system for analyzing and optimizing factory production efficiency in real time. This system includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, an emotion recognition means, and a presentation material creation means.

[0349] Data collection methods

[0350] The data collection method is a system that collects operational status and quality data from automated equipment in factories. Specifically, sensors and IoT devices are attached to each piece of automated equipment, which collect data in real time and send it to a server.

[0351] Data Analysis Methods

[0352] The server receives data from the data collection means and analyzes it using the data analysis means. Data analysis uses machine learning algorithms such as Python's Pandas library and Sklearn to identify important indicators and trends. For example, linear regression can be used to analyze trends in production efficiency.

[0353] Real-time acquisition of accounting data

[0354] Additionally, the server connects to external financial data systems via APIs to obtain real-time accounting data, which is then analyzed and used to adjust production plans and manage costs.

[0355] Generation AI means

[0356] Generative AI methods extract important information based on the results of data analysis and automatically generate materials. The generated information is visualized as appropriate graphs and charts. An example of this is to use a generative AI model and set the input prompt as follows:

[0357] "Analyze the user's emotional state and choose the appropriate style for your presentation materials."

[0358] How to create presentation materials

[0359] The presentation material creation means uses the data provided by the generation AI means to create visually easy-to-understand presentation materials. At this time, the design and item layout are adjusted taking into account the user's emotional state. The emotion recognition means analyzes the user's emotions in real time and selects the style of the presentation materials based on the results. For example, if the user is feeling stressed, it will generate materials with a simple, easy-to-read design.

[0360] emotion recognition means

[0361] The emotion recognition engine uses facial recognition technology and voice analysis to identify the user's emotional state. This function optimizes the effectiveness of information transmission while maintaining a comfortable user experience. Specifically, if the user is calm, the system creates materials containing a lot of detailed data, and if the user is stressed, it provides simple, easy-to-understand materials.

[0362] As described above, the present invention realizes a system that analyzes production efficiency in a factory in real time and automatically generates optimal presentation materials according to the user's emotional state, thereby improving the efficiency of production plan adjustments and cost management and reducing the burden on operators and managers.

[0363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0364] Step 1:

[0365] The terminal collects operational status and quality data from sensors and IoT devices attached to automated equipment in the factory. The data obtained from these devices is sent to a server via the network. The input data is the operational status and quality information of each automated device, and the output data is raw data stored on the server.

[0366] Step 2:

[0367] The server receives the raw data obtained by the data collection means and analyzes it using the data analysis means. Specifically, it organizes the data using Python's Pandas library and applies machine learning algorithms such as Sklearn to identify important indicators and trends. The input data is the collected raw data, and the output data is the indicators and trends obtained through the analysis.

[0368] Step 3:

[0369] The server obtains accounting data in real time from an external financial data system through the accounting software's API. This is done by using an API key or connection information to obtain data in real time and store it directly on the server. The input data is real-time data from the accounting software, and the output data is the latest financial information stored on the server.

[0370] Step 4:

[0371] The server passes the data collected and analyzed by the data analysis means and real-time accounting data acquisition means to the generation AI means, which extracts important information and automatically generates presentation materials. Here, a generative AI model is used to prepare prompt text. As an example, we use the prompt, "Analyze the user's emotional state and select an appropriate style for the presentation materials." The input data are the analysis results and real-time accounting data, and the output data is the generated presentation content.

[0372] Step 5:

[0373] The emotion recognition means uses facial recognition technology and voice analysis to identify the emotional state of the user when using the system. For example, it analyzes facial expressions and voice tone in real time using a camera and microphone. The input data is the user's facial expression data and voice data, and the output data is data indicating the user's emotional state.

[0374] Step 6:

[0375] The server adjusts the design and layout of the presentation materials created by the generation AI means based on the user's emotional state obtained from the emotion recognition means. For example, if the user is feeling stressed, it selects a simple, easy-to-read design, and if the user is calm, it selects a complex graph containing detailed data. The input data is the user's emotional data and the generated presentation materials, and the output data is the optimal presentation materials after adjustment.

[0376] Step 7:

[0377] The terminal displays the final, adjusted presentation materials to the user. Here, the screen display function and PDF generation function are used to provide the materials visually. The input data are the adjusted presentation materials, and the output data are the presentation materials displayed to the user.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Second embodiment]

[0382] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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."

[0394] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, and a means for creating presentation materials.

[0395] Data collection

[0396] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[0397] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[0398] Data analysis

[0399] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[0400] Real-time acquisition of accounting data

[0401] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[0402] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[0403] Creating presentation materials using generative AI

[0404] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[0405] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0406] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0407] Specific examples

[0408] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0409] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0410] As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated. The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[0411] In this way, companies can obtain high-quality presentation materials quickly and efficiently, significantly reducing the time and effort required to create them and enabling timely decision-making.

[0412] The processing flow will be explained below.

[0413] Data collection and analysis process steps

[0414] Step 1: Configure the Data Source

[0415] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[0416] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[0417] Step 2: Collect data

[0418] The server periodically accesses the data source set by the user and collects the required data.

[0419] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[0420] Step 3: Store the data

[0421] The server stores the collected data in a data warehouse.

[0422] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[0423] Step 4: Analyze the data

[0424] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[0425] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[0426] Processing steps for real-time accounting data capture

[0427] Step 1: Set up your accounting software

[0428] The user sets up collaboration with accounting software (for example, general accounting software).

[0429] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[0430] Step 2: Obtaining accounting data

[0431] The server retrieves real-time financial data from the accounting software via API.

[0432] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[0433] Step 3: Store and update data

[0434] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[0435] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[0436] Processing steps for creating presentation materials using generative AI

[0437] Step 1: Request for materials

[0438] The user inputs the period and target for which the presentation materials are needed.

[0439] How it works: You specify the requirements for the materials on your device and send them to the server.

[0440] Step 2: Data acquisition and analysis

[0441] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[0442] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[0443] Step 3: Extracting and visualizing information

[0444] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[0445] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[0446] Step 4: Optimize your presentation

[0447] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[0448] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[0449] Step 5: View the material

[0450] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[0451] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[0452] Example 1

[0453] 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."

[0454] Modern companies need to collect information from a variety of data sources, analyze it, and use it to make decisions. However, the processes of collecting and analyzing data, acquiring accounting data in real time, and creating presentation materials are complex and require time and resources. Therefore, there is a need for a method to streamline these processes and quickly create high-quality presentation materials.

[0455] 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.

[0456] In this invention, the server includes a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generative AI means, a means for creating presentation materials, a means for a user to use a terminal to input authentication information for a data source and send it to the server, a means for the server to periodically access the data source and store the data in a data retention system, a means for the server to analyze the data acquired from the data retention system using an analysis tool, a means for a user to use a terminal to set up a connection with accounting software and send it to the server, a means for the server to acquire accounting data in real time using the accounting software's API and store it in the data retention system, a means for a user to use a terminal to input requirements for creating presentation materials and send it to the server, and a means for the server to acquire data from the data retention system based on a specified period, analyze it using a generative AI model, and generate presentation materials. This enables companies to efficiently collect and analyze information from multiple data sources, acquire the latest accounting data in real time, and automatically generate high-quality presentation materials quickly.

[0457] The "data collection means" is a function that periodically accesses multiple data sources set by the user and acquires the necessary data.

[0458] "Data analysis means" is a function that analyzes collected data using analytical tools and algorithms to extract important indicators and trends.

[0459] "Means for acquiring accounting data in real time" refers to a function that acquires accounting data in real time using the accounting software's API and stores it in a data retention system.

[0460] "Generative AI means" is a function that uses a generative AI model to analyze collected and analyzed data, extract important information, and generate output such as presentation materials.

[0461] The "presentation material creation means" is a function that generates appropriate graphs, charts, and tables based on the analysis results of the generation AI means, and optimizes the layout and design of presentation materials.

[0462] "Means for entering authentication information for a data source using a terminal and sending it to a server" refers to the process in which a user uses a terminal to enter authentication information and connection settings for accessing a data source and sends them to a server.

[0463] "Means for the server to periodically access the data source and store the data in the data retention system" refers to the process by which the server accesses the data source at scheduled intervals and securely stores the retrieved data in the data retention system.

[0464] "Means by which the server analyzes data obtained from the data retention system using an analysis tool" refers to the process by which the server obtains data from the data retention system and analyzes it using an analysis tool (e.g., Python's Pandas or Scikit-Learn).

[0465] "Means by which a user uses a terminal to set up a connection with accounting software and send it to a server" refers to the process by which a user uses a terminal to enter the API key and connection information for the accounting software and send it to a server.

[0466] "Means by which the server obtains accounting data in real time using the accounting software's API and stores it in the data retention system" refers to the process by which the server obtains data from the accounting software in real time through the API and stores it in the data retention system.

[0467] "Means for a user to use a terminal to input the requirements for creating presentation materials and send them to a server" refers to the process by which a user inputs the period and target required for creating presentation materials through a terminal and sends that information to a server.

[0468] "Means in which the server obtains data from the data retention system based on a specified period, analyzes it using a generative AI model, and generates presentation materials" refers to the process in which the server obtains data for a specified period from the data retention system, analyzes it using a generative AI model, and generates presentation materials based on the results.

[0469] The present invention is a system that combines multiple functions to enable companies to efficiently collect and analyze data, obtain the latest accounting data in real time, and automatically generate high-quality presentation materials based on that data.

[0470] First, the user uses the terminal to configure the company's data sources, such as ERP systems (e.g., SAP, Oracle) and CRM systems (e.g., Salesforce, HubSpot). The user enters the authentication information and connection settings for these data sources on the terminal and sends them to the server. The server then stores the received authentication information and connection settings in its database.

[0471] The server periodically accesses the data source and collects the required data using a data collection script that runs at scheduled intervals, for example using a cron job, and stores the collected data in a data warehouse (e.g., Amazon Redshift, Google BigQuery).

[0472] The server then analyzes the collected data using a data analysis methodology, using tools such as Python's Pandas library and Scikit-Learn, to extract key metrics and trends. The results of this analysis are passed to a generative AI methodology.

[0473] The user also uses the device to set up the connection with the accounting software. Specifically, they enter the accounting software's API key and connection information, and send it to the server. The server retrieves accounting data in real time via the API and stores it in a data warehouse. This process ensures that the company's financial information is always kept up to date.

[0474] Furthermore, the user specifies the period and target for which the presentation materials need to be created through the terminal. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means. The generative AI means uses a generative AI model such as GPT-4, which performs analysis by inputting a prompt statement. An example of a prompt statement is "Create a performance report for Q3 2023."

[0475] The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables. Finally, the presentation material creation means optimizes the layout and design of the presentation to create high-quality presentation materials containing the necessary information. These materials are then displayed to the user via their device.

[0476] For example, if a company were to create a "2023 Q3 performance report," the user would configure the system to collect data from the ERP system and CRM, and also configure integration settings to obtain financial data from accounting software in real time. When the user inputs the requirements for creating the "2023 Q3 performance report" into the system, the server retrieves the data for the relevant period from the data warehouse, and the generation AI means analyzes it. As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated, and the presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via their device.

[0477] This system allows companies to quickly and efficiently obtain high-quality presentation materials, significantly reducing the time and effort required to create them and enabling timely decision-making.

[0478] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0479] Step 1:

[0480] Step Name: Configure Data Source

[0481] The user uses the terminal to enter authentication information for data sources such as the company's ERP system or CRM. Specifically, they enter authentication information for each system (user name, password, API key, etc.) and set up the connection. The entered information is sent to the server via the terminal. The server stores the received authentication information and connection settings in a database.

[0482] Input: ERP system or CRM system credentials, connection settings

[0483] Data processing: converting credentials and settings into the appropriate format

[0484] Output: Store in database

[0485] Specific operation: User enters authentication information on the device → Data is sent from the device to the server → Server receives the data → Data is stored in the database

[0486] Step 2:

[0487] Step Name: Data Collection

[0488] The server periodically runs a data collection script, accessing data sources and collecting the necessary data. The script is scheduled using a cron job. Specifically, it retrieves sales and customer data from ERP and CRM systems. The collected data is then stored in a data warehouse.

[0489] Input: Data source credentials and access settings

[0490] Data processing: Acquiring data from data sources and converting data formats

[0491] Output: Data storage in a data warehouse

[0492] Specific operation: The server periodically executes the script → accesses the data source → collects data → stores it in the data warehouse

[0493] Step 3:

[0494] Step Name: Data Analysis

[0495] The server retrieves the collected data from the data storage system and performs analysis using analytical tools. Statistical analysis and machine learning algorithms are applied using Python's Pandas library and Scikit-Learn. Key indicators and trends are extracted as a result of the analysis and then fed into the generative AI method.

[0496] Input: Data stored in a data warehouse

[0497] Data processing: Data analysis, extraction of indicators and trends

[0498] Output: Analysis results

[0499] Specific operations: The server acquires data → Analyzes the data using an analysis tool → Extracts important indicators and trends → Stores the analysis results

[0500] Step 4:

[0501] Step Name: Real-time accounting data acquisition

[0502] The user enters the API key and connection information for the accounting software using a terminal and sends it to the server, which retrieves accounting data (e.g., sales, expenses, profits, etc.) in real time via the API and stores it in a data warehouse.

[0503] Input: Accounting software API key, connection information

[0504] Data processing: Acquiring accounting data through API

[0505] Output: Data storage in a data warehouse

[0506] Specific operation: User enters API key on device → Data is sent to server → Server retrieves data via API → Stores in data warehouse

[0507] Step 5:

[0508] Step Name: Generate presentation materials

[0509] The user uses the device to specify the period and subject for which the presentation material is to be created. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means (e.g., GPT-4 generative AI model). Graphs, charts, and tables are generated as analysis results. Finally, the presentation material creation means automatically creates high-quality presentation materials based on this data and displays them to the user via the device.

[0510] Input: Presentation material creation period, target information

[0511] Data processing: Data acquisition, analysis using generative AI models, and document creation

[0512] Output: Presentation materials

[0513] Specific operation: User inputs requirements on the device → Server acquires data → Analyzes using the generation AI means → Document creation means generates presentation materials → Display on the device

[0514] (Application example 1)

[0515] 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."

[0516] In order for companies to carry out marketing activities and decision-making quickly and effectively, they need to analyze and visualize data collected from various data sources in real time. However, since collecting and analyzing data and creating presentation materials requires a lot of time and effort, a system is needed to streamline these processes.

[0517] 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.

[0518] In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, a visual data generation means, and an automatic report generation means, which enables a company to analyze data collected from multiple data sources in real time and automatically generate high-quality presentation materials and reports using the generation AI.

[0519] A "data collection tool" is a tool for periodically collecting data from multiple data sources within an enterprise.

[0520] "Data analysis tools" are tools used to analyze collected data and identify important indicators and trends.

[0521] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time in conjunction with accounting software.

[0522] "Generative AI means" refers to artificial intelligence means for automatically generating important information based on analysis results.

[0523] The "presentation material creation means" is a means for optimizing the layout and design of the presentation and creating materials based on the data generated by the generation AI means.

[0524] The "visual data generation means" is a means for generating visual data such as graphs and charts based on the analysis results.

[0525] The "automatic report generation means" is a means for automatically creating a report by combining the generated visual data and analysis results.

[0526] "Data storage" refers to a storage device for safely and efficiently storing collected data.

[0527] A system embodying this invention operates by combining the following means. First, a data collection means periodically collects data from multiple data sources of a company, such as an ERP system or CRM. A user uses a terminal to enter the necessary authentication information and connection settings and transmits them to a server. The server accesses the data sources set by the user and stores the collected data in a data storage.

[0528] The data analysis means then analyzes the collected data. The server uses data analysis tools and algorithms to identify important indicators and trends. The analysis results are used for subsequent data processing by the generative AI means.

[0529] The server uses a real-time accounting data acquisition method to connect with the company's accounting software, acquires accounting data in real time via API, and stores this data in data storage, ensuring that the company's financial information is always kept up to date.

[0530] The user specifies the period and subject for which presentation materials are to be created. This information is sent to the server via the device. The server retrieves data corresponding to the specified period from data storage and analyzes the data using the generation AI means. The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0531] Furthermore, the visual data generation means generates visual data based on the analysis results. The generated data is used to create professional presentation materials and reports. The presentation material creation means combines the generated visual data and analysis results to automatically create presentation materials taking into consideration the optimal layout and design.

[0532] As a specific example, consider the case where a company wants to create an "Entertainment Report based on viewing data and advertising effectiveness data for Q3 2023." The user configures the system to collect viewing data from an ERP system or CRM, and also configures integration to obtain advertising data from accounting software in real time. When the user inputs the requirements for report creation into the system, the server retrieves the data for the relevant period from the data storage, analyzes it, and generates visual data. Finally, a report is automatically created based on the generated data.

[0533] The generative AI model uses an advanced generative AI such as GPT-3, and an example prompt based on the analysis results is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create presentation materials including graphs of viewing trends and advertising effectiveness." This allows for the rapid generation of visually easy-to-understand reports and presentation materials, which can be useful for corporate marketing activities and decision-making.

[0534] The hardware used includes servers, user devices, data storage, etc. The software used includes API clients, data collection scripts, data analysis tools such as Python's Pandas and Scikit-learn, and generative AI tools (such as OpenAI's GPT-3), which automate each processing step and enable efficient data analysis and information generation.

[0535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0536] Step 1:

[0537] The user enters authentication information and connection settings using a terminal and sends them to the server. This input data includes the necessary authentication information to access data sources such as ERP systems and CRM. Upon receiving this authentication information, the server configures the settings for periodic access.

[0538] Step 2:

[0539] The server periodically accesses the data sources configured by the user and collects the required data. At this stage, the server connects to each data source using an API client, organizes the collected data, and stores it in data storage. The input is data from the data source, and the output is storage in the data storage.

[0540] Step 3:

[0541] The server analyzes the collected data using data analysis tools such as Python's Pandas and Scikit-learn to extract trends and patterns. In this step, the input is the data stored in the data storage, and the output is the indicators and trends that are the analysis results.

[0542] Step 4:

[0543] The server uses a real-time accounting data acquisition method to link with the company's accounting software and acquires accounting data in real time via API. This data is also stored in the data storage. The input is accounting data from the accounting software, and the output is storage in the data storage.

[0544] Step 5:

[0545] The user specifies the period and subject required for creating presentation materials and sends this information to the server via the terminal. The input is the information about the period and subject from the user, and the output is transmission to the server.

[0546] Step 6:

[0547] The server retrieves data corresponding to the specified period from data storage and analyzes the data using generative AI means. Using a generative AI model (e.g., GPT-3), it automatically generates important information based on the analysis results. The input is the data for the specified period, and the output is text data as the analysis results.

[0548] Step 7:

[0549] The server uses the visual data generation means to generate visual data such as appropriate graphs and charts based on the generated text data. The input is the text data as the analysis result, and the output is the visual data.

[0550] Step 8:

[0551] The server combines the visual data generated using the presentation material creation tool with the analysis results, and automatically creates presentation materials taking into consideration the optimal layout and design. The input is the visual data and the analysis results, and the output is the completed presentation materials.

[0552] Step 9:

[0553] The user can check the final generated presentation materials and reports through the terminal and download them as necessary. The input is the completed presentation materials, and the output is the materials provided to the user. As a specific example, the prompt statement used is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create a presentation material that includes graphs of viewing trends and advertising effectiveness."

[0554] 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.

[0555] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[0556] Data collection

[0557] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[0558] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[0559] Data analysis

[0560] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[0561] Real-time acquisition of accounting data

[0562] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[0563] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[0564] Creating presentation materials using generative AI

[0565] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[0566] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0567] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0568] Introducing the Emotion Engine

[0569] When a user uses the system, the emotion engine automatically recognizes the user's emotions, using facial recognition technology and voice analysis to identify the user's current emotional state.

[0570] The server receives the data from the emotion engine and selects a data visualization method based on the user's emotional state, thereby generating graphs and charts in a format that best suits the user's emotions.

[0571] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[0572] Specific examples

[0573] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0574] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0575] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[0576] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[0577] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[0578] The processing flow will be explained below.

[0579] Data collection and analysis process steps

[0580] Step 1: Configure the Data Source

[0581] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[0582] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[0583] Step 2: Collect data

[0584] The server periodically accesses the data source set by the user and collects the required data.

[0585] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[0586] Step 3: Store the data

[0587] The server stores the collected data in a data warehouse.

[0588] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[0589] Step 4: Analyze the data

[0590] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[0591] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[0592] Processing steps for real-time accounting data capture

[0593] Step 1: Set up your accounting software

[0594] The user sets up collaboration with accounting software (for example, general accounting software).

[0595] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[0596] Step 2: Obtaining accounting data

[0597] The server retrieves real-time financial data from the accounting software via API.

[0598] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[0599] Step 3: Store and update data

[0600] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[0601] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[0602] Processing steps for creating presentation materials using generative AI

[0603] Step 1: Request for materials

[0604] The user inputs the period and target for which the presentation materials are needed.

[0605] How it works: You specify the requirements for the materials on your device and send them to the server.

[0606] Step 2: Data acquisition and analysis

[0607] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[0608] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[0609] Step 3: Extracting and visualizing information

[0610] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[0611] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[0612] Step 4: Optimize your presentation

[0613] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[0614] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[0615] Step 5: View the material

[0616] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[0617] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[0618] Steps to implement the Emotion Engine

[0619] Step 1: Recognizing user emotions

[0620] When a user uses the system, the emotion engine automatically recognizes the user's emotions.

[0621] How it works: Uses the device's built-in camera and microphone to analyze the user's facial expressions and tone of voice to determine their emotional state.

[0622] Step 2: Processing the emotion data

[0623] The server receives the emotional data from the emotion engine and selects a data visualization means based on the user's emotional state.

[0624] What it does: Analyzes the data received from the emotion engine to identify the user's emotional state (e.g., happy, sad, stressed).

[0625] Step 3: Sentiment-based graph generation

[0626] The generative AI means generates appropriate graphs, charts, and tables based on the user's emotions.

[0627] Action: If users are experiencing stress, choose visualizations that are simple and easy to understand at a glance and customize your presentation.

[0628] Step 4: Adjust your design based on emotion

[0629] The server optimizes the layout and design of presentation materials based on the output of the emotion engine.

[0630] Behavior: If the user is calm, create a presentation with complex graphs and detailed data. Conversely, if the user is anxious, choose a simple, easy-to-understand design.

[0631] Specific examples

[0632] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0633] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0634] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[0635] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[0636] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[0637] Example 2

[0638] 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."

[0639] In conventional systems, the processes of data collection, data analysis, accounting data acquisition, and presentation material creation were all performed separately, which required time and effort to integrate them.In addition, there was also the issue of not being able to optimize data display and presentation materials based on the user's emotional state, which resulted in a lack of improvement in the user experience.

[0640] 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. In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, and an emotion engine. This makes it possible to collect data from multiple data sources of a company, acquire accounting data in real time, analyze the data through the generation AI means, and create optimal presentation materials according to the user's emotional state.

[0641] The "data collection means" is a means having a function of acquiring necessary data from multiple data sources and transmitting it to a server.

[0642] "Data analytics tools" are tools and algorithms used to analyze collected data and identify key indicators and trends.

[0643] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time using the API of accounting software and storing it in a data warehouse.

[0644] "Generative AI methods" are methods that extract important information and generate appropriate graphs, charts, and tables based on collected and analyzed data.

[0645] The "presentation material creation means" is a means for automatically creating presentation materials by optimizing the layout and design of the presentation based on the generated data.

[0646] An "emotion engine" is a means including facial recognition technology and voice analysis technology for recognizing a user's emotions.

[0647] A "data warehouse" is a data storage system that centrally stores collected data and allows for efficient subsequent data analysis and use.

[0648] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[0649] Data collection methods

[0650] Users configure the system with multiple corporate data sources (e.g., ERP systems, CRM) by using a terminal to enter the necessary authentication information and connection settings, which are then sent to the server, including API keys, usernames, and passwords.

[0651] The server periodically accesses the data sources configured by the user and sends API requests to collect the required data, which is then stored in the data warehouse.

[0652] Data Analysis Methods

[0653] The server analyzes the data stored in the data warehouse using data analysis tools and algorithms, such as Python's Pandas and Scikit-learn, to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI tools.

[0654] Real-time acquisition of accounting data

[0655] The user sets up the connection with the accounting software by entering the API key and connection information on the device and sending it to the server.

[0656] The server accesses the accounting software's API in real time to retrieve new accounting data, which is then stored in a data warehouse, ensuring that the company's financial information is always up-to-date.

[0657] Generation AI means

[0658] The user specifies the period and target for which presentation materials are to be created, and this information is sent to the server via the terminal.

[0659] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI methods (e.g., GPT model, BERT model), which extract important information based on the analysis results and generate appropriate graphs, charts, and tables.

[0660] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0661] Emotion Engine

[0662] When users use the system, their emotions are automatically recognized by the emotion engine, which uses facial recognition technology and voice analysis.

[0663] The server receives the data from the emotion engine, identifies the user's emotional state, and selects a data visualization method based on the identified emotional state to generate an optimal graph or chart.

[0664] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[0665] Specific examples

[0666] For example, when a company prepares its "2023 Q3 Performance Report," it follows these steps:

[0667] Users configure the system to collect data from ERP systems and CRMs, and then set up integrations to retrieve financial data from accounting software in real time.

[0668] When a user inputs the requirements for creating a "2023 Q3 performance report" into the system, the server retrieves data for that period from the data warehouse. The generation AI means generates analysis results such as a line graph of sales trends or a pie chart of customer distribution. At this time, the emotion engine recognizes the user's emotions, and if the user is calm, for example, a complex graph containing detailed data will be selected. Conversely, if the user is stressed, a simple visualization means that can be understood at a glance will be selected.

[0669] The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[0670] Prompt Sentence Examples

[0671] Here are some example prompts to input to the generative AI model:

[0672] "Collect data from your ERP system and CRM, and use data from your accounting software for Q3 2023 to generate performance reports optimized based on user sentiment."

[0673] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0674] Step 1:

[0675] The user enters the connection information for the company's data source (e.g., ERP system, CRM) into the device, enters authentication information, and then sends this to the server. The server accesses the data source based on the entered authentication information and establishes a connection using an API key, username, and password. Based on this input, preparations are made to collect the necessary data. The output is a database that stores the authentication information and connection information.

[0676] Step 2:

[0677] The server periodically sends API requests to the data source set by the user to collect the necessary data. The collected data is stored in a data warehouse after filtering out duplicate data and standardizing the data format. The input is the sent API request, and the output is the retrieved data.

[0678] Step 3:

[0679] The user uses a terminal to enter the API key and connection information for the accounting software and sends it to the server. The server uses this information to establish a connection with the accounting software and prepares to obtain accounting data in real time. The input is the API key and connection information, and the output is the status that the connection is ready.

[0680] Step 4:

[0681] The server accesses the accounting software's API in real time to periodically retrieve new accounting data. The retrieved accounting data is stored in a data warehouse. The input is the API request, and the output is the retrieved accounting data.

[0682] Step 5:

[0683] The server periodically checks the data stored in the data warehouse and, if new data is available, invokes data analysis tools such as Python's Pandas and Scikit-learn to identify important indicators and trends. The input is the new data, and the output is the analysis results.

[0684] Step 6:

[0685] The user inputs the requirements for creating presentation materials (e.g., creation period, target) into the terminal and sends them to the server. The server retrieves the necessary data based on the user's requirements from the data warehouse and activates the generation AI means. The input is the requirement information, and the output is data based on the requirements.

[0686] Step 7:

[0687] The server uses generative AI methods (e.g., GPT model, BERT model) to analyze the data and extract key information. Based on this, it generates appropriate graphs, charts, and tables. The input is the data based on the requirements, and the output is the analyzed information and visualization data.

[0688] Step 8:

[0689] The presentation creation tool optimizes the layout and design of the presentation based on the generated information. Finally, the presentation material is created and displayed to the user via a terminal. The input is visualization data, and the output is the completed presentation material.

[0690] Step 9:

[0691] When using the system, the user completes the settings for facial recognition and voice analysis, which allows the emotion engine to recognize the user's emotions. The input is the necessary hardware permission settings, and the output is the completion of emotion recognition preparation.

[0692] Step 10:

[0693] The emotion engine recognizes the user's emotional state and sends the data to the server. The server selects a data visualization method based on the emotional state and generates graphs and charts that best suit the user's emotions. The input is the user's emotional data, and the output is visualization data that corresponds to the emotional state.

[0694] Step 11:

[0695] The generative AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected. The input is visualization data corresponding to the user's emotional state, and the output is presentation materials optimized for that emotion.

[0696] This series of processes not only enables companies to quickly and efficiently create high-quality presentation materials, but also enables them to provide optimal information according to the user's emotional state.

[0697] (Application example 2)

[0698] 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."

[0699] The purpose of this invention is to improve the efficiency of data collection and analysis in production facilities and to improve production planning and reduce stress for operators and managers by generating optimal presentation materials based on human emotions. Specifically, the purpose is to provide a system that monitors and adjusts production efficiency in real time, visualizes data, and generates presentation materials that are tailored to human emotional states.

[0700] 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 means for acquiring accounting data in real time, a generation AI means, a presentation material creation means, an emotion recognition means, a means for collecting operational status and quality data of automated equipment in a facility, and a means for analyzing and optimizing production efficiency in real time. This enables real-time collection and analysis of production data in a facility, and further enables the generation AI and emotion engine to automatically generate optimal presentation materials that take into account the user's emotions.

[0701] "Data collection means" refers to devices or software used to collect operational status and quality data from automated equipment within a facility.

[0702] "Data analytics tools" are algorithms and tools used to analyze collected data and identify key indicators and trends.

[0703] "Means for obtaining accounting data in real time" refers to an API or interface for obtaining financial data in real time from an external accounting system.

[0704] A "generative AI means" is a system that uses artificial intelligence to analyze data and automatically generate the necessary information.

[0705] The "presentation material creation means" is software for automatically creating visually easy-to-understand presentation materials based on the analyzed data.

[0706] An "emotion recognition means" is an engine or algorithm that analyzes emotions from the user's face and voice and identifies their emotional state.

[0707] The "means for analyzing and optimizing production efficiency in real time" is a system that uses data collected within the facility to continuously analyze the efficiency of the production line and generate optimal production plans and maintenance schedules.

[0708] The present invention provides a system for analyzing and optimizing factory production efficiency in real time. This system includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, an emotion recognition means, and a presentation material creation means.

[0709] Data collection methods

[0710] The data collection method is a system that collects operational status and quality data from automated equipment in factories. Specifically, sensors and IoT devices are attached to each piece of automated equipment, which collect data in real time and send it to a server.

[0711] Data Analysis Methods

[0712] The server receives data from the data collection means and analyzes it using the data analysis means. Data analysis uses machine learning algorithms such as Python's Pandas library and Sklearn to identify important indicators and trends. For example, linear regression can be used to analyze trends in production efficiency.

[0713] Real-time acquisition of accounting data

[0714] Additionally, the server connects to external financial data systems via APIs to obtain real-time accounting data, which is then analyzed and used to adjust production plans and manage costs.

[0715] Generation AI means

[0716] Generative AI methods extract important information based on the results of data analysis and automatically generate materials. The generated information is visualized as appropriate graphs and charts. An example of this is to use a generative AI model and set the input prompt as follows:

[0717] "Analyze the user's emotional state and choose the appropriate style for your presentation materials."

[0718] How to create presentation materials

[0719] The presentation material creation means uses the data provided by the generation AI means to create visually easy-to-understand presentation materials. At this time, the design and item layout are adjusted taking into account the user's emotional state. The emotion recognition means analyzes the user's emotions in real time and selects the style of the presentation materials based on the results. For example, if the user is feeling stressed, it will generate materials with a simple, easy-to-read design.

[0720] emotion recognition means

[0721] The emotion recognition engine uses facial recognition technology and voice analysis to identify the user's emotional state. This function optimizes the effectiveness of information transmission while maintaining a comfortable user experience. Specifically, if the user is calm, the system creates materials containing a lot of detailed data, and if the user is stressed, it provides simple, easy-to-understand materials.

[0722] As described above, the present invention realizes a system that analyzes production efficiency in a factory in real time and automatically generates optimal presentation materials according to the user's emotional state, thereby improving the efficiency of production plan adjustments and cost management and reducing the burden on operators and managers.

[0723] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0724] Step 1:

[0725] The terminal collects operational status and quality data from sensors and IoT devices attached to automated equipment in the factory. The data obtained from these devices is sent to a server via the network. The input data is the operational status and quality information of each automated device, and the output data is raw data stored on the server.

[0726] Step 2:

[0727] The server receives the raw data obtained by the data collection means and analyzes it using the data analysis means. Specifically, it organizes the data using Python's Pandas library and applies machine learning algorithms such as Sklearn to identify important indicators and trends. The input data is the collected raw data, and the output data is the indicators and trends obtained through the analysis.

[0728] Step 3:

[0729] The server obtains accounting data in real time from an external financial data system through the accounting software's API. This is done by using an API key or connection information to obtain data in real time and store it directly on the server. The input data is real-time data from the accounting software, and the output data is the latest financial information stored on the server.

[0730] Step 4:

[0731] The server passes the data collected and analyzed by the data analysis means and real-time accounting data acquisition means to the generation AI means, which extracts important information and automatically generates presentation materials. Here, a generative AI model is used to prepare prompt text. As an example, we use the prompt, "Analyze the user's emotional state and select an appropriate style for the presentation materials." The input data are the analysis results and real-time accounting data, and the output data is the generated presentation content.

[0732] Step 5:

[0733] The emotion recognition means uses facial recognition technology and voice analysis to identify the emotional state of the user when using the system. For example, it analyzes facial expressions and voice tone in real time using a camera and microphone. The input data is the user's facial expression data and voice data, and the output data is data indicating the user's emotional state.

[0734] Step 6:

[0735] The server adjusts the design and layout of the presentation materials created by the generation AI means based on the user's emotional state obtained from the emotion recognition means. For example, if the user is feeling stressed, it selects a simple, easy-to-read design, and if the user is calm, it selects a complex graph containing detailed data. The input data is the user's emotional data and the generated presentation materials, and the output data is the optimal presentation materials after adjustment.

[0736] Step 7:

[0737] The terminal displays the final, adjusted presentation materials to the user. Here, the screen display function and PDF generation function are used to provide the materials visually. The input data are the adjusted presentation materials, and the output data are the presentation materials displayed to the user.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] [Third embodiment]

[0742] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0743] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0744] 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).

[0745] 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.

[0746] 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.

[0747] 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).

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, and a means for creating presentation materials.

[0755] Data collection

[0756] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[0757] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[0758] Data analysis

[0759] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[0760] Real-time acquisition of accounting data

[0761] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[0762] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[0763] Creating presentation materials using generative AI

[0764] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[0765] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0766] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0767] Specific examples

[0768] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0769] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0770] As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated. The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[0771] In this way, companies can obtain high-quality presentation materials quickly and efficiently, significantly reducing the time and effort required to create them and enabling timely decision-making.

[0772] The processing flow will be explained below.

[0773] Data collection and analysis process steps

[0774] Step 1: Configure the Data Source

[0775] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[0776] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[0777] Step 2: Collect data

[0778] The server periodically accesses the data source set by the user and collects the required data.

[0779] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[0780] Step 3: Store the data

[0781] The server stores the collected data in a data warehouse.

[0782] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[0783] Step 4: Analyze the data

[0784] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[0785] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[0786] Processing steps for real-time accounting data capture

[0787] Step 1: Set up your accounting software

[0788] The user sets up collaboration with accounting software (for example, general accounting software).

[0789] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[0790] Step 2: Obtaining accounting data

[0791] The server retrieves real-time financial data from the accounting software via API.

[0792] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[0793] Step 3: Store and update data

[0794] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[0795] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[0796] Processing steps for creating presentation materials using generative AI

[0797] Step 1: Request for materials

[0798] The user inputs the period and target for which the presentation materials are needed.

[0799] How it works: You specify the requirements for the materials on your device and send them to the server.

[0800] Step 2: Data acquisition and analysis

[0801] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[0802] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[0803] Step 3: Extracting and visualizing information

[0804] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[0805] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[0806] Step 4: Optimize your presentation

[0807] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[0808] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[0809] Step 5: View the material

[0810] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[0811] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[0812] Example 1

[0813] 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."

[0814] Modern companies need to collect information from a variety of data sources, analyze it, and use it to make decisions. However, the processes of collecting and analyzing data, acquiring accounting data in real time, and creating presentation materials are complex and require time and resources. Therefore, there is a need for a method to streamline these processes and quickly create high-quality presentation materials.

[0815] 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.

[0816] In this invention, the server includes a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generative AI means, a means for creating presentation materials, a means for a user to use a terminal to input authentication information for a data source and send it to the server, a means for the server to periodically access the data source and store the data in a data retention system, a means for the server to analyze the data acquired from the data retention system using an analysis tool, a means for a user to use a terminal to set up a connection with accounting software and send it to the server, a means for the server to acquire accounting data in real time using the accounting software's API and store it in the data retention system, a means for a user to use a terminal to input requirements for creating presentation materials and send it to the server, and a means for the server to acquire data from the data retention system based on a specified period, analyze it using a generative AI model, and generate presentation materials. This enables companies to efficiently collect and analyze information from multiple data sources, acquire the latest accounting data in real time, and automatically generate high-quality presentation materials quickly.

[0817] The "data collection means" is a function that periodically accesses multiple data sources set by the user and acquires the necessary data.

[0818] "Data analysis means" is a function that analyzes collected data using analytical tools and algorithms to extract important indicators and trends.

[0819] "Means for acquiring accounting data in real time" refers to a function that acquires accounting data in real time using the accounting software's API and stores it in a data retention system.

[0820] "Generative AI means" is a function that uses a generative AI model to analyze collected and analyzed data, extract important information, and generate output such as presentation materials.

[0821] The "presentation material creation means" is a function that generates appropriate graphs, charts, and tables based on the analysis results of the generation AI means, and optimizes the layout and design of presentation materials.

[0822] "Means for entering authentication information for a data source using a terminal and sending it to a server" refers to the process in which a user uses a terminal to enter authentication information and connection settings for accessing a data source and sends them to a server.

[0823] "Means for the server to periodically access the data source and store the data in the data retention system" refers to the process by which the server accesses the data source at scheduled intervals and securely stores the retrieved data in the data retention system.

[0824] "Means by which the server analyzes data obtained from the data retention system using an analysis tool" refers to the process by which the server obtains data from the data retention system and analyzes it using an analysis tool (e.g., Python's Pandas or Scikit-Learn).

[0825] "Means by which a user uses a terminal to set up a connection with accounting software and send it to a server" refers to the process by which a user uses a terminal to enter the API key and connection information for the accounting software and send it to a server.

[0826] "Means by which the server obtains accounting data in real time using the accounting software's API and stores it in the data retention system" refers to the process by which the server obtains data from the accounting software in real time through the API and stores it in the data retention system.

[0827] "Means for a user to use a terminal to input the requirements for creating presentation materials and send them to a server" refers to the process by which a user inputs the period and target required for creating presentation materials through a terminal and sends that information to a server.

[0828] "Means in which the server obtains data from the data retention system based on a specified period, analyzes it using a generative AI model, and generates presentation materials" refers to the process in which the server obtains data for a specified period from the data retention system, analyzes it using a generative AI model, and generates presentation materials based on the results.

[0829] The present invention is a system that combines multiple functions to enable companies to efficiently collect and analyze data, obtain the latest accounting data in real time, and automatically generate high-quality presentation materials based on that data.

[0830] First, the user uses the terminal to configure the company's data sources, such as ERP systems (e.g., SAP, Oracle) and CRM systems (e.g., Salesforce, HubSpot). The user enters the authentication information and connection settings for these data sources on the terminal and sends them to the server. The server then stores the received authentication information and connection settings in its database.

[0831] The server periodically accesses the data source and collects the required data using a data collection script that runs at scheduled intervals, for example using a cron job, and stores the collected data in a data warehouse (e.g., Amazon Redshift, Google BigQuery).

[0832] The server then analyzes the collected data using a data analysis methodology, using tools such as Python's Pandas library and Scikit-Learn, to extract key metrics and trends. The results of this analysis are passed to a generative AI methodology.

[0833] The user also uses the device to set up the connection with the accounting software. Specifically, they enter the accounting software's API key and connection information, and send it to the server. The server retrieves accounting data in real time via the API and stores it in a data warehouse. This process ensures that the company's financial information is always kept up to date.

[0834] Furthermore, the user specifies the period and target for which the presentation materials need to be created through the terminal. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means. The generative AI means uses a generative AI model such as GPT-4, which performs analysis by inputting a prompt statement. An example of a prompt statement is "Create a performance report for Q3 2023."

[0835] The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables. Finally, the presentation material creation means optimizes the layout and design of the presentation to create high-quality presentation materials containing the necessary information. These materials are then displayed to the user via their device.

[0836] For example, if a company were to create a "2023 Q3 performance report," the user would configure the system to collect data from the ERP system and CRM, and also configure integration settings to obtain financial data from accounting software in real time. When the user inputs the requirements for creating the "2023 Q3 performance report" into the system, the server retrieves the data for the relevant period from the data warehouse, and the generation AI means analyzes it. As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated, and the presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via their device.

[0837] This system allows companies to quickly and efficiently obtain high-quality presentation materials, significantly reducing the time and effort required to create them and enabling timely decision-making.

[0838] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0839] Step 1:

[0840] Step Name: Configure Data Source

[0841] The user uses the terminal to enter authentication information for data sources such as the company's ERP system or CRM. Specifically, they enter authentication information for each system (user name, password, API key, etc.) and set up the connection. The entered information is sent to the server via the terminal. The server stores the received authentication information and connection settings in a database.

[0842] Input: ERP system or CRM system credentials, connection settings

[0843] Data processing: converting credentials and settings into the appropriate format

[0844] Output: Store in database

[0845] Specific operation: User enters authentication information on the device → Data is sent from the device to the server → Server receives the data → Data is stored in the database

[0846] Step 2:

[0847] Step Name: Data Collection

[0848] The server periodically runs a data collection script, accessing data sources and collecting the necessary data. The script is scheduled using a cron job. Specifically, it retrieves sales and customer data from ERP and CRM systems. The collected data is then stored in a data warehouse.

[0849] Input: Data source credentials and access settings

[0850] Data processing: Acquiring data from data sources and converting data formats

[0851] Output: Data storage in a data warehouse

[0852] Specific operation: The server periodically executes the script → accesses the data source → collects data → stores it in the data warehouse

[0853] Step 3:

[0854] Step Name: Data Analysis

[0855] The server retrieves the collected data from the data storage system and performs analysis using analytical tools. Statistical analysis and machine learning algorithms are applied using Python's Pandas library and Scikit-Learn. Key indicators and trends are extracted as a result of the analysis and then fed into the generative AI method.

[0856] Input: Data stored in a data warehouse

[0857] Data processing: Data analysis, extraction of indicators and trends

[0858] Output: Analysis results

[0859] Specific operations: The server acquires data → Analyzes the data using an analysis tool → Extracts important indicators and trends → Stores the analysis results

[0860] Step 4:

[0861] Step Name: Real-time accounting data acquisition

[0862] The user enters the API key and connection information for the accounting software using a terminal and sends it to the server, which retrieves accounting data (e.g., sales, expenses, profits, etc.) in real time via the API and stores it in a data warehouse.

[0863] Input: Accounting software API key, connection information

[0864] Data processing: Acquiring accounting data through API

[0865] Output: Data storage in a data warehouse

[0866] Specific operation: User enters API key on device → Data is sent to server → Server retrieves data via API → Stores in data warehouse

[0867] Step 5:

[0868] Step Name: Generate presentation materials

[0869] The user uses the device to specify the period and subject for which the presentation material is to be created. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means (e.g., GPT-4 generative AI model). Graphs, charts, and tables are generated as analysis results. Finally, the presentation material creation means automatically creates high-quality presentation materials based on this data and displays them to the user via the device.

[0870] Input: Presentation material creation period, target information

[0871] Data processing: Data acquisition, analysis using generative AI models, and document creation

[0872] Output: Presentation materials

[0873] Specific operation: User inputs requirements on the device → Server acquires data → Analyzes using the generation AI means → Document creation means generates presentation materials → Display on the device

[0874] (Application example 1)

[0875] 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."

[0876] In order for companies to carry out marketing activities and decision-making quickly and effectively, they need to analyze and visualize data collected from various data sources in real time. However, since collecting and analyzing data and creating presentation materials requires a lot of time and effort, a system is needed to streamline these processes.

[0877] 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.

[0878] In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, a visual data generation means, and an automatic report generation means, which enables a company to analyze data collected from multiple data sources in real time and automatically generate high-quality presentation materials and reports using the generation AI.

[0879] A "data collection tool" is a tool for periodically collecting data from multiple data sources within an enterprise.

[0880] "Data analysis tools" are tools used to analyze collected data and identify important indicators and trends.

[0881] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time in conjunction with accounting software.

[0882] "Generative AI means" refers to artificial intelligence means for automatically generating important information based on analysis results.

[0883] The "presentation material creation means" is a means for optimizing the layout and design of the presentation and creating materials based on the data generated by the generation AI means.

[0884] The "visual data generation means" is a means for generating visual data such as graphs and charts based on the analysis results.

[0885] The "automatic report generation means" is a means for automatically creating a report by combining the generated visual data and analysis results.

[0886] "Data storage" refers to a storage device for safely and efficiently storing collected data.

[0887] A system embodying this invention operates by combining the following means. First, a data collection means periodically collects data from multiple data sources of a company, such as an ERP system or CRM. A user uses a terminal to enter the necessary authentication information and connection settings and transmits them to a server. The server accesses the data sources set by the user and stores the collected data in a data storage.

[0888] The data analysis means then analyzes the collected data. The server uses data analysis tools and algorithms to identify important indicators and trends. The analysis results are used for subsequent data processing by the generative AI means.

[0889] The server uses a real-time accounting data acquisition method to connect with the company's accounting software, acquires accounting data in real time via API, and stores this data in data storage, ensuring that the company's financial information is always kept up to date.

[0890] The user specifies the period and subject for which presentation materials are to be created. This information is sent to the server via the device. The server retrieves data corresponding to the specified period from data storage and analyzes the data using the generation AI means. The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0891] Furthermore, the visual data generation means generates visual data based on the analysis results. The generated data is used to create professional presentation materials and reports. The presentation material creation means combines the generated visual data and analysis results to automatically create presentation materials taking into consideration the optimal layout and design.

[0892] As a specific example, consider the case where a company wants to create an "Entertainment Report based on viewing data and advertising effectiveness data for Q3 2023." The user configures the system to collect viewing data from an ERP system or CRM, and also configures integration to obtain advertising data from accounting software in real time. When the user inputs the requirements for report creation into the system, the server retrieves the data for the relevant period from the data storage, analyzes it, and generates visual data. Finally, a report is automatically created based on the generated data.

[0893] The generative AI model uses an advanced generative AI such as GPT-3, and an example prompt based on the analysis results is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create presentation materials including graphs of viewing trends and advertising effectiveness." This allows for the rapid generation of visually easy-to-understand reports and presentation materials, which can be useful for corporate marketing activities and decision-making.

[0894] The hardware used includes servers, user devices, data storage, etc. The software used includes API clients, data collection scripts, data analysis tools such as Python's Pandas and Scikit-learn, and generative AI tools (such as OpenAI's GPT-3), which automate each processing step and enable efficient data analysis and information generation.

[0895] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0896] Step 1:

[0897] The user enters authentication information and connection settings using a terminal and sends them to the server. This input data includes the necessary authentication information to access data sources such as ERP systems and CRM. Upon receiving this authentication information, the server configures the settings for periodic access.

[0898] Step 2:

[0899] The server periodically accesses the data sources configured by the user and collects the required data. At this stage, the server connects to each data source using an API client, organizes the collected data, and stores it in data storage. The input is data from the data source, and the output is storage in the data storage.

[0900] Step 3:

[0901] The server analyzes the collected data using data analysis tools such as Python's Pandas and Scikit-learn to extract trends and patterns. In this step, the input is the data stored in the data storage, and the output is the indicators and trends that are the analysis results.

[0902] Step 4:

[0903] The server uses a real-time accounting data acquisition method to link with the company's accounting software and acquires accounting data in real time via API. This data is also stored in the data storage. The input is accounting data from the accounting software, and the output is storage in the data storage.

[0904] Step 5:

[0905] The user specifies the period and subject required for creating presentation materials and sends this information to the server via the terminal. The input is the information about the period and subject from the user, and the output is transmission to the server.

[0906] Step 6:

[0907] The server retrieves data corresponding to the specified period from data storage and analyzes the data using generative AI means. Using a generative AI model (e.g., GPT-3), it automatically generates important information based on the analysis results. The input is the data for the specified period, and the output is text data as the analysis results.

[0908] Step 7:

[0909] The server uses the visual data generation means to generate visual data such as appropriate graphs and charts based on the generated text data. The input is the text data as the analysis result, and the output is the visual data.

[0910] Step 8:

[0911] The server combines the visual data generated using the presentation material creation tool with the analysis results, and automatically creates presentation materials taking into consideration the optimal layout and design. The input is the visual data and the analysis results, and the output is the completed presentation materials.

[0912] Step 9:

[0913] The user can check the final generated presentation materials and reports through the terminal and download them as necessary. The input is the completed presentation materials, and the output is the materials provided to the user. As a specific example, the prompt statement used is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create a presentation material that includes graphs of viewing trends and advertising effectiveness."

[0914] 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.

[0915] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[0916] Data collection

[0917] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[0918] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[0919] Data analysis

[0920] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[0921] Real-time acquisition of accounting data

[0922] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[0923] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[0924] Creating presentation materials using generative AI

[0925] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[0926] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[0927] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[0928] Introducing the Emotion Engine

[0929] When a user uses the system, the emotion engine automatically recognizes the user's emotions, using facial recognition technology and voice analysis to identify the user's current emotional state.

[0930] The server receives the data from the emotion engine and selects a data visualization method based on the user's emotional state, thereby generating graphs and charts in a format that best suits the user's emotions.

[0931] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[0932] Specific examples

[0933] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0934] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0935] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[0936] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[0937] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[0938] The processing flow will be explained below.

[0939] Data collection and analysis process steps

[0940] Step 1: Configure the Data Source

[0941] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[0942] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[0943] Step 2: Collect data

[0944] The server periodically accesses the data source set by the user and collects the required data.

[0945] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[0946] Step 3: Store the data

[0947] The server stores the collected data in a data warehouse.

[0948] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[0949] Step 4: Analyze the data

[0950] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[0951] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[0952] Processing steps for real-time accounting data capture

[0953] Step 1: Set up your accounting software

[0954] The user sets up collaboration with accounting software (for example, general accounting software).

[0955] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[0956] Step 2: Obtaining accounting data

[0957] The server retrieves real-time financial data from the accounting software via API.

[0958] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[0959] Step 3: Store and update data

[0960] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[0961] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[0962] Processing steps for creating presentation materials using generative AI

[0963] Step 1: Request for materials

[0964] The user inputs the period and target for which the presentation materials are needed.

[0965] How it works: You specify the requirements for the materials on your device and send them to the server.

[0966] Step 2: Data acquisition and analysis

[0967] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[0968] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[0969] Step 3: Extracting and visualizing information

[0970] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[0971] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[0972] Step 4: Optimize your presentation

[0973] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[0974] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[0975] Step 5: View the material

[0976] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[0977] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[0978] Steps to implement the Emotion Engine

[0979] Step 1: Recognizing user emotions

[0980] When a user uses the system, the emotion engine automatically recognizes the user's emotions.

[0981] How it works: Uses the device's built-in camera and microphone to analyze the user's facial expressions and tone of voice to determine their emotional state.

[0982] Step 2: Processing the emotion data

[0983] The server receives the emotional data from the emotion engine and selects a data visualization means based on the user's emotional state.

[0984] What it does: Analyzes the data received from the emotion engine to identify the user's emotional state (e.g., happy, sad, stressed).

[0985] Step 3: Sentiment-based graph generation

[0986] The generative AI means generates appropriate graphs, charts, and tables based on the user's emotions.

[0987] Action: If users are experiencing stress, choose visualizations that are simple and easy to understand at a glance and customize your presentation.

[0988] Step 4: Adjust your design based on emotion

[0989] The server optimizes the layout and design of presentation materials based on the output of the emotion engine.

[0990] Behavior: If the user is calm, create a presentation with complex graphs and detailed data. Conversely, if the user is anxious, choose a simple, easy-to-understand design.

[0991] Specific examples

[0992] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[0993] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[0994] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[0995] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[0996] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[0997] Example 2

[0998] 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."

[0999] In conventional systems, the processes of data collection, data analysis, accounting data acquisition, and presentation material creation were all performed separately, which required time and effort to integrate them.In addition, there was also the issue of not being able to optimize data display and presentation materials based on the user's emotional state, which resulted in a lack of improvement in the user experience.

[1000] 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. In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, and an emotion engine. This makes it possible to collect data from multiple data sources of a company, acquire accounting data in real time, analyze the data through the generation AI means, and create optimal presentation materials according to the user's emotional state.

[1001] The "data collection means" is a means having a function of acquiring necessary data from multiple data sources and transmitting it to a server.

[1002] "Data analytics tools" are tools and algorithms used to analyze collected data and identify key indicators and trends.

[1003] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time using the API of accounting software and storing it in a data warehouse.

[1004] "Generative AI methods" are methods that extract important information and generate appropriate graphs, charts, and tables based on collected and analyzed data.

[1005] The "presentation material creation means" is a means for automatically creating presentation materials by optimizing the layout and design of the presentation based on the generated data.

[1006] An "emotion engine" is a means including facial recognition technology and voice analysis technology for recognizing a user's emotions.

[1007] A "data warehouse" is a data storage system that centrally stores collected data and allows for efficient subsequent data analysis and use.

[1008] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[1009] Data collection methods

[1010] Users configure the system with multiple corporate data sources (e.g., ERP systems, CRM) by using a terminal to enter the necessary authentication information and connection settings, which are then sent to the server, including API keys, usernames, and passwords.

[1011] The server periodically accesses the data sources configured by the user and sends API requests to collect the required data, which is then stored in the data warehouse.

[1012] Data Analysis Methods

[1013] The server analyzes the data stored in the data warehouse using data analysis tools and algorithms, such as Python's Pandas and Scikit-learn, to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI tools.

[1014] Real-time acquisition of accounting data

[1015] The user sets up the connection with the accounting software by entering the API key and connection information on the device and sending it to the server.

[1016] The server accesses the accounting software's API in real time to retrieve new accounting data, which is then stored in a data warehouse, ensuring that the company's financial information is always up-to-date.

[1017] Generation AI means

[1018] The user specifies the period and target for which presentation materials are to be created, and this information is sent to the server via the terminal.

[1019] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI methods (e.g., GPT model, BERT model), which extract important information based on the analysis results and generate appropriate graphs, charts, and tables.

[1020] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[1021] Emotion Engine

[1022] When users use the system, their emotions are automatically recognized by the emotion engine, which uses facial recognition technology and voice analysis.

[1023] The server receives the data from the emotion engine, identifies the user's emotional state, and selects a data visualization method based on the identified emotional state to generate an optimal graph or chart.

[1024] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[1025] Specific examples

[1026] For example, when a company prepares its "2023 Q3 Performance Report," it follows these steps:

[1027] Users configure the system to collect data from ERP systems and CRMs, and then set up integrations to retrieve financial data from accounting software in real time.

[1028] When a user inputs the requirements for creating a "2023 Q3 performance report" into the system, the server retrieves data for that period from the data warehouse. The generation AI means generates analysis results such as a line graph of sales trends or a pie chart of customer distribution. At this time, the emotion engine recognizes the user's emotions, and if the user is calm, for example, a complex graph containing detailed data will be selected. Conversely, if the user is stressed, a simple visualization means that can be understood at a glance will be selected.

[1029] The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[1030] Prompt Sentence Examples

[1031] Here are some example prompts to input to the generative AI model:

[1032] "Collect data from your ERP system and CRM, and use data from your accounting software for Q3 2023 to generate performance reports optimized based on user sentiment."

[1033] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] The user enters the connection information for the company's data source (e.g., ERP system, CRM) into the device, enters authentication information, and then sends this to the server. The server accesses the data source based on the entered authentication information and establishes a connection using an API key, username, and password. Based on this input, preparations are made to collect the necessary data. The output is a database that stores the authentication information and connection information.

[1036] Step 2:

[1037] The server periodically sends API requests to the data source set by the user to collect the necessary data. The collected data is stored in a data warehouse after filtering out duplicate data and standardizing the data format. The input is the sent API request, and the output is the retrieved data.

[1038] Step 3:

[1039] The user uses a terminal to enter the API key and connection information for the accounting software and sends it to the server. The server uses this information to establish a connection with the accounting software and prepares to obtain accounting data in real time. The input is the API key and connection information, and the output is the status that the connection is ready.

[1040] Step 4:

[1041] The server accesses the accounting software's API in real time to periodically retrieve new accounting data. The retrieved accounting data is stored in a data warehouse. The input is the API request, and the output is the retrieved accounting data.

[1042] Step 5:

[1043] The server periodically checks the data stored in the data warehouse and, if new data is available, invokes data analysis tools such as Python's Pandas and Scikit-learn to identify important indicators and trends. The input is the new data, and the output is the analysis results.

[1044] Step 6:

[1045] The user inputs the requirements for creating presentation materials (e.g., creation period, target) into the terminal and sends them to the server. The server retrieves the necessary data based on the user's requirements from the data warehouse and activates the generation AI means. The input is the requirement information, and the output is data based on the requirements.

[1046] Step 7:

[1047] The server uses generative AI methods (e.g., GPT model, BERT model) to analyze the data and extract key information. Based on this, it generates appropriate graphs, charts, and tables. The input is the data based on the requirements, and the output is the analyzed information and visualization data.

[1048] Step 8:

[1049] The presentation creation tool optimizes the layout and design of the presentation based on the generated information. Finally, the presentation material is created and displayed to the user via a terminal. The input is visualization data, and the output is the completed presentation material.

[1050] Step 9:

[1051] When using the system, the user completes the settings for facial recognition and voice analysis, which allows the emotion engine to recognize the user's emotions. The input is the necessary hardware permission settings, and the output is the completion of emotion recognition preparation.

[1052] Step 10:

[1053] The emotion engine recognizes the user's emotional state and sends the data to the server. The server selects a data visualization method based on the emotional state and generates graphs and charts that best suit the user's emotions. The input is the user's emotional data, and the output is visualization data that corresponds to the emotional state.

[1054] Step 11:

[1055] The generative AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected. The input is visualization data corresponding to the user's emotional state, and the output is presentation materials optimized for that emotion.

[1056] This series of processes not only enables companies to quickly and efficiently create high-quality presentation materials, but also enables them to provide optimal information according to the user's emotional state.

[1057] (Application example 2)

[1058] 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."

[1059] The purpose of this invention is to improve the efficiency of data collection and analysis in production facilities and to improve production planning and reduce stress for operators and managers by generating optimal presentation materials based on human emotions. Specifically, the purpose is to provide a system that monitors and adjusts production efficiency in real time, visualizes data, and generates presentation materials that are tailored to human emotional states.

[1060] 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 means for acquiring accounting data in real time, a generation AI means, a presentation material creation means, an emotion recognition means, a means for collecting operational status and quality data of automated equipment in a facility, and a means for analyzing and optimizing production efficiency in real time. This enables real-time collection and analysis of production data in a facility, and further enables the generation AI and emotion engine to automatically generate optimal presentation materials that take into account the user's emotions.

[1061] "Data collection means" refers to devices or software used to collect operational status and quality data from automated equipment within a facility.

[1062] "Data analytics tools" are algorithms and tools used to analyze collected data and identify key indicators and trends.

[1063] "Means for obtaining accounting data in real time" refers to an API or interface for obtaining financial data in real time from an external accounting system.

[1064] A "generative AI means" is a system that uses artificial intelligence to analyze data and automatically generate the necessary information.

[1065] The "presentation material creation means" is software for automatically creating visually easy-to-understand presentation materials based on the analyzed data.

[1066] An "emotion recognition means" is an engine or algorithm that analyzes emotions from the user's face and voice and identifies their emotional state.

[1067] The "means for analyzing and optimizing production efficiency in real time" is a system that uses data collected within the facility to continuously analyze the efficiency of the production line and generate optimal production plans and maintenance schedules.

[1068] The present invention provides a system for analyzing and optimizing factory production efficiency in real time. This system includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, an emotion recognition means, and a presentation material creation means.

[1069] Data collection methods

[1070] The data collection method is a system that collects operational status and quality data from automated equipment in factories. Specifically, sensors and IoT devices are attached to each piece of automated equipment, which collect data in real time and send it to a server.

[1071] Data Analysis Methods

[1072] The server receives data from the data collection means and analyzes it using the data analysis means. Data analysis uses machine learning algorithms such as Python's Pandas library and Sklearn to identify important indicators and trends. For example, linear regression can be used to analyze trends in production efficiency.

[1073] Real-time acquisition of accounting data

[1074] Additionally, the server connects to external financial data systems via APIs to obtain real-time accounting data, which is then analyzed and used to adjust production plans and manage costs.

[1075] Generation AI means

[1076] Generative AI methods extract important information based on the results of data analysis and automatically generate materials. The generated information is visualized as appropriate graphs and charts. An example of this is to use a generative AI model and set the input prompt as follows:

[1077] "Analyze the user's emotional state and choose the appropriate style for your presentation materials."

[1078] How to create presentation materials

[1079] The presentation material creation means uses the data provided by the generation AI means to create visually easy-to-understand presentation materials. At this time, the design and item layout are adjusted taking into account the user's emotional state. The emotion recognition means analyzes the user's emotions in real time and selects the style of the presentation materials based on the results. For example, if the user is feeling stressed, it will generate materials with a simple, easy-to-read design.

[1080] emotion recognition means

[1081] The emotion recognition engine uses facial recognition technology and voice analysis to identify the user's emotional state. This function optimizes the effectiveness of information transmission while maintaining a comfortable user experience. Specifically, if the user is calm, the system creates materials containing a lot of detailed data, and if the user is stressed, it provides simple, easy-to-understand materials.

[1082] As described above, the present invention realizes a system that analyzes production efficiency in a factory in real time and automatically generates optimal presentation materials according to the user's emotional state, thereby improving the efficiency of production plan adjustments and cost management and reducing the burden on operators and managers.

[1083] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1084] Step 1:

[1085] The terminal collects operational status and quality data from sensors and IoT devices attached to automated equipment in the factory. The data obtained from these devices is sent to a server via the network. The input data is the operational status and quality information of each automated device, and the output data is raw data stored on the server.

[1086] Step 2:

[1087] The server receives the raw data obtained by the data collection means and analyzes it using the data analysis means. Specifically, it organizes the data using Python's Pandas library and applies machine learning algorithms such as Sklearn to identify important indicators and trends. The input data is the collected raw data, and the output data is the indicators and trends obtained through the analysis.

[1088] Step 3:

[1089] The server obtains accounting data in real time from an external financial data system through the accounting software's API. This is done by using an API key or connection information to obtain data in real time and store it directly on the server. The input data is real-time data from the accounting software, and the output data is the latest financial information stored on the server.

[1090] Step 4:

[1091] The server passes the data collected and analyzed by the data analysis means and real-time accounting data acquisition means to the generation AI means, which extracts important information and automatically generates presentation materials. Here, a generative AI model is used to prepare prompt text. As an example, we use the prompt, "Analyze the user's emotional state and select an appropriate style for the presentation materials." The input data are the analysis results and real-time accounting data, and the output data is the generated presentation content.

[1092] Step 5:

[1093] The emotion recognition means uses facial recognition technology and voice analysis to identify the emotional state of the user when using the system. For example, it analyzes facial expressions and voice tone in real time using a camera and microphone. The input data is the user's facial expression data and voice data, and the output data is data indicating the user's emotional state.

[1094] Step 6:

[1095] The server adjusts the design and layout of the presentation materials created by the generation AI means based on the user's emotional state obtained from the emotion recognition means. For example, if the user is feeling stressed, it selects a simple, easy-to-read design, and if the user is calm, it selects a complex graph containing detailed data. The input data is the user's emotional data and the generated presentation materials, and the output data is the optimal presentation materials after adjustment.

[1096] Step 7:

[1097] The terminal displays the final, adjusted presentation materials to the user. Here, the screen display function and PDF generation function are used to provide the materials visually. The input data are the adjusted presentation materials, and the output data are the presentation materials displayed to the user.

[1098] 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.

[1099] 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.

[1100] 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.

[1101] [Fourth embodiment]

[1102] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1103] 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.

[1104] 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).

[1105] 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.

[1106] 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.

[1107] 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).

[1108] 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.

[1109] 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.

[1110] 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.

[1111] 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.

[1112] 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.

[1113] 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.

[1114] 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."

[1115] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, and a means for creating presentation materials.

[1116] Data collection

[1117] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[1118] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[1119] Data analysis

[1120] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[1121] Real-time acquisition of accounting data

[1122] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[1123] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[1124] Creating presentation materials using generative AI

[1125] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[1126] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[1127] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[1128] Specific examples

[1129] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[1130] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[1131] As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated. The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[1132] In this way, companies can obtain high-quality presentation materials quickly and efficiently, significantly reducing the time and effort required to create them and enabling timely decision-making.

[1133] The processing flow will be explained below.

[1134] Data collection and analysis process steps

[1135] Step 1: Configure the Data Source

[1136] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[1137] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[1138] Step 2: Collect data

[1139] The server periodically accesses the data source set by the user and collects the required data.

[1140] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[1141] Step 3: Store the data

[1142] The server stores the collected data in a data warehouse.

[1143] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[1144] Step 4: Analyze the data

[1145] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[1146] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[1147] Processing steps for real-time accounting data capture

[1148] Step 1: Set up your accounting software

[1149] The user sets up collaboration with accounting software (for example, general accounting software).

[1150] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[1151] Step 2: Obtaining accounting data

[1152] The server retrieves real-time financial data from the accounting software via API.

[1153] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[1154] Step 3: Store and update data

[1155] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[1156] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[1157] Processing steps for creating presentation materials using generative AI

[1158] Step 1: Request for materials

[1159] The user inputs the period and target for which the presentation materials are needed.

[1160] How it works: You specify the requirements for the materials on your device and send them to the server.

[1161] Step 2: Data acquisition and analysis

[1162] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[1163] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[1164] Step 3: Extracting and visualizing information

[1165] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[1166] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[1167] Step 4: Optimize your presentation

[1168] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[1169] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[1170] Step 5: View the material

[1171] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[1172] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[1173] Example 1

[1174] 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."

[1175] Modern companies need to collect information from a variety of data sources, analyze it, and use it to make decisions. However, the processes of collecting and analyzing data, acquiring accounting data in real time, and creating presentation materials are complex and require time and resources. Therefore, there is a need for a method to streamline these processes and quickly create high-quality presentation materials.

[1176] 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.

[1177] In this invention, the server includes a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generative AI means, a means for creating presentation materials, a means for a user to use a terminal to input authentication information for a data source and send it to the server, a means for the server to periodically access the data source and store the data in a data retention system, a means for the server to analyze the data acquired from the data retention system using an analysis tool, a means for a user to use a terminal to set up a connection with accounting software and send it to the server, a means for the server to acquire accounting data in real time using the accounting software's API and store it in the data retention system, a means for a user to use a terminal to input requirements for creating presentation materials and send it to the server, and a means for the server to acquire data from the data retention system based on a specified period, analyze it using a generative AI model, and generate presentation materials. This enables companies to efficiently collect and analyze information from multiple data sources, acquire the latest accounting data in real time, and automatically generate high-quality presentation materials quickly.

[1178] The "data collection means" is a function that periodically accesses multiple data sources set by the user and acquires the necessary data.

[1179] "Data analysis means" is a function that analyzes collected data using analytical tools and algorithms to extract important indicators and trends.

[1180] "Means for acquiring accounting data in real time" refers to a function that acquires accounting data in real time using the accounting software's API and stores it in a data retention system.

[1181] "Generative AI means" is a function that uses a generative AI model to analyze collected and analyzed data, extract important information, and generate output such as presentation materials.

[1182] The "presentation material creation means" is a function that generates appropriate graphs, charts, and tables based on the analysis results of the generation AI means, and optimizes the layout and design of presentation materials.

[1183] "Means for entering authentication information for a data source using a terminal and sending it to a server" refers to the process in which a user uses a terminal to enter authentication information and connection settings for accessing a data source and sends them to a server.

[1184] "Means for the server to periodically access the data source and store the data in the data retention system" refers to the process by which the server accesses the data source at scheduled intervals and securely stores the retrieved data in the data retention system.

[1185] "Means by which the server analyzes data obtained from the data retention system using an analysis tool" refers to the process by which the server obtains data from the data retention system and analyzes it using an analysis tool (e.g., Python's Pandas or Scikit-Learn).

[1186] "Means by which a user uses a terminal to set up a connection with accounting software and send it to a server" refers to the process by which a user uses a terminal to enter the API key and connection information for the accounting software and send it to a server.

[1187] "Means by which the server obtains accounting data in real time using the accounting software's API and stores it in the data retention system" refers to the process by which the server obtains data from the accounting software in real time through the API and stores it in the data retention system.

[1188] "Means for a user to use a terminal to input the requirements for creating presentation materials and send them to a server" refers to the process by which a user inputs the period and target required for creating presentation materials through a terminal and sends that information to a server.

[1189] "Means in which the server obtains data from the data retention system based on a specified period, analyzes it using a generative AI model, and generates presentation materials" refers to the process in which the server obtains data for a specified period from the data retention system, analyzes it using a generative AI model, and generates presentation materials based on the results.

[1190] The present invention is a system that combines multiple functions to enable companies to efficiently collect and analyze data, obtain the latest accounting data in real time, and automatically generate high-quality presentation materials based on that data.

[1191] First, the user uses the terminal to configure the company's data sources, such as ERP systems (e.g., SAP, Oracle) and CRM systems (e.g., Salesforce, HubSpot). The user enters the authentication information and connection settings for these data sources on the terminal and sends them to the server. The server then stores the received authentication information and connection settings in its database.

[1192] The server periodically accesses the data source and collects the required data using a data collection script that runs at scheduled intervals, for example using a cron job, and stores the collected data in a data warehouse (e.g., Amazon Redshift, Google BigQuery).

[1193] The server then analyzes the collected data using a data analysis methodology, using tools such as Python's Pandas library and Scikit-Learn, to extract key metrics and trends. The results of this analysis are passed to a generative AI methodology.

[1194] The user also uses the device to set up the connection with the accounting software. Specifically, they enter the accounting software's API key and connection information, and send it to the server. The server retrieves accounting data in real time via the API and stores it in a data warehouse. This process ensures that the company's financial information is always kept up to date.

[1195] Furthermore, the user specifies the period and target for which the presentation materials need to be created through the terminal. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means. The generative AI means uses a generative AI model such as GPT-4, which performs analysis by inputting a prompt statement. An example of a prompt statement is "Create a performance report for Q3 2023."

[1196] The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables. Finally, the presentation material creation means optimizes the layout and design of the presentation to create high-quality presentation materials containing the necessary information. These materials are then displayed to the user via their device.

[1197] For example, if a company were to create a "2023 Q3 performance report," the user would configure the system to collect data from the ERP system and CRM, and also configure integration settings to obtain financial data from accounting software in real time. When the user inputs the requirements for creating the "2023 Q3 performance report" into the system, the server retrieves the data for the relevant period from the data warehouse, and the generation AI means analyzes it. As a result of the analysis, a line graph of sales trends and a pie chart of customer distribution are generated, and the presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via their device.

[1198] This system allows companies to quickly and efficiently obtain high-quality presentation materials, significantly reducing the time and effort required to create them and enabling timely decision-making.

[1199] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1200] Step 1:

[1201] Step Name: Configure Data Source

[1202] The user uses the terminal to enter authentication information for data sources such as the company's ERP system or CRM. Specifically, they enter authentication information for each system (user name, password, API key, etc.) and set up the connection. The entered information is sent to the server via the terminal. The server stores the received authentication information and connection settings in a database.

[1203] Input: ERP system or CRM system credentials, connection settings

[1204] Data processing: converting credentials and settings into the appropriate format

[1205] Output: Store in database

[1206] Specific operation: User enters authentication information on the device → Data is sent from the device to the server → Server receives the data → Data is stored in the database

[1207] Step 2:

[1208] Step Name: Data Collection

[1209] The server periodically runs a data collection script, accessing data sources and collecting the necessary data. The script is scheduled using a cron job. Specifically, it retrieves sales and customer data from ERP and CRM systems. The collected data is then stored in a data warehouse.

[1210] Input: Data source credentials and access settings

[1211] Data processing: Acquiring data from data sources and converting data formats

[1212] Output: Data storage in a data warehouse

[1213] Specific operation: The server periodically executes the script → accesses the data source → collects data → stores it in the data warehouse

[1214] Step 3:

[1215] Step Name: Data Analysis

[1216] The server retrieves the collected data from the data storage system and performs analysis using analytical tools. Statistical analysis and machine learning algorithms are applied using Python's Pandas library and Scikit-Learn. Key indicators and trends are extracted as a result of the analysis and then fed into the generative AI method.

[1217] Input: Data stored in a data warehouse

[1218] Data processing: Data analysis, extraction of indicators and trends

[1219] Output: Analysis results

[1220] Specific operations: The server acquires data → Analyzes the data using an analysis tool → Extracts important indicators and trends → Stores the analysis results

[1221] Step 4:

[1222] Step Name: Real-time accounting data acquisition

[1223] The user enters the API key and connection information for the accounting software using a terminal and sends it to the server, which retrieves accounting data (e.g., sales, expenses, profits, etc.) in real time via the API and stores it in a data warehouse.

[1224] Input: Accounting software API key, connection information

[1225] Data processing: Acquiring accounting data through API

[1226] Output: Data storage in a data warehouse

[1227] Specific operation: User enters API key on device → Data is sent to server → Server retrieves data via API → Stores in data warehouse

[1228] Step 5:

[1229] Step Name: Generate presentation materials

[1230] The user uses the device to specify the period and subject for which the presentation material is to be created. This information is sent to the server. The server retrieves data from the data warehouse based on the specified period and analyzes the data using a generative AI means (e.g., GPT-4 generative AI model). Graphs, charts, and tables are generated as analysis results. Finally, the presentation material creation means automatically creates high-quality presentation materials based on this data and displays them to the user via the device.

[1231] Input: Presentation material creation period, target information

[1232] Data processing: Data acquisition, analysis using generative AI models, and document creation

[1233] Output: Presentation materials

[1234] Specific operation: User inputs requirements on the device → Server acquires data → Analyzes using the generation AI means → Document creation means generates presentation materials → Display on the device

[1235] (Application example 1)

[1236] 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."

[1237] In order for companies to carry out marketing activities and decision-making quickly and effectively, they need to analyze and visualize data collected from various data sources in real time. However, since collecting and analyzing data and creating presentation materials requires a lot of time and effort, a system is needed to streamline these processes.

[1238] 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.

[1239] In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, a visual data generation means, and an automatic report generation means, which enables a company to analyze data collected from multiple data sources in real time and automatically generate high-quality presentation materials and reports using the generation AI.

[1240] A "data collection tool" is a tool for periodically collecting data from multiple data sources within an enterprise.

[1241] "Data analysis tools" are tools used to analyze collected data and identify important indicators and trends.

[1242] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time in conjunction with accounting software.

[1243] "Generative AI means" refers to artificial intelligence means for automatically generating important information based on analysis results.

[1244] The "presentation material creation means" is a means for optimizing the layout and design of the presentation and creating materials based on the data generated by the generation AI means.

[1245] The "visual data generation means" is a means for generating visual data such as graphs and charts based on the analysis results.

[1246] The "automatic report generation means" is a means for automatically creating a report by combining the generated visual data and analysis results.

[1247] "Data storage" refers to a storage device for safely and efficiently storing collected data.

[1248] A system embodying this invention operates by combining the following means. First, a data collection means periodically collects data from multiple data sources of a company, such as an ERP system or CRM. A user uses a terminal to enter the necessary authentication information and connection settings and transmits them to a server. The server accesses the data sources set by the user and stores the collected data in a data storage.

[1249] The data analysis means then analyzes the collected data. The server uses data analysis tools and algorithms to identify important indicators and trends. The analysis results are used for subsequent data processing by the generative AI means.

[1250] The server uses a real-time accounting data acquisition method to connect with the company's accounting software, acquires accounting data in real time via API, and stores this data in data storage, ensuring that the company's financial information is always kept up to date.

[1251] The user specifies the period and subject for which presentation materials are to be created. This information is sent to the server via the device. The server retrieves data corresponding to the specified period from data storage and analyzes the data using the generation AI means. The generation AI means extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[1252] Furthermore, the visual data generation means generates visual data based on the analysis results. The generated data is used to create professional presentation materials and reports. The presentation material creation means combines the generated visual data and analysis results to automatically create presentation materials taking into consideration the optimal layout and design.

[1253] As a specific example, consider the case where a company wants to create an "Entertainment Report based on viewing data and advertising effectiveness data for Q3 2023." The user configures the system to collect viewing data from an ERP system or CRM, and also configures integration to obtain advertising data from accounting software in real time. When the user inputs the requirements for report creation into the system, the server retrieves the data for the relevant period from the data storage, analyzes it, and generates visual data. Finally, a report is automatically created based on the generated data.

[1254] The generative AI model uses an advanced generative AI such as GPT-3, and an example prompt based on the analysis results is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create presentation materials including graphs of viewing trends and advertising effectiveness." This allows for the rapid generation of visually easy-to-understand reports and presentation materials, which can be useful for corporate marketing activities and decision-making.

[1255] The hardware used includes servers, user devices, data storage, etc. The software used includes API clients, data collection scripts, data analysis tools such as Python's Pandas and Scikit-learn, and generative AI tools (such as OpenAI's GPT-3), which automate each processing step and enable efficient data analysis and information generation.

[1256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1257] Step 1:

[1258] The user enters authentication information and connection settings using a terminal and sends them to the server. This input data includes the necessary authentication information to access data sources such as ERP systems and CRM. Upon receiving this authentication information, the server configures the settings for periodic access.

[1259] Step 2:

[1260] The server periodically accesses the data sources configured by the user and collects the required data. At this stage, the server connects to each data source using an API client, organizes the collected data, and stores it in data storage. The input is data from the data source, and the output is storage in the data storage.

[1261] Step 3:

[1262] The server analyzes the collected data using data analysis tools such as Python's Pandas and Scikit-learn to extract trends and patterns. In this step, the input is the data stored in the data storage, and the output is the indicators and trends that are the analysis results.

[1263] Step 4:

[1264] The server uses a real-time accounting data acquisition method to link with the company's accounting software and acquires accounting data in real time via API. This data is also stored in the data storage. The input is accounting data from the accounting software, and the output is storage in the data storage.

[1265] Step 5:

[1266] The user specifies the period and subject required for creating presentation materials and sends this information to the server via the terminal. The input is the information about the period and subject from the user, and the output is transmission to the server.

[1267] Step 6:

[1268] The server retrieves data corresponding to the specified period from data storage and analyzes the data using generative AI means. Using a generative AI model (e.g., GPT-3), it automatically generates important information based on the analysis results. The input is the data for the specified period, and the output is text data as the analysis results.

[1269] Step 7:

[1270] The server uses the visual data generation means to generate visual data such as appropriate graphs and charts based on the generated text data. The input is the text data as the analysis result, and the output is the visual data.

[1271] Step 8:

[1272] The server combines the visual data generated using the presentation material creation tool with the analysis results, and automatically creates presentation materials taking into consideration the optimal layout and design. The input is the visual data and the analysis results, and the output is the completed presentation materials.

[1273] Step 9:

[1274] The user can check the final generated presentation materials and reports through the terminal and download them as necessary. The input is the completed presentation materials, and the output is the materials provided to the user. As a specific example, the prompt statement used is, "Generate an entertainment report based on viewing data and advertising effectiveness data for Q3 2023, and create a presentation material that includes graphs of viewing trends and advertising effectiveness."

[1275] 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.

[1276] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[1277] Data collection

[1278] The user configures the system with multiple company data sources (such as ERP systems and CRM), by entering the necessary authentication information and connection settings using a terminal and sending them to the server.

[1279] The server periodically accesses the data sources set by the user to collect the necessary data, which is then stored in a data warehouse and analyzed by the data analysis means.

[1280] Data analysis

[1281] The server analyzes the collected data using the data analysis means. Data analysis tools and algorithms are used to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI means.

[1282] Real-time acquisition of accounting data

[1283] The user sets up a connection with accounting software (for example, mentioning the software without using a specific name, rather than the name of a general accounting software), enters the accounting software's API key and connection information on the device, and sends it to the server.

[1284] The server retrieves accounting data in real time via API and stores it in a data warehouse, ensuring that a company's financial information is always up-to-date.

[1285] Creating presentation materials using generative AI

[1286] The user specifies the period and target for which the presentation materials are to be created, and this information is sent to the server via the terminal.

[1287] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using the generative AI method, which extracts important information based on the analysis results and generates appropriate graphs, charts, and tables.

[1288] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[1289] Introducing the Emotion Engine

[1290] When a user uses the system, the emotion engine automatically recognizes the user's emotions, using facial recognition technology and voice analysis to identify the user's current emotional state.

[1291] The server receives the data from the emotion engine and selects a data visualization method based on the user's emotional state, thereby generating graphs and charts in a format that best suits the user's emotions.

[1292] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[1293] Specific examples

[1294] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[1295] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[1296] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[1297] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[1298] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[1299] The processing flow will be explained below.

[1300] Data collection and analysis process steps

[1301] Step 1: Configure the Data Source

[1302] The user configures the system with multiple data sources from the company (for example, ERP systems and CRM).

[1303] Operation: Enter the required authentication information and connection settings into the terminal's input form and send it to the server.

[1304] Step 2: Collect data

[1305] The server periodically accesses the data source set by the user and collects the required data.

[1306] How it works: Communicates with each source through APIs and database connections to retrieve, for example, sales data, customer data, etc.

[1307] Step 3: Store the data

[1308] The server stores the collected data in a data warehouse.

[1309] What it does: Creates the tables needed for the data warehouse and organizes and stores the collected data.

[1310] Step 4: Analyze the data

[1311] The server uses data analysis tools to identify key metrics and trends from the collected data and provide insights.

[1312] How it works: Apply machine learning algorithms and statistical analysis to extract key insights, such as sales trends, customer behavior patterns, and cost analysis.

[1313] Processing steps for real-time accounting data capture

[1314] Step 1: Set up your accounting software

[1315] The user sets up collaboration with accounting software (for example, general accounting software).

[1316] How it works: Enter the accounting software's API key and connection information on the device and send it to the server.

[1317] Step 2: Obtaining accounting data

[1318] The server retrieves real-time financial data from the accounting software via API.

[1319] How it works: Calls APIs periodically or based on real-time triggers to retrieve data such as income statements and balance sheets.

[1320] Step 3: Store and update data

[1321] The server stores the acquired accounting data in a data warehouse and updates the latest financial indicators.

[1322] What it does: Integrates collected data into existing tables to keep your financial data up to date.

[1323] Processing steps for creating presentation materials using generative AI

[1324] Step 1: Request for materials

[1325] The user inputs the period and target for which the presentation materials are needed.

[1326] How it works: You specify the requirements for the materials on your device and send them to the server.

[1327] Step 2: Data acquisition and analysis

[1328] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI.

[1329] How it works: Query the required data from the data warehouse and pass it as input to the generative AI, which analyzes the data to identify key metrics and trends.

[1330] Step 3: Extracting and visualizing information

[1331] Generative AI extracts key information and generates appropriate graphs, charts, and tables.

[1332] How it works: Based on the analysis results, it creates, for example, a line graph of sales trends or a pie chart of customer distribution.

[1333] Step 4: Optimize your presentation

[1334] The server optimizes the presentation layout and design based on the generated data and generates the final materials.

[1335] How it works: Auto-generates slides based on user requirements, adjusting layout and design.

[1336] Step 5: View the material

[1337] The terminal displays the generated presentation materials to the user and makes them available for download as needed.

[1338] What it does: Provides the user with a completed presentation in PDF or PowerPoint format and a download link.

[1339] Steps to implement the Emotion Engine

[1340] Step 1: Recognizing user emotions

[1341] When a user uses the system, the emotion engine automatically recognizes the user's emotions.

[1342] How it works: Uses the device's built-in camera and microphone to analyze the user's facial expressions and tone of voice to determine their emotional state.

[1343] Step 2: Processing the emotion data

[1344] The server receives the emotional data from the emotion engine and selects a data visualization means based on the user's emotional state.

[1345] What it does: Analyzes the data received from the emotion engine to identify the user's emotional state (e.g., happy, sad, stressed).

[1346] Step 3: Sentiment-based graph generation

[1347] The generative AI means generates appropriate graphs, charts, and tables based on the user's emotions.

[1348] Action: If users are experiencing stress, choose visualizations that are simple and easy to understand at a glance and customize your presentation.

[1349] Step 4: Adjust your design based on emotion

[1350] The server optimizes the layout and design of presentation materials based on the output of the emotion engine.

[1351] Behavior: If the user is calm, create a presentation with complex graphs and detailed data. Conversely, if the user is anxious, choose a simple, easy-to-understand design.

[1352] Specific examples

[1353] For example, suppose a company is creating a "Performance Report for Q3 2023." The user configures the system to collect data from ERP systems and CRM, and also configures integrations to retrieve financial data from accounting software in real time.

[1354] When a user inputs the requirement to create a "Performance Report for Q3 2023" into the system, the server retrieves the data for that period from the data warehouse, and the generating AI means analyzes them.

[1355] The analysis results in a line graph of sales trends and a pie chart of customer distribution. The emotion engine recognizes the user's emotions and selects a complex graph with detailed data if the user is calm, for example. Conversely, if the user is feeling stressed, a simple visualization that can be understood at a glance is selected.

[1356] The presentation material creation means automatically creates professional presentation materials based on these data and displays them to the user via the terminal.

[1357] In this way, companies can obtain high-quality presentation materials quickly and efficiently. The introduction of the emotion engine generates materials that are optimally suited to the user's emotional state, improving the effectiveness of using the materials.

[1358] Example 2

[1359] 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."

[1360] In conventional systems, the processes of data collection, data analysis, accounting data acquisition, and presentation material creation were all performed separately, which required time and effort to integrate them.In addition, there was also the issue of not being able to optimize data display and presentation materials based on the user's emotional state, which resulted in a lack of improvement in the user experience.

[1361] 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. In this invention, the server includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, a presentation material creation means, and an emotion engine. This makes it possible to collect data from multiple data sources of a company, acquire accounting data in real time, analyze the data through the generation AI means, and create optimal presentation materials according to the user's emotional state.

[1362] The "data collection means" is a means having a function of acquiring necessary data from multiple data sources and transmitting it to a server.

[1363] "Data analytics tools" are tools and algorithms used to analyze collected data and identify key indicators and trends.

[1364] "Means for acquiring accounting data in real time" refers to a means for acquiring accounting data in real time using the API of accounting software and storing it in a data warehouse.

[1365] "Generative AI methods" are methods that extract important information and generate appropriate graphs, charts, and tables based on collected and analyzed data.

[1366] The "presentation material creation means" is a means for automatically creating presentation materials by optimizing the layout and design of the presentation based on the generated data.

[1367] An "emotion engine" is a means including facial recognition technology and voice analysis technology for recognizing a user's emotions.

[1368] A "data warehouse" is a data storage system that centrally stores collected data and allows for efficient subsequent data analysis and use.

[1369] The present invention relates to a system including a data collection means, a data analysis means, a means for acquiring accounting data in real time, a generation AI means, a means for creating presentation materials, and an emotion engine for recognizing user emotions.

[1370] Data collection methods

[1371] Users configure the system with multiple corporate data sources (e.g., ERP systems, CRM) by using a terminal to enter the necessary authentication information and connection settings, which are then sent to the server, including API keys, usernames, and passwords.

[1372] The server periodically accesses the data sources configured by the user and sends API requests to collect the required data, which is then stored in the data warehouse.

[1373] Data Analysis Methods

[1374] The server analyzes the data stored in the data warehouse using data analysis tools and algorithms, such as Python's Pandas and Scikit-learn, to identify important indicators and trends. The results of this analysis are used for subsequent data processing by the generative AI tools.

[1375] Real-time acquisition of accounting data

[1376] The user sets up the connection with the accounting software by entering the API key and connection information on the device and sending it to the server.

[1377] The server accesses the accounting software's API in real time to retrieve new accounting data, which is then stored in a data warehouse, ensuring that the company's financial information is always up-to-date.

[1378] Generation AI means

[1379] The user specifies the period and target for which presentation materials are to be created, and this information is sent to the server via the terminal.

[1380] The server retrieves data corresponding to the specified period from the data warehouse and analyzes the data using generative AI methods (e.g., GPT model, BERT model), which extract important information based on the analysis results and generate appropriate graphs, charts, and tables.

[1381] Finally, based on the data generated by the generation AI means, the presentation material creation means optimizes the layout and design of the presentation and creates the presentation materials.

[1382] Emotion Engine

[1383] When users use the system, their emotions are automatically recognized by the emotion engine, which uses facial recognition technology and voice analysis.

[1384] The server receives the data from the emotion engine, identifies the user's emotional state, and selects a data visualization method based on the identified emotional state to generate an optimal graph or chart.

[1385] Furthermore, the generation AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected.

[1386] Specific examples

[1387] For example, when a company prepares its "2023 Q3 Performance Report," it follows these steps:

[1388] Users configure the system to collect data from ERP systems and CRMs, and then set up integrations to retrieve financial data from accounting software in real time.

[1389] When a user inputs the requirements for creating a "2023 Q3 performance report" into the system, the server retrieves data for that period from the data warehouse. The generation AI means generates analysis results such as a line graph of sales trends or a pie chart of customer distribution. At this time, the emotion engine recognizes the user's emotions, and if the user is calm, for example, a complex graph containing detailed data will be selected. Conversely, if the user is stressed, a simple visualization means that can be understood at a glance will be selected.

[1390] The presentation material creation means automatically creates professional presentation materials based on this data and displays them to the user via the terminal.

[1391] Prompt Sentence Examples

[1392] Here are some example prompts to input to the generative AI model:

[1393] "Collect data from your ERP system and CRM, and use data from your accounting software for Q3 2023 to generate performance reports optimized based on user sentiment."

[1394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1395] Step 1:

[1396] The user enters the connection information for the company's data source (e.g., ERP system, CRM) into the device, enters authentication information, and then sends this to the server. The server accesses the data source based on the entered authentication information and establishes a connection using an API key, username, and password. Based on this input, preparations are made to collect the necessary data. The output is a database that stores the authentication information and connection information.

[1397] Step 2:

[1398] The server periodically sends API requests to the data source set by the user to collect the necessary data. The collected data is stored in a data warehouse after filtering out duplicate data and standardizing the data format. The input is the sent API request, and the output is the retrieved data.

[1399] Step 3:

[1400] The user uses a terminal to enter the API key and connection information for the accounting software and sends it to the server. The server uses this information to establish a connection with the accounting software and prepares to obtain accounting data in real time. The input is the API key and connection information, and the output is the status that the connection is ready.

[1401] Step 4:

[1402] The server accesses the accounting software's API in real time to periodically retrieve new accounting data. The retrieved accounting data is stored in a data warehouse. The input is the API request, and the output is the retrieved accounting data.

[1403] Step 5:

[1404] The server periodically checks the data stored in the data warehouse and, if new data is available, invokes data analysis tools such as Python's Pandas and Scikit-learn to identify important indicators and trends. The input is the new data, and the output is the analysis results.

[1405] Step 6:

[1406] The user inputs the requirements for creating presentation materials (e.g., creation period, target) into the terminal and sends them to the server. The server retrieves the necessary data based on the user's requirements from the data warehouse and activates the generation AI means. The input is the requirement information, and the output is data based on the requirements.

[1407] Step 7:

[1408] The server uses generative AI methods (e.g., GPT model, BERT model) to analyze the data and extract key information. Based on this, it generates appropriate graphs, charts, and tables. The input is the data based on the requirements, and the output is the analyzed information and visualization data.

[1409] Step 8:

[1410] The presentation creation tool optimizes the layout and design of the presentation based on the generated information. Finally, the presentation material is created and displayed to the user via a terminal. The input is visualization data, and the output is the completed presentation material.

[1411] Step 9:

[1412] When using the system, the user completes the settings for facial recognition and voice analysis, which allows the emotion engine to recognize the user's emotions. The input is the necessary hardware permission settings, and the output is the completion of emotion recognition preparation.

[1413] Step 10:

[1414] The emotion engine recognizes the user's emotional state and sends the data to the server. The server selects a data visualization method based on the emotional state and generates graphs and charts that best suit the user's emotions. The input is the user's emotional data, and the output is visualization data that corresponds to the emotional state.

[1415] Step 11:

[1416] The generative AI means and presentation material creation means adjust the layout and design of the presentation materials based on the output of the emotion engine. For example, if the user is feeling stressed, a simpler, more readable design will be selected. The input is visualization data corresponding to the user's emotional state, and the output is presentation materials optimized for that emotion.

[1417] This series of processes not only enables companies to quickly and efficiently create high-quality presentation materials, but also enables them to provide optimal information according to the user's emotional state.

[1418] (Application example 2)

[1419] 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."

[1420] The purpose of this invention is to improve the efficiency of data collection and analysis in production facilities and to improve production planning and reduce stress for operators and managers by generating optimal presentation materials based on human emotions. Specifically, the purpose is to provide a system that monitors and adjusts production efficiency in real time, visualizes data, and generates presentation materials that are tailored to human emotional states.

[1421] 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 means for acquiring accounting data in real time, a generation AI means, a presentation material creation means, an emotion recognition means, a means for collecting operational status and quality data of automated equipment in a facility, and a means for analyzing and optimizing production efficiency in real time. This enables real-time collection and analysis of production data in a facility, and further enables the generation AI and emotion engine to automatically generate optimal presentation materials that take into account the user's emotions.

[1422] "Data collection means" refers to devices or software used to collect operational status and quality data from automated equipment within a facility.

[1423] "Data analytics tools" are algorithms and tools used to analyze collected data and identify key indicators and trends.

[1424] "Means for obtaining accounting data in real time" refers to an API or interface for obtaining financial data in real time from an external accounting system.

[1425] A "generative AI means" is a system that uses artificial intelligence to analyze data and automatically generate the necessary information.

[1426] The "presentation material creation means" is software for automatically creating visually easy-to-understand presentation materials based on the analyzed data.

[1427] An "emotion recognition means" is an engine or algorithm that analyzes emotions from the user's face and voice and identifies their emotional state.

[1428] The "means for analyzing and optimizing production efficiency in real time" is a system that uses data collected within the facility to continuously analyze the efficiency of the production line and generate optimal production plans and maintenance schedules.

[1429] The present invention provides a system for analyzing and optimizing factory production efficiency in real time. This system includes a data collection means, a data analysis means, a real-time accounting data acquisition means, a generation AI means, an emotion recognition means, and a presentation material creation means.

[1430] Data collection methods

[1431] The data collection method is a system that collects operational status and quality data from automated equipment in factories. Specifically, sensors and IoT devices are attached to each piece of automated equipment, which collect data in real time and send it to a server.

[1432] Data Analysis Methods

[1433] The server receives data from the data collection means and analyzes it using the data analysis means. Data analysis uses machine learning algorithms such as Python's Pandas library and Sklearn to identify important indicators and trends. For example, linear regression can be used to analyze trends in production efficiency.

[1434] Real-time acquisition of accounting data

[1435] Additionally, the server connects to external financial data systems via APIs to obtain real-time accounting data, which is then analyzed and used to adjust production plans and manage costs.

[1436] Generation AI means

[1437] Generative AI methods extract important information based on the results of data analysis and automatically generate materials. The generated information is visualized as appropriate graphs and charts. An example of this is to use a generative AI model and set the input prompt as follows:

[1438] "Analyze the user's emotional state and choose the appropriate style for your presentation materials."

[1439] How to create presentation materials

[1440] The presentation material creation means uses the data provided by the generation AI means to create visually easy-to-understand presentation materials. At this time, the design and item layout are adjusted taking into account the user's emotional state. The emotion recognition means analyzes the user's emotions in real time and selects the style of the presentation materials based on the results. For example, if the user is feeling stressed, it will generate materials with a simple, easy-to-read design.

[1441] emotion recognition means

[1442] The emotion recognition engine uses facial recognition technology and voice analysis to identify the user's emotional state. This function optimizes the effectiveness of information transmission while maintaining a comfortable user experience. Specifically, if the user is calm, the system creates materials containing a lot of detailed data, and if the user is stressed, it provides simple, easy-to-understand materials.

[1443] As described above, the present invention realizes a system that analyzes production efficiency in a factory in real time and automatically generates optimal presentation materials according to the user's emotional state, thereby improving the efficiency of production plan adjustments and cost management and reducing the burden on operators and managers.

[1444] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1445] Step 1:

[1446] The terminal collects operational status and quality data from sensors and IoT devices attached to automated equipment in the factory. The data obtained from these devices is sent to a server via the network. The input data is the operational status and quality information of each automated device, and the output data is raw data stored on the server.

[1447] Step 2:

[1448] The server receives the raw data obtained by the data collection means and analyzes it using the data analysis means. Specifically, it organizes the data using Python's Pandas library and applies machine learning algorithms such as Sklearn to identify important indicators and trends. The input data is the collected raw data, and the output data is the indicators and trends obtained through the analysis.

[1449] Step 3:

[1450] The server obtains accounting data in real time from an external financial data system through the accounting software's API. This is done by using an API key or connection information to obtain data in real time and store it directly on the server. The input data is real-time data from the accounting software, and the output data is the latest financial information stored on the server.

[1451] Step 4:

[1452] The server passes the data collected and analyzed by the data analysis means and real-time accounting data acquisition means to the generation AI means, which extracts important information and automatically generates presentation materials. Here, a generative AI model is used to prepare prompt text. As an example, we use the prompt, "Analyze the user's emotional state and select an appropriate style for the presentation materials." The input data are the analysis results and real-time accounting data, and the output data is the generated presentation content.

[1453] Step 5:

[1454] The emotion recognition means uses facial recognition technology and voice analysis to identify the emotional state of the user when using the system. For example, it analyzes facial expressions and voice tone in real time using a camera and microphone. The input data is the user's facial expression data and voice data, and the output data is data indicating the user's emotional state.

[1455] Step 6:

[1456] The server adjusts the design and layout of the presentation materials created by the generation AI means based on the user's emotional state obtained from the emotion recognition means. For example, if the user is feeling stressed, it selects a simple, easy-to-read design, and if the user is calm, it selects a complex graph containing detailed data. The input data is the user's emotional data and the generated presentation materials, and the output data is the optimal presentation materials after adjustment.

[1457] Step 7:

[1458] The terminal displays the final, adjusted presentation materials to the user. Here, the screen display function and PDF generation function are used to provide the materials visually. The input data are the adjusted presentation materials, and the output data are the presentation materials displayed to the user.

[1459] 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.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1464] 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.

[1465] 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).

[1466] 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.

[1467] 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."

[1468] 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.

[1469] 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).

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] 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.

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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.

[1480] The following is further disclosed regarding the above embodiment.

[1481] (Claim 1)

[1482] data collection means;

[1483] Data analysis means;

[1484] A means of obtaining accounting data in real time;

[1485] Generative AI means;

[1486] Presentation material creation means,

[1487] A system including:

[1488] (Claim 2)

[1489] 10. The system of claim 1, further comprising means for collecting data from a plurality of data sources of an enterprise.

[1490] (Claim 3)

[1491] 10. The system of claim 1, further comprising a data warehouse for storing the collected data.

[1492] (Claim 4)

[1493] 10. The system of claim 1, including means for utilizing data analysis tools to identify important metrics and trends.

[1494] (Claim 5)

[1495] 10. The system of claim 1, further comprising means for obtaining accounting data in real time through integration with accounting software.

[1496] (Claim 6)

[1497] 10. The system of claim 1, further comprising means for generating appropriate graphs, charts, and tables from the collected data using generative AI.

[1498] (Claim 7)

[1499] 10. The system of claim 1, further comprising means for optimizing the layout and design of the presentation based on the generated data.

[1500] "Example 1"

[1501] (Claim 1)

[1502] data collection means;

[1503] Data analysis means;

[1504] A means of obtaining accounting data in real time;

[1505] Generative AI means;

[1506] Presentation material creation means,

[1507] a means for a user to enter data source authentication information using a terminal and transmit the information to a server;

[1508] means for the server to periodically access the data source and store the data in a data retention system;

[1509] A means for analyzing the data acquired by the server from the data retention system using an analysis tool;

[1510] A means for a user to use a terminal to set up a link with accounting software and transmit the link to a server;

[1511] A means for the server to acquire accounting data in real time using the API of the accounting software and store it in a data retention system;

[1512] A means for a user to input requirements for creating presentation materials using a terminal and transmit the input to a server;

[1513] A means for the server to acquire data from the data retention system based on a specified period, analyze the data using a generative AI model, and generate presentation materials;

[1514] A system including:

[1515] (Claim 2)

[1516] 10. The system of claim 1, further comprising means for collecting data from a plurality of data sources of an enterprise.

[1517] (Claim 3)

[1518] 10. The system of claim 1, further comprising a data retention system for storing the collected data.

[1519] "Application Example 1"

[1520] (Claim 1)

[1521] data collection means;

[1522] Data analysis means;

[1523] A means of obtaining accounting data in real time;

[1524] Generative AI means;

[1525] Presentation material creation means,

[1526] visual data generation means;

[1527] an automatic report generation means;

[1528] A system including:

[1529] (Claim 2)

[1530] 10. The system of claim 1, further comprising means for collecting data from a plurality of data sources of an enterprise.

[1531] (Claim 3)

[1532] 10. The system of claim 1, further comprising a data storage for storing the collected data.

[1533] "Example 2: Combining Emotion Engines"

[1534] (Claim 1)

[1535] data collection means;

[1536] Data analysis means;

[1537] A means of obtaining accounting data in real time;

[1538] Generative AI means;

[1539] Presentation material creation means,

[1540] A system that includes an emotion engine.

[1541] (Claim 2)

[1542] 10. The system of claim 1, further comprising means for collecting data from a plurality of data sources of an enterprise.

[1543] (Claim 3)

[1544] 10. The system of claim 1, further comprising a data warehouse for storing the collected data.

[1545] "Application example 2 when combining emotion engines"

[1546] (Claim 1)

[1547] data collection means;

[1548] Data analysis means;

[1549] A means of obtaining accounting data in real time;

[1550] Generative AI means;

[1551] Presentation material creation means,

[1552] An emotion recognition means;

[1553] A means of collecting operational status and quality data for automated equipment within the facility;

[1554] A means to analyze and optimize production efficiency in real time;

[1555] A system including:

[1556] (Claim 2)

[1557] 10. The system of claim 1, further comprising means for collecting data from a plurality of data sources of an enterprise.

[1558] (Claim 3)

[1559] 10. The system of claim 1, further comprising a data warehouse for storing the collected data. [Explanation of symbols]

[1560] 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; A means of obtaining accounting data in real time; Generative AI means; Presentation material creation means, A system including:

2. The system of claim 1 , further comprising means for collecting data from a plurality of data sources of an enterprise.

3. The system of claim 1 , further comprising a data warehouse for storing the collected data.

4. 10. The system of claim 1, further comprising means for utilizing data analysis tools to identify important metrics and trends.

5. The system of claim 1, further comprising means for acquiring accounting data in real time in cooperation with accounting software.

6. 10. The system of claim 1, further comprising means for generating appropriate graphs, charts, and tables from the collected data using generative AI.

7. 10. The system of claim 1, further comprising means for optimizing the layout and design of the presentation based on the generated data.

Images