system

Abstract

Provide a system. 【Solution means】 Means for collecting data from an external information source, Means for analyzing past data and real-time numerical information, Means for automatically generating a budget plan, Means for presenting the generated budget via a solid terminal, Means for managing the adjustment and approval of the budget by the user, Means for monitoring the implementation status of the budget and detecting anomalies, Means for sending an alert for the detected anomaly, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in 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] Enterprise budget management is a complex and time-consuming process that requires a lot of data collection and analysis operations. In this process, it is difficult to grasp the real-time budget situation and formulate a budget considering external and internal factors, so budget surpluses and deficits often occur. There is also a problem that it is difficult to quickly identify the difference between the predicted value and the actual value and take appropriate measures.

Means for Solving the Problems

[0005] This invention provides a means for automatically generating a budget proposal by collecting data from external sources and analyzing historical data and real-time numerical information. This system presents the generated budget to the user via a solid-state terminal and manages the user's budget adjustment and approval. Furthermore, it includes means for monitoring the budget implementation status, detecting anomalies, and issuing alerts for detected anomalies. This improves the efficiency and accuracy of budget management.

[0006] "External information sources" refer to systems and services outside the company that provide the data necessary for a company's budget management.

[0007] "Means of data collection" refers to the methods or processes for obtaining necessary data from external sources or internal systems.

[0008] "Means of analysis" refers to the methods or processes used to analyze collected data and derive meaningful insights or predictions.

[0009] "Means for automatically generating budget proposals" refers to a method or system that automatically creates an appropriate budget proposal based on collected data and analysis results.

[0010] A "solid-state terminal" refers to a device that a user can directly operate, used for presenting and adjusting budget proposals.

[0011] "Means for managing user budget adjustments and approvals" refers to methods that support the process of users reviewing, revising, and approving newly proposed budgets.

[0012] "Means of monitoring and detecting anomalies" refers to methods or technologies for monitoring budget implementation in real time and immediately detecting fluctuations that exceed normal limits.

[0013] "Means of issuing alerts" refers to methods or systems for promptly notifying users of detected anomalies and prompting them to resolve the problem. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is an automated system for efficiently managing a company's budget. This system primarily consists of three components: a server, terminals, and users.

[0036] The server has the capability to collect necessary data from external sources and internal databases and analyze it. Machine learning algorithms are used for data analysis to clarify past trends and predict future trends. Based on these results, the server automatically generates a budget proposal that takes multiple factors into account and sends it to the physical device.

[0037] The terminal functions as a user interface, visualizing and presenting the generated budget proposal to the user. The user reviews and adjusts the budget details through the terminal, and sends the adjustments to the server to proceed with the approval process.

[0038] The user has the role of adjusting and approving the budget. Specifically, they make necessary modifications to the budget proposal presented on the terminal and make the final decision. This operation is set up to ensure that appropriate budget allocation is achieved based on the user's judgment.

[0039] As an example of how this system works, consider the budget management process for a company starting a new project. The server generates a budget proposal based on data from similar past projects. This proposal includes necessary resources and schedules and is presented to the user on a terminal. The user reviews the presented budget proposal and makes adjustments as needed to match the project's priorities and strategy. The final approved budget is returned to the server, and monitoring of its implementation status begins. In this way, the system significantly improves transparency and efficiency in budget management.

[0040] This system also has the capability to detect unusual expenditures and revenue fluctuations in real time, and the server immediately issues alerts. This allows users to take swift action and maintain the financial health of their businesses.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects revenue and expenditure data from external sources and internal corporate databases. This includes using APIs to interact with external systems and obtain up-to-date financial data.

[0044] Step 2:

[0045] The server cleanses the collected data and ensures data quality. It detects outliers and incomplete data and corrects or supplements them as needed.

[0046] Step 3:

[0047] The server uses machine learning models to analyze historical revenue and expenditure data and perform trend analysis. It also uses time series analysis and regression models to predict future revenues and expenses.

[0048] Step 4:

[0049] The server automatically generates budget proposals for each project and department based on the prediction results. This process ensures appropriate resource allocation, taking into account current business objectives and strategies.

[0050] Step 5:

[0051] The server transfers the generated budget proposal to the terminal and prepares it for presentation to the user.

[0052] Step 6:

[0053] The device displays the budget proposal to the user using visualization tools, allowing the user to easily evaluate the budget details.

[0054] Step 7:

[0055] Users operate the terminal to review the budget proposal and make revisions as needed. The revised content is then sent to the server for approval.

[0056] Step 8:

[0057] The server records the proposed revisions received from users and reflects the final approved budget in the system.

[0058] Step 9:

[0059] The server monitors budget implementation in real time and generates periodic reports, including budget utilization rates and detection of anomalous expenditures.

[0060] Step 10:

[0061] If the server detects unusual spending or revenue fluctuations, it immediately issues an alert. The terminal notifies the user of the alert and prompts them to take prompt action.

[0062] (Example 1)

[0063] 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."

[0064] Corporate budget management is often inefficient, error-prone, and involves a great deal of manual work. Furthermore, it's difficult to detect unusual expenditures or revenue fluctuations requiring immediate attention in real time. In such circumstances, corporate financial planning is frequently inaccurate, degrading the quality of decision-making. Therefore, there is a need for a method that simultaneously achieves both efficiency and accuracy in budget management.

[0065] 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.

[0066] In this invention, the server includes means for acquiring information from an external data source, means for using a mathematical model to analyze historical data and real-time numerical information, and means for automatically creating a budget proposal. This enables efficient and accurate budget management and early detection of anomalies.

[0067] An "external data source" refers to an external information provision device or system used to acquire information from both inside and outside a company.

[0068] "Means of acquiring information" refers to system functions for collecting necessary data from external data sources.

[0069] "Historical data" refers to a collection of numerical information and records that have been collected and stored in the past.

[0070] "Real-time numerical information" refers to data provided immediately that reflects the current situation and transactions.

[0071] A "mathematical model for analysis" is a mathematical method used to analyze numerical and data patterns to provide future predictions and insights.

[0072] "Methods for automatically generating budget proposals" refers to functions that automatically generate budget plans using machine learning algorithms and computational methods.

[0073] An "indicating device" is a device or interface used to display information or results to a user.

[0074] "Means of control" refer to methods for managing processes and information based on user input and decisions.

[0075] "Means for detecting anomalies" are functions for identifying data patterns or events that are different from the norm.

[0076] A "notification system" is a mechanism for quickly communicating abnormal or important information to users.

[0077] "Means of analyzing visual information" refers to processing technologies for extracting information from images and videos.

[0078] "Multiple plan options" refer to several strategic forecast plans formulated based on different assumptions and conditions.

[0079] This invention is an automated system designed to streamline corporate budget management and consists of three main components: a server, a terminal, and a user.

[0080] The server collects necessary data from external data sources and internal databases. Internal data is managed using an SQL database, while external data is obtained through API calls and web scraping techniques. Programming libraries such as "requests" and "Beautiful Soup" support this process. The collected data is analyzed within the server. Machine learning algorithms such as "Scikit-learn" and "TENSORFLOW®" are used for analysis, analyzing past trends and making future predictions. This allows the server to automatically generate budget proposals.

[0081] The generated budget proposal is sent to the terminal via the network. On the terminal, a user interface using "React.js" or "Vue.js" is activated, visualizing and presenting the budget proposal to the user. The terminal provides an interactive presentation using graphs and tables. The user can easily manipulate the displayed data and review the contents of the budget proposal.

[0082] Users can evaluate the budget proposal presented through their device and make adjustments as needed. These adjustments may involve using spreadsheet software such as Excel or Google Sheets. The adjusted budget proposal is sent to the server via an API. The server receives the adjustments and performs the necessary approval process.

[0083] This system has the capability to detect unusual expenditures and revenue fluctuations in real time and issue immediate alerts. This allows companies to quickly understand their financial situation and take necessary actions.

[0084] As a concrete example, when implementing a new development project, the server generates a budget proposal based on data from similar past projects. This budget proposal includes the necessary resources and timeframes and is presented to the user on their terminal. The user can then use this information to make adjustments in line with the project strategy and obtain final approval. This process makes the company's budget management process highly transparent and efficient.

[0085] For example, you can give instructions to a generative AI model using prompts as follows:

[0086] "Based on past data from similar projects, please propose a budget for the new project, divided into five main categories. For each category, please include the estimated cost and the reasoning behind it."

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The server collects necessary data from external data sources and internal databases. Inputs consist of external information supplied via APIs and internal data obtained via SQL queries. The server collects these inputs and generates an integrated dataset. Specifically, it uses the "requests" library to retrieve data from external APIs and "SQLAlchemy" to send queries to the internal database. The output is the dataset required for analysis.

[0090] Step 2:

[0091] The server analyzes the collected dataset to identify past trends and make future predictions. The input is the integrated dataset obtained in Step 1. The server uses "Scikit-learn" and "TensorFlow" to train machine learning models and analyze the data. Data analysis includes regression analysis for trend identification and budget forecasting. The output is insights and numerical information based on the predictions.

[0092] Step 3:

[0093] The server automatically generates a budget proposal based on the analysis results. The inputs are the insights and predicted figures obtained in Step 2. The server uses this data to construct the budget proposal. This process is governed by specific calculation logic and business rules. The output is the proposed budget proposal, structured in JSON format.

[0094] Step 4:

[0095] The server sends the generated budget proposal to the terminal. The input is the budget proposal generated in step 3. The server sends the budget proposal to the terminal via the network communication protocol. The output is the presentation of the budget proposal to the terminal.

[0096] Step 5:

[0097] The terminal visualizes the received budget proposal through a user interface. The input is the budget proposal sent from the server. The terminal uses React.js or Vue.js to display the budget proposal as an interactive graph or table. This display allows the user to easily grasp the detailed information of the budget proposal. The output is the visualized budget proposal.

[0098] Step 6:

[0099] The user reviews the budget proposal presented through the terminal and makes adjustments as needed. The input is the budget proposal visualized in step 5. The user edits each item of the budget and creates an adjustment proposal. Spreadsheet software may be used for this. The output is the adjusted budget proposal.

[0100] Step 7:

[0101] The terminal sends the budget proposal adjusted by the user to the server. The input is the budget proposal modified by the user in step 6. The terminal sends the adjusted proposal to the server via the API, and the output is the budget proposal data for approval.

[0102] Step 8:

[0103] The server receives the revised budget proposal and proceeds with the approval process. The input is the revised budget proposal sent from the terminal. The server executes the logic to request approval and notifies relevant parties as needed. The output is the approved budget proposal and its record.

[0104] (Application Example 1)

[0105] 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."

[0106] Budget management in businesses is essential for efficient resource allocation and ensuring financial soundness, but the usual management process is often complex and time-consuming. Furthermore, real-time information verification and adjustment are difficult, potentially hindering appropriate budget allocation. Conventional systems also suffer from delays in anomaly detection and alert generation, making rapid response difficult. This invention aims to solve these problems and further improve the real-time and efficiency of budget management in factory environments.

[0107] 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.

[0108] In this invention, the server includes means for collecting data from external information sources, means for analyzing historical data and real-time numerical information, means for automatically generating a budget proposal, means for presenting the generated budget via a visualization terminal, means for managing budget adjustments and approvals by users, means for monitoring the budget implementation status and detecting anomalies, means for transmitting a warning signal when an anomaly is detected, and means for displaying budget information in real time using a smart information display device and allowing users to directly adjust the information. This enables immediate information access and adjustment in the factory environment, facilitating budget optimization and rapid response to abnormal situations.

[0109] "Means of collecting data from external sources" refers to technologies and devices for automatically or manually obtaining necessary information from various databases and online resources both inside and outside a company.

[0110] "Means for analyzing historical data and real-time numerical information" refer to algorithms and software that use accumulated historical data and currently occurring data to analyze the current situation and make future predictions.

[0111] "Methods for automatically generating budget proposals" refer to technologies that automate the process of creating budget proposals based on collected data, taking into account the allocation of necessary resources and costs.

[0112] "Means of presenting generated budgets via visualization terminals" refers to functions that communicate budget information to users using devices or interfaces for visually displaying budget information, such as displays or smart glasses.

[0113] "Means for managing user budget adjustments and approvals" refers to assistive technologies that allow users to review proposed budgets, make modifications as needed, and then proceed with the final approval process.

[0114] "Means for monitoring budget implementation and detecting anomalies" refers to analytical techniques that check whether actual expenditures and revenues match the budget plan and issue warnings if anomalies are found.

[0115] "Means for sending warning signals when an anomaly is detected" refers to a function that promptly notifies users or administrators when expenditures exceeding set standards or discrepancies are found.

[0116] "A means of displaying budget information in real time using a smart information display device and allowing users to directly adjust the information" refers to a system that uses interactive devices such as smart glasses or head-mounted displays to allow users to instantly manipulate and modify budget data.

[0117] To implement this invention, a system is constructed in which the server, terminals, and users each play specific roles. The server is the main data processing center, generating information necessary for budget management based on data collected from external sources and internal databases. Specifically, it is responsible for the process of analyzing past trends using machine learning algorithms and automatically generating future budget proposals. This analysis utilizes data processing libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow) using Python.

[0118] The terminal functions as a user interface, visualizing and presenting budget information generated on the server to the user. This display utilizes a front-end framework such as Vue.js, and the information is presented clearly via smart glasses or a head-mounted display. Through this, the user can review the presented budget and make adjustments as needed. The adjusted information is then sent back to the server for the final approval process. This allows for real-time monitoring of resource allocation within the factory.

[0119] Examples of user interactions include prompts such as, "Material costs in the factory have exceeded the budget. Please suggest adjustments in real time," or "Please tell me the optimal staffing arrangement to stay within budget." This allows users to instantly utilize the generated AI model to obtain the necessary information and make more accurate budget adjustments.

[0120] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0121] Step 1:

[0122] The server collects data from external sources and internal databases. Various data sources are accepted as input, and Python is used to execute API calls and database queries to collect budget-related data. The output is an integrated dataset.

[0123] Step 2:

[0124] The server performs data analysis on the collected data. It supplies the input data to machine learning algorithms to perform historical trend analysis and build predictive models. Specifically, it uses scikit-learn and TensorFlow to train and predict data. The output is the prediction results necessary for generating budget proposals.

[0125] Step 3:

[0126] The server automatically generates a budget proposal based on the prediction results. Using this input data, an AI model calculates the optimal budget allocation solution, and the generated budget proposal is output. The budget proposal includes the resources to be considered and their associated costs.

[0127] Step 4:

[0128] The server sends the generated budget proposal to the terminal, which then visualizes it. The system receives the generated budget proposal as input and uses frontend technologies such as Vue.js to display the information on smart glasses. The output is visualized budget data, which is then presented to the user.

[0129] Step 5:

[0130] The user reviews the displayed budget proposal and makes adjustments as needed. The input is visualized budget data, which the user manipulates using the adjustment interface. The output is the adjusted budget proposal.

[0131] Step 6:

[0132] The terminal sends the adjusted budget proposal to the server, which then proceeds with the final approval process. The server receives the adjusted data as input, performs final verification and necessary processing, and outputs the results. The finalized budget proposal is then prepared for implementation.

[0133] Step 7:

[0134] The server monitors budget implementation and detects anomalies. It analyzes real-time expenditure data as input to detect discrepancies in data patterns and budget overruns. When an anomaly is detected, it generates and outputs a warning signal.

[0135] Step 8:

[0136] The server immediately sends an alert to the user after detecting an anomaly. The alert notification is based on the detected anomaly. The user receives the alert and takes prompt action as a result.

[0137] 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.

[0138] This invention is a system that combines an emotion engine to enable more effective budget management for users. The system consists of three main components: a server, a terminal, and the user, with the emotion engine also functioning.

[0139] The server collects revenue and expenditure data from external sources and internal databases, cleanses that data, and analyzes it. Based on the analysis results, it has a function to automatically generate budget proposals using machine learning models. Multiple budget proposals are created for different scenarios, and the optimal resource allocation under various conditions is considered.

[0140] The generated budget proposal is displayed on the device with an optimized user interface. Crucially, the emotion engine plays a key role here; the device incorporates an emotion recognition sensor that recognizes the user's emotions from their facial expressions and voice. This emotion data is sent to a server and used to present and adjust the budget proposal.

[0141] Users receive interactive budget proposals based on emotional data via their devices, which they then review and adjust. Based on this information, the user's stress level and engagement are determined, and the budget proposal is flexibly modified accordingly.

[0142] For example, if the emotion engine detects a stress response while a user is viewing a project budget proposal, the system will immediately redisplay the budget proposal in a simplified format. Furthermore, if the user expresses positive emotions towards the budget proposal, adjustments will be made to improve the user experience, such as presenting detailed analysis results or additional scenarios.

[0143] Finally, the user submits the revised budget proposal to the server for final approval. The approved budget is then reflected in the entire system by the server, and the budget implementation status is monitored in real time. If any anomalies are detected, an alert is immediately issued and notified to the user on their terminal.

[0144] Thus, by incorporating an emotion engine, the present invention provides a new dimension of effectiveness to conventional budget management processes and promotes an improved user experience.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The server collects necessary revenue and expenditure data from external sources and internal corporate databases. This data collection includes integration with external systems using APIs.

[0148] Step 2:

[0149] The server cleanses the collected data and analyzes it using machine learning models. Based on the analysis results, it automatically generates multiple budget proposals based on past trends and future economic indicators.

[0150] Step 3:

[0151] The generated budget proposal is sent from the server to the terminal and displayed on the user interface. The terminal uses visualization tools to present the user with budget proposals for multiple scenarios.

[0152] Step 4:

[0153] The device incorporates an emotion recognition sensor that recognizes the user's emotions in real time from their facial expressions and voice.

[0154] Step 5:

[0155] The emotion engine analyzes the user's emotional data and optimizes the display format of the budget proposal according to the detected emotions. For example, if the user is feeling stressed, the amount of information is simplified and redisplayed in an easy-to-understand format.

[0156] Step 6:

[0157] Users can review the budget proposal presented on their device and make adjustments as needed. For example, if additional resources are required for a particular project, they can reflect that in the budget.

[0158] Step 7:

[0159] The user submits the budget proposal, which they have completed adjusting, to the server for approval. The approved budget proposal is then reflected throughout the system by the server.

[0160] Step 8:

[0161] The server monitors budget implementation in real time and immediately sends an alert to the terminal if it detects any significant anomalies or unexpected fluctuations in revenue and expenditure.

[0162] Step 9:

[0163] Users receive alerts on their devices and take appropriate action. For example, if project costs exceed the budget, they reassess the required budget. In this way, the emotion engine improves the user experience and enables efficient budget management.

[0164] (Example 2)

[0165] 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".

[0166] Traditional budget management systems often fail to adequately adapt to users because they unilaterally provide information without considering their emotions or psychological state. Furthermore, users may experience stress when adjusting budget proposals, or the information may be overwhelming and difficult to understand. Additionally, there may be delays in detecting anomalies and issuing alerts, requiring a rapid response. To address these challenges, there is a need to develop a system that manages budgets more effectively while considering user emotions.

[0167] 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.

[0168] In this invention, the server includes a unit that collects numerical information from an external data source, a unit that evaluates the user's psychological state using emotion recognition technology, and a module that transmits alarms for identified anomalies. This enables flexible budget management in response to the user's emotions and rapid detection and response to anomalies.

[0169] "External data sources" is a general term for external information providers or databases that a system uses to collect information.

[0170] "Numerical information" refers to data expressed numerically that a system needs to analyze and process.

[0171] A "unit" refers to a portion of hardware or software configured to perform a specific function.

[0172] "Emotion recognition technology" is a technology that analyzes a user's facial expressions, voice, etc., to determine their current psychological state.

[0173] "Psychological state" refers to the mental state or emotions inferred from the user's facial expressions, voice, and actions.

[0174] "Evaluating" refers to the process of analyzing information based on specific criteria and drawing conclusions.

[0175] An "anomaly" refers to data or situations that deviate from the normal, predictable range, and signifies a situation that requires attention or improvement.

[0176] An "alarm" refers to a means of notifying a user or related system of an anomaly when it occurs.

[0177] A "module" refers to an independent part of a program that is responsible for a specific function.

[0178] This invention provides a system that utilizes emotion recognition to effectively manage user budgets. The system primarily consists of three main components: a server, a terminal, and a user. The roles and specific implementation methods of each component are described below.

[0179] The server plays a central role in data processing and analysis. It collects necessary numerical information from external data sources via APIs, and cleans and analyzes the data using data analysis software such as Python or R. Based on the analysis results, the server automatically generates multiple budget proposals using generative AI models (e.g., TensorFlow or PyTorch). This creates forecasts based on historical data and budget proposals for various scenarios.

[0180] The device is a crucial component responsible for user interaction. It incorporates emotion recognition technology, capturing the user's facial expressions and voice through hardware such as cameras and microphones. This data is analyzed using emotion analysis software (e.g., a common emotion recognition API) to determine the user's psychological state. Along with this emotion data, the device proposes a budget plan to the user through an optimized interface. The presentation style and level of detail are adjusted according to the user's emotions.

[0181] Users can review the budget proposal presented on their device and interactively adjust it to suit their needs. For example, if the system detects user stress while viewing a project budget proposal, the device will simplify and display the budget proposal. Conversely, if the user shows a positive reaction, additional information will be provided to improve the user experience.

[0182] Here's a concrete example of a prompt: "Create optimal budget proposals for next month's marketing project, broken down by scenario. Dynamically modify user suggestions based on sentiment data." This type of prompt is given to a generative AI model to generate the budget proposals.

[0183] This system takes user emotions into account in real time, enabling a flexible and effective management method that goes beyond traditional budget management processes.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The server collects numerical information from external data sources. It retrieves real-time revenue and expenditure data from financial institutions and market analysis databases via APIs. Inputs include user authentication information and data request parameters, while output is the retrieved revenue and expenditure data.

[0187] Step 2:

[0188] The server cleanses the collected data. Using Python or R, it processes the data to correct inaccuracies and fill in missing values. The input is raw data, and the output is clean data in an analyzable format.

[0189] Step 3:

[0190] The server performs data analysis using clean data. Data analysis tools are utilized to perform data calculations to identify past revenue and expenditure trends and patterns. The input is clean data, and the output is the analysis results.

[0191] Step 4:

[0192] The server generates budget proposals using a generative AI model. Based on the analysis results, it applies machine learning models such as TensorFlow to create budget proposals based on multiple scenarios. The input is the analysis results, and the output is the budget proposal for each scenario.

[0193] Step 5:

[0194] The device captures the user's facial expressions and voice using emotion recognition technology. Data obtained from the built-in camera and microphone is processed by an emotion analysis API. Input is the user's facial expression data and voice data, and output is the result of the emotional state evaluation.

[0195] Step 6:

[0196] The device displays a budget proposal based on the user's emotional state. Based on the evaluation results, it presents appropriate information through the user interface. The inputs are emotional data and the budget proposal, and the output is an interactive budget proposal presented to the user.

[0197] Step 7:

[0198] Users review the budget proposal on their device and make adjustments as needed. Modifications are made via touch controls and voice commands through the interface. Input consists of the displayed budget proposal and user feedback, while output is the adjusted budget proposal.

[0199] Step 8:

[0200] The user submits the final adjusted budget proposal to the server. The server then performs an approval process and reflects it in the overall system. The input is the adjusted budget proposal, and the output is the final approved budget.

[0201] Step 9:

[0202] The server monitors the budget implementation process. It monitors the revenue and expenditure status in real time and detects anomalies. The input is the actual revenue and expenditure data, and the output is the result of the anomaly detection.

[0203] Step 10:

[0204] The server issues an alert when an anomaly is detected. It sends a warning message to the user's terminal to draw their attention. The input is the anomaly detection result, and the output is the user notification.

[0205] (Application Example 2)

[0206] 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".

[0207] For modern consumers, managing daily spending is a crucial challenge. However, traditional methods often fail to consider the user's emotional needs, leading to stress and frustration. In particular, a lack of budget flexibility and the inability to respond to sudden emotional reactions are problematic. To address this, there is a need for a system that can dynamically adjust budget proposals based on the user's emotions.

[0208] 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.

[0209] In this invention, the server includes means for collecting information from external information sources, means for analyzing historical data and real-time numerical information, and means for extracting and analyzing emotional data. This makes it possible to adaptively adjust the presentation of budget proposals based on the user's emotions.

[0210] "External information sources" refer to information collected from external sources such as the internet, corporate databases, and financial institutions.

[0211] "Means of collecting information" refers to hardware or software processes or devices used to acquire data.

[0212] "Means of analysis" refers to the processes and devices used to analyze data and extract useful information.

[0213] "Methods for automatically generating budget proposals" refers to processes or devices that mechanically create budget plans based on collected data.

[0214] "Electronic devices" refer to equipment that performs calculations and information processing, such as smartphones and tablet devices.

[0215] A "warning" is a notification or alarm designed to alert the user to an abnormal situation or malfunction.

[0216] "Emotional data" refers to information about a user's emotional state obtained from their facial expressions and voice.

[0217] "Means of adaptive adjustment" refer to processes or devices that dynamically change the operation of a system in response to the situation or user's reaction.

[0218] This invention is a system consisting of three main components: a server, a terminal, and a user. The server is responsible for collecting income and expenditure data from external sources and internal databases, and for cleansing and analyzing it. The analysis uses programming languages ​​such as Python and R, as well as data analysis tools. Machine learning models are used for acquiring and analyzing sentiment data, leveraging software such as TensorFlow and Keras.

[0219] The server automatically generates a budget proposal based on the analysis results and sends it to the terminal through an optimized user interface. The terminal has a built-in emotion recognition sensor that uses a camera and microphone to detect the user's facial expressions and voice. This is processed in real time using libraries such as OpenCV. This data is analyzed by an emotion engine, and the budget proposal is dynamically adjusted according to the user's emotions.

[0220] For example, if the emotion sensor detects stress while a user is reviewing a budget proposal on their device, the system reduces the user's burden by redisplaying the budget proposal in a more concise format. Furthermore, if the user shows interest, more detailed information and additional scenarios are presented. This allows users to manage their budget in a more comfortable environment.

[0221] An example of a prompt to input into the generative AI model would be text such as, "Suggest a savings plan that the user might be interested in. The emotion data is 'anxious'." This allows the system to provide information that is best suited to the user.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] The server collects revenue and expenditure data from external sources. This is done by obtaining financial institution and market data via APIs. The input is the URL or API key of the external data source, and the output is the collected raw revenue and expenditure data.

[0225] Step 2:

[0226] The server cleanses and analyzes the collected income and expenditure data. It uses data analysis libraries such as Pandas to impute missing values ​​and remove noise. The input is raw income and expenditure data, and the output is cleansed, analyzable data.

[0227] Step 3:

[0228] The server analyzes emotional data using an emotion engine. It classifies the user's emotional state using a pre-trained generative AI model. The input is the user's facial expressions and voice data, and the output is the analyzed emotion label.

[0229] Step 4:

[0230] The server automatically generates budget proposals based on analyzed revenue and expenditure data and sentiment data. It generates multiple budget patterns according to the scenario and selects the optimal one. A machine learning model is used in this process. The input is an analyzed dataset, and the output is a budget proposal for each scenario.

[0231] Step 5:

[0232] The terminal displays the budget proposal received from the server in an optimized user interface. An emotion sensor detects the user's reactions in real time and adjusts the displayed content as needed. The input is the budget proposal and the user's real-time emotion data, and the output is the displayed content of the adjusted budget.

[0233] Step 6:

[0234] Users review the budget proposal through their device and make adjustments as needed. They make adjustments via touch input, referencing suggested changes based on sentiment data. The input is the budget proposal before adjustments, and the output is the budget proposal after user adjustments.

[0235] Step 7:

[0236] The server saves the user-approved budget proposal to the final database and reflects it in the overall system. It continuously monitors data to detect anomalies and monitors budget implementation in real time. The input is the approved budget proposal, and the output is the updated system data.

[0237] Through the steps outlined above, flexible budget management tailored to user emotions can be achieved.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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".

[0254] This invention is an automated system for efficiently managing a company's budget. This system primarily consists of three components: a server, terminals, and users.

[0255] The server has the capability to collect necessary data from external sources and internal databases and analyze it. Machine learning algorithms are used for data analysis to clarify past trends and predict future trends. Based on these results, the server automatically generates a budget proposal that takes multiple factors into account and sends it to the physical device.

[0256] The terminal functions as a user interface, visualizing and presenting the generated budget proposal to the user. The user reviews and adjusts the budget details through the terminal, and sends the adjustments to the server to proceed with the approval process.

[0257] The user has the role of adjusting and approving the budget. Specifically, they make necessary modifications to the budget proposal presented on the terminal and make the final decision. This operation is set up to ensure that appropriate budget allocation is achieved based on the user's judgment.

[0258] As an example of how this system works, consider the budget management process for a company starting a new project. The server generates a budget proposal based on data from similar past projects. This proposal includes necessary resources and schedules and is presented to the user on a terminal. The user reviews the presented budget proposal and makes adjustments as needed to match the project's priorities and strategy. The final approved budget is returned to the server, and monitoring of its implementation status begins. In this way, the system significantly improves transparency and efficiency in budget management.

[0259] This system also has the capability to detect unusual expenditures and revenue fluctuations in real time, and the server immediately issues alerts. This allows users to take swift action and maintain the financial health of their businesses.

[0260] The following describes the processing flow.

[0261] Step 1:

[0262] The server collects revenue and expenditure data from external sources and internal corporate databases. This includes using APIs to interact with external systems and obtain up-to-date financial data.

[0263] Step 2:

[0264] The server cleanses the collected data and ensures data quality. It detects outliers and incomplete data and corrects or supplements them as needed.

[0265] Step 3:

[0266] The server uses machine learning models to analyze historical revenue and expenditure data and perform trend analysis. It also uses time series analysis and regression models to predict future revenues and expenses.

[0267] Step 4:

[0268] The server automatically generates budget proposals for each project and department based on the prediction results. This process ensures appropriate resource allocation, taking into account current business objectives and strategies.

[0269] Step 5:

[0270] The server transfers the generated budget proposal to the terminal and prepares it for presentation to the user.

[0271] Step 6:

[0272] The device displays the budget proposal to the user using visualization tools, allowing the user to easily evaluate the budget details.

[0273] Step 7:

[0274] Users operate the terminal to review the budget proposal and make revisions as needed. The revised content is then sent to the server for approval.

[0275] Step 8:

[0276] The server records the proposed revisions received from users and reflects the final approved budget in the system.

[0277] Step 9:

[0278] The server monitors the budget implementation status in real time and generates reports periodically. This includes the detection of budget digestion rates and abnormal expenditures.

[0279] Step 10:

[0280] When the server detects abnormal expenditures or revenue fluctuations, it immediately issues an alert. The terminal notifies the user of the alert and prompts for a quick response.

[0281] (Example 1)

[0282] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] Enterprise budget management involves a lot of manual work, with problems such as low efficiency and high error rates. Furthermore, it is difficult to detect abnormal expenditures and revenue fluctuations that require quick responses in real time. In such situations, the financial plans of enterprises are often inaccurate, reducing the quality of decision-making. Therefore, a method is required to simultaneously improve the efficiency and accuracy of budget management.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0285] In this invention, the server includes means for obtaining information from an external data source, means for using a mathematical model for analyzing past data and real-time numerical information, and means for automatically creating a budget plan. This enables efficient and accurate budget management and early detection of abnormalities.

[0286] The "external data source" is an external information providing device or system used to obtain information from within and outside the enterprise.

[0287] [[ID=3,6]]The "means for obtaining information" is a system function for collecting necessary data from an external data source.

[0288] "Historical data" refers to a collection of numerical information and records that have been collected and stored in the past.

[0289] "Real-time numerical information" refers to data provided immediately that reflects the current situation and transactions.

[0290] A "mathematical model for analysis" is a mathematical method used to analyze numerical and data patterns to provide future predictions and insights.

[0291] "Methods for automatically generating budget proposals" refers to functions that automatically generate budget plans using machine learning algorithms and computational methods.

[0292] An "indicating device" is a device or interface used to display information or results to a user.

[0293] "Means of control" refer to methods for managing processes and information based on user input and decisions.

[0294] "Means for detecting anomalies" are functions for identifying data patterns or events that are different from the norm.

[0295] A "notification system" is a mechanism for quickly communicating abnormal or important information to users.

[0296] "Means of analyzing visual information" refers to processing technologies for extracting information from images and videos.

[0297] "Multiple plan options" refer to several strategic forecast plans formulated based on different assumptions and conditions.

[0298] This invention is an automated system designed to streamline corporate budget management and consists of three main components: a server, a terminal, and a user.

[0299] The server collects necessary data from external data sources and internal databases. Internal data is managed using an SQL database, while external data is obtained through API calls and web scraping techniques. Programming libraries such as "requests" and "Beautiful Soup" support this process. The collected data is analyzed within the server. Machine learning algorithms such as "Scikit-learn" and "TensorFlow" are used for analysis, analyzing past trends and making future predictions. This allows the server to automatically generate budget proposals.

[0300] The generated budget proposal is sent to the terminal via the network. On the terminal, a user interface using "React.js" or "Vue.js" is activated, visualizing and presenting the budget proposal to the user. The terminal provides an interactive presentation using graphs and tables. The user can easily manipulate the displayed data and review the contents of the budget proposal.

[0301] Users can evaluate the budget proposal presented through their device and make adjustments as needed. These adjustments may involve using spreadsheet software such as Excel or Google Sheets. The adjusted budget proposal is sent to the server via an API. The server receives the adjustments and performs the necessary approval process.

[0302] This system has the capability to detect unusual expenditures and revenue fluctuations in real time and issue immediate alerts. This allows companies to quickly understand their financial situation and take necessary actions.

[0303] As a specific example, when implementing a new development project, the server may generate a budget plan based on past similar project data. This budget plan includes the necessary resources and time frame and is presented to the user on the terminal. The user can make adjustments in line with the project strategy based on this information and obtain final approval. Through this process, the company's budget management process becomes highly transparent and efficient.

[0304] As an example of a prompt sentence, instructions can be given to the generative AI model as follows:

[0305] "Please propose a budget for the new project by dividing it into five main items, referring to the past data of similar projects. Please describe the predicted costs and reasons for each item."

[0306] The flow of the specific process in Example 1 will be described using FIG. 11.

[0307] Step 1:

[0308] The server collects the necessary data from external data sources and internal databases. The input is external information supplied via an API and internal data obtained via an SQL query. The server collects these inputs and generates an integrated dataset. Specifically, data is obtained from an external API using the "requests" library, and a query is sent to the internal database using "SQLAlchemy". The output is a dataset required for analysis.

[0309] Step 2:

[0310] The server analyzes the collected dataset to identify past trends and make future predictions. The input is the integrated dataset obtained in Step 1. The server uses "Scikit-learn" and "TensorFlow" to train a machine learning model and analyze the data. Data analysis includes trend identification and regression analysis for budget prediction. The output is insights and numerical information based on the predictions.

[0311] Step 3:

[0312] The server automatically generates a budget proposal based on the analysis results. The inputs are the insights and predicted figures obtained in Step 2. The server uses this data to construct the budget proposal. This process is governed by specific calculation logic and business rules. The output is the proposed budget proposal, structured in JSON format.

[0313] Step 4:

[0314] The server sends the generated budget proposal to the terminal. The input is the budget proposal generated in step 3. The server sends the budget proposal to the terminal via the network communication protocol. The output is the presentation of the budget proposal to the terminal.

[0315] Step 5:

[0316] The terminal visualizes the received budget proposal through a user interface. The input is the budget proposal sent from the server. The terminal uses React.js or Vue.js to display the budget proposal as an interactive graph or table. This display allows the user to easily grasp the detailed information of the budget proposal. The output is the visualized budget proposal.

[0317] Step 6:

[0318] The user reviews the budget proposal presented through the terminal and makes adjustments as needed. The input is the budget proposal visualized in step 5. The user edits each item of the budget and creates an adjustment proposal. Spreadsheet software may be used for this. The output is the adjusted budget proposal.

[0319] Step 7:

[0320] The terminal sends the budget proposal adjusted by the user to the server. The input is the budget proposal modified by the user in step 6. The terminal sends the adjusted proposal to the server via the API, and the output is the budget proposal data for approval.

[0321] Step 8:

[0322] The server receives the revised budget proposal and proceeds with the approval process. The input is the revised budget proposal sent from the terminal. The server executes the logic to request approval and notifies relevant parties as needed. The output is the approved budget proposal and its record.

[0323] (Application Example 1)

[0324] 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."

[0325] Budget management in businesses is essential for efficient resource allocation and ensuring financial soundness, but the usual management process is often complex and time-consuming. Furthermore, real-time information verification and adjustment are difficult, potentially hindering appropriate budget allocation. Conventional systems also suffer from delays in anomaly detection and alert generation, making rapid response difficult. This invention aims to solve these problems and further improve the real-time and efficiency of budget management in factory environments.

[0326] 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.

[0327] In this invention, the server includes means for collecting data from external information sources, means for analyzing historical data and real-time numerical information, means for automatically generating a budget proposal, means for presenting the generated budget via a visualization terminal, means for managing budget adjustments and approvals by users, means for monitoring the budget implementation status and detecting anomalies, means for transmitting a warning signal when an anomaly is detected, and means for displaying budget information in real time using a smart information display device and allowing users to directly adjust the information. This enables immediate information access and adjustment in the factory environment, facilitating budget optimization and rapid response to abnormal situations.

[0328] "Means of collecting data from external sources" refers to technologies and devices for automatically or manually obtaining necessary information from various databases and online resources both inside and outside a company.

[0329] "Means for analyzing historical data and real-time numerical information" refer to algorithms and software that use accumulated historical data and currently occurring data to analyze the current situation and make future predictions.

[0330] "Methods for automatically generating budget proposals" refer to technologies that automate the process of creating budget proposals based on collected data, taking into account the allocation of necessary resources and costs.

[0331] "Means of presenting generated budgets via visualization terminals" refers to functions that communicate budget information to users using devices or interfaces for visually displaying budget information, such as displays or smart glasses.

[0332] "Means for managing user budget adjustments and approvals" refers to assistive technologies that allow users to review proposed budgets, make modifications as needed, and then proceed with the final approval process.

[0333] "Means for monitoring budget implementation and detecting anomalies" refers to analytical techniques that check whether actual expenditures and revenues match the budget plan and issue warnings if anomalies are found.

[0334] "Means for sending warning signals when an anomaly is detected" refers to a function that promptly notifies users or administrators when expenditures exceeding set standards or discrepancies are found.

[0335] "A means of displaying budget information in real time using a smart information display device and allowing users to directly adjust the information" refers to a system that uses interactive devices such as smart glasses or head-mounted displays to allow users to instantly manipulate and modify budget data.

[0336] To implement this invention, a system is constructed in which the server, terminals, and users each play specific roles. The server is the main data processing center, generating information necessary for budget management based on data collected from external sources and internal databases. Specifically, it is responsible for the process of analyzing past trends using machine learning algorithms and automatically generating future budget proposals. This analysis utilizes data processing libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow) using Python.

[0337] The terminal functions as a user interface, visualizing and presenting budget information generated on the server to the user. This display utilizes a front-end framework such as Vue.js, and the information is presented clearly via smart glasses or a head-mounted display. Through this, the user can review the presented budget and make adjustments as needed. The adjusted information is then sent back to the server for the final approval process. This allows for real-time monitoring of resource allocation within the factory.

[0338] Examples of user interactions include prompts such as, "Material costs in the factory have exceeded the budget. Please suggest adjustments in real time," or "Please tell me the optimal staffing arrangement to stay within budget." This allows users to instantly utilize the generated AI model to obtain the necessary information and make more accurate budget adjustments.

[0339] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0340] Step 1:

[0341] The server collects data from external sources and internal databases. Various data sources are accepted as input, and Python is used to execute API calls and database queries to collect budget-related data. The output is an integrated dataset.

[0342] Step 2:

[0343] The server performs data analysis on the collected data. It supplies the input data to machine learning algorithms to perform historical trend analysis and build predictive models. Specifically, it uses scikit-learn and TensorFlow to train and predict data. The output is the prediction results necessary for generating budget proposals.

[0344] Step 3:

[0345] The server automatically generates a budget proposal based on the prediction results. Using this input data, an AI model calculates the optimal budget allocation solution, and the generated budget proposal is output. The budget proposal includes the resources to be considered and their associated costs.

[0346] Step 4:

[0347] The server sends the generated budget proposal to the terminal, which then visualizes it. The system receives the generated budget proposal as input and uses frontend technologies such as Vue.js to display the information on smart glasses. The output is visualized budget data, which is then presented to the user.

[0348] Step 5:

[0349] The user reviews the displayed budget proposal and makes adjustments as needed. The input is visualized budget data, which the user manipulates using the adjustment interface. The output is the adjusted budget proposal.

[0350] Step 6:

[0351] The terminal sends the adjusted budget proposal to the server, which then proceeds with the final approval process. The server receives the adjusted data as input, performs final verification and necessary processing, and outputs the results. The finalized budget proposal is then prepared for implementation.

[0352] Step 7:

[0353] The server monitors budget implementation and detects anomalies. It analyzes real-time expenditure data as input to detect discrepancies in data patterns and budget overruns. When an anomaly is detected, it generates and outputs a warning signal.

[0354] Step 8:

[0355] The server immediately sends an alert to the user after detecting an anomaly. The alert notification is based on the detected anomaly. The user receives the alert and takes prompt action as a result.

[0356] 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.

[0357] This invention is a system that combines an emotion engine to enable more effective budget management for users. The system consists of three main components: a server, a terminal, and the user, with the emotion engine also functioning.

[0358] The server collects revenue and expenditure data from external sources and internal databases, cleanses that data, and analyzes it. Based on the analysis results, it has a function to automatically generate budget proposals using machine learning models. Multiple budget proposals are created for different scenarios, and the optimal resource allocation under various conditions is considered.

[0359] The generated budget proposal is displayed on the device with an optimized user interface. Crucially, the emotion engine plays a key role here; the device incorporates an emotion recognition sensor that recognizes the user's emotions from their facial expressions and voice. This emotion data is sent to a server and used to present and adjust the budget proposal.

[0360] Users receive interactive budget proposals based on emotional data via their devices, which they then review and adjust. Based on this information, the user's stress level and engagement are determined, and the budget proposal is flexibly modified accordingly.

[0361] For example, if the emotion engine detects a stress response while a user is viewing a project budget proposal, the system will immediately redisplay the budget proposal in a simplified format. Furthermore, if the user expresses positive emotions towards the budget proposal, adjustments will be made to improve the user experience, such as presenting detailed analysis results or additional scenarios.

[0362] Finally, the user submits the revised budget proposal to the server for final approval. The approved budget is then reflected in the entire system by the server, and the budget implementation status is monitored in real time. If any anomalies are detected, an alert is immediately issued and notified to the user on their terminal.

[0363] Thus, by incorporating an emotion engine, the present invention provides a new dimension of effectiveness to conventional budget management processes and promotes an improved user experience.

[0364] The following describes the processing flow.

[0365] Step 1:

[0366] The server collects necessary revenue and expenditure data from external sources and internal corporate databases. This data collection includes integration with external systems using APIs.

[0367] Step 2:

[0368] The server cleanses the collected data and analyzes it using machine learning models. Based on the analysis results, it automatically generates multiple budget proposals based on past trends and future economic indicators.

[0369] Step 3:

[0370] The generated budget proposal is sent from the server to the terminal and displayed on the user interface. The terminal uses visualization tools to present the user with budget proposals for multiple scenarios.

[0371] Step 4:

[0372] The device incorporates an emotion recognition sensor that recognizes the user's emotions in real time from their facial expressions and voice.

[0373] Step 5:

[0374] The emotion engine analyzes the user's emotional data and optimizes the display format of the budget proposal according to the detected emotions. For example, if the user is feeling stressed, the amount of information is simplified and redisplayed in an easy-to-understand format.

[0375] Step 6:

[0376] Users can review the budget proposal presented on their device and make adjustments as needed. For example, if additional resources are required for a particular project, they can reflect that in the budget.

[0377] Step 7:

[0378] The user submits the budget proposal, which they have completed adjusting, to the server for approval. The approved budget proposal is then reflected throughout the system by the server.

[0379] Step 8:

[0380] The server monitors budget implementation in real time and immediately sends an alert to the terminal if it detects any significant anomalies or unexpected fluctuations in revenue and expenditure.

[0381] Step 9:

[0382] Users receive alerts on their devices and take appropriate action. For example, if project costs exceed the budget, they reassess the required budget. In this way, the emotion engine improves the user experience and enables efficient budget management.

[0383] (Example 2)

[0384] 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".

[0385] Traditional budget management systems often fail to adequately adapt to users because they unilaterally provide information without considering their emotions or psychological state. Furthermore, users may experience stress when adjusting budget proposals, or the information may be overwhelming and difficult to understand. Additionally, there may be delays in detecting anomalies and issuing alerts, requiring a rapid response. To address these challenges, there is a need to develop a system that manages budgets more effectively while considering user emotions.

[0386] 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.

[0387] In this invention, the server includes a unit that collects numerical information from an external data source, a unit that evaluates the user's psychological state using emotion recognition technology, and a module that transmits alarms for identified anomalies. This enables flexible budget management in response to the user's emotions and rapid detection and response to anomalies.

[0388] "External data sources" is a general term for external information providers or databases that a system uses to collect information.

[0389] "Numerical information" refers to data expressed numerically that a system needs to analyze and process.

[0390] A "unit" refers to a portion of hardware or software configured to perform a specific function.

[0391] "Emotion recognition technology" is a technology that analyzes a user's facial expressions, voice, etc., to determine their current psychological state.

[0392] "Psychological state" refers to the mental state or emotions inferred from the user's facial expressions, voice, and actions.

[0393] "Evaluating" refers to the process of analyzing information based on specific criteria and drawing conclusions.

[0394] An "anomaly" refers to data or situations that deviate from the normal, predictable range, and signifies a situation that requires attention or improvement.

[0395] An "alarm" refers to a means of notifying a user or related system of an anomaly when it occurs.

[0396] A "module" refers to an independent part of a program that is responsible for a specific function.

[0397] This invention provides a system that utilizes emotion recognition to effectively manage user budgets. The system primarily consists of three main components: a server, a terminal, and a user. The roles and specific implementation methods of each component are described below.

[0398] The server plays a central role in data processing and analysis. It collects necessary numerical information from external data sources via APIs, and cleans and analyzes the data using data analysis software such as Python or R. Based on the analysis results, the server automatically generates multiple budget proposals using generative AI models (e.g., TensorFlow or PyTorch). This creates forecasts based on historical data and budget proposals for various scenarios.

[0399] The device is a crucial component responsible for user interaction. It incorporates emotion recognition technology, capturing the user's facial expressions and voice through hardware such as cameras and microphones. This data is analyzed using emotion analysis software (e.g., a common emotion recognition API) to determine the user's psychological state. Along with this emotion data, the device proposes a budget plan to the user through an optimized interface. The presentation style and level of detail are adjusted according to the user's emotions.

[0400] Users can review the budget proposal presented on their device and interactively adjust it to suit their needs. For example, if the system detects user stress while viewing a project budget proposal, the device will simplify and display the budget proposal. Conversely, if the user shows a positive reaction, additional information will be provided to improve the user experience.

[0401] Here's a concrete example of a prompt: "Create optimal budget proposals for next month's marketing project, broken down by scenario. Dynamically modify user suggestions based on sentiment data." This type of prompt is given to a generative AI model to generate the budget proposals.

[0402] This system takes user emotions into account in real time, enabling a flexible and effective management method that goes beyond traditional budget management processes.

[0403] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0404] Step 1:

[0405] The server collects numerical information from external data sources. It retrieves real-time revenue and expenditure data from financial institutions and market analysis databases via APIs. Inputs include user authentication information and data request parameters, while output is the retrieved revenue and expenditure data.

[0406] Step 2:

[0407] The server cleanses the collected data. Using Python or R, it processes the data to correct inaccuracies and fill in missing values. The input is raw data, and the output is clean data in an analyzable format.

[0408] Step 3:

[0409] The server performs data analysis using clean data. Data analysis tools are utilized to perform data calculations to identify past revenue and expenditure trends and patterns. The input is clean data, and the output is the analysis results.

[0410] Step 4:

[0411] The server generates budget proposals using a generative AI model. Based on the analysis results, it applies machine learning models such as TensorFlow to create budget proposals based on multiple scenarios. The input is the analysis results, and the output is the budget proposal for each scenario.

[0412] Step 5:

[0413] The device captures the user's facial expressions and voice using emotion recognition technology. Data obtained from the built-in camera and microphone is processed by an emotion analysis API. Input is the user's facial expression data and voice data, and output is the result of the emotional state evaluation.

[0414] Step 6:

[0415] The device displays a budget proposal based on the user's emotional state. Based on the evaluation results, it presents appropriate information through the user interface. The inputs are emotional data and the budget proposal, and the output is an interactive budget proposal presented to the user.

[0416] Step 7:

[0417] Users review the budget proposal on their device and make adjustments as needed. Modifications are made via touch controls and voice commands through the interface. Input consists of the displayed budget proposal and user feedback, while output is the adjusted budget proposal.

[0418] Step 8:

[0419] The user submits the final adjusted budget proposal to the server. The server then performs an approval process and reflects it in the overall system. The input is the adjusted budget proposal, and the output is the final approved budget.

[0420] Step 9:

[0421] The server monitors the budget implementation process. It monitors the revenue and expenditure status in real time and detects anomalies. The input is the actual revenue and expenditure data, and the output is the result of the anomaly detection.

[0422] Step 10:

[0423] The server issues an alert when an anomaly is detected. It sends a warning message to the user's terminal to draw their attention. The input is the anomaly detection result, and the output is the user notification.

[0424] (Application Example 2)

[0425] 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."

[0426] For modern consumers, managing daily spending is a crucial challenge. However, traditional methods often fail to consider the user's emotional needs, leading to stress and frustration. In particular, a lack of budget flexibility and the inability to respond to sudden emotional reactions are problematic. To address this, there is a need for a system that can dynamically adjust budget proposals based on the user's emotions.

[0427] 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.

[0428] In this invention, the server includes means for collecting information from external information sources, means for analyzing historical data and real-time numerical information, and means for extracting and analyzing emotional data. This makes it possible to adaptively adjust the presentation of budget proposals based on the user's emotions.

[0429] "External information sources" refer to information collected from external sources such as the internet, corporate databases, and financial institutions.

[0430] "Means of collecting information" refers to hardware or software processes or devices used to acquire data.

[0431] "Means of analysis" refers to the processes and devices used to analyze data and extract useful information.

[0432] "Methods for automatically generating budget proposals" refers to processes or devices that mechanically create budget plans based on collected data.

[0433] "Electronic devices" refer to equipment that performs calculations and information processing, such as smartphones and tablet devices.

[0434] A "warning" is a notification or alarm designed to alert the user to an abnormal situation or malfunction.

[0435] "Emotional data" refers to information about a user's emotional state obtained from their facial expressions and voice.

[0436] "Means of adaptive adjustment" refer to processes or devices that dynamically change the operation of a system in response to the situation or user's reaction.

[0437] This invention is a system consisting of three main components: a server, a terminal, and a user. The server is responsible for collecting income and expenditure data from external sources and internal databases, and for cleansing and analyzing it. The analysis uses programming languages ​​such as Python and R, as well as data analysis tools. Machine learning models are used for acquiring and analyzing sentiment data, leveraging software such as TensorFlow and Keras.

[0438] The server automatically generates a budget proposal based on the analysis results and sends it to the terminal through an optimized user interface. The terminal has a built-in emotion recognition sensor that uses a camera and microphone to detect the user's facial expressions and voice. This is processed in real time using libraries such as OpenCV. This data is analyzed by an emotion engine, and the budget proposal is dynamically adjusted according to the user's emotions.

[0439] For example, if the emotion sensor detects stress while a user is reviewing a budget proposal on their device, the system reduces the user's burden by redisplaying the budget proposal in a more concise format. Furthermore, if the user shows interest, more detailed information and additional scenarios are presented. This allows users to manage their budget in a more comfortable environment.

[0440] An example of a prompt to input into the generative AI model would be text such as, "Suggest a savings plan that the user might be interested in. The emotion data is 'anxious'." This allows the system to provide information that is best suited to the user.

[0441] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0442] Step 1:

[0443] The server collects revenue and expenditure data from external sources. This is done by obtaining financial institution and market data via APIs. The input is the URL or API key of the external data source, and the output is the collected raw revenue and expenditure data.

[0444] Step 2:

[0445] The server cleanses and analyzes the collected income and expenditure data. It uses data analysis libraries such as Pandas to impute missing values ​​and remove noise. The input is raw income and expenditure data, and the output is cleansed, analyzable data.

[0446] Step 3:

[0447] The server analyzes emotional data using an emotion engine. It classifies the user's emotional state using a pre-trained generative AI model. The input is the user's facial expressions and voice data, and the output is the analyzed emotion label.

[0448] Step 4:

[0449] The server automatically generates budget proposals based on analyzed revenue and expenditure data and sentiment data. It generates multiple budget patterns according to the scenario and selects the optimal one. A machine learning model is used in this process. The input is an analyzed dataset, and the output is a budget proposal for each scenario.

[0450] Step 5:

[0451] The terminal displays the budget proposal received from the server in an optimized user interface. An emotion sensor detects the user's reactions in real time and adjusts the displayed content as needed. The input is the budget proposal and the user's real-time emotion data, and the output is the displayed content of the adjusted budget.

[0452] Step 6:

[0453] Users review the budget proposal through their device and make adjustments as needed. They make adjustments via touch input, referencing suggested changes based on sentiment data. The input is the budget proposal before adjustments, and the output is the budget proposal after user adjustments.

[0454] Step 7:

[0455] The server saves the user-approved budget proposal to the final database and reflects it in the overall system. It continuously monitors data to detect anomalies and monitors budget implementation in real time. The input is the approved budget proposal, and the output is the updated system data.

[0456] Through the steps outlined above, flexible budget management tailored to user emotions can be achieved.

[0457] 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.

[0458] 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.

[0459] 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.

[0460] [Third Embodiment]

[0461] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0462] 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.

[0463] 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).

[0464] 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.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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".

[0473] This invention is an automated system for efficiently managing a company's budget. This system primarily consists of three components: a server, terminals, and users.

[0474] The server has the capability to collect necessary data from external sources and internal databases and analyze it. Machine learning algorithms are used for data analysis to clarify past trends and predict future trends. Based on these results, the server automatically generates a budget proposal that takes multiple factors into account and sends it to the physical device.

[0475] The terminal functions as a user interface, visualizing and presenting the generated budget proposal to the user. The user reviews and adjusts the budget details through the terminal, and sends the adjustments to the server to proceed with the approval process.

[0476] The user has the role of adjusting and approving the budget. Specifically, they make necessary modifications to the budget proposal presented on the terminal and make the final decision. This operation is set up to ensure that appropriate budget allocation is achieved based on the user's judgment.

[0477] As an example of how this system works, consider the budget management process for a company starting a new project. The server generates a budget proposal based on data from similar past projects. This proposal includes necessary resources and schedules and is presented to the user on a terminal. The user reviews the presented budget proposal and makes adjustments as needed to match the project's priorities and strategy. The final approved budget is returned to the server, and monitoring of its implementation status begins. In this way, the system significantly improves transparency and efficiency in budget management.

[0478] This system also has the capability to detect unusual expenditures and revenue fluctuations in real time, and the server immediately issues alerts. This allows users to take swift action and maintain the financial health of their businesses.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] The server collects revenue and expenditure data from external sources and internal corporate databases. This includes using APIs to interact with external systems and obtain up-to-date financial data.

[0482] Step 2:

[0483] The server cleanses the collected data and ensures data quality. It detects outliers and incomplete data and corrects or supplements them as needed.

[0484] Step 3:

[0485] The server uses machine learning models to analyze historical revenue and expenditure data and perform trend analysis. It also uses time series analysis and regression models to predict future revenues and expenses.

[0486] Step 4:

[0487] The server automatically generates budget proposals for each project and department based on the prediction results. This process ensures appropriate resource allocation, taking into account current business objectives and strategies.

[0488] Step 5:

[0489] The server transfers the generated budget proposal to the terminal and prepares it for presentation to the user.

[0490] Step 6:

[0491] The device displays the budget proposal to the user using visualization tools, allowing the user to easily evaluate the budget details.

[0492] Step 7:

[0493] Users operate the terminal to review the budget proposal and make revisions as needed. The revised content is then sent to the server for approval.

[0494] Step 8:

[0495] The server records the proposed revisions received from users and reflects the final approved budget in the system.

[0496] Step 9:

[0497] The server monitors budget implementation in real time and generates periodic reports, including budget utilization rates and detection of anomalous expenditures.

[0498] Step 10:

[0499] If the server detects unusual spending or revenue fluctuations, it immediately issues an alert. The terminal notifies the user of the alert and prompts them to take prompt action.

[0500] (Example 1)

[0501] 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."

[0502] Corporate budget management is often inefficient, error-prone, and involves a great deal of manual work. Furthermore, it's difficult to detect unusual expenditures or revenue fluctuations requiring immediate attention in real time. In such circumstances, corporate financial planning is frequently inaccurate, degrading the quality of decision-making. Therefore, there is a need for a method that simultaneously achieves both efficiency and accuracy in budget management.

[0503] 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.

[0504] In this invention, the server includes means for acquiring information from an external data source, means for using a mathematical model to analyze historical data and real-time numerical information, and means for automatically creating a budget proposal. This enables efficient and accurate budget management and early detection of anomalies.

[0505] An "external data source" refers to an external information provision device or system used to acquire information from both inside and outside a company.

[0506] "Means of acquiring information" refers to system functions for collecting necessary data from external data sources.

[0507] "Historical data" refers to a collection of numerical information and records that have been collected and stored in the past.

[0508] "Real-time numerical information" refers to data provided immediately that reflects the current situation and transactions.

[0509] A "mathematical model for analysis" is a mathematical method used to analyze numerical and data patterns to provide future predictions and insights.

[0510] "Methods for automatically generating budget proposals" refers to functions that automatically generate budget plans using machine learning algorithms and computational methods.

[0511] An "indicating device" is a device or interface used to display information or results to a user.

[0512] "Means of control" refer to methods for managing processes and information based on user input and decisions.

[0513] "Means for detecting anomalies" are functions for identifying data patterns or events that are different from the norm.

[0514] A "notification system" is a mechanism for quickly communicating abnormal or important information to users.

[0515] "Means of analyzing visual information" refers to processing technologies for extracting information from images and videos.

[0516] "Multiple plan options" refer to several strategic forecast plans formulated based on different assumptions and conditions.

[0517] This invention is an automated system designed to streamline corporate budget management and consists of three main components: a server, a terminal, and a user.

[0518] The server collects necessary data from external data sources and internal databases. Internal data is managed using an SQL database, while external data is obtained through API calls and web scraping techniques. Programming libraries such as "requests" and "Beautiful Soup" support this process. The collected data is analyzed within the server. Machine learning algorithms such as "Scikit-learn" and "TensorFlow" are used for analysis, analyzing past trends and making future predictions. This allows the server to automatically generate budget proposals.

[0519] The generated budget proposal is sent to the terminal via the network. On the terminal, a user interface using "React.js" or "Vue.js" is activated, visualizing and presenting the budget proposal to the user. The terminal provides an interactive presentation using graphs and tables. The user can easily manipulate the displayed data and review the contents of the budget proposal.

[0520] Users can evaluate the budget proposal presented through their device and make adjustments as needed. These adjustments may involve using spreadsheet software such as Excel or Google Sheets. The adjusted budget proposal is sent to the server via an API. The server receives the adjustments and performs the necessary approval process.

[0521] This system has the capability to detect unusual expenditures and revenue fluctuations in real time and issue immediate alerts. This allows companies to quickly understand their financial situation and take necessary actions.

[0522] As a concrete example, when implementing a new development project, the server generates a budget proposal based on data from similar past projects. This budget proposal includes the necessary resources and timeframes and is presented to the user on their terminal. The user can then use this information to make adjustments in line with the project strategy and obtain final approval. This process makes the company's budget management process highly transparent and efficient.

[0523] For example, you can give instructions to a generative AI model using prompts as follows:

[0524] "Based on past data from similar projects, please propose a budget for the new project, divided into five main categories. For each category, please include the estimated cost and the reasoning behind it."

[0525] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0526] Step 1:

[0527] The server collects necessary data from external data sources and internal databases. Inputs consist of external information supplied via APIs and internal data obtained via SQL queries. The server collects these inputs and generates an integrated dataset. Specifically, it uses the "requests" library to retrieve data from external APIs and "SQLAlchemy" to send queries to the internal database. The output is the dataset required for analysis.

[0528] Step 2:

[0529] The server analyzes the collected dataset to identify past trends and make future predictions. The input is the integrated dataset obtained in Step 1. The server uses "Scikit-learn" and "TensorFlow" to train machine learning models and analyze the data. Data analysis includes regression analysis for trend identification and budget forecasting. The output is insights and numerical information based on the predictions.

[0530] Step 3:

[0531] The server automatically generates a budget proposal based on the analysis results. The inputs are the insights and predicted figures obtained in Step 2. The server uses this data to construct the budget proposal. This process is governed by specific calculation logic and business rules. The output is the proposed budget proposal, structured in JSON format.

[0532] Step 4:

[0533] The server sends the generated budget proposal to the terminal. The input is the budget proposal generated in step 3. The server sends the budget proposal to the terminal via the network communication protocol. The output is the presentation of the budget proposal to the terminal.

[0534] Step 5:

[0535] The terminal visualizes the received budget proposal through a user interface. The input is the budget proposal sent from the server. The terminal uses React.js or Vue.js to display the budget proposal as an interactive graph or table. This display allows the user to easily grasp the detailed information of the budget proposal. The output is the visualized budget proposal.

[0536] Step 6:

[0537] The user reviews the budget proposal presented through the terminal and makes adjustments as needed. The input is the budget proposal visualized in step 5. The user edits each item of the budget and creates an adjustment proposal. Spreadsheet software may be used for this. The output is the adjusted budget proposal.

[0538] Step 7:

[0539] The terminal sends the budget proposal adjusted by the user to the server. The input is the budget proposal modified by the user in step 6. The terminal sends the adjusted proposal to the server via the API, and the output is the budget proposal data for approval.

[0540] Step 8:

[0541] The server receives the revised budget proposal and proceeds with the approval process. The input is the revised budget proposal sent from the terminal. The server executes the logic to request approval and notifies relevant parties as needed. The output is the approved budget proposal and its record.

[0542] (Application Example 1)

[0543] 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."

[0544] Budget management in businesses is essential for efficient resource allocation and ensuring financial soundness, but the usual management process is often complex and time-consuming. Furthermore, real-time information verification and adjustment are difficult, potentially hindering appropriate budget allocation. Conventional systems also suffer from delays in anomaly detection and alert generation, making rapid response difficult. This invention aims to solve these problems and further improve the real-time and efficiency of budget management in factory environments.

[0545] 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.

[0546] In this invention, the server includes means for collecting data from external information sources, means for analyzing historical data and real-time numerical information, means for automatically generating a budget proposal, means for presenting the generated budget via a visualization terminal, means for managing budget adjustments and approvals by users, means for monitoring the budget implementation status and detecting anomalies, means for transmitting a warning signal when an anomaly is detected, and means for displaying budget information in real time using a smart information display device and allowing users to directly adjust the information. This enables immediate information access and adjustment in the factory environment, facilitating budget optimization and rapid response to abnormal situations.

[0547] "Means of collecting data from external sources" refers to technologies and devices for automatically or manually obtaining necessary information from various databases and online resources both inside and outside a company.

[0548] "Means for analyzing historical data and real-time numerical information" refer to algorithms and software that use accumulated historical data and currently occurring data to analyze the current situation and make future predictions.

[0549] "Methods for automatically generating budget proposals" refer to technologies that automate the process of creating budget proposals based on collected data, taking into account the allocation of necessary resources and costs.

[0550] "Means of presenting generated budgets via visualization terminals" refers to functions that communicate budget information to users using devices or interfaces for visually displaying budget information, such as displays or smart glasses.

[0551] "Means for managing user budget adjustments and approvals" refers to assistive technologies that allow users to review proposed budgets, make modifications as needed, and then proceed with the final approval process.

[0552] "Means for monitoring budget implementation and detecting anomalies" refers to analytical techniques that check whether actual expenditures and revenues match the budget plan and issue warnings if anomalies are found.

[0553] "Means for sending warning signals when an anomaly is detected" refers to a function that promptly notifies users or administrators when expenditures exceeding set standards or discrepancies are found.

[0554] "A means of displaying budget information in real time using a smart information display device and allowing users to directly adjust the information" refers to a system that uses interactive devices such as smart glasses or head-mounted displays to allow users to instantly manipulate and modify budget data.

[0555] To implement this invention, a system is constructed in which the server, terminals, and users each play specific roles. The server is the main data processing center, generating information necessary for budget management based on data collected from external sources and internal databases. Specifically, it is responsible for the process of analyzing past trends using machine learning algorithms and automatically generating future budget proposals. This analysis utilizes data processing libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow) using Python.

[0556] The terminal functions as a user interface, visualizing and presenting budget information generated on the server to the user. This display utilizes a front-end framework such as Vue.js, and the information is presented clearly via smart glasses or a head-mounted display. Through this, the user can review the presented budget and make adjustments as needed. The adjusted information is then sent back to the server for the final approval process. This allows for real-time monitoring of resource allocation within the factory.

[0557] Examples of user interactions include prompts such as, "Material costs in the factory have exceeded the budget. Please suggest adjustments in real time," or "Please tell me the optimal staffing arrangement to stay within budget." This allows users to instantly utilize the generated AI model to obtain the necessary information and make more accurate budget adjustments.

[0558] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0559] Step 1:

[0560] The server collects data from external sources and internal databases. Various data sources are accepted as input, and Python is used to execute API calls and database queries to collect budget-related data. The output is an integrated dataset.

[0561] Step 2:

[0562] The server performs data analysis on the collected data. It supplies the input data to machine learning algorithms to perform historical trend analysis and build predictive models. Specifically, it uses scikit-learn and TensorFlow to train and predict data. The output is the prediction results necessary for generating budget proposals.

[0563] Step 3:

[0564] The server automatically generates a budget proposal based on the prediction results. Using this input data, an AI model calculates the optimal budget allocation solution, and the generated budget proposal is output. The budget proposal includes the resources to be considered and their associated costs.

[0565] Step 4:

[0566] The server sends the generated budget proposal to the terminal, which then visualizes it. The system receives the generated budget proposal as input and uses frontend technologies such as Vue.js to display the information on smart glasses. The output is visualized budget data, which is then presented to the user.

[0567] Step 5:

[0568] The user reviews the displayed budget proposal and makes adjustments as needed. The input is visualized budget data, which the user manipulates using the adjustment interface. The output is the adjusted budget proposal.

[0569] Step 6:

[0570] The terminal sends the adjusted budget proposal to the server, which then proceeds with the final approval process. The server receives the adjusted data as input, performs final verification and necessary processing, and outputs the results. The finalized budget proposal is then prepared for implementation.

[0571] Step 7:

[0572] The server monitors budget implementation and detects anomalies. It analyzes real-time expenditure data as input to detect discrepancies in data patterns and budget overruns. When an anomaly is detected, it generates and outputs a warning signal.

[0573] Step 8:

[0574] The server immediately sends an alert to the user after detecting an anomaly. The alert notification is based on the detected anomaly. The user receives the alert and takes prompt action as a result.

[0575] 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.

[0576] This invention is a system that combines an emotion engine to enable more effective budget management for users. The system consists of three main components: a server, a terminal, and the user, with the emotion engine also functioning.

[0577] The server collects revenue and expenditure data from external sources and internal databases, cleanses that data, and analyzes it. Based on the analysis results, it has a function to automatically generate budget proposals using machine learning models. Multiple budget proposals are created for different scenarios, and the optimal resource allocation under various conditions is considered.

[0578] The generated budget proposal is displayed on the device with an optimized user interface. Crucially, the emotion engine plays a key role here; the device incorporates an emotion recognition sensor that recognizes the user's emotions from their facial expressions and voice. This emotion data is sent to a server and used to present and adjust the budget proposal.

[0579] Users receive interactive budget proposals based on emotional data via their devices, which they then review and adjust. Based on this information, the user's stress level and engagement are determined, and the budget proposal is flexibly modified accordingly.

[0580] For example, if the emotion engine detects a stress response while a user is viewing a project budget proposal, the system will immediately redisplay the budget proposal in a simplified format. Furthermore, if the user expresses positive emotions towards the budget proposal, adjustments will be made to improve the user experience, such as presenting detailed analysis results or additional scenarios.

[0581] Finally, the user submits the revised budget proposal to the server for final approval. The approved budget is then reflected in the entire system by the server, and the budget implementation status is monitored in real time. If any anomalies are detected, an alert is immediately issued and notified to the user on their terminal.

[0582] Thus, by incorporating an emotion engine, the present invention provides a new dimension of effectiveness to conventional budget management processes and promotes an improved user experience.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The server collects necessary revenue and expenditure data from external sources and internal corporate databases. This data collection includes integration with external systems using APIs.

[0586] Step 2:

[0587] The server cleanses the collected data and analyzes it using machine learning models. Based on the analysis results, it automatically generates multiple budget proposals based on past trends and future economic indicators.

[0588] Step 3:

[0589] The generated budget proposal is sent from the server to the terminal and displayed on the user interface. The terminal uses visualization tools to present the user with budget proposals for multiple scenarios.

[0590] Step 4:

[0591] The device incorporates an emotion recognition sensor that recognizes the user's emotions in real time from their facial expressions and voice.

[0592] Step 5:

[0593] The emotion engine analyzes the user's emotional data and optimizes the display format of the budget proposal according to the detected emotions. For example, if the user is feeling stressed, the amount of information is simplified and redisplayed in an easy-to-understand format.

[0594] Step 6:

[0595] Users can review the budget proposal presented on their device and make adjustments as needed. For example, if additional resources are required for a particular project, they can reflect that in the budget.

[0596] Step 7:

[0597] The user submits the budget proposal, which they have completed adjusting, to the server for approval. The approved budget proposal is then reflected throughout the system by the server.

[0598] Step 8:

[0599] The server monitors budget implementation in real time and immediately sends an alert to the terminal if it detects any significant anomalies or unexpected fluctuations in revenue and expenditure.

[0600] Step 9:

[0601] Users receive alerts on their devices and take appropriate action. For example, if project costs exceed the budget, they reassess the required budget. In this way, the emotion engine improves the user experience and enables efficient budget management.

[0602] (Example 2)

[0603] 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."

[0604] Traditional budget management systems often fail to adequately adapt to users because they unilaterally provide information without considering their emotions or psychological state. Furthermore, users may experience stress when adjusting budget proposals, or the information may be overwhelming and difficult to understand. Additionally, there may be delays in detecting anomalies and issuing alerts, requiring a rapid response. To address these challenges, there is a need to develop a system that manages budgets more effectively while considering user emotions.

[0605] 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.

[0606] In this invention, the server includes a unit that collects numerical information from an external data source, a unit that evaluates the user's psychological state using emotion recognition technology, and a module that transmits alarms for identified anomalies. This enables flexible budget management in response to the user's emotions and rapid detection and response to anomalies.

[0607] "External data sources" is a general term for external information providers or databases that a system uses to collect information.

[0608] "Numerical information" refers to data expressed numerically that a system needs to analyze and process.

[0609] A "unit" refers to a portion of hardware or software configured to perform a specific function.

[0610] "Emotion recognition technology" is a technology that analyzes a user's facial expressions, voice, etc., to determine their current psychological state.

[0611] "Psychological state" refers to the mental state or emotions inferred from the user's facial expressions, voice, and actions.

[0612] "Evaluating" refers to the process of analyzing information based on specific criteria and drawing conclusions.

[0613] An "anomaly" refers to data or situations that deviate from the normal, predictable range, and signifies a situation that requires attention or improvement.

[0614] An "alarm" refers to a means of notifying a user or related system of an anomaly when it occurs.

[0615] A "module" refers to an independent part of a program that is responsible for a specific function.

[0616] This invention provides a system that utilizes emotion recognition to effectively manage user budgets. The system primarily consists of three main components: a server, a terminal, and a user. The roles and specific implementation methods of each component are described below.

[0617] The server plays a central role in data processing and analysis. It collects necessary numerical information from external data sources via APIs, and cleans and analyzes the data using data analysis software such as Python or R. Based on the analysis results, the server automatically generates multiple budget proposals using generative AI models (e.g., TensorFlow or PyTorch). This creates forecasts based on historical data and budget proposals for various scenarios.

[0618] The device is a crucial component responsible for user interaction. It incorporates emotion recognition technology, capturing the user's facial expressions and voice through hardware such as cameras and microphones. This data is analyzed using emotion analysis software (e.g., a common emotion recognition API) to determine the user's psychological state. Along with this emotion data, the device proposes a budget plan to the user through an optimized interface. The presentation style and level of detail are adjusted according to the user's emotions.

[0619] Users can review the budget proposal presented on their device and interactively adjust it to suit their needs. For example, if the system detects user stress while viewing a project budget proposal, the device will simplify and display the budget proposal. Conversely, if the user shows a positive reaction, additional information will be provided to improve the user experience.

[0620] Here's a concrete example of a prompt: "Create optimal budget proposals for next month's marketing project, broken down by scenario. Dynamically modify user suggestions based on sentiment data." This type of prompt is given to a generative AI model to generate the budget proposals.

[0621] This system takes user emotions into account in real time, enabling a flexible and effective management method that goes beyond traditional budget management processes.

[0622] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0623] Step 1:

[0624] The server collects numerical information from external data sources. It retrieves real-time revenue and expenditure data from financial institutions and market analysis databases via APIs. Inputs include user authentication information and data request parameters, while output is the retrieved revenue and expenditure data.

[0625] Step 2:

[0626] The server cleanses the collected data. Using Python or R, it processes the data to correct inaccuracies and fill in missing values. The input is raw data, and the output is clean data in an analyzable format.

[0627] Step 3:

[0628] The server performs data analysis using clean data. Data analysis tools are utilized to perform data calculations to identify past revenue and expenditure trends and patterns. The input is clean data, and the output is the analysis results.

[0629] Step 4:

[0630] The server generates budget proposals using a generative AI model. Based on the analysis results, it applies machine learning models such as TensorFlow to create budget proposals based on multiple scenarios. The input is the analysis results, and the output is the budget proposal for each scenario.

[0631] Step 5:

[0632] The device captures the user's facial expressions and voice using emotion recognition technology. Data obtained from the built-in camera and microphone is processed by an emotion analysis API. Input is the user's facial expression data and voice data, and output is the result of the emotional state evaluation.

[0633] Step 6:

[0634] The device displays a budget proposal based on the user's emotional state. Based on the evaluation results, it presents appropriate information through the user interface. The inputs are emotional data and the budget proposal, and the output is an interactive budget proposal presented to the user.

[0635] Step 7:

[0636] Users review the budget proposal on their device and make adjustments as needed. Modifications are made via touch controls and voice commands through the interface. Input consists of the displayed budget proposal and user feedback, while output is the adjusted budget proposal.

[0637] Step 8:

[0638] The user submits the final adjusted budget proposal to the server. The server then performs an approval process and reflects it in the overall system. The input is the adjusted budget proposal, and the output is the final approved budget.

[0639] Step 9:

[0640] The server monitors the budget implementation process. It monitors the revenue and expenditure status in real time and detects anomalies. The input is the actual revenue and expenditure data, and the output is the result of the anomaly detection.

[0641] Step 10:

[0642] The server issues an alert when an anomaly is detected. It sends a warning message to the user's terminal to draw their attention. The input is the anomaly detection result, and the output is the user notification.

[0643] (Application Example 2)

[0644] 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."

[0645] For modern consumers, managing daily spending is a crucial challenge. However, traditional methods often fail to consider the user's emotional needs, leading to stress and frustration. In particular, a lack of budget flexibility and the inability to respond to sudden emotional reactions are problematic. To address this, there is a need for a system that can dynamically adjust budget proposals based on the user's emotions.

[0646] 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.

[0647] In this invention, the server includes means for collecting information from external information sources, means for analyzing historical data and real-time numerical information, and means for extracting and analyzing emotional data. This makes it possible to adaptively adjust the presentation of budget proposals based on the user's emotions.

[0648] "External information sources" refer to information collected from external sources such as the internet, corporate databases, and financial institutions.

[0649] "Means of collecting information" refers to hardware or software processes or devices used to acquire data.

[0650] "Means of analysis" refers to the processes and devices used to analyze data and extract useful information.

[0651] "Methods for automatically generating budget proposals" refers to processes or devices that mechanically create budget plans based on collected data.

[0652] "Electronic devices" refer to equipment that performs calculations and information processing, such as smartphones and tablet devices.

[0653] A "warning" is a notification or alarm designed to alert the user to an abnormal situation or malfunction.

[0654] "Emotional data" refers to information about a user's emotional state obtained from their facial expressions and voice.

[0655] "Means of adaptive adjustment" refer to processes or devices that dynamically change the operation of a system in response to the situation or user's reaction.

[0656] This invention is a system consisting of three main components: a server, a terminal, and a user. The server is responsible for collecting income and expenditure data from external sources and internal databases, and for cleansing and analyzing it. The analysis uses programming languages ​​such as Python and R, as well as data analysis tools. Machine learning models are used for acquiring and analyzing sentiment data, leveraging software such as TensorFlow and Keras.

[0657] The server automatically generates a budget proposal based on the analysis results and sends it to the terminal through an optimized user interface. The terminal has a built-in emotion recognition sensor that uses a camera and microphone to detect the user's facial expressions and voice. This is processed in real time using libraries such as OpenCV. This data is analyzed by an emotion engine, and the budget proposal is dynamically adjusted according to the user's emotions.

[0658] For example, if the emotion sensor detects stress while a user is reviewing a budget proposal on their device, the system reduces the user's burden by redisplaying the budget proposal in a more concise format. Furthermore, if the user shows interest, more detailed information and additional scenarios are presented. This allows users to manage their budget in a more comfortable environment.

[0659] An example of a prompt to input into the generative AI model would be text such as, "Suggest a savings plan that the user might be interested in. The emotion data is 'anxious'." This allows the system to provide information that is best suited to the user.

[0660] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0661] Step 1:

[0662] The server collects revenue and expenditure data from external sources. This is done by obtaining financial institution and market data via APIs. The input is the URL or API key of the external data source, and the output is the collected raw revenue and expenditure data.

[0663] Step 2:

[0664] The server cleanses and analyzes the collected income and expenditure data. It uses data analysis libraries such as Pandas to impute missing values ​​and remove noise. The input is raw income and expenditure data, and the output is cleansed, analyzable data.

[0665] Step 3:

[0666] The server analyzes emotional data using an emotion engine. It classifies the user's emotional state using a pre-trained generative AI model. The input is the user's facial expressions and voice data, and the output is the analyzed emotion label.

[0667] Step 4:

[0668] The server automatically generates budget proposals based on analyzed revenue and expenditure data and sentiment data. It generates multiple budget patterns according to the scenario and selects the optimal one. A machine learning model is used in this process. The input is an analyzed dataset, and the output is a budget proposal for each scenario.

[0669] Step 5:

[0670] The terminal displays the budget proposal received from the server in an optimized user interface. An emotion sensor detects the user's reactions in real time and adjusts the displayed content as needed. The input is the budget proposal and the user's real-time emotion data, and the output is the displayed content of the adjusted budget.

[0671] Step 6:

[0672] Users review the budget proposal through their device and make adjustments as needed. They make adjustments via touch input, referencing suggested changes based on sentiment data. The input is the budget proposal before adjustments, and the output is the budget proposal after user adjustments.

[0673] Step 7:

[0674] The server saves the user-approved budget proposal to the final database and reflects it in the overall system. It continuously monitors data to detect anomalies and monitors budget implementation in real time. The input is the approved budget proposal, and the output is the updated system data.

[0675] Through the steps outlined above, flexible budget management tailored to user emotions can be achieved.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] [Fourth Embodiment]

[0680] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0681] 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.

[0682] 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).

[0683] 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.

[0684] 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.

[0685] 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).

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] 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".

[0693] This invention is an automated system for efficiently managing a company's budget. This system primarily consists of three components: a server, terminals, and users.

[0694] The server has the capability to collect necessary data from external sources and internal databases and analyze it. Machine learning algorithms are used for data analysis to clarify past trends and predict future trends. Based on these results, the server automatically generates a budget proposal that takes multiple factors into account and sends it to the physical device.

[0695] The terminal functions as a user interface, visualizing and presenting the generated budget proposal to the user. The user reviews and adjusts the budget details through the terminal, and sends the adjustments to the server to proceed with the approval process.

[0696] The user has the role of adjusting and approving the budget. Specifically, they make necessary modifications to the budget proposal presented on the terminal and make the final decision. This operation is set up to ensure that appropriate budget allocation is achieved based on the user's judgment.

[0697] As an example of how this system works, consider the budget management process for a company starting a new project. The server generates a budget proposal based on data from similar past projects. This proposal includes necessary resources and schedules and is presented to the user on a terminal. The user reviews the presented budget proposal and makes adjustments as needed to match the project's priorities and strategy. The final approved budget is returned to the server, and monitoring of its implementation status begins. In this way, the system significantly improves transparency and efficiency in budget management.

[0698] This system also has the capability to detect unusual expenditures and revenue fluctuations in real time, and the server immediately issues alerts. This allows users to take swift action and maintain the financial health of their businesses.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server collects revenue and expenditure data from external sources and internal corporate databases. This includes using APIs to interact with external systems and obtain up-to-date financial data.

[0702] Step 2:

[0703] The server cleanses the collected data and ensures data quality. It detects outliers and incomplete data and corrects or supplements them as needed.

[0704] Step 3:

[0705] The server uses machine learning models to analyze historical revenue and expenditure data and perform trend analysis. It also uses time series analysis and regression models to predict future revenues and expenses.

[0706] Step 4:

[0707] The server automatically generates budget proposals for each project and department based on the prediction results. This process ensures appropriate resource allocation, taking into account current business objectives and strategies.

[0708] Step 5:

[0709] The server transfers the generated budget proposal to the terminal and prepares it for presentation to the user.

[0710] Step 6:

[0711] The device displays the budget proposal to the user using visualization tools, allowing the user to easily evaluate the budget details.

[0712] Step 7:

[0713] Users operate the terminal to review the budget proposal and make revisions as needed. The revised content is then sent to the server for approval.

[0714] Step 8:

[0715] The server records the proposed revisions received from users and reflects the final approved budget in the system.

[0716] Step 9:

[0717] The server monitors budget implementation in real time and generates periodic reports, including budget utilization rates and detection of anomalous expenditures.

[0718] Step 10:

[0719] If the server detects unusual spending or revenue fluctuations, it immediately issues an alert. The terminal notifies the user of the alert and prompts them to take prompt action.

[0720] (Example 1)

[0721] 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".

[0722] Corporate budget management is often inefficient, error-prone, and involves a great deal of manual work. Furthermore, it's difficult to detect unusual expenditures or revenue fluctuations requiring immediate attention in real time. In such circumstances, corporate financial planning is frequently inaccurate, degrading the quality of decision-making. Therefore, there is a need for a method that simultaneously achieves both efficiency and accuracy in budget management.

[0723] 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.

[0724] In this invention, the server includes means for acquiring information from an external data source, means for using a mathematical model to analyze historical data and real-time numerical information, and means for automatically creating a budget proposal. This enables efficient and accurate budget management and early detection of anomalies.

[0725] An "external data source" refers to an external information provision device or system used to acquire information from both inside and outside a company.

[0726] "Means of acquiring information" refers to system functions for collecting necessary data from external data sources.

[0727] "Historical data" refers to a collection of numerical information and records that have been collected and stored in the past.

[0728] "Real-time numerical information" refers to data provided immediately that reflects the current situation and transactions.

[0729] A "mathematical model for analysis" is a mathematical method used to analyze numerical and data patterns to provide future predictions and insights.

[0730] "Methods for automatically generating budget proposals" refers to functions that automatically generate budget plans using machine learning algorithms and computational methods.

[0731] An "indicating device" is a device or interface used to display information or results to a user.

[0732] "Means of control" refer to methods for managing processes and information based on user input and decisions.

[0733] "Means for detecting anomalies" are functions for identifying data patterns or events that are different from the norm.

[0734] A "notification system" is a mechanism for quickly communicating abnormal or important information to users.

[0735] "Means of analyzing visual information" refers to processing technologies for extracting information from images and videos.

[0736] "Multiple plan options" refer to several strategic forecast plans formulated based on different assumptions and conditions.

[0737] This invention is an automated system designed to streamline corporate budget management and consists of three main components: a server, a terminal, and a user.

[0738] The server collects necessary data from external data sources and internal databases. Internal data is managed using an SQL database, while external data is obtained through API calls and web scraping techniques. Programming libraries such as "requests" and "Beautiful Soup" support this process. The collected data is analyzed within the server. Machine learning algorithms such as "Scikit-learn" and "TensorFlow" are used for analysis, analyzing past trends and making future predictions. This allows the server to automatically generate budget proposals.

[0739] The generated budget proposal is sent to the terminal via the network. On the terminal, a user interface using "React.js" or "Vue.js" is activated, visualizing and presenting the budget proposal to the user. The terminal provides an interactive presentation using graphs and tables. The user can easily manipulate the displayed data and review the contents of the budget proposal.

[0740] Users can evaluate the budget proposal presented through their device and make adjustments as needed. These adjustments may involve using spreadsheet software such as Excel or Google Sheets. The adjusted budget proposal is sent to the server via an API. The server receives the adjustments and performs the necessary approval process.

[0741] This system has the capability to detect unusual expenditures and revenue fluctuations in real time and issue immediate alerts. This allows companies to quickly understand their financial situation and take necessary actions.

[0742] As a concrete example, when implementing a new development project, the server generates a budget proposal based on data from similar past projects. This budget proposal includes the necessary resources and timeframes and is presented to the user on their terminal. The user can then use this information to make adjustments in line with the project strategy and obtain final approval. This process makes the company's budget management process highly transparent and efficient.

[0743] For example, you can give instructions to a generative AI model using prompts as follows:

[0744] "Based on past data from similar projects, please propose a budget for the new project, divided into five main categories. For each category, please include the estimated cost and the reasoning behind it."

[0745] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0746] Step 1:

[0747] The server collects necessary data from external data sources and internal databases. Inputs consist of external information supplied via APIs and internal data obtained via SQL queries. The server collects these inputs and generates an integrated dataset. Specifically, it uses the "requests" library to retrieve data from external APIs and "SQLAlchemy" to send queries to the internal database. The output is the dataset required for analysis.

[0748] Step 2:

[0749] The server analyzes the collected dataset to identify past trends and make future predictions. The input is the integrated dataset obtained in Step 1. The server uses "Scikit-learn" and "TensorFlow" to train machine learning models and analyze the data. Data analysis includes regression analysis for trend identification and budget forecasting. The output is insights and numerical information based on the predictions.

[0750] Step 3:

[0751] The server automatically generates a budget proposal based on the analysis results. The inputs are the insights and predicted figures obtained in Step 2. The server uses this data to construct the budget proposal. This process is governed by specific calculation logic and business rules. The output is the proposed budget proposal, structured in JSON format.

[0752] Step 4:

[0753] The server sends the generated budget proposal to the terminal. The input is the budget proposal generated in step 3. The server sends the budget proposal to the terminal via the network communication protocol. The output is the presentation of the budget proposal to the terminal.

[0754] Step 5:

[0755] The terminal visualizes the received budget proposal through a user interface. The input is the budget proposal sent from the server. The terminal uses React.js or Vue.js to display the budget proposal as an interactive graph or table. This display allows the user to easily grasp the detailed information of the budget proposal. The output is the visualized budget proposal.

[0756] Step 6:

[0757] The user reviews the budget proposal presented through the terminal and makes adjustments as needed. The input is the budget proposal visualized in step 5. The user edits each item of the budget and creates an adjustment proposal. Spreadsheet software may be used for this. The output is the adjusted budget proposal.

[0758] Step 7:

[0759] The terminal sends the budget proposal adjusted by the user to the server. The input is the budget proposal modified by the user in step 6. The terminal sends the adjusted proposal to the server via the API, and the output is the budget proposal data for approval.

[0760] Step 8:

[0761] The server receives the revised budget proposal and proceeds with the approval process. The input is the revised budget proposal sent from the terminal. The server executes the logic to request approval and notifies relevant parties as needed. The output is the approved budget proposal and its record.

[0762] (Application Example 1)

[0763] 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".

[0764] Budget management in businesses is essential for efficient resource allocation and ensuring financial soundness, but the usual management process is often complex and time-consuming. Furthermore, real-time information verification and adjustment are difficult, potentially hindering appropriate budget allocation. Conventional systems also suffer from delays in anomaly detection and alert generation, making rapid response difficult. This invention aims to solve these problems and further improve the real-time and efficiency of budget management in factory environments.

[0765] 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.

[0766] In this invention, the server includes means for collecting data from external information sources, means for analyzing historical data and real-time numerical information, means for automatically generating a budget proposal, means for presenting the generated budget via a visualization terminal, means for managing budget adjustments and approvals by users, means for monitoring the budget implementation status and detecting anomalies, means for transmitting a warning signal when an anomaly is detected, and means for displaying budget information in real time using a smart information display device and allowing users to directly adjust the information. This enables immediate information access and adjustment in the factory environment, facilitating budget optimization and rapid response to abnormal situations.

[0767] "Means of collecting data from external sources" refers to technologies and devices for automatically or manually obtaining necessary information from various databases and online resources both inside and outside a company.

[0768] "Means for analyzing historical data and real-time numerical information" refer to algorithms and software that use accumulated historical data and currently occurring data to analyze the current situation and make future predictions.

[0769] "Methods for automatically generating budget proposals" refer to technologies that automate the process of creating budget proposals based on collected data, taking into account the allocation of necessary resources and costs.

[0770] "Means of presenting generated budgets via visualization terminals" refers to functions that communicate budget information to users using devices or interfaces for visually displaying budget information, such as displays or smart glasses.

[0771] "Means for managing user budget adjustments and approvals" refers to assistive technologies that allow users to review proposed budgets, make modifications as needed, and then proceed with the final approval process.

[0772] "Means for monitoring budget implementation and detecting anomalies" refers to analytical techniques that check whether actual expenditures and revenues match the budget plan and issue warnings if anomalies are found.

[0773] "Means for sending warning signals when an anomaly is detected" refers to a function that promptly notifies users or administrators when expenditures exceeding set standards or discrepancies are found.

[0774] "A means of displaying budget information in real time using a smart information display device and allowing users to directly adjust the information" refers to a system that uses interactive devices such as smart glasses or head-mounted displays to allow users to instantly manipulate and modify budget data.

[0775] To implement this invention, a system is constructed in which the server, terminals, and users each play specific roles. The server is the main data processing center, generating information necessary for budget management based on data collected from external sources and internal databases. Specifically, it is responsible for the process of analyzing past trends using machine learning algorithms and automatically generating future budget proposals. This analysis utilizes data processing libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow) using Python.

[0776] The terminal functions as a user interface, visualizing and presenting budget information generated on the server to the user. This display utilizes a front-end framework such as Vue.js, and the information is presented clearly via smart glasses or a head-mounted display. Through this, the user can review the presented budget and make adjustments as needed. The adjusted information is then sent back to the server for the final approval process. This allows for real-time monitoring of resource allocation within the factory.

[0777] Examples of user interactions include prompts such as, "Material costs in the factory have exceeded the budget. Please suggest adjustments in real time," or "Please tell me the optimal staffing arrangement to stay within budget." This allows users to instantly utilize the generated AI model to obtain the necessary information and make more accurate budget adjustments.

[0778] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0779] Step 1:

[0780] The server collects data from external sources and internal databases. Various data sources are accepted as input, and Python is used to execute API calls and database queries to collect budget-related data. The output is an integrated dataset.

[0781] Step 2:

[0782] The server performs data analysis on the collected data. It supplies the input data to machine learning algorithms to perform historical trend analysis and build predictive models. Specifically, it uses scikit-learn and TensorFlow to train and predict data. The output is the prediction results necessary for generating budget proposals.

[0783] Step 3:

[0784] The server automatically generates a budget proposal based on the prediction results. Using this input data, an AI model calculates the optimal budget allocation solution, and the generated budget proposal is output. The budget proposal includes the resources to be considered and their associated costs.

[0785] Step 4:

[0786] The server sends the generated budget proposal to the terminal, which then visualizes it. The system receives the generated budget proposal as input and uses frontend technologies such as Vue.js to display the information on smart glasses. The output is visualized budget data, which is then presented to the user.

[0787] Step 5:

[0788] The user reviews the displayed budget proposal and makes adjustments as needed. The input is visualized budget data, which the user manipulates using the adjustment interface. The output is the adjusted budget proposal.

[0789] Step 6:

[0790] The terminal sends the adjusted budget proposal to the server, which then proceeds with the final approval process. The server receives the adjusted data as input, performs final verification and necessary processing, and outputs the results. The finalized budget proposal is then prepared for implementation.

[0791] Step 7:

[0792] The server monitors budget implementation and detects anomalies. It analyzes real-time expenditure data as input to detect discrepancies in data patterns and budget overruns. When an anomaly is detected, it generates and outputs a warning signal.

[0793] Step 8:

[0794] The server immediately sends an alert to the user after detecting an anomaly. The alert notification is based on the detected anomaly. The user receives the alert and takes prompt action as a result.

[0795] 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.

[0796] This invention is a system that combines an emotion engine to enable more effective budget management for users. The system consists of three main components: a server, a terminal, and the user, with the emotion engine also functioning.

[0797] The server collects revenue and expenditure data from external sources and internal databases, cleanses that data, and analyzes it. Based on the analysis results, it has a function to automatically generate budget proposals using machine learning models. Multiple budget proposals are created for different scenarios, and the optimal resource allocation under various conditions is considered.

[0798] The generated budget proposal is displayed on the device with an optimized user interface. Crucially, the emotion engine plays a key role here; the device incorporates an emotion recognition sensor that recognizes the user's emotions from their facial expressions and voice. This emotion data is sent to a server and used to present and adjust the budget proposal.

[0799] Users receive interactive budget proposals based on emotional data via their devices, which they then review and adjust. Based on this information, the user's stress level and engagement are determined, and the budget proposal is flexibly modified accordingly.

[0800] For example, if the emotion engine detects a stress response while a user is viewing a project budget proposal, the system will immediately redisplay the budget proposal in a simplified format. Furthermore, if the user expresses positive emotions towards the budget proposal, adjustments will be made to improve the user experience, such as presenting detailed analysis results or additional scenarios.

[0801] Finally, the user submits the revised budget proposal to the server for final approval. The approved budget is then reflected in the entire system by the server, and the budget implementation status is monitored in real time. If any anomalies are detected, an alert is immediately issued and notified to the user on their terminal.

[0802] Thus, by incorporating an emotion engine, the present invention provides a new dimension of effectiveness to conventional budget management processes and promotes an improved user experience.

[0803] The following describes the processing flow.

[0804] Step 1:

[0805] The server collects necessary revenue and expenditure data from external sources and internal corporate databases. This data collection includes integration with external systems using APIs.

[0806] Step 2:

[0807] The server cleanses the collected data and analyzes it using machine learning models. Based on the analysis results, it automatically generates multiple budget proposals based on past trends and future economic indicators.

[0808] Step 3:

[0809] The generated budget proposal is sent from the server to the terminal and displayed on the user interface. The terminal uses visualization tools to present the user with budget proposals for multiple scenarios.

[0810] Step 4:

[0811] The device incorporates an emotion recognition sensor that recognizes the user's emotions in real time from their facial expressions and voice.

[0812] Step 5:

[0813] The emotion engine analyzes the user's emotional data and optimizes the display format of the budget proposal according to the detected emotions. For example, if the user is feeling stressed, the amount of information is simplified and redisplayed in an easy-to-understand format.

[0814] Step 6:

[0815] Users can review the budget proposal presented on their device and make adjustments as needed. For example, if additional resources are required for a particular project, they can reflect that in the budget.

[0816] Step 7:

[0817] The user submits the budget proposal, which they have completed adjusting, to the server for approval. The approved budget proposal is then reflected throughout the system by the server.

[0818] Step 8:

[0819] The server monitors budget implementation in real time and immediately sends an alert to the terminal if it detects any significant anomalies or unexpected fluctuations in revenue and expenditure.

[0820] Step 9:

[0821] Users receive alerts on their devices and take appropriate action. For example, if project costs exceed the budget, they reassess the required budget. In this way, the emotion engine improves the user experience and enables efficient budget management.

[0822] (Example 2)

[0823] 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".

[0824] Traditional budget management systems often fail to adequately adapt to users because they unilaterally provide information without considering their emotions or psychological state. Furthermore, users may experience stress when adjusting budget proposals, or the information may be overwhelming and difficult to understand. Additionally, there may be delays in detecting anomalies and issuing alerts, requiring a rapid response. To address these challenges, there is a need to develop a system that manages budgets more effectively while considering user emotions.

[0825] 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.

[0826] In this invention, the server includes a unit that collects numerical information from an external data source, a unit that evaluates the user's psychological state using emotion recognition technology, and a module that transmits alarms for identified anomalies. This enables flexible budget management in response to the user's emotions and rapid detection and response to anomalies.

[0827] "External data sources" is a general term for external information providers or databases that a system uses to collect information.

[0828] "Numerical information" refers to data expressed numerically that a system needs to analyze and process.

[0829] A "unit" refers to a portion of hardware or software configured to perform a specific function.

[0830] "Emotion recognition technology" is a technology that analyzes a user's facial expressions, voice, etc., to determine their current psychological state.

[0831] "Psychological state" refers to the mental state or emotions inferred from the user's facial expressions, voice, and actions.

[0832] "Evaluating" refers to the process of analyzing information based on specific criteria and drawing conclusions.

[0833] An "anomaly" refers to data or situations that deviate from the normal, predictable range, and signifies a situation that requires attention or improvement.

[0834] An "alarm" refers to a means of notifying a user or related system of an anomaly when it occurs.

[0835] A "module" refers to an independent part of a program that is responsible for a specific function.

[0836] This invention provides a system that utilizes emotion recognition to effectively manage user budgets. The system primarily consists of three main components: a server, a terminal, and a user. The roles and specific implementation methods of each component are described below.

[0837] The server plays a central role in data processing and analysis. It collects necessary numerical information from external data sources via APIs, and cleans and analyzes the data using data analysis software such as Python or R. Based on the analysis results, the server automatically generates multiple budget proposals using generative AI models (e.g., TensorFlow or PyTorch). This creates forecasts based on historical data and budget proposals for various scenarios.

[0838] The device is a crucial component responsible for user interaction. It incorporates emotion recognition technology, capturing the user's facial expressions and voice through hardware such as cameras and microphones. This data is analyzed using emotion analysis software (e.g., a common emotion recognition API) to determine the user's psychological state. Along with this emotion data, the device proposes a budget plan to the user through an optimized interface. The presentation style and level of detail are adjusted according to the user's emotions.

[0839] Users can review the budget proposal presented on their device and interactively adjust it to suit their needs. For example, if the system detects user stress while viewing a project budget proposal, the device will simplify and display the budget proposal. Conversely, if the user shows a positive reaction, additional information will be provided to improve the user experience.

[0840] Here's a concrete example of a prompt: "Create optimal budget proposals for next month's marketing project, broken down by scenario. Dynamically modify user suggestions based on sentiment data." This type of prompt is given to a generative AI model to generate the budget proposals.

[0841] This system takes user emotions into account in real time, enabling a flexible and effective management method that goes beyond traditional budget management processes.

[0842] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0843] Step 1:

[0844] The server collects numerical information from external data sources. It retrieves real-time revenue and expenditure data from financial institutions and market analysis databases via APIs. Inputs include user authentication information and data request parameters, while output is the retrieved revenue and expenditure data.

[0845] Step 2:

[0846] The server cleanses the collected data. Using Python or R, it processes the data to correct inaccuracies and fill in missing values. The input is raw data, and the output is clean data in an analyzable format.

[0847] Step 3:

[0848] The server performs data analysis using clean data. Data analysis tools are utilized to perform data calculations to identify past revenue and expenditure trends and patterns. The input is clean data, and the output is the analysis results.

[0849] Step 4:

[0850] The server generates budget proposals using a generative AI model. Based on the analysis results, it applies machine learning models such as TensorFlow to create budget proposals based on multiple scenarios. The input is the analysis results, and the output is the budget proposal for each scenario.

[0851] Step 5:

[0852] The device captures the user's facial expressions and voice using emotion recognition technology. Data obtained from the built-in camera and microphone is processed by an emotion analysis API. Input is the user's facial expression data and voice data, and output is the result of the emotional state evaluation.

[0853] Step 6:

[0854] The device displays a budget proposal based on the user's emotional state. Based on the evaluation results, it presents appropriate information through the user interface. The inputs are emotional data and the budget proposal, and the output is an interactive budget proposal presented to the user.

[0855] Step 7:

[0856] Users review the budget proposal on their device and make adjustments as needed. Modifications are made via touch controls and voice commands through the interface. Input consists of the displayed budget proposal and user feedback, while output is the adjusted budget proposal.

[0857] Step 8:

[0858] The user submits the final adjusted budget proposal to the server. The server then performs an approval process and reflects it in the overall system. The input is the adjusted budget proposal, and the output is the final approved budget.

[0859] Step 9:

[0860] The server monitors the budget implementation process. It monitors the revenue and expenditure status in real time and detects anomalies. The input is the actual revenue and expenditure data, and the output is the result of the anomaly detection.

[0861] Step 10:

[0862] The server issues an alert when an anomaly is detected. It sends a warning message to the user's terminal to draw their attention. The input is the anomaly detection result, and the output is the user notification.

[0863] (Application Example 2)

[0864] 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".

[0865] For modern consumers, managing daily spending is a crucial challenge. However, traditional methods often fail to consider the user's emotional needs, leading to stress and frustration. In particular, a lack of budget flexibility and the inability to respond to sudden emotional reactions are problematic. To address this, there is a need for a system that can dynamically adjust budget proposals based on the user's emotions.

[0866] 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.

[0867] In this invention, the server includes means for collecting information from external information sources, means for analyzing historical data and real-time numerical information, and means for extracting and analyzing emotional data. This makes it possible to adaptively adjust the presentation of budget proposals based on the user's emotions.

[0868] "External information sources" refer to information collected from external sources such as the internet, corporate databases, and financial institutions.

[0869] "Means of collecting information" refers to hardware or software processes or devices used to acquire data.

[0870] "Means of analysis" refers to the processes and devices used to analyze data and extract useful information.

[0871] "Methods for automatically generating budget proposals" refers to processes or devices that mechanically create budget plans based on collected data.

[0872] "Electronic devices" refer to equipment that performs calculations and information processing, such as smartphones and tablet devices.

[0873] A "warning" is a notification or alarm designed to alert the user to an abnormal situation or malfunction.

[0874] "Emotional data" refers to information about a user's emotional state obtained from their facial expressions and voice.

[0875] "Means of adaptive adjustment" refer to processes or devices that dynamically change the operation of a system in response to the situation or user's reaction.

[0876] This invention is a system consisting of three main components: a server, a terminal, and a user. The server is responsible for collecting income and expenditure data from external sources and internal databases, and for cleansing and analyzing it. The analysis uses programming languages ​​such as Python and R, as well as data analysis tools. Machine learning models are used for acquiring and analyzing sentiment data, leveraging software such as TensorFlow and Keras.

[0877] The server automatically generates a budget proposal based on the analysis results and sends it to the terminal through an optimized user interface. The terminal has a built-in emotion recognition sensor that uses a camera and microphone to detect the user's facial expressions and voice. This is processed in real time using libraries such as OpenCV. This data is analyzed by an emotion engine, and the budget proposal is dynamically adjusted according to the user's emotions.

[0878] For example, if the emotion sensor detects stress while a user is reviewing a budget proposal on their device, the system reduces the user's burden by redisplaying the budget proposal in a more concise format. Furthermore, if the user shows interest, more detailed information and additional scenarios are presented. This allows users to manage their budget in a more comfortable environment.

[0879] An example of a prompt to input into the generative AI model would be text such as, "Suggest a savings plan that the user might be interested in. The emotion data is 'anxious'." This allows the system to provide information that is best suited to the user.

[0880] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0881] Step 1:

[0882] The server collects revenue and expenditure data from external sources. This is done by obtaining financial institution and market data via APIs. The input is the URL or API key of the external data source, and the output is the collected raw revenue and expenditure data.

[0883] Step 2:

[0884] The server cleanses and analyzes the collected income and expenditure data. It uses data analysis libraries such as Pandas to impute missing values ​​and remove noise. The input is raw income and expenditure data, and the output is cleansed, analyzable data.

[0885] Step 3:

[0886] The server analyzes emotional data using an emotion engine. It classifies the user's emotional state using a pre-trained generative AI model. The input is the user's facial expressions and voice data, and the output is the analyzed emotion label.

[0887] Step 4:

[0888] The server automatically generates budget proposals based on analyzed revenue and expenditure data and sentiment data. It generates multiple budget patterns according to the scenario and selects the optimal one. A machine learning model is used in this process. The input is an analyzed dataset, and the output is a budget proposal for each scenario.

[0889] Step 5:

[0890] The terminal displays the budget proposal received from the server in an optimized user interface. An emotion sensor detects the user's reactions in real time and adjusts the displayed content as needed. The input is the budget proposal and the user's real-time emotion data, and the output is the displayed content of the adjusted budget.

[0891] Step 6:

[0892] Users review the budget proposal through their device and make adjustments as needed. They make adjustments via touch input, referencing suggested changes based on sentiment data. The input is the budget proposal before adjustments, and the output is the budget proposal after user adjustments.

[0893] Step 7:

[0894] The server saves the user-approved budget proposal to the final database and reflects it in the overall system. It continuously monitors data to detect anomalies and monitors budget implementation in real time. The input is the approved budget proposal, and the output is the updated system data.

[0895] Through the steps outlined above, flexible budget management tailored to user emotions can be achieved.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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."

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] The following is further disclosed regarding the embodiments described above.

[0918] (Claim 1)

[0919] Means of collecting data from external sources,

[0920] A means of analyzing historical data and real-time numerical information,

[0921] A means of automatically generating a budget proposal,

[0922] A means of presenting the generated budget via a solid-state terminal,

[0923] A means of managing budget adjustments and approvals by users,

[0924] A means to monitor the status of budget implementation and detect anomalies,

[0925] A means of issuing alerts for detected anomalies,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, comprising means for analyzing images in order to extract income and expenditure information as text data.

[0929] (Claim 3)

[0930] The system according to claim 1, comprising means for generating different budget proposals based on multiple planning scenarios.

[0931] "Example 1"

[0932] (Claim 1)

[0933] Means of obtaining information from external data sources,

[0934] Means of using mathematical models to analyze historical data and real-time numerical information,

[0935] A means to automatically create a budget proposal,

[0936] A means of presenting the created budget via an instruction device,

[0937] A means of controlling user budget adjustments and approvals,

[0938] A means to monitor the status of budget implementation and detect anomalies,

[0939] A means of issuing notifications for detected anomalies,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, comprising means for analyzing visual information in order to extract expenditure and revenue information as text data.

[0943] (Claim 3)

[0944] The system according to claim 1, comprising means for creating different budget proposals based on multiple plan proposals.

[0945] "Application Example 1"

[0946] (Claim 1)

[0947] Means of collecting data from external sources,

[0948] A means of analyzing historical data and real-time numerical information,

[0949] A means of automatically generating a budget proposal,

[0950] A means of presenting the generated budget via a visualization terminal,

[0951] A means of managing budget adjustments and approvals by users,

[0952] A means to monitor the status of budget implementation and detect anomalies,

[0953] A means for transmitting a warning signal when an abnormality is detected,

[0954] A smart information display device is used to display budget information in real time, and a means for users to directly adjust the information.

[0955] A system that includes this.

[0956] (Claim 2)

[0957] The system according to claim 1, comprising means for analyzing images in order to extract income and expenditure information as text data, and transferring the data to a smart information display device.

[0958] (Claim 3)

[0959] The system according to claim 1, comprising means for generating different budget proposals based on multiple planning scenarios, and optimizing the budget proposals by utilizing input prompts to a generating AI model.

[0960] "Example 2 of combining an emotion engine"

[0961] (Claim 1)

[0962] A unit that collects numerical information from external data sources,

[0963] A unit that purifies information in order to analyze patterns,

[0964] A unit that analyzes digital information to build budget plans,

[0965] A unit that visually communicates the planned budget through a device,

[0966] A unit that evaluates the user's psychological state using emotion recognition technology,

[0967] A management unit where users make budget changes and final approvals,

[0968] A module that observes the budget implementation process and identifies anomalies,

[0969] A module that transmits an alarm for identified anomalies,

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, which has a function for examining visual materials in order to extract recorded information as textual information.

[0973] (Claim 3)

[0974] The system according to claim 1, comprising a unit for formulating different budgets based on multiple plan proposals.

[0975] "Application example 2 when combining with an emotional engine"

[0976] (Claim 1)

[0977] Means of collecting information from external sources,

[0978] A means of analyzing historical data and real-time numerical information,

[0979] A means of automatically generating a budget proposal,

[0980] A means of presenting the generated budget via an electronic device,

[0981] A means of managing budget adjustments and approvals by users,

[0982] A means to monitor the status of budget implementation and detect anomalies,

[0983] A means of issuing a warning for detected anomalies,

[0984] Methods for extracting and analyzing emotional data,

[0985] A means of adaptively adjusting the information presented based on the user's emotions,

[0986] A system that includes this.

[0987] (Claim 2)

[0988] The system according to claim 1, comprising means for analyzing an image in order to extract numerical information as text data.

[0989] (Claim 3)

[0990] The system according to claim 1, comprising means for generating different budget proposals based on multiple planning scenarios. [Explanation of Symbols]

[0991] 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. Means of collecting data from external sources, A means of analyzing historical data and real-time numerical information, A means of automatically generating a budget proposal, A means of presenting the generated budget via a solid-state terminal, A means of managing budget adjustments and approvals by users, A means to monitor the status of budget implementation and detect anomalies, A means of issuing alerts for detected anomalies, A system that includes this.

2. The system according to claim 1, comprising means for analyzing images in order to extract income and expenditure information as text data.

3. The system according to claim 1, comprising means for generating different budget proposals based on multiple planning scenarios.

Images