system

Abstract

We provide the system. [Solution] A means of receiving corporate attribute information and generating an optimal data management format using a machine learning algorithm, A means of inputting raw data, automatically generating master data based on the input data, and registering it in a database, A means of extracting information from a database and automatically generating management accounting reports, Means for providing the report, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The purpose is to improve the efficiency of data management and the accuracy of management accounting in enterprises. The present invention aims to automate optimal data management according to the industry type and scale of enterprises, enable rapid generation of accurate management accounting reports based thereon, thereby achieving efficient utilization of management resources and effectively supporting corporate decision-making. Further, it is intended to remove obstacles to sustainable growth, which is considered for enterprises that are lagging in responding to new business models due to insufficient data preparation and limitations of human resources.

Means for Solving the Problems

[0005] This invention provides a means for automatically generating an optimal data management format using a machine learning algorithm based on a company's attribute information. Furthermore, it includes means for automatically generating master data using AI and registering it in a database based on raw data entered by a user. It also proposes a means to streamline corporate decision-making support by automatically generating management accounting reports using AI through information extraction from the database and providing them to the user. In addition, it includes means to improve data accuracy by performing automatic verification and correction of missing data when raw data is entered, and means to add analytical information for decision-making support to the generated reports.

[0006] "Company attribute information" refers to a collection of basic information about a company's activities and organizational structure, including its industry, size, and business model.

[0007] A "machine learning algorithm" is a computational method that learns patterns and rules from data to perform predictions and classifications.

[0008] A "data management format" is a framework that defines the data structure and item settings necessary for effectively managing and processing data.

[0009] "Raw data" refers to unprocessed data provided to the system by the user, and is a collection of information that is subject to analysis and processing.

[0010] "Master data" refers to fundamental and continuously used data within an organization's business processes, serving as the foundation for other data.

[0011] A "database" is a computer system for systematically storing and managing data in a digital format.

[0012] A "management accounting report" is a report containing financial and performance-related information intended for decision-making and business analysis within a company.

[0013] "AI" is an abbreviation for artificial intelligence, which refers to computer systems and programs that mimic human intellectual activity.

[0014] A "user" refers to a subscriber or operator who operates the system and inputs or uses data.

[0015] "Verification" refers to the process of checking whether input data and processing results are accurate and consistent.

[0016] "Missing data" refers to a state where information is incomplete, and some of the necessary data is missing. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

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

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

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

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

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0038] This invention is a system for streamlining data management and management accounting in a company, and its embodiments will be described. This system functions through the cooperation of three entities: a server, a terminal, and a user.

[0039] First, users input company attribute information into the server using their devices. This information includes the company's industry, size, and business model. The server receives this information and uses machine learning algorithms to generate an optimal data management format. This enables customized data management tailored to the specific characteristics of each company.

[0040] Next, the user inputs raw data into the terminal based on the proposed format. This could include transaction data or product information. The server receives the input raw data and verifies the data format. Then, using AI technology, it automatically generates master data and registers it in the database. This process also includes automatic correction of missing data and outliers, ensuring high data accuracy.

[0041] Furthermore, the server periodically extracts information from the database and generates management accounting reports. These reports include basic financial indicators such as sales, expenses, and profit margins, as well as analytical information aimed at supporting decision-making. The generated reports are provided to users via terminals.

[0042] By using this system, companies can centralize data management and significantly improve the accuracy and speed of generating management accounting reports. As a result, they can effectively utilize management resources and make quick business decisions. This entire process strongly supports the sustainable growth of companies and enhances their competitiveness.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses a terminal to input attribute information such as the company's industry, size, and business model. The terminal then sends this information to the server.

[0046] Step 2:

[0047] The server uses the received company attribute information to execute a machine learning algorithm. This generates the optimal data management format and sends it back to the terminal.

[0048] Step 3:

[0049] The user inputs raw data from the terminal according to the proposed data management format. The terminal then transfers the input data to the server.

[0050] Step 4:

[0051] The server verifies the raw data received from the terminal and checks if the data format matches the specified format. If there are any inconsistencies, it requests correction.

[0052] Step 5:

[0053] The server analyzes the verified data using AI technology and automatically generates master data. During this process, it automatically corrects any missing data or outliers.

[0054] Step 6:

[0055] The generated master data is registered in the database by the server. This ensures centralized data management.

[0056] Step 7:

[0057] The server periodically extracts necessary information from the database and generates management accounting reports. These reports include sales revenue, expenses, profit margins, and other metrics.

[0058] Step 8:

[0059] The generated management accounting report is sent from the server to the terminal and presented to the user. The user makes management decisions based on this report.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In organizational data management and management accounting processes, centralizing information is often difficult, leading to inefficient data entry and processing. Furthermore, there is a need for methods to appropriately correct outliers and missing information while maintaining data accuracy, as well as the desire for rapid report generation to support decision-making.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for receiving attribute information using a data processing device and generating an optimized data management format using a machine learning algorithm; means for receiving initial data entered by a user with the processing device, automatically generating main data based on said data, and storing it in a storage device; and means for periodically retrieving information from the storage device and automatically creating a management accounting processing report. This makes it possible to streamline data management within an organization and realize accurate and rapid management accounting processing.

[0065] A "data processing device" is a general term for electronic devices that have the functions of receiving, analyzing, and transmitting information, and is a central device for data management.

[0066] "Attribute information" refers to data that indicates the characteristics of the organization or individual in question, and includes basic information such as industry and size.

[0067] A "machine learning algorithm" is a type of program that analyzes large amounts of data, derives patterns and rules, and automatically provides the optimal solution.

[0068] An "optimized data management format" is a data management method or format that has been adapted to specific needs using machine learning algorithms.

[0069] A "user terminal" is an input and display device used by a user to perform operations, and is used for receiving and transmitting data.

[0070] "Initial data" refers to the original information entered into the system, which forms the basis for subsequent data processing and analysis.

[0071] "Primary data" refers to important information used for organizational operations and decision-making, obtained by processing initial data.

[0072] A "storage device" is hardware used to store data long-term and retrieve it as needed.

[0073] A "management accounting report" is an analytical report of data generated to evaluate the current state and performance of a company's finances.

[0074] This invention is a system that supports efficient data management and rapid management accounting processing within an organization. The system consists of three elements: a server, terminals, and users.

[0075] First, the user uses a terminal to input initial data into the system. This includes attribute information such as the organization's industry and size. For example, the user can input information such as "manufacturing, medium-sized" into the system. The terminal immediately sends this information to the server.

[0076] The server utilizes data processing equipment and machine learning algorithms to generate an optimized data management format based on the received attribute information. During this process, the server uses a generation AI model to analyze the data and dynamically propose a format tailored to the specific characteristics of each company.

[0077] Next, the user inputs initial data, such as actual transaction data and product information, into the terminal based on the format provided by the server. This data is first validated on the terminal before being sent to the server. The server receives the input data, automatically generates main data using AI technology, and registers it in storage. This process also includes a function to automatically correct outliers and missing values ​​in the data, ensuring high data accuracy.

[0078] Furthermore, the server periodically accesses the storage device, extracts necessary information, and automatically generates management accounting reports. These reports include necessary financial indicators and analytical information to support decision-making. The generated reports are provided to users via terminals.

[0079] For example, a system may automatically generate financial reports using monthly sales data. These reports are used in management meetings to facilitate quick decision-making.

[0080] As an example of a prompt, entering "Generate a data management format suitable for a medium-sized manufacturing company" will allow the server to immediately suggest a suitable format.

[0081] This system enables organizations to centralize data management, leading to faster data processing and improved reporting accuracy. As a result, it allows for the effective use of management resources and faster decision-making.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users input organizational attribute information using a terminal. This information includes "industry," "size," and "business model." The terminal sends this input data to the server in real time. The server analyzes the received data and uses machine learning algorithms to generate a data management format optimized for the organization. Specifically, the server processes the received data, extracts features, and presents format candidates.

[0085] Step 2:

[0086] The user inputs raw data, such as transaction data and product information, into the terminal based on the data management format proposed by the server. The terminal performs initial verification, checking the data format and for any deficiencies, before transferring the data to the server. The server further verifies the data, cleans it using machine learning models, and performs data imputation and correction of outliers. Finally, the server stores this cleaned data as the primary data in its storage device. Specifically, the process involves formatting and cleaning the data to ensure consistency.

[0087] Step 3:

[0088] The server periodically extracts primary data from storage and generates management accounting reports. This utilizes a generative AI model, which automatically aggregates financial indicators based on past patterns and new information, providing analysis results. The generated reports include not only basic financial indicators such as sales, expenses, and profit margins, but also analytical information to support future decision-making. Specifically, the server performs aggregation processing, converts the data into a visually easy-to-interpret format, and creates the reports.

[0089] Step 4:

[0090] The server sends the generated management accounting report to the user's terminal in a format that the user can view. The report is displayed directly on the terminal's dashboard, allowing the user to make quick decisions based on the report. Specifically, after the report is generated, the data is appropriately distributed over the network to ensure it reaches the end user quickly.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] Companies process vast amounts of transaction information daily, and there is a need for systems that can manage this information efficiently and accurately, and provide it in a format that aids in decision-making. Furthermore, there is a demand for real-time financial assessment and rapid proposal of improvement measures, which traditional methods have been time-consuming and cumbersome, making accurate judgment difficult.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for receiving corporate characteristic information and generating an optimal information management format using machine learning techniques; means for inputting information and automatically generating foundational information based on the input information and registering it in an information storage device; and means for extracting information from the information storage device and automatically generating performance management reports. This enables efficient management of transaction information and the generation and provision of performance management reports in real time.

[0096] "Company characteristic information" refers to information about a company's attributes, including its industry, size, and business model.

[0097] "Machine learning techniques" are technologies that allow computers to learn patterns from large amounts of data and use them to make predictions and decisions.

[0098] An "information management format" refers to a format or structure for organizing, effectively managing, and utilizing data.

[0099] "Basic information" refers to information that is automatically generated based on raw data and registered in a database.

[0100] An "information storage device" is a system or storage device for accumulating and saving data and retrieving it as needed.

[0101] A "performance management report" is a document that shows the financial situation and management indicators, and is used to analyze the management status of a company.

[0102] "Analytical information" refers to information added to data for the purpose of analyzing it and supporting decision-making.

[0103] A "mobile communication terminal" refers to a highly portable communication device such as a smartphone or tablet.

[0104] "Transaction information" refers to records related to purchases, sales, and service provision conducted by a company.

[0105] "Real-time" refers to situations where processing or displaying information almost simultaneously is required, demanding immediacy.

[0106] This invention is a system for efficiently managing corporate transaction information and generating and providing performance management reports in real time. The server uses machine learning techniques to generate the optimal information management format based on the company's characteristic information. Users input daily transaction information using a mobile communication terminal, and the server automatically generates foundational information based on this and registers it in the information storage device. The information is analyzed in real time, and abnormal values ​​and missing data are automatically corrected as needed.

[0107] The server periodically extracts information from the data storage device, generates performance management reports in real time, and provides them to the user's terminal. These reports include analytical information to support the user's rapid decision-making. Specifically, by using mobile communication devices such as smartphones or smart glasses to photograph and input transaction information, the financial situation and improvement measures are displayed intuitively.

[0108] This system consists of a data analysis backend using Python and a user interface using Flutter®, with TENSORFLOW® used for machine learning. Firebase is also utilized for real-time data management. As a concrete example, when a company executive reads transaction information from multiple suppliers using smart glasses, the system instantly displays the monthly spending situation and cost-reduction improvements.

[0109] An example of a prompt message would be, "Load restaurant transaction data and generate an optimization plan for spending," and the system would automatically perform the analysis. In this way, a company's daily transaction information is processed efficiently, enabling real-time performance analysis and suggestions for improvement.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] Users input characteristic information about a company using a mobile communication terminal. This input includes the company's industry, size, and business model. Once this information is sent to the server, the server utilizes machine learning techniques to generate an optimal information management format. The input is characteristic information, and the output is a customized information management format.

[0113] Step 2:

[0114] Users input daily transaction information using a mobile communication terminal. This input is sent to a server, which automatically generates foundational information based on it. Data validation is performed simultaneously, and any abnormal values ​​or missing data are automatically corrected. The input is transaction information, and the output is validated foundational information.

[0115] Step 3:

[0116] The server periodically extracts foundational information from the data storage device and generates performance management reports in real time. The reports include basic financial indicators such as sales, expenses, and profit margins, and also include analytical information. The input is foundational information, and the output is the performance management report.

[0117] Step 4:

[0118] The server provides the generated performance management report to the user's terminal. Users can view this report via smart glasses or a smartphone, gaining an intuitive understanding of the current state of business and potential improvements. The input is the performance management report, and the output is the displayed information.

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] The present invention aims to provide a more advanced user experience by combining a system that streamlines corporate data management and management accounting with an emotion engine that recognizes user emotions. Embodiments of the present invention are described below.

[0121] This system consists of a server, terminals, users, and an emotion engine. Users input attribute information such as the company's industry, size, and business model through the terminal. The terminal sends this information to the server, which uses machine learning algorithms to generate the optimal data management format based on the received information. At this stage, the emotion engine analyzes the user's emotions and fine-tunes the suggested format according to their emotional state.

[0122] The user inputs raw data into the terminal according to the generated format. At this time, the emotion engine recognizes the user's emotions again and adjusts the input screen interface to reduce the burden of operation and improve input efficiency. The data sent from the terminal to the server is verified by the server, and if there are no problems, master data is automatically generated using AI technology and registered in the database.

[0123] Furthermore, the server extracts information from the database and automatically generates management accounting reports. During this process, the emotion engine dynamically displays information within the report based on the user's emotional state, highlighting data of high importance. The generated reports are presented to the user via their terminal and used in decision-making.

[0124] For example, when the emotion engine determines that a user is stressed, it simplifies the data input interface and visually highlights the most important metrics in reports. This provides data interaction optimized according to the user's emotional state. This system supports companies in strengthening their competitiveness by linking centralized data management, rapid report generation, and improved user experience.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user uses a terminal to enter company attribute information. This information includes industry, size, and business model. The terminal then sends this information to the server.

[0128] Step 2:

[0129] The server executes a machine learning algorithm based on the received attribute information to generate the optimal data management format. The emotion engine simultaneously analyzes the user's emotions and adjusts the suggestions accordingly.

[0130] Step 3:

[0131] The user inputs raw data from the device according to the generated data management format. The emotion engine identifies the user's current emotional state and customizes the device interface to facilitate user input.

[0132] Step 4:

[0133] The terminal sends the raw data it receives to the server. The server verifies the data and automatically corrects any inconsistencies. Correct input data is generated as master data by AI and registered in the database.

[0134] Step 5:

[0135] The server periodically extracts information from the database and generates management accounting reports. The sentiment engine highlights information in the generated reports according to the user's emotions, making important data stand out.

[0136] Step 6:

[0137] The generated report is sent from the server to the terminal. The terminal presents the report to the user, and the sentiment engine continuously monitors the user's response, providing additional support information as needed.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] In modern business management, efficient data management and management accounting are essential. However, traditional systems often fail to adequately consider user experience, leading to increased user burden and decreased operational efficiency. Furthermore, management accounting reports lacked the means to appropriately highlight important information and enable users to make effective decisions. Therefore, a dynamic system that recognizes and reflects the emotional state of users was needed.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for receiving corporate characteristic information and generating an optimal information management structure using computational technology, means for dynamically adjusting the information management structure based on the user's emotions using emotion recognition elements, and means for extracting information from storage devices and automatically generating management information reports. This enables the provision of data interactions optimized according to the user's emotional state and efficient decision support.

[0143] "Company characteristic information" refers to information that indicates the characteristics of a company, such as its industry, size, and business model.

[0144] "Computational technology" refers to the technology of analyzing and processing data using machine learning algorithms and artificial intelligence.

[0145] An "information management structure" is a collection of formats and templates designed to efficiently manage data within a company.

[0146] "Emotion recognition elements" are technologies that analyze the user's emotional state and adjust the system's operation and expression accordingly.

[0147] "Initial data" refers to the raw, unfiltered information that the user inputs into the system.

[0148] "Basic data" refers to pre-processed data automatically generated based on initial data, used for analysis and processing.

[0149] A "storage device" is a part of the hardware and software used to store and manage data within a computer.

[0150] A "management information report" is a document or digital data that compiles information necessary for a company's management and business operations and is used for decision-making.

[0151] Modes for carrying out the invention

[0152] This invention is a system that efficiently manages data and management accounting within a company while simultaneously improving the user experience. Specifically, it consists of a combination of a server, terminal, user, and emotion recognition elements.

[0153] System Configuration and Data Processing

[0154] server

[0155] The server receives characteristic information about a company and uses computational techniques, specifically machine learning algorithms, based on that information to generate an optimal information management structure. This system includes data integrity verification and automatic generation of basic data, and registers the generated data in storage. It also plays a role in extracting necessary information from the database and automatically generating and providing management information reports.

[0156] terminal

[0157] The device provides an interface for receiving initial data entered by the user. It also uses emotion recognition elements to adjust the UI / UX according to the user's emotional state. For example, if the system detects that the user is stressed, it provides an environment where information can be manipulated more intuitively.

[0158] User

[0159] Users input company characteristics and initial data through the system's terminals. The data entered by users is verified by the server and, if appropriate, stored in storage as foundational data. Users can also view management information reports optimized according to their emotional state, which can aid in decision-making.

[0160] Specific example

[0161] For example, if a user wants to analyze their business performance, they can use the system to input company characteristics and update real-time financial data. If the system detects that the user is feeling anxious, it will highlight important data and provide optimal visual feedback to the user.

[0162] Example of a prompt

[0163] Examples of prompt statements include the following:

[0164] "Please describe a system that generates an optimal data entry interface based on the user's emotional state and automates reports that help reduce stress."

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user inputs company characteristics information into a terminal. This information includes industry, size, and business model. This information is sent to a server, and the output from the terminal becomes input to the server. The server then uses this information for analysis.

[0168] Step 2:

[0169] The server uses computational technology to generate an optimal information management structure based on the received company characteristic information. At this stage, machine learning algorithms are applied to classify and organize the information. The resulting management structure is a customized format tailored to the user's business requirements.

[0170] Step 3:

[0171] The server checks the user's emotional state in order to adjust the generated information management structure using emotion recognition elements. User emotion data is collected and analyzed in real time. Based on this analysis, the complexity of the format is reduced, and colors and layouts are adjusted. The adjusted format based on emotion is output.

[0172] Step 4:

[0173] The user enters initial data into the terminal using a pre-formatted system. This data includes sales figures and customer information, which is then sent back to the server. The data entered by the terminal becomes the raw material for further processing by the server.

[0174] Step 5:

[0175] The server verifies the initial data it receives and automatically generates foundational data using computational techniques. Here, data integrity is confirmed, and necessary transformations and corrections are applied by the AI ​​model. Once the foundational data is generated, it is stored in memory and used for subsequent processing.

[0176] Step 6:

[0177] The server automatically generates management information reports based on the underlying data stored in the storage device. A generation AI model analyzes the data, extracts and organizes key metrics for the report. The generated report serves as an information resource for users to use in decision-making.

[0178] Step 7:

[0179] The server sends the generated management information report to the terminal. The terminal optimizes the display method, taking into account the user's emotional state, and provides the information in a way that is easy for the user to understand. Ultimately, the user can view the report and use it to help make business decisions.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] While improving efficiency in data management and accounting processes, there is a need to provide a user-friendly and intuitive interface. However, the lack of interface adjustments that respond to the user's emotional state makes data entry and report interpretation burdensome for users. The objective of this invention is to resolve this situation and improve the user experience.

[0183] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0184] In this invention, the server includes means for receiving organizational attribute information and generating an optimal data management format using machine learning techniques, means for inputting individual data and automatically generating core data based on the input data and registering it in a storage device, and emotion processing engine means for recognizing the user's emotional state and adapting the input interface. This enables flexible interface adjustment according to the user's emotional state and rapid and efficient accounting data management.

[0185] "Organizational attribute information" refers to information that indicates the basic characteristics of a company or organization, such as its industry, size, and business model.

[0186] "Machine learning techniques" is a general term for algorithms that allow computers to learn patterns and rules from data and perform predictions and classifications.

[0187] An "optimal data management format" is a format with an ideal structure that efficiently organizes and makes usable the data being entered.

[0188] "Individual data" refers to a dataset containing detailed information about a specific entity, which is uniquely identifiable.

[0189] "Core data" refers to essential data that forms the core of an organization's operations and processes, and is the foundation for continuous business activities.

[0190] A "storage device" is a system of hardware and software that can store data and retrieve it as needed.

[0191] "User emotional state" refers to information that indicates a user's temporary psychological state or emotions.

[0192] An "emotion processing engine" is a software component that recognizes a user's emotions and generates an appropriate system response based on those emotions.

[0193] "Interface optimization" is the process of optimizing how information is displayed and input according to user needs.

[0194] "Rapid and efficient accounting data management" refers to a method of managing data quickly while ensuring smooth business processes and minimizing errors.

[0195] To implement this invention, it is necessary to build a system that optimizes organizational data management and decision support. This system consists of a server, a terminal, a user, and an emotion processing engine. First, the user uses the terminal to input organizational attribute information. This information is sent to the server, which uses machine learning techniques to generate an optimal data management format. In this process, it is desirable to use TensorFlow, a common platform.

[0196] Next, the user inputs individual data via the device. The device has a built-in emotion processing engine that uses emotion recognition APIs such as Affectiva to analyze the user's emotional state in real time. Based on this information, the device adjusts the input interface to provide a user-friendly environment.

[0197] The entered data is sent to the server and registered as core data in the database. Using cloud services such as Firebase enables scalable data management. Furthermore, the server automatically generates accounting reports based on the data stored in the storage device. In this process, the system considers the user's emotional state and dynamically adjusts how the report is displayed to support decision-making.

[0198] For example, if the emotion processing engine detects that a user is feeling fatigued while reviewing a cost report, the server will highlight the most important data and suggest removing unnecessary information. This can reduce the user's burden.

[0199] An example of a prompt might be the instruction, "Analyze the user's emotions and adjust the input interface accordingly." By inputting such a prompt into the generating AI model, the system can perform actions according to those instructions.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] Users input organizational attribute information using a terminal. This information includes basic characteristics such as industry, size, and business model. The entered attribute information is sent from the terminal to the server. Based on the received attribute information, the server generates an optimal data management format using machine learning techniques. In this process, TensorFlow is used to recognize data patterns and construct the format.

[0203] Step 2:

[0204] The user inputs individual data via a terminal. During this data input, an emotion processing engine built into the terminal activates, using an emotion recognition API such as Affectiva to analyze the user's emotions in real time. The input individual data and emotion information are sent from the terminal to a server. Here, the input interface is adjusted according to the user's stress level using the emotion information.

[0205] Step 3:

[0206] The server receives individual data sent from the terminal and stores it in a database. Cloud services such as Firebase are used here to ensure data scalability and reliability. During this process, data preprocessing is performed to correct outliers and impute missing data.

[0207] Step 4:

[0208] The server automatically generates accounting reports based on individual data stored in the database. During this process, it dynamically adjusts the display method of the generated reports, taking into account the user's emotional state. It supports users in efficiently understanding the information by highlighting important data, for example. For example, for tired users, easily understandable information is placed at the forefront.

[0209] Step 5:

[0210] Users view accounting reports generated on their devices. An interface optimized by an emotion processing engine allows users to easily understand the information and make informed decisions. User feedback is sent from the device to the server and used to further improve the system.

[0211] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0212] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0213] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0217] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0218] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0219] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0220] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0221] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0222] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0223] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0224] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0225] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0226] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0227] This invention is a system for streamlining data management and management accounting in a company, and its embodiments will be described. This system functions through the cooperation of three entities: a server, a terminal, and a user.

[0228] First, users input company attribute information into the server using their devices. This information includes the company's industry, size, and business model. The server receives this information and uses machine learning algorithms to generate an optimal data management format. This enables customized data management tailored to the specific characteristics of each company.

[0229] Next, the user inputs raw data into the terminal based on the proposed format. This could include transaction data or product information. The server receives the input raw data and verifies the data format. Then, using AI technology, it automatically generates master data and registers it in the database. This process also includes automatic correction of missing data and outliers, ensuring high data accuracy.

[0230] Furthermore, the server periodically extracts information from the database and generates management accounting reports. These reports include basic financial indicators such as sales, expenses, and profit margins, as well as analytical information aimed at supporting decision-making. The generated reports are provided to users via terminals.

[0231] By using this system, companies can centralize data management and significantly improve the accuracy and speed of generating management accounting reports. As a result, they can effectively utilize management resources and make quick business decisions. This entire process strongly supports the sustainable growth of companies and enhances their competitiveness.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user uses a terminal to input attribute information such as the company's industry, size, and business model. The terminal then sends this information to the server.

[0235] Step 2:

[0236] The server uses the received company attribute information to execute a machine learning algorithm. This generates the optimal data management format and sends it back to the terminal.

[0237] Step 3:

[0238] The user inputs raw data from the terminal according to the proposed data management format. The terminal then transfers the input data to the server.

[0239] Step 4:

[0240] The server verifies the raw data received from the terminal and checks if the data format matches the specified format. If there are any inconsistencies, it requests correction.

[0241] Step 5:

[0242] The server analyzes the verified data using AI technology and automatically generates master data. During this process, it automatically corrects any missing data or outliers.

[0243] Step 6:

[0244] The generated master data is registered in the database by the server. This ensures centralized data management.

[0245] Step 7:

[0246] The server periodically extracts necessary information from the database and generates management accounting reports. These reports include sales revenue, expenses, profit margins, and other metrics.

[0247] Step 8:

[0248] The generated management accounting report is sent from the server to the terminal and presented to the user. The user makes management decisions based on this report.

[0249] (Example 1)

[0250] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0251] In organizational data management and management accounting processes, centralizing information is often difficult, leading to inefficient data entry and processing. Furthermore, there is a need for methods to appropriately correct outliers and missing information while maintaining data accuracy, as well as the desire for rapid report generation to support decision-making.

[0252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0253] In this invention, the server includes means for receiving attribute information using a data processing device and generating an optimized data management format using a machine learning algorithm; means for receiving initial data entered by a user with the processing device, automatically generating main data based on said data, and storing it in a storage device; and means for periodically retrieving information from the storage device and automatically creating a management accounting processing report. This makes it possible to streamline data management within an organization and realize accurate and rapid management accounting processing.

[0254] A "data processing device" is a general term for electronic devices that have the functions of receiving, analyzing, and transmitting information, and is a central device for data management.

[0255] "Attribute information" refers to data that indicates the characteristics of the organization or individual in question, and includes basic information such as industry and size.

[0256] A "machine learning algorithm" is a type of program that analyzes large amounts of data, derives patterns and rules, and automatically provides the optimal solution.

[0257] An "optimized data management format" is a data management method or format that has been adapted to specific needs using machine learning algorithms.

[0258] A "user terminal" is an input and display device used by a user to perform operations, and is used for receiving and transmitting data.

[0259] "Initial data" refers to the original information entered into the system, which forms the basis for subsequent data processing and analysis.

[0260] "Primary data" refers to important information used for organizational operations and decision-making, obtained by processing initial data.

[0261] A "storage device" is hardware used to store data long-term and retrieve it as needed.

[0262] A "management accounting report" is an analytical report of data generated to evaluate the current state and performance of a company's finances.

[0263] This invention is a system that supports efficient data management and rapid management accounting processing within an organization. The system consists of three elements: a server, terminals, and users.

[0264] First, the user uses a terminal to input initial data into the system. This includes attribute information such as the organization's industry and size. For example, the user can input information such as "manufacturing, medium-sized" into the system. The terminal immediately sends this information to the server.

[0265] The server utilizes data processing equipment and machine learning algorithms to generate an optimized data management format based on the received attribute information. During this process, the server uses a generation AI model to analyze the data and dynamically propose a format tailored to the specific characteristics of each company.

[0266] Next, the user inputs initial data, such as actual transaction data and product information, into the terminal based on the format provided by the server. This data is first validated on the terminal before being sent to the server. The server receives the input data, automatically generates main data using AI technology, and registers it in storage. This process also includes a function to automatically correct outliers and missing values ​​in the data, ensuring high data accuracy.

[0267] Furthermore, the server periodically accesses the storage device, extracts necessary information, and automatically generates management accounting reports. These reports include necessary financial indicators and analytical information to support decision-making. The generated reports are provided to users via terminals.

[0268] For example, a system may automatically generate financial reports using monthly sales data. These reports are used in management meetings to facilitate quick decision-making.

[0269] As an example of a prompt, entering "Generate a data management format suitable for a medium-sized manufacturing company" will allow the server to immediately suggest a suitable format.

[0270] This system enables organizations to centralize data management, leading to faster data processing and improved reporting accuracy. As a result, it allows for the effective use of management resources and faster decision-making.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] Users input organizational attribute information using a terminal. This information includes "industry," "size," and "business model." The terminal sends this input data to the server in real time. The server analyzes the received data and uses machine learning algorithms to generate a data management format optimized for the organization. Specifically, the server processes the received data, extracts features, and presents format candidates.

[0274] Step 2:

[0275] The user inputs raw data, such as transaction data and product information, into the terminal based on the data management format proposed by the server. The terminal performs initial verification, checking the data format and for any deficiencies, before transferring the data to the server. The server further verifies the data, cleans it using machine learning models, and performs data imputation and correction of outliers. Finally, the server stores this cleaned data as the primary data in its storage device. Specifically, the process involves formatting and cleaning the data to ensure consistency.

[0276] Step 3:

[0277] The server periodically extracts primary data from storage and generates management accounting reports. This utilizes a generative AI model, which automatically aggregates financial indicators based on past patterns and new information, providing analysis results. The generated reports include not only basic financial indicators such as sales, expenses, and profit margins, but also analytical information to support future decision-making. Specifically, the server performs aggregation processing, converts the data into a visually easy-to-interpret format, and creates the reports.

[0278] Step 4:

[0279] The server sends the generated management accounting report to the user's terminal in a format that the user can view. The report is displayed directly on the terminal's dashboard, allowing the user to make quick decisions based on the report. Specifically, after the report is generated, the data is appropriately distributed over the network to ensure it reaches the end user quickly.

[0280] (Application Example 1)

[0281] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0282] Companies process a large amount of transaction information every day, but there is a need for a system that can manage this information efficiently and accurately and provide it in a form useful for decision-making. In addition, there is a demand for real-time understanding of the financial situation and the proposal of prompt improvement measures. Conventional methods are time-consuming and labor-intensive, and it is difficult to make highly accurate judgments.

[0283] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following respective means.

[0284] In this invention, the server includes means for receiving the characteristic information of a company, generating an optimal information management format using a machine learning method, inputting information, automatically generating base information based on the input information, and registering it in an information storage device, and means for extracting information from the information storage device and automatically generating a performance management report. As a result, efficient management of transaction information and generation and provision of a performance management report in real time become possible.

[0285] The "characteristic information of a company" is information regarding attributes including the industry type, scale, business model, etc. of the company.

[0286] The "machine learning method" is a technology in which a computer learns patterns from a large amount of data and makes predictions and judgments.

[0287] The "information management format" is a format and structure for organizing data and effectively managing and utilizing it.

[0288] The "base information" is information automatically generated based on raw data and registered in a database.

[0289] The "information storage device" is a system or storage for accumulating and storing data and retrieving it as needed.

[0290] The "performance management report" is a document that shows the financial situation and management indicators and analyzes the business state of a company.

[0291] "Analytical information" refers to information added to data for the purpose of analyzing it and supporting decision-making.

[0292] A "mobile communication terminal" refers to a highly portable communication device such as a smartphone or tablet.

[0293] "Transaction information" refers to records related to purchases, sales, and service provision conducted by a company.

[0294] "Real-time" refers to situations where processing or displaying information almost simultaneously is required, demanding immediacy.

[0295] This invention is a system for efficiently managing corporate transaction information and generating and providing performance management reports in real time. The server uses machine learning techniques to generate the optimal information management format based on the company's characteristic information. Users input daily transaction information using a mobile communication terminal, and the server automatically generates foundational information based on this and registers it in the information storage device. The information is analyzed in real time, and abnormal values ​​and missing data are automatically corrected as needed.

[0296] The server periodically extracts information from the data storage device, generates performance management reports in real time, and provides them to the user's terminal. These reports include analytical information to support the user's rapid decision-making. Specifically, by using mobile communication devices such as smartphones or smart glasses to photograph and input transaction information, the financial situation and improvement measures are displayed intuitively.

[0297] This system consists of a data analysis backend using Python and a user interface using Flutter, with TensorFlow used for machine learning. Firebase is also utilized for real-time data management. As a concrete example, when a company executive reads transaction information from multiple suppliers using smart glasses, the system instantly displays the monthly spending situation and cost-reduction improvements.

[0298] An example of a prompt message would be, "Load restaurant transaction data and generate an optimization plan for spending," and the system would automatically perform the analysis. In this way, a company's daily transaction information is processed efficiently, enabling real-time performance analysis and suggestions for improvement.

[0299] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0300] Step 1:

[0301] Users input characteristic information about a company using a mobile communication terminal. This input includes the company's industry, size, and business model. Once this information is sent to the server, the server utilizes machine learning techniques to generate an optimal information management format. The input is characteristic information, and the output is a customized information management format.

[0302] Step 2:

[0303] Users input daily transaction information using a mobile communication terminal. This input is sent to a server, which automatically generates foundational information based on it. Data validation is performed simultaneously, and any abnormal values ​​or missing data are automatically corrected. The input is transaction information, and the output is validated foundational information.

[0304] Step 3:

[0305] The server periodically extracts foundational information from the data storage device and generates performance management reports in real time. The reports include basic financial indicators such as sales, expenses, and profit margins, and also include analytical information. The input is foundational information, and the output is the performance management report.

[0306] Step 4:

[0307] The server provides the generated performance management report to the user's terminal. The user can view this report through smart glasses or a smartphone and intuitively grasp the current state of the business and improvement measures. The input is the performance management report, and the output is display information.

[0308] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0309] The purpose of the present invention is to provide a higher-level user experience by combining an emotion engine that recognizes the user's emotion with a system for efficient corporate data management and management accounting. Embodiments of the present invention will be described below.

[0310] This system is composed of a server, a terminal, a user, and an emotion engine. The user inputs attribute information such as the industry type, scale, and business model of the company through the terminal. The terminal transmits this information to the server, and the server generates an optimal data management format using a machine learning algorithm based on the received information. At this stage, the emotion engine analyzes the user's emotion and fine-tunes the proposed format according to the emotional state.

[0311] The user inputs raw data into the terminal according to the generated format. At this time, the emotion engine recognizes the user's emotion again and reduces the operation burden and improves the input efficiency by adjusting the interface of the input screen. The data transmitted from the terminal to the server is verified by the server, and if there is no problem, master data is automatically generated using AI technology and registered in the database.

[0312] Furthermore, the server extracts information from the database and automatically generates management accounting reports. During this process, the emotion engine dynamically displays information within the report based on the user's emotional state, highlighting data of high importance. The generated reports are presented to the user via their terminal and used in decision-making.

[0313] For example, when the emotion engine determines that a user is stressed, it simplifies the data input interface and visually highlights the most important metrics in reports. This provides data interaction optimized according to the user's emotional state. This system supports companies in strengthening their competitiveness by linking centralized data management, rapid report generation, and improved user experience.

[0314] The following describes the processing flow.

[0315] Step 1:

[0316] The user uses a terminal to enter company attribute information. This information includes industry, size, and business model. The terminal then sends this information to the server.

[0317] Step 2:

[0318] The server executes a machine learning algorithm based on the received attribute information to generate the optimal data management format. The emotion engine simultaneously analyzes the user's emotions and adjusts the suggestions accordingly.

[0319] Step 3:

[0320] The user inputs raw data from the device according to the generated data management format. The emotion engine identifies the user's current emotional state and customizes the device interface to facilitate user input.

[0321] Step 4:

[0322] The terminal sends the raw data it receives to the server. The server verifies the data and automatically corrects any inconsistencies. Correct input data is generated as master data by AI and registered in the database.

[0323] Step 5:

[0324] The server periodically extracts information from the database and generates management accounting reports. The sentiment engine highlights information in the generated reports according to the user's emotions, making important data stand out.

[0325] Step 6:

[0326] The generated report is sent from the server to the terminal. The terminal presents the report to the user, and the sentiment engine continuously monitors the user's response, providing additional support information as needed.

[0327] (Example 2)

[0328] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0329] In modern business management, efficient data management and management accounting are essential. However, traditional systems often fail to adequately consider user experience, leading to increased user burden and decreased operational efficiency. Furthermore, management accounting reports lacked the means to appropriately highlight important information and enable users to make effective decisions. Therefore, a dynamic system that recognizes and reflects the emotional state of users was needed.

[0330] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0331] In this invention, the server includes means for receiving corporate characteristic information and generating an optimal information management structure using computational technology, means for dynamically adjusting the information management structure based on the user's emotions using emotion recognition elements, and means for extracting information from storage devices and automatically generating management information reports. This enables the provision of data interactions optimized according to the user's emotional state and efficient decision support.

[0332] "Company characteristic information" refers to information that indicates the characteristics of a company, such as its industry, size, and business model.

[0333] "Computational technology" refers to the technology of analyzing and processing data using machine learning algorithms and artificial intelligence.

[0334] An "information management structure" is a collection of formats and templates designed to efficiently manage data within a company.

[0335] "Emotion recognition elements" are technologies that analyze the user's emotional state and adjust the system's operation and expression accordingly.

[0336] "Initial data" refers to the raw, unfiltered information that the user inputs into the system.

[0337] "Basic data" refers to pre-processed data automatically generated based on initial data, used for analysis and processing.

[0338] A "storage device" is a part of the hardware and software used to store and manage data within a computer.

[0339] A "management information report" is a document or digital data that compiles information necessary for a company's management and business operations and is used for decision-making.

[0340] Modes for carrying out the invention

[0341] This invention is a system that efficiently manages data and management accounting within a company while simultaneously improving the user experience. Specifically, it consists of a combination of a server, terminal, user, and emotion recognition elements.

[0342] System Configuration and Data Processing

[0343] server

[0344] The server receives characteristic information about a company and uses computational techniques, specifically machine learning algorithms, based on that information to generate an optimal information management structure. This system includes data integrity verification and automatic generation of basic data, and registers the generated data in storage. It also plays a role in extracting necessary information from the database and automatically generating and providing management information reports.

[0345] terminal

[0346] The device provides an interface for receiving initial data entered by the user. It also uses emotion recognition elements to adjust the UI / UX according to the user's emotional state. For example, if the system detects that the user is stressed, it provides an environment where information can be manipulated more intuitively.

[0347] User

[0348] Users input company characteristics and initial data through the system's terminals. The data entered by users is verified by the server and, if appropriate, stored in storage as foundational data. Users can also view management information reports optimized according to their emotional state, which can aid in decision-making.

[0349] Specific example

[0350] For example, if a user wants to analyze their business performance, they can use the system to input company characteristics and update real-time financial data. If the system detects that the user is feeling anxious, it will highlight important data and provide optimal visual feedback to the user.

[0351] Example of a prompt

[0352] Examples of prompt statements include the following:

[0353] "Please describe a system that generates an optimal data entry interface based on the user's emotional state and automates reports that help reduce stress."

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user inputs company characteristics information into a terminal. This information includes industry, size, and business model. This information is sent to a server, and the output from the terminal becomes input to the server. The server then uses this information for analysis.

[0357] Step 2:

[0358] The server uses computational technology to generate an optimal information management structure based on the received company characteristic information. At this stage, machine learning algorithms are applied to classify and organize the information. The resulting management structure is a customized format tailored to the user's business requirements.

[0359] Step 3:

[0360] The server checks the user's emotional state in order to adjust the generated information management structure using emotion recognition elements. User emotion data is collected and analyzed in real time. Based on this analysis, the complexity of the format is reduced, and colors and layouts are adjusted. The adjusted format based on emotion is output.

[0361] Step 4:

[0362] The user enters initial data into the terminal using a pre-formatted system. This data includes sales figures and customer information, which is then sent back to the server. The data entered by the terminal becomes the raw material for further processing by the server.

[0363] Step 5:

[0364] The server verifies the initial data it receives and automatically generates foundational data using computational techniques. Here, data integrity is confirmed, and necessary transformations and corrections are applied by the AI ​​model. Once the foundational data is generated, it is stored in memory and used for subsequent processing.

[0365] Step 6:

[0366] The server automatically generates management information reports based on the underlying data stored in the storage device. A generation AI model analyzes the data, extracts and organizes key metrics for the report. The generated report serves as an information resource for users to use in decision-making.

[0367] Step 7:

[0368] The server sends the generated management information report to the terminal. The terminal optimizes the display method, taking into account the user's emotional state, and provides the information in a way that is easy for the user to understand. Ultimately, the user can view the report and use it to help make business decisions.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] While improving efficiency in data management and accounting processes, there is a need to provide a user-friendly and intuitive interface. However, the lack of interface adjustments that respond to the user's emotional state makes data entry and report interpretation burdensome for users. The objective of this invention is to resolve this situation and improve the user experience.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes means for receiving organizational attribute information and generating an optimal data management format using machine learning techniques, means for inputting individual data and automatically generating core data based on the input data and registering it in a storage device, and emotion processing engine means for recognizing the user's emotional state and adapting the input interface. This enables flexible interface adjustment according to the user's emotional state and rapid and efficient accounting data management.

[0374] "Organizational attribute information" refers to information that indicates the basic characteristics of a company or organization, such as its industry, size, and business model.

[0375] "Machine learning techniques" is a general term for algorithms that allow computers to learn patterns and rules from data and perform predictions and classifications.

[0376] An "optimal data management format" is a format with an ideal structure that efficiently organizes and makes usable the data being entered.

[0377] "Individual data" refers to a dataset containing detailed information about a specific entity, which is uniquely identifiable.

[0378] "Core data" refers to essential data that forms the core of an organization's operations and processes, and is the foundation for continuous business activities.

[0379] A "storage device" is a system of hardware and software that can store data and retrieve it as needed.

[0380] "User emotional state" refers to information that indicates a user's temporary psychological state or emotions.

[0381] An "emotion processing engine" is a software component that recognizes a user's emotions and generates an appropriate system response based on those emotions.

[0382] "Interface optimization" is the process of optimizing how information is displayed and input according to user needs.

[0383] "Rapid and efficient accounting data management" refers to a method of managing data quickly while ensuring smooth business processes and minimizing errors.

[0384] To implement this invention, it is necessary to build a system that optimizes organizational data management and decision support. This system consists of a server, a terminal, a user, and an emotion processing engine. First, the user uses the terminal to input organizational attribute information. This information is sent to the server, which uses machine learning techniques to generate an optimal data management format. In this process, it is desirable to use TensorFlow, a common platform.

[0385] Next, the user inputs individual data via the device. The device has a built-in emotion processing engine that uses emotion recognition APIs such as Affectiva to analyze the user's emotional state in real time. Based on this information, the device adjusts the input interface to provide a user-friendly environment.

[0386] The entered data is sent to the server and registered as core data in the database. Using cloud services such as Firebase enables scalable data management. Furthermore, the server automatically generates accounting reports based on the data stored in the storage device. In this process, the system considers the user's emotional state and dynamically adjusts how the report is displayed to support decision-making.

[0387] For example, if the emotion processing engine detects that a user is feeling fatigued while reviewing a cost report, the server will highlight the most important data and suggest removing unnecessary information. This can reduce the user's burden.

[0388] An example of a prompt might be the instruction, "Analyze the user's emotions and adjust the input interface accordingly." By inputting such a prompt into the generating AI model, the system can perform actions according to those instructions.

[0389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0390] Step 1:

[0391] Users input organizational attribute information using a terminal. This information includes basic characteristics such as industry, size, and business model. The entered attribute information is sent from the terminal to the server. Based on the received attribute information, the server generates an optimal data management format using machine learning techniques. In this process, TensorFlow is used to recognize data patterns and construct the format.

[0392] Step 2:

[0393] The user inputs individual data via a terminal. During this data input, an emotion processing engine built into the terminal activates, using an emotion recognition API such as Affectiva to analyze the user's emotions in real time. The input individual data and emotion information are sent from the terminal to a server. Here, the input interface is adjusted according to the user's stress level using the emotion information.

[0394] Step 3:

[0395] The server receives individual data sent from the terminal and stores it in a database. Cloud services such as Firebase are used here to ensure data scalability and reliability. During this process, data preprocessing is performed to correct outliers and impute missing data.

[0396] Step 4:

[0397] The server automatically generates accounting reports based on individual data stored in the database. During this process, it dynamically adjusts the display method of the generated reports, taking into account the user's emotional state. It supports users in efficiently understanding the information by highlighting important data, for example. For example, for tired users, easily understandable information is placed at the forefront.

[0398] Step 5:

[0399] Users view accounting reports generated on their devices. An interface optimized by an emotion processing engine allows users to easily understand the information and make informed decisions. User feedback is sent from the device to the server and used to further improve the system.

[0400] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0401] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0402] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0406] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0407] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0408] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0409] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0410] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0411] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0412] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0413] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0414] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0415] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0416] This invention is a system for streamlining data management and management accounting in a company, and its embodiments will be described. This system functions through the cooperation of three entities: a server, a terminal, and a user.

[0417] First, users input company attribute information into the server using their devices. This information includes the company's industry, size, and business model. The server receives this information and uses machine learning algorithms to generate an optimal data management format. This enables customized data management tailored to the specific characteristics of each company.

[0418] Next, the user inputs raw data into the terminal based on the proposed format. This could include transaction data or product information. The server receives the input raw data and verifies the data format. Then, using AI technology, it automatically generates master data and registers it in the database. This process also includes automatic correction of missing data and outliers, ensuring high data accuracy.

[0419] Furthermore, the server periodically extracts information from the database and generates management accounting reports. These reports include basic financial indicators such as sales, expenses, and profit margins, as well as analytical information aimed at supporting decision-making. The generated reports are provided to users via terminals.

[0420] By using this system, companies can centralize data management and significantly improve the accuracy and speed of generating management accounting reports. As a result, they can effectively utilize management resources and make quick business decisions. This entire process strongly supports the sustainable growth of companies and enhances their competitiveness.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The user uses a terminal to input attribute information such as the company's industry, size, and business model. The terminal then sends this information to the server.

[0424] Step 2:

[0425] The server uses the received company attribute information to execute a machine learning algorithm. This generates the optimal data management format and sends it back to the terminal.

[0426] Step 3:

[0427] The user inputs raw data from the terminal according to the proposed data management format. The terminal then transfers the input data to the server.

[0428] Step 4:

[0429] The server verifies the raw data received from the terminal and checks if the data format matches the specified format. If there are any inconsistencies, it requests correction.

[0430] Step 5:

[0431] The server analyzes the verified data using AI technology and automatically generates master data. During this process, it automatically corrects any missing data or outliers.

[0432] Step 6:

[0433] The generated master data is registered in the database by the server. This ensures centralized data management.

[0434] Step 7:

[0435] The server periodically extracts necessary information from the database and generates management accounting reports. These reports include sales revenue, expenses, profit margins, and other metrics.

[0436] Step 8:

[0437] The generated management accounting report is sent from the server to the terminal and presented to the user. The user makes management decisions based on this report.

[0438] (Example 1)

[0439] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0440] In organizational data management and management accounting processes, centralizing information is often difficult, leading to inefficient data entry and processing. Furthermore, there is a need for methods to appropriately correct outliers and missing information while maintaining data accuracy, as well as the desire for rapid report generation to support decision-making.

[0441] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0442] In this invention, the server includes means for receiving attribute information using a data processing device and generating an optimized data management format using a machine learning algorithm; means for receiving initial data entered by a user with the processing device, automatically generating main data based on said data, and storing it in a storage device; and means for periodically retrieving information from the storage device and automatically creating a management accounting processing report. This makes it possible to streamline data management within an organization and realize accurate and rapid management accounting processing.

[0443] A "data processing device" is a general term for electronic devices that have the functions of receiving, analyzing, and transmitting information, and is a central device for data management.

[0444] "Attribute information" refers to data that indicates the characteristics of the organization or individual in question, and includes basic information such as industry and size.

[0445] A "machine learning algorithm" is a type of program that analyzes large amounts of data, derives patterns and rules, and automatically provides the optimal solution.

[0446] An "optimized data management format" is a data management method or format that has been adapted to specific needs using machine learning algorithms.

[0447] A "user terminal" is an input and display device used by a user to perform operations, and is used for receiving and transmitting data.

[0448] "Initial data" refers to the original information entered into the system, which forms the basis for subsequent data processing and analysis.

[0449] "Primary data" refers to important information used for organizational operations and decision-making, obtained by processing initial data.

[0450] A "storage device" is hardware used to store data long-term and retrieve it as needed.

[0451] A "management accounting report" is an analytical report of data generated to evaluate the current state and performance of a company's finances.

[0452] This invention is a system that supports efficient data management and rapid management accounting processing within an organization. The system consists of three elements: a server, terminals, and users.

[0453] First, the user uses a terminal to input initial data into the system. This includes attribute information such as the organization's industry and size. For example, the user can input information such as "manufacturing, medium-sized" into the system. The terminal immediately sends this information to the server.

[0454] The server utilizes data processing equipment and machine learning algorithms to generate an optimized data management format based on the received attribute information. During this process, the server uses a generation AI model to analyze the data and dynamically propose a format tailored to the specific characteristics of each company.

[0455] Next, the user inputs initial data, such as actual transaction data and product information, into the terminal based on the format provided by the server. This data is first validated on the terminal before being sent to the server. The server receives the input data, automatically generates main data using AI technology, and registers it in storage. This process also includes a function to automatically correct outliers and missing values ​​in the data, ensuring high data accuracy.

[0456] Furthermore, the server periodically accesses the storage device, extracts necessary information, and automatically generates management accounting reports. These reports include necessary financial indicators and analytical information to support decision-making. The generated reports are provided to users via terminals.

[0457] For example, a system may automatically generate financial reports using monthly sales data. These reports are used in management meetings to facilitate quick decision-making.

[0458] As an example of a prompt, entering "Generate a data management format suitable for a medium-sized manufacturing company" will allow the server to immediately suggest a suitable format.

[0459] This system enables organizations to centralize data management, leading to faster data processing and improved reporting accuracy. As a result, it allows for the effective use of management resources and faster decision-making.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1:

[0462] Users input organizational attribute information using a terminal. This information includes "industry," "size," and "business model." The terminal sends this input data to the server in real time. The server analyzes the received data and uses machine learning algorithms to generate a data management format optimized for the organization. Specifically, the server processes the received data, extracts features, and presents format candidates.

[0463] Step 2:

[0464] The user inputs raw data, such as transaction data and product information, into the terminal based on the data management format proposed by the server. The terminal performs initial verification, checking the data format and for any deficiencies, before transferring the data to the server. The server further verifies the data, cleans it using machine learning models, and performs data imputation and correction of outliers. Finally, the server stores this cleaned data as the primary data in its storage device. Specifically, the process involves formatting and cleaning the data to ensure consistency.

[0465] Step 3:

[0466] The server periodically extracts primary data from storage and generates management accounting reports. This utilizes a generative AI model, which automatically aggregates financial indicators based on past patterns and new information, providing analysis results. The generated reports include not only basic financial indicators such as sales, expenses, and profit margins, but also analytical information to support future decision-making. Specifically, the server performs aggregation processing, converts the data into a visually easy-to-interpret format, and creates the reports.

[0467] Step 4:

[0468] The server sends the generated management accounting report to the user's terminal in a format that the user can view. The report is displayed directly on the terminal's dashboard, allowing the user to make quick decisions based on the report. Specifically, after the report is generated, the data is appropriately distributed over the network to ensure it reaches the end user quickly.

[0469] (Application Example 1)

[0470] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0471] Companies process vast amounts of transaction information daily, and there is a need for systems that can manage this information efficiently and accurately, and provide it in a format that aids in decision-making. Furthermore, there is a demand for real-time financial assessment and rapid proposal of improvement measures, which traditional methods have been time-consuming and cumbersome, making accurate judgment difficult.

[0472] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0473] In this invention, the server includes means for receiving corporate characteristic information and generating an optimal information management format using machine learning techniques; means for inputting information and automatically generating foundational information based on the input information and registering it in an information storage device; and means for extracting information from the information storage device and automatically generating performance management reports. This enables efficient management of transaction information and the generation and provision of performance management reports in real time.

[0474] "Company characteristic information" refers to information about a company's attributes, including its industry, size, and business model.

[0475] "Machine learning techniques" are technologies that allow computers to learn patterns from large amounts of data and use them to make predictions and decisions.

[0476] An "information management format" refers to a format or structure for organizing, effectively managing, and utilizing data.

[0477] "Basic information" refers to information that is automatically generated based on raw data and registered in a database.

[0478] An "information storage device" is a system or storage device for accumulating and saving data and retrieving it as needed.

[0479] A "performance management report" is a document that shows the financial situation and management indicators, and is used to analyze the management status of a company.

[0480] "Analytical information" refers to information added to data for the purpose of analyzing it and supporting decision-making.

[0481] A "mobile communication terminal" refers to a highly portable communication device such as a smartphone or tablet.

[0482] "Transaction information" refers to records related to purchases, sales, and service provision conducted by a company.

[0483] "Real-time" refers to situations where processing or displaying information almost simultaneously is required, demanding immediacy.

[0484] This invention is a system for efficiently managing corporate transaction information and generating and providing performance management reports in real time. The server uses machine learning techniques to generate the optimal information management format based on the company's characteristic information. Users input daily transaction information using a mobile communication terminal, and the server automatically generates foundational information based on this and registers it in the information storage device. The information is analyzed in real time, and abnormal values ​​and missing data are automatically corrected as needed.

[0485] The server periodically extracts information from the data storage device, generates performance management reports in real time, and provides them to the user's terminal. These reports include analytical information to support the user's rapid decision-making. Specifically, by using mobile communication devices such as smartphones or smart glasses to photograph and input transaction information, the financial situation and improvement measures are displayed intuitively.

[0486] This system consists of a data analysis backend using Python and a user interface using Flutter, with TensorFlow used for machine learning. Firebase is also utilized for real-time data management. As a concrete example, when a company executive reads transaction information from multiple suppliers using smart glasses, the system instantly displays the monthly spending situation and cost-reduction improvements.

[0487] An example of a prompt message would be, "Load restaurant transaction data and generate an optimization plan for spending," and the system would automatically perform the analysis. In this way, a company's daily transaction information is processed efficiently, enabling real-time performance analysis and suggestions for improvement.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] Users input characteristic information about a company using a mobile communication terminal. This input includes the company's industry, size, and business model. Once this information is sent to the server, the server utilizes machine learning techniques to generate an optimal information management format. The input is characteristic information, and the output is a customized information management format.

[0491] Step 2:

[0492] Users input daily transaction information using a mobile communication terminal. This input is sent to a server, which automatically generates foundational information based on it. Data validation is performed simultaneously, and any abnormal values ​​or missing data are automatically corrected. The input is transaction information, and the output is validated foundational information.

[0493] Step 3:

[0494] The server periodically extracts foundational information from the data storage device and generates performance management reports in real time. The reports include basic financial indicators such as sales, expenses, and profit margins, and also include analytical information. The input is foundational information, and the output is the performance management report.

[0495] Step 4:

[0496] The server provides the generated performance management report to the user's terminal. Users can view this report via smart glasses or a smartphone, gaining an intuitive understanding of the current state of business and potential improvements. The input is the performance management report, and the output is the displayed information.

[0497] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0498] The present invention aims to provide a more advanced user experience by combining a system that streamlines corporate data management and management accounting with an emotion engine that recognizes user emotions. Embodiments of the present invention are described below.

[0499] This system consists of a server, terminals, users, and an emotion engine. Users input attribute information such as the company's industry, size, and business model through the terminal. The terminal sends this information to the server, which uses machine learning algorithms to generate the optimal data management format based on the received information. At this stage, the emotion engine analyzes the user's emotions and fine-tunes the suggested format according to their emotional state.

[0500] The user inputs raw data into the terminal according to the generated format. At this time, the emotion engine recognizes the user's emotions again and adjusts the input screen interface to reduce the burden of operation and improve input efficiency. The data sent from the terminal to the server is verified by the server, and if there are no problems, master data is automatically generated using AI technology and registered in the database.

[0501] Furthermore, the server extracts information from the database and automatically generates management accounting reports. During this process, the emotion engine dynamically displays information within the report based on the user's emotional state, highlighting data of high importance. The generated reports are presented to the user via their terminal and used in decision-making.

[0502] For example, when the emotion engine determines that a user is stressed, it simplifies the data input interface and visually highlights the most important metrics in reports. This provides data interaction optimized according to the user's emotional state. This system supports companies in strengthening their competitiveness by linking centralized data management, rapid report generation, and improved user experience.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] The user uses a terminal to enter company attribute information. This information includes industry, size, and business model. The terminal then sends this information to the server.

[0506] Step 2:

[0507] The server executes a machine learning algorithm based on the received attribute information to generate the optimal data management format. The emotion engine simultaneously analyzes the user's emotions and adjusts the suggestions accordingly.

[0508] Step 3:

[0509] The user inputs raw data from the device according to the generated data management format. The emotion engine identifies the user's current emotional state and customizes the device interface to facilitate user input.

[0510] Step 4:

[0511] The terminal sends the raw data it receives to the server. The server verifies the data and automatically corrects any inconsistencies. Correct input data is generated as master data by AI and registered in the database.

[0512] Step 5:

[0513] The server periodically extracts information from the database and generates management accounting reports. The sentiment engine highlights information in the generated reports according to the user's emotions, making important data stand out.

[0514] Step 6:

[0515] The generated report is sent from the server to the terminal. The terminal presents the report to the user, and the sentiment engine continuously monitors the user's response, providing additional support information as needed.

[0516] (Example 2)

[0517] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] In modern business management, efficient data management and management accounting are essential. However, traditional systems often fail to adequately consider user experience, leading to increased user burden and decreased operational efficiency. Furthermore, management accounting reports lacked the means to appropriately highlight important information and enable users to make effective decisions. Therefore, a dynamic system that recognizes and reflects the emotional state of users was needed.

[0519] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0520] In this invention, the server includes means for receiving corporate characteristic information and generating an optimal information management structure using computational technology, means for dynamically adjusting the information management structure based on the user's emotions using emotion recognition elements, and means for extracting information from storage devices and automatically generating management information reports. This enables the provision of data interactions optimized according to the user's emotional state and efficient decision support.

[0521] "Company characteristic information" refers to information that indicates the characteristics of a company, such as its industry, size, and business model.

[0522] "Computational technology" refers to the technology of analyzing and processing data using machine learning algorithms and artificial intelligence.

[0523] An "information management structure" is a collection of formats and templates designed to efficiently manage data within a company.

[0524] "Emotion recognition elements" are technologies that analyze the user's emotional state and adjust the system's operation and expression accordingly.

[0525] "Initial data" refers to the raw, unfiltered information that the user inputs into the system.

[0526] "Basic data" refers to pre-processed data automatically generated based on initial data, used for analysis and processing.

[0527] A "storage device" is a part of the hardware and software used to store and manage data within a computer.

[0528] A "management information report" is a document or digital data that compiles information necessary for a company's management and business operations and is used for decision-making.

[0529] Modes for carrying out the invention

[0530] This invention is a system that efficiently manages data and management accounting within a company while simultaneously improving the user experience. Specifically, it consists of a combination of a server, terminal, user, and emotion recognition elements.

[0531] System Configuration and Data Processing

[0532] server

[0533] The server receives characteristic information about a company and uses computational techniques, specifically machine learning algorithms, based on that information to generate an optimal information management structure. This system includes data integrity verification and automatic generation of basic data, and registers the generated data in storage. It also plays a role in extracting necessary information from the database and automatically generating and providing management information reports.

[0534] terminal

[0535] The device provides an interface for receiving initial data entered by the user. It also uses emotion recognition elements to adjust the UI / UX according to the user's emotional state. For example, if the system detects that the user is stressed, it provides an environment where information can be manipulated more intuitively.

[0536] User

[0537] Users input company characteristics and initial data through the system's terminals. The data entered by users is verified by the server and, if appropriate, stored in storage as foundational data. Users can also view management information reports optimized according to their emotional state, which can aid in decision-making.

[0538] Specific example

[0539] For example, if a user wants to analyze their business performance, they can use the system to input company characteristics and update real-time financial data. If the system detects that the user is feeling anxious, it will highlight important data and provide optimal visual feedback to the user.

[0540] Example of a prompt

[0541] Examples of prompt statements include the following:

[0542] "Please describe a system that generates an optimal data entry interface based on the user's emotional state and automates reports that help reduce stress."

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The user inputs company characteristics information into a terminal. This information includes industry, size, and business model. This information is sent to a server, and the output from the terminal becomes input to the server. The server then uses this information for analysis.

[0546] Step 2:

[0547] The server uses computational technology to generate an optimal information management structure based on the received company characteristic information. At this stage, machine learning algorithms are applied to classify and organize the information. The resulting management structure is a customized format tailored to the user's business requirements.

[0548] Step 3:

[0549] The server checks the user's emotional state in order to adjust the generated information management structure using emotion recognition elements. User emotion data is collected and analyzed in real time. Based on this analysis, the complexity of the format is reduced, and colors and layouts are adjusted. The adjusted format based on emotion is output.

[0550] Step 4:

[0551] The user enters initial data into the terminal using a pre-formatted system. This data includes sales figures and customer information, which is then sent back to the server. The data entered by the terminal becomes the raw material for further processing by the server.

[0552] Step 5:

[0553] The server verifies the initial data it receives and automatically generates foundational data using computational techniques. Here, data integrity is confirmed, and necessary transformations and corrections are applied by the AI ​​model. Once the foundational data is generated, it is stored in memory and used for subsequent processing.

[0554] Step 6:

[0555] The server automatically generates management information reports based on the underlying data stored in the storage device. A generation AI model analyzes the data, extracts and organizes key metrics for the report. The generated report serves as an information resource for users to use in decision-making.

[0556] Step 7:

[0557] The server sends the generated management information report to the terminal. The terminal optimizes the display method, taking into account the user's emotional state, and provides the information in a way that is easy for the user to understand. Ultimately, the user can view the report and use it to help make business decisions.

[0558] (Application Example 2)

[0559] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0560] While improving efficiency in data management and accounting processes, there is a need to provide a user-friendly and intuitive interface. However, the lack of interface adjustments that respond to the user's emotional state makes data entry and report interpretation burdensome for users. The objective of this invention is to resolve this situation and improve the user experience.

[0561] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0562] In this invention, the server includes means for receiving organizational attribute information and generating an optimal data management format using machine learning techniques, means for inputting individual data and automatically generating core data based on the input data and registering it in a storage device, and emotion processing engine means for recognizing the user's emotional state and adapting the input interface. This enables flexible interface adjustment according to the user's emotional state and rapid and efficient accounting data management.

[0563] "Organizational attribute information" refers to information that indicates the basic characteristics of a company or organization, such as its industry, size, and business model.

[0564] "Machine learning techniques" is a general term for algorithms that allow computers to learn patterns and rules from data and perform predictions and classifications.

[0565] An "optimal data management format" is a format with an ideal structure that efficiently organizes and makes usable the data being entered.

[0566] "Individual data" refers to a dataset containing detailed information about a specific entity, which is uniquely identifiable.

[0567] "Core data" refers to essential data that forms the core of an organization's operations and processes, and is the foundation for continuous business activities.

[0568] A "storage device" is a system of hardware and software that can store data and retrieve it as needed.

[0569] "User emotional state" refers to information that indicates a user's temporary psychological state or emotions.

[0570] An "emotion processing engine" is a software component that recognizes a user's emotions and generates an appropriate system response based on those emotions.

[0571] "Interface optimization" is the process of optimizing how information is displayed and input according to user needs.

[0572] "Rapid and efficient accounting data management" refers to a method of managing data quickly while ensuring smooth business processes and minimizing errors.

[0573] To implement this invention, it is necessary to build a system that optimizes organizational data management and decision support. This system consists of a server, a terminal, a user, and an emotion processing engine. First, the user uses the terminal to input organizational attribute information. This information is sent to the server, which uses machine learning techniques to generate an optimal data management format. In this process, it is desirable to use TensorFlow, a common platform.

[0574] Next, the user inputs individual data via the device. The device has a built-in emotion processing engine that uses emotion recognition APIs such as Affectiva to analyze the user's emotional state in real time. Based on this information, the device adjusts the input interface to provide a user-friendly environment.

[0575] The entered data is sent to the server and registered as core data in the database. Using cloud services such as Firebase enables scalable data management. Furthermore, the server automatically generates accounting reports based on the data stored in the storage device. In this process, the system considers the user's emotional state and dynamically adjusts how the report is displayed to support decision-making.

[0576] For example, if the emotion processing engine detects that a user is feeling fatigued while reviewing a cost report, the server will highlight the most important data and suggest removing unnecessary information. This can reduce the user's burden.

[0577] An example of a prompt might be the instruction, "Analyze the user's emotions and adjust the input interface accordingly." By inputting such a prompt into the generating AI model, the system can perform actions according to those instructions.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] Users input organizational attribute information using a terminal. This information includes basic characteristics such as industry, size, and business model. The entered attribute information is sent from the terminal to the server. Based on the received attribute information, the server generates an optimal data management format using machine learning techniques. In this process, TensorFlow is used to recognize data patterns and construct the format.

[0581] Step 2:

[0582] The user inputs individual data via a terminal. During this data input, an emotion processing engine built into the terminal activates, using an emotion recognition API such as Affectiva to analyze the user's emotions in real time. The input individual data and emotion information are sent from the terminal to a server. Here, the input interface is adjusted according to the user's stress level using the emotion information.

[0583] Step 3:

[0584] The server receives individual data sent from the terminal and stores it in a database. Cloud services such as Firebase are used here to ensure data scalability and reliability. During this process, data preprocessing is performed to correct outliers and impute missing data.

[0585] Step 4:

[0586] The server automatically generates accounting reports based on individual data stored in the database. During this process, it dynamically adjusts the display method of the generated reports, taking into account the user's emotional state. It supports users in efficiently understanding the information by highlighting important data, for example. For example, for tired users, easily understandable information is placed at the forefront.

[0587] Step 5:

[0588] Users view accounting reports generated on their devices. An interface optimized by an emotion processing engine allows users to easily understand the information and make informed decisions. User feedback is sent from the device to the server and used to further improve the system.

[0589] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0590] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0591] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0592] [Fourth Embodiment]

[0593] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0594] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0595] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0596] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0597] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0598] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0599] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0600] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0601] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0602] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0603] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0604] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0605] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] This invention is a system for streamlining data management and management accounting in a company, and its embodiments will be described. This system functions through the cooperation of three entities: a server, a terminal, and a user.

[0607] First, users input company attribute information into the server using their devices. This information includes the company's industry, size, and business model. The server receives this information and uses machine learning algorithms to generate an optimal data management format. This enables customized data management tailored to the specific characteristics of each company.

[0608] Next, the user inputs raw data into the terminal based on the proposed format. This could include transaction data or product information. The server receives the input raw data and verifies the data format. Then, using AI technology, it automatically generates master data and registers it in the database. This process also includes automatic correction of missing data and outliers, ensuring high data accuracy.

[0609] Furthermore, the server periodically extracts information from the database and generates management accounting reports. These reports include basic financial indicators such as sales, expenses, and profit margins, as well as analytical information aimed at supporting decision-making. The generated reports are provided to users via terminals.

[0610] By using this system, companies can centralize data management and significantly improve the accuracy and speed of generating management accounting reports. As a result, they can effectively utilize management resources and make quick business decisions. This entire process strongly supports the sustainable growth of companies and enhances their competitiveness.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The user uses a terminal to input attribute information such as the company's industry, size, and business model. The terminal then sends this information to the server.

[0614] Step 2:

[0615] The server uses the received company attribute information to execute a machine learning algorithm. This generates the optimal data management format and sends it back to the terminal.

[0616] Step 3:

[0617] The user inputs raw data from the terminal according to the proposed data management format. The terminal then transfers the input data to the server.

[0618] Step 4:

[0619] The server verifies the raw data received from the terminal and checks if the data format matches the specified format. If there are any inconsistencies, it requests correction.

[0620] Step 5:

[0621] The server analyzes the verified data using AI technology and automatically generates master data. During this process, it automatically corrects any missing data or outliers.

[0622] Step 6:

[0623] The generated master data is registered in the database by the server. This ensures centralized data management.

[0624] Step 7:

[0625] The server periodically extracts necessary information from the database and generates management accounting reports. These reports include sales revenue, expenses, profit margins, and other metrics.

[0626] Step 8:

[0627] The generated management accounting report is sent from the server to the terminal and presented to the user. The user makes management decisions based on this report.

[0628] (Example 1)

[0629] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0630] In organizational data management and management accounting processes, centralizing information is often difficult, leading to inefficient data entry and processing. Furthermore, there is a need for methods to appropriately correct outliers and missing information while maintaining data accuracy, as well as the desire for rapid report generation to support decision-making.

[0631] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0632] In this invention, the server includes means for receiving attribute information using a data processing device and generating an optimized data management format using a machine learning algorithm; means for receiving initial data entered by a user with the processing device, automatically generating main data based on said data, and storing it in a storage device; and means for periodically retrieving information from the storage device and automatically creating a management accounting processing report. This makes it possible to streamline data management within an organization and realize accurate and rapid management accounting processing.

[0633] A "data processing device" is a general term for electronic devices that have the functions of receiving, analyzing, and transmitting information, and is a central device for data management.

[0634] "Attribute information" refers to data that indicates the characteristics of the organization or individual in question, and includes basic information such as industry and size.

[0635] A "machine learning algorithm" is a type of program that analyzes large amounts of data, derives patterns and rules, and automatically provides the optimal solution.

[0636] An "optimized data management format" is a data management method or format that has been adapted to specific needs using machine learning algorithms.

[0637] A "user terminal" is an input and display device used by a user to perform operations, and is used for receiving and transmitting data.

[0638] "Initial data" refers to the original information entered into the system, which forms the basis for subsequent data processing and analysis.

[0639] "Primary data" refers to important information used for organizational operations and decision-making, obtained by processing initial data.

[0640] A "storage device" is hardware used to store data long-term and retrieve it as needed.

[0641] A "management accounting report" is an analytical report of data generated to evaluate the current state and performance of a company's finances.

[0642] This invention is a system that supports efficient data management and rapid management accounting processing within an organization. The system consists of three elements: a server, terminals, and users.

[0643] First, the user uses a terminal to input initial data into the system. This includes attribute information such as the organization's industry and size. For example, the user can input information such as "manufacturing, medium-sized" into the system. The terminal immediately sends this information to the server.

[0644] The server utilizes data processing equipment and machine learning algorithms to generate an optimized data management format based on the received attribute information. During this process, the server uses a generation AI model to analyze the data and dynamically propose a format tailored to the specific characteristics of each company.

[0645] Next, the user inputs initial data, such as actual transaction data and product information, into the terminal based on the format provided by the server. This data is first validated on the terminal before being sent to the server. The server receives the input data, automatically generates main data using AI technology, and registers it in storage. This process also includes a function to automatically correct outliers and missing values ​​in the data, ensuring high data accuracy.

[0646] Furthermore, the server periodically accesses the storage device, extracts necessary information, and automatically generates management accounting reports. These reports include necessary financial indicators and analytical information to support decision-making. The generated reports are provided to users via terminals.

[0647] For example, a system may automatically generate financial reports using monthly sales data. These reports are used in management meetings to facilitate quick decision-making.

[0648] As an example of a prompt, entering "Generate a data management format suitable for a medium-sized manufacturing company" will allow the server to immediately suggest a suitable format.

[0649] This system enables organizations to centralize data management, leading to faster data processing and improved reporting accuracy. As a result, it allows for the effective use of management resources and faster decision-making.

[0650] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0651] Step 1:

[0652] Users input organizational attribute information using a terminal. This information includes "industry," "size," and "business model." The terminal sends this input data to the server in real time. The server analyzes the received data and uses machine learning algorithms to generate a data management format optimized for the organization. Specifically, the server processes the received data, extracts features, and presents format candidates.

[0653] Step 2:

[0654] The user inputs raw data, such as transaction data and product information, into the terminal based on the data management format proposed by the server. The terminal performs initial verification, checking the data format and for any deficiencies, before transferring the data to the server. The server further verifies the data, cleans it using machine learning models, and performs data imputation and correction of outliers. Finally, the server stores this cleaned data as the primary data in its storage device. Specifically, the process involves formatting and cleaning the data to ensure consistency.

[0655] Step 3:

[0656] The server periodically extracts primary data from storage and generates management accounting reports. This utilizes a generative AI model, which automatically aggregates financial indicators based on past patterns and new information, providing analysis results. The generated reports include not only basic financial indicators such as sales, expenses, and profit margins, but also analytical information to support future decision-making. Specifically, the server performs aggregation processing, converts the data into a visually easy-to-interpret format, and creates the reports.

[0657] Step 4:

[0658] The server sends the generated management accounting report to the user's terminal in a format that the user can view. The report is displayed directly on the terminal's dashboard, allowing the user to make quick decisions based on the report. Specifically, after the report is generated, the data is appropriately distributed over the network to ensure it reaches the end user quickly.

[0659] (Application Example 1)

[0660] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] Companies process vast amounts of transaction information daily, and there is a need for systems that can manage this information efficiently and accurately, and provide it in a format that aids in decision-making. Furthermore, there is a demand for real-time financial assessment and rapid proposal of improvement measures, which traditional methods have been time-consuming and cumbersome, making accurate judgment difficult.

[0662] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0663] In this invention, the server includes means for receiving corporate characteristic information and generating an optimal information management format using machine learning techniques; means for inputting information and automatically generating foundational information based on the input information and registering it in an information storage device; and means for extracting information from the information storage device and automatically generating performance management reports. This enables efficient management of transaction information and the generation and provision of performance management reports in real time.

[0664] "Company characteristic information" refers to information about a company's attributes, including its industry, size, and business model.

[0665] "Machine learning techniques" are technologies that allow computers to learn patterns from large amounts of data and use them to make predictions and decisions.

[0666] An "information management format" refers to a format or structure for organizing, effectively managing, and utilizing data.

[0667] "Basic information" refers to information that is automatically generated based on raw data and registered in a database.

[0668] An "information storage device" is a system or storage device for accumulating and saving data and retrieving it as needed.

[0669] A "performance management report" is a document that shows the financial situation and management indicators, and is used to analyze the management status of a company.

[0670] "Analytical information" refers to information added to data for the purpose of analyzing it and supporting decision-making.

[0671] A "mobile communication terminal" refers to a highly portable communication device such as a smartphone or tablet.

[0672] "Transaction information" refers to records related to purchases, sales, and service provision conducted by a company.

[0673] "Real-time" refers to situations where processing or displaying information almost simultaneously is required, demanding immediacy.

[0674] This invention is a system for efficiently managing corporate transaction information and generating and providing performance management reports in real time. The server uses machine learning techniques to generate the optimal information management format based on the company's characteristic information. Users input daily transaction information using a mobile communication terminal, and the server automatically generates foundational information based on this and registers it in the information storage device. The information is analyzed in real time, and abnormal values ​​and missing data are automatically corrected as needed.

[0675] The server periodically extracts information from the data storage device, generates performance management reports in real time, and provides them to the user's terminal. These reports include analytical information to support the user's rapid decision-making. Specifically, by using mobile communication devices such as smartphones or smart glasses to photograph and input transaction information, the financial situation and improvement measures are displayed intuitively.

[0676] This system consists of a data analysis backend using Python and a user interface using Flutter, with TensorFlow used for machine learning. Firebase is also utilized for real-time data management. As a concrete example, when a company executive reads transaction information from multiple suppliers using smart glasses, the system instantly displays the monthly spending situation and cost-reduction improvements.

[0677] An example of a prompt message would be, "Load restaurant transaction data and generate an optimization plan for spending," and the system would automatically perform the analysis. In this way, a company's daily transaction information is processed efficiently, enabling real-time performance analysis and suggestions for improvement.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] Users input characteristic information about a company using a mobile communication terminal. This input includes the company's industry, size, and business model. Once this information is sent to the server, the server utilizes machine learning techniques to generate an optimal information management format. The input is characteristic information, and the output is a customized information management format.

[0681] Step 2:

[0682] Users input daily transaction information using a mobile communication terminal. This input is sent to a server, which automatically generates foundational information based on it. Data validation is performed simultaneously, and any abnormal values ​​or missing data are automatically corrected. The input is transaction information, and the output is validated foundational information.

[0683] Step 3:

[0684] The server periodically extracts foundational information from the data storage device and generates performance management reports in real time. The reports include basic financial indicators such as sales, expenses, and profit margins, and also include analytical information. The input is foundational information, and the output is the performance management report.

[0685] Step 4:

[0686] The server provides the generated performance management report to the user's terminal. Users can view this report via smart glasses or a smartphone, gaining an intuitive understanding of the current state of business and potential improvements. The input is the performance management report, and the output is the displayed information.

[0687] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0688] The present invention aims to provide a more advanced user experience by combining a system that streamlines corporate data management and management accounting with an emotion engine that recognizes user emotions. Embodiments of the present invention are described below.

[0689] This system consists of a server, terminals, users, and an emotion engine. Users input attribute information such as the company's industry, size, and business model through the terminal. The terminal sends this information to the server, which uses machine learning algorithms to generate the optimal data management format based on the received information. At this stage, the emotion engine analyzes the user's emotions and fine-tunes the suggested format according to their emotional state.

[0690] The user inputs raw data into the terminal according to the generated format. At this time, the emotion engine recognizes the user's emotions again and adjusts the input screen interface to reduce the burden of operation and improve input efficiency. The data sent from the terminal to the server is verified by the server, and if there are no problems, master data is automatically generated using AI technology and registered in the database.

[0691] Furthermore, the server extracts information from the database and automatically generates management accounting reports. During this process, the emotion engine dynamically displays information within the report based on the user's emotional state, highlighting data of high importance. The generated reports are presented to the user via their terminal and used in decision-making.

[0692] For example, when the emotion engine determines that a user is stressed, it simplifies the data input interface and visually highlights the most important metrics in reports. This provides data interaction optimized according to the user's emotional state. This system supports companies in strengthening their competitiveness by linking centralized data management, rapid report generation, and improved user experience.

[0693] The following describes the processing flow.

[0694] Step 1:

[0695] The user uses a terminal to enter company attribute information. This information includes industry, size, and business model. The terminal then sends this information to the server.

[0696] Step 2:

[0697] The server executes a machine learning algorithm based on the received attribute information to generate the optimal data management format. The emotion engine simultaneously analyzes the user's emotions and adjusts the suggestions accordingly.

[0698] Step 3:

[0699] The user inputs raw data from the device according to the generated data management format. The emotion engine identifies the user's current emotional state and customizes the device interface to facilitate user input.

[0700] Step 4:

[0701] The terminal sends the raw data it receives to the server. The server verifies the data and automatically corrects any inconsistencies. Correct input data is generated as master data by AI and registered in the database.

[0702] Step 5:

[0703] The server periodically extracts information from the database and generates management accounting reports. The sentiment engine highlights information in the generated reports according to the user's emotions, making important data stand out.

[0704] Step 6:

[0705] The generated report is sent from the server to the terminal. The terminal presents the report to the user, and the sentiment engine continuously monitors the user's response, providing additional support information as needed.

[0706] (Example 2)

[0707] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0708] In modern business management, efficient data management and management accounting are essential. However, traditional systems often fail to adequately consider user experience, leading to increased user burden and decreased operational efficiency. Furthermore, management accounting reports lacked the means to appropriately highlight important information and enable users to make effective decisions. Therefore, a dynamic system that recognizes and reflects the emotional state of users was needed.

[0709] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0710] In this invention, the server includes means for receiving corporate characteristic information and generating an optimal information management structure using computational technology, means for dynamically adjusting the information management structure based on the user's emotions using emotion recognition elements, and means for extracting information from storage devices and automatically generating management information reports. This enables the provision of data interactions optimized according to the user's emotional state and efficient decision support.

[0711] "Company characteristic information" refers to information that indicates the characteristics of a company, such as its industry, size, and business model.

[0712] "Computational technology" refers to the technology of analyzing and processing data using machine learning algorithms and artificial intelligence.

[0713] An "information management structure" is a collection of formats and templates designed to efficiently manage data within a company.

[0714] "Emotion recognition elements" are technologies that analyze the user's emotional state and adjust the system's operation and expression accordingly.

[0715] "Initial data" refers to the raw, unfiltered information that the user inputs into the system.

[0716] "Basic data" refers to pre-processed data automatically generated based on initial data, used for analysis and processing.

[0717] A "storage device" is a part of the hardware and software used to store and manage data within a computer.

[0718] A "management information report" is a document or digital data that compiles information necessary for a company's management and business operations and is used for decision-making.

[0719] Modes for carrying out the invention

[0720] This invention is a system that efficiently manages data and management accounting within a company while simultaneously improving the user experience. Specifically, it consists of a combination of a server, terminal, user, and emotion recognition elements.

[0721] System Configuration and Data Processing

[0722] server

[0723] The server receives characteristic information about a company and uses computational techniques, specifically machine learning algorithms, based on that information to generate an optimal information management structure. This system includes data integrity verification and automatic generation of basic data, and registers the generated data in storage. It also plays a role in extracting necessary information from the database and automatically generating and providing management information reports.

[0724] terminal

[0725] The device provides an interface for receiving initial data entered by the user. It also uses emotion recognition elements to adjust the UI / UX according to the user's emotional state. For example, if the system detects that the user is stressed, it provides an environment where information can be manipulated more intuitively.

[0726] User

[0727] Users input company characteristics and initial data through the system's terminals. The data entered by users is verified by the server and, if appropriate, stored in storage as foundational data. Users can also view management information reports optimized according to their emotional state, which can aid in decision-making.

[0728] Specific example

[0729] For example, if a user wants to analyze their business performance, they can use the system to input company characteristics and update real-time financial data. If the system detects that the user is feeling anxious, it will highlight important data and provide optimal visual feedback to the user.

[0730] Example of a prompt

[0731] Examples of prompt statements include the following:

[0732] "Please describe a system that generates an optimal data entry interface based on the user's emotional state and automates reports that help reduce stress."

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The user inputs company characteristics information into a terminal. This information includes industry, size, and business model. This information is sent to a server, and the output from the terminal becomes input to the server. The server then uses this information for analysis.

[0736] Step 2:

[0737] The server uses computational technology to generate an optimal information management structure based on the received company characteristic information. At this stage, machine learning algorithms are applied to classify and organize the information. The resulting management structure is a customized format tailored to the user's business requirements.

[0738] Step 3:

[0739] The server checks the user's emotional state in order to adjust the generated information management structure using emotion recognition elements. User emotion data is collected and analyzed in real time. Based on this analysis, the complexity of the format is reduced, and colors and layouts are adjusted. The adjusted format based on emotion is output.

[0740] Step 4:

[0741] The user enters initial data into the terminal using a pre-formatted system. This data includes sales figures and customer information, which is then sent back to the server. The data entered by the terminal becomes the raw material for further processing by the server.

[0742] Step 5:

[0743] The server verifies the initial data it receives and automatically generates foundational data using computational techniques. Here, data integrity is confirmed, and necessary transformations and corrections are applied by the AI ​​model. Once the foundational data is generated, it is stored in memory and used for subsequent processing.

[0744] Step 6:

[0745] The server automatically generates management information reports based on the underlying data stored in the storage device. A generation AI model analyzes the data, extracts and organizes key metrics for the report. The generated report serves as an information resource for users to use in decision-making.

[0746] Step 7:

[0747] The server sends the generated management information report to the terminal. The terminal optimizes the display method, taking into account the user's emotional state, and provides the information in a way that is easy for the user to understand. Ultimately, the user can view the report and use it to help make business decisions.

[0748] (Application Example 2)

[0749] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0750] While improving efficiency in data management and accounting processes, there is a need to provide a user-friendly and intuitive interface. However, the lack of interface adjustments that respond to the user's emotional state makes data entry and report interpretation burdensome for users. The objective of this invention is to resolve this situation and improve the user experience.

[0751] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0752] In this invention, the server includes means for receiving organizational attribute information and generating an optimal data management format using machine learning techniques, means for inputting individual data and automatically generating core data based on the input data and registering it in a storage device, and emotion processing engine means for recognizing the user's emotional state and adapting the input interface. This enables flexible interface adjustment according to the user's emotional state and rapid and efficient accounting data management.

[0753] "Organizational attribute information" refers to information that indicates the basic characteristics of a company or organization, such as its industry, size, and business model.

[0754] "Machine learning techniques" is a general term for algorithms that allow computers to learn patterns and rules from data and perform predictions and classifications.

[0755] An "optimal data management format" is a format with an ideal structure that efficiently organizes and makes usable the data being entered.

[0756] "Individual data" refers to a dataset containing detailed information about a specific entity, which is uniquely identifiable.

[0757] "Core data" refers to essential data that forms the core of an organization's operations and processes, and is the foundation for continuous business activities.

[0758] A "storage device" is a system of hardware and software that can store data and retrieve it as needed.

[0759] "User emotional state" refers to information that indicates a user's temporary psychological state or emotions.

[0760] An "emotion processing engine" is a software component that recognizes a user's emotions and generates an appropriate system response based on those emotions.

[0761] "Interface optimization" is the process of optimizing how information is displayed and input according to user needs.

[0762] "Rapid and efficient accounting data management" refers to a method of managing data quickly while ensuring smooth business processes and minimizing errors.

[0763] To implement this invention, it is necessary to build a system that optimizes organizational data management and decision support. This system consists of a server, a terminal, a user, and an emotion processing engine. First, the user uses the terminal to input organizational attribute information. This information is sent to the server, which uses machine learning techniques to generate an optimal data management format. In this process, it is desirable to use TensorFlow, a common platform.

[0764] Next, the user inputs individual data via the device. The device has a built-in emotion processing engine that uses emotion recognition APIs such as Affectiva to analyze the user's emotional state in real time. Based on this information, the device adjusts the input interface to provide a user-friendly environment.

[0765] The entered data is sent to the server and registered as core data in the database. Using cloud services such as Firebase enables scalable data management. Furthermore, the server automatically generates accounting reports based on the data stored in the storage device. In this process, the system considers the user's emotional state and dynamically adjusts how the report is displayed to support decision-making.

[0766] For example, if the emotion processing engine detects that a user is feeling fatigued while reviewing a cost report, the server will highlight the most important data and suggest removing unnecessary information. This can reduce the user's burden.

[0767] An example of a prompt might be the instruction, "Analyze the user's emotions and adjust the input interface accordingly." By inputting such a prompt into the generating AI model, the system can perform actions according to those instructions.

[0768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0769] Step 1:

[0770] Users input organizational attribute information using a terminal. This information includes basic characteristics such as industry, size, and business model. The entered attribute information is sent from the terminal to the server. Based on the received attribute information, the server generates an optimal data management format using machine learning techniques. In this process, TensorFlow is used to recognize data patterns and construct the format.

[0771] Step 2:

[0772] The user inputs individual data via a terminal. During this data input, an emotion processing engine built into the terminal activates, using an emotion recognition API such as Affectiva to analyze the user's emotions in real time. The input individual data and emotion information are sent from the terminal to a server. Here, the input interface is adjusted according to the user's stress level using the emotion information.

[0773] Step 3:

[0774] The server receives individual data sent from the terminal and stores it in a database. Cloud services such as Firebase are used here to ensure data scalability and reliability. During this process, data preprocessing is performed to correct outliers and impute missing data.

[0775] Step 4:

[0776] The server automatically generates accounting reports based on individual data stored in the database. During this process, it dynamically adjusts the display method of the generated reports, taking into account the user's emotional state. It supports users in efficiently understanding the information by highlighting important data, for example. For example, for tired users, easily understandable information is placed at the forefront.

[0777] Step 5:

[0778] Users view accounting reports generated on their devices. An interface optimized by an emotion processing engine allows users to easily understand the information and make informed decisions. User feedback is sent from the device to the server and used to further improve the system.

[0779] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0780] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0781] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0782] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0783] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0784] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0785] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0786] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0787] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0788] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0789] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0790] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0791] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0792] 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.

[0793] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0794] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0795] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0796] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0797] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0798] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0799] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] A means of receiving corporate attribute information and generating an optimal data management format using a machine learning algorithm,

[0803] A means of inputting raw data, automatically generating master data based on the input data, and registering it in a database,

[0804] A means of extracting information from a database and automatically generating management accounting reports,

[0805] Means for providing the report,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, further comprising means for automatically verifying raw data entered by a user and correcting outliers and missing data.

[0809] (Claim 3)

[0810] The system according to claim 1, further comprising means for adding analytical information for decision support to the generated management accounting report.

[0811] "Example 1"

[0812] (Claim 1)

[0813] A means for receiving attribute information using a data processing device and generating an optimized data management format using a machine learning algorithm,

[0814] A means for receiving initial data entered by the user in a processing device, automatically generating main data based on that data, and storing it in a storage device,

[0815] A means of periodically retrieving information from storage devices and automatically generating management accounting reports,

[0816] Means for providing the aforementioned report to the user terminal,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, further comprising means for performing automatic verification during initial data input and correcting abnormal values ​​and missing information.

[0820] (Claim 3)

[0821] The system according to claim 1, further comprising means for adding analytical information for decision support to the generated management accounting processing report.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] A means of receiving characteristic information of a company and generating an optimal information management format using machine learning techniques,

[0825] A means for inputting information, automatically generating base information based on the input information, and registering it in an information storage device,

[0826] A means for extracting information from an information storage device and automatically generating a performance management report,

[0827] Means for supplying the report through a display device,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, further comprising means for automatically verifying data entered by a user and correcting abnormal values ​​or missing data.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means for adding analytical information for decision support to the generated performance management report.

[0833] (Claim 4)

[0834] The system according to claim 1, comprising means for collecting transaction information via a mobile communication terminal and providing performance management reports in real time.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] A means of receiving characteristic information of a company and generating an optimal information management structure using computational technology,

[0838] A means of dynamically adjusting the information management structure based on the user's emotions using emotion recognition elements,

[0839] A means for inputting initial data, automatically generating basic data based on the input data, and registering it in a storage device,

[0840] A means for extracting information from a storage device and automatically generating a management information report,

[0841] Means for presenting the report,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for automatically verifying initial data entered by the user and correcting outliers and missing data.

[0845] (Claim 3)

[0846] The system according to claim 1, further comprising means for adding analytical information for decision support to the generated management information report.

[0847] "Application example 2 of combining emotional engines"

[0848] (Claim 1)

[0849] A means of receiving organizational attribute information and generating an optimal data management format using machine learning techniques,

[0850] A means for inputting individual data, automatically generating core data based on the input data, and registering it in a storage device,

[0851] A means for extracting information from a storage device and automatically generating accounting reports,

[0852] Means for providing the accounting report,

[0853] A system including an emotion processing engine that recognizes the user's emotional state and adapts the input interface accordingly.

[0854] (Claim 2)

[0855] The system according to claim 1, further comprising means for automatically verifying individual data entered by a user and correcting outliers and missing data.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for adding analytical information for decision support to the generated accounting report and for dynamically displaying data based on emotional state. [Explanation of symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving corporate attribute information and generating an optimal data management format using a machine learning algorithm, A means of inputting raw data, automatically generating master data based on that input data, and registering it in a database, A means of extracting information from a database and automatically generating management accounting reports, Means for providing the report, A system that includes this.

2. The system according to claim 1, further comprising means for automatically verifying raw data entered by a user and correcting outliers and missing data.

3. The system according to claim 1, further comprising means for adding analytical information for decision support to the generated management accounting report.

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