system

Abstract

We provide the system. [Solution] A means of automatically collecting work data and computer operation information, A means for automatically calculating salary information based on the collected information, A means of automatically processing social security and taxation based on the calculated salary information, A means of proposing individual financial plans based on the salary information, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] Conventional salary calculation and personnel processing rely on a lot of manual work. Therefore, not only does it take a long time for the work, but there may also be calculation errors and delays in administrative procedures. In addition, the support for the financial plans of individual employees is insufficient. When employees themselves do not have sufficient knowledge about asset management and investment, it is difficult to formulate an effective financial strategy. The present invention aims to solve these problems and provide an efficient and beneficial system for both enterprises and employees.

Means for Solving the Problems

[0005] This invention provides a system that automatically collects work data and computer operation information, and automatically calculates salary information based on this data. Furthermore, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. In addition, by incorporating means for analyzing work hours to evaluate work efficiency and means for automatically responding to user input using natural language processing, it solves conventional problems and aims to improve operational efficiency and optimize employee financial strategies.

[0006] "Work data" refers to information related to employees' attendance, departure times, and working hours.

[0007] "Computer operation information" refers to information about computer applications used by employees for work and the time spent using them.

[0008] "Salary information" refers to information about the compensation that employees receive for their work, including base salary, allowances, and deductions.

[0009] "Social security" refers to social welfare systems such as health insurance, pensions, and unemployment insurance, and includes the procedures related to them.

[0010] "Taxation" refers to the procedures related to taxes that individuals or corporations pay to the government based on their income or assets.

[0011] A "financial plan" is a financial plan undertaken by an individual or company in anticipation of future funding needs, and includes strategies for saving and investing.

[0012] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0013] "Business efficiency" refers to the ratio of resources invested in a particular task to the results obtained from those resources, and is a measure used to evaluate the productivity of a task. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] An example of an embodiment of the system according to the technology of the present disclosure will be described below 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 implemented as a system that automatically collects employee work data and computer operation information, and automatically calculates salary information based on this data. In addition, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. Furthermore, it evaluates operational efficiency and has the functionality to automatically respond to operations and questions in human language using natural language processing.

[0036] In a specific implementation, a server acts as a central administrator, periodically collecting work data and computer operation logs from each employee's terminal. When an employee logs in at the start of their workday, the terminal generates and sends this information to the server. The server aggregates and manages this data, and uses AI algorithms to analyze work hours and work efficiency.

[0037] In payroll calculations, the server calculates the base salary based on each employee's pre-configured salary components. Furthermore, it calculates various deductions based on the latest tax and social security laws to determine the employee's final take-home pay. This result is automatically delivered to terminals, allowing employees to easily check their individual pay stubs.

[0038] Furthermore, regarding financial planning proposals, the server analyzes each employee's salary history and past payment data to create individualized financial strategies. These proposals are optimized to each employee's lifestyle and future plans and are presented to them via their terminals. Employees can review the proposed plans in detail and provide feedback to the server in natural language.

[0039] For example, if an employee is considering purchasing a home, the system simulates future income and expenses and proposes an optimal loan plan based on their current income. This allows the employee to gain a deeper understanding of financial product selection. Through this process, the server improves operational efficiency and automatically provides each employee with useful financial advice.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The terminal generates login information when an employee starts work and sends it to the server. This includes the login time and identification information.

[0043] Step 2:

[0044] The terminal generates PC operation logs at set time intervals and periodically sends this data to the server. The operation logs record the names of the applications used and the duration of their use.

[0045] Step 3:

[0046] The server stores received login information and operation logs in a centralized database. This database serves as foundational information for use in subsequent processing steps.

[0047] Step 4:

[0048] The server uses AI algorithms to analyze collected work data and evaluate each employee's working hours and work efficiency. This evaluation result is then reflected in subsequent payroll calculations.

[0049] Step 5:

[0050] The server starts the payroll module and calculates each employee's base salary based on pre-configured payroll rules. Then, taking into account the latest tax laws and social security regulations, it deducts the necessary amounts to calculate the final take-home pay.

[0051] Step 6:

[0052] The server sends the generated payroll information to each employee's terminal and displays their payslip. This information helps users accurately understand their own salary structure.

[0053] Step 7:

[0054] The server analyzes users' payroll history and payment data to generate individualized financial plans. These plans present optimal strategies to meet employees' future financial needs.

[0055] Step 8:

[0056] The device displays the generated financial plan to the user. The user can then provide feedback and questions in natural language.

[0057] Step 9:

[0058] The server analyzes user input using natural language processing and generates automated responses. This allows for immediate addressing of user questions and requests.

[0059] (Example 1)

[0060] 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."

[0061] The challenge lies in providing a system that efficiently manages employee work information and computer operation history, accurately calculates compensation information, automates social welfare and tax processing, and optimizes and presents individual asset plans, thereby improving employee work efficiency and supporting enhanced asset management capabilities.

[0062] 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.

[0063] In this invention, the server includes means for automatically collecting employee work information and computer operation history, means for automatically calculating compensation information based on the collected information, means for automatically processing social welfare and taxation based on the calculated compensation information, and means for analyzing each employee's salary history and settlement history to optimize individual asset plans. This makes it possible to simplify employee salary management and tax procedures, and further supports employees in making financially meaningful decisions by proposing individually optimized asset plans.

[0064] "Employee work information" refers to information related to work, including employees' arrival and departure times, working hours, and break times.

[0065] "Computer operation history" refers to information that shows a record of operations performed on computers and other digital devices.

[0066] "Compensation information" refers to information that shows monetary compensation for an employee's work, such as salary, wages, and bonuses.

[0067] "Social welfare" is a term that refers to public welfare services and benefits provided under the social security system.

[0068] "Taxation" refers to taxes that the government collects from individuals and corporations in accordance with the law.

[0069] An "individualized asset plan" is a savings, investment, and spending strategy optimized based on the financial situation of an individual or organization.

[0070] "Means of automatic data collection" refers to functions or devices in which a machine or software collects data without human intervention.

[0071] "Means of calculation" refers to a device or algorithm that performs calculations based on data or input information and derives a result.

[0072] "Means of processing" refers to the technology or system used to manage, modify, or analyze data for a specific purpose.

[0073] "Means of analysis and optimization" refers to methods or techniques for analyzing data to derive the most efficient solutions or results.

[0074] This system automatically collects each employee's work information and computer operation history, and calculates compensation information based on that data. Specifically, it manages information by linking the server and terminals, and is designed to allow users to efficiently check and operate the information.

[0075] The server acts as a central management system, periodically collecting work information and computer operation history transmitted from each employee's terminal. The server stores this data in a database and analyzes and processes it using AI algorithms. These AI algorithms are useful for analyzing work hours and calculating various legal deductions. The server also automatically performs tax processing in accordance with the latest tax and social welfare laws.

[0076] Each employee is provided with a terminal, which generates work information when they log in at the start of their daily workday. The terminal sends this information to the server and also has the function of displaying compensation information and individual asset plans received from the server to the user. Through the terminal, users can check asset plan suggestions in real time and provide feedback in natural language as needed.

[0077] For example, if a user is considering purchasing a new home, the server will perform a future income and expenditure simulation based on collected salary history and payment information, and propose the optimal loan plan. The user can receive an immediate response from the server by entering a prompt message on their terminal such as, "I would like to purchase a home in the future, so please propose a financial plan."

[0078] In this way, this system utilizes a generative AI model and natural language processing technology to enable accurate and rapid responses, thereby achieving efficient business operations and user support.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] When an employee logs into the terminal at the start of their workday, it generates their work start time and employee ID. This becomes the input information. The terminal periodically sends this information to the server, forming the basic data for work information. The server receives this information and records it in the database.

[0082] Step 2:

[0083] The server inputs received work information and operation history into an AI algorithm to analyze work hours and work efficiency. This analysis outputs work efficiency patterns for each employee. Specifically, the server calculates how much time was spent on each task and adds the results to the profile as an indicator of work efficiency.

[0084] Step 3:

[0085] The server initiates a process to calculate compensation information based on the analysis results. This calculation involves data processing that takes into account working hours, salary components (basic salary, allowances, etc.), and legal deductions (taxes, social insurance contributions). This results in the output of accurate net compensation amounts, which are then saved as customized salary data for each employee.

[0086] Step 4:

[0087] The server uses the calculated compensation information to analyze each employee's current and past salary and payment history, and generates asset planning suggestions. The AI ​​model derives the optimal savings plan and investment strategy, which is then provided as output. Specifically, it proposes appropriate financial products and asset management methods based on the employee's lifestyle and future plans.

[0088] Step 5:

[0089] Users can view asset plans and reward information provided by the server through their terminal. They can provide feedback by entering prompt messages. For example, by entering "I would like to buy a house in the future, so please propose a financial plan" into the terminal, they will receive a specific proposal from the server. Based on this feedback, the server will further modify the plan or provide additional information.

[0090] (Application Example 1)

[0091] 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."

[0092] There is a need to automate accurate and efficient payroll calculation and fund management using employee work information and data from information processing equipment operations. Furthermore, there is a demand for information that helps individuals achieve a more prosperous life by proposing savings and investment strategies optimized for each individual. Traditional methods are time-consuming and cumbersome, and struggle to keep up with the latest tax and public insurance regulations.

[0093] 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.

[0094] In this invention, the server includes means for automatically collecting work information and data on the operation of the information processing device, means for automatically calculating salary data based on the collected data, and means for automatically processing public insurance and taxes. This enables accurate and efficient payroll calculation and fund management.

[0095] "Work information" refers to data on employees' working hours, job duties, and activities during work, and forms the basis for payroll calculation and work efficiency analysis.

[0096] "Data from information processing device operations" refers to records of operation logs, input history, and applications used in connection with the use of information processing devices, and is information used to evaluate employees' work activities.

[0097] "Salary data" refers to wage information calculated for each individual employee, reflecting work information, various deductions, tax obligations, etc., to show the final take-home pay.

[0098] "Public insurance" refers to insurance based on the social security system, including health insurance and pension insurance, which are paid for by both employees and employers.

[0099] "Taxation" refers to the obligation to pay taxes based on an employee's income in accordance with the law, and includes taxation such as income tax and local inhabitant tax.

[0100] A "financial plan" is a financial strategy created based on an individual employee's income, expenses, lifestyle, and future goals, outlining their savings and investment strategies.

[0101] "Means" refer to the processes or mechanical / technical methods employed to achieve a specific objective, and are essential components in the execution of a system.

[0102] In the system that implements this application example, the server is the central component. The server automatically collects each employee's work information and information processing device operation data, and stores it in a database. The server uses the collected data to calculate salary data, and then applies deductions based on the latest public insurance and tax information to calculate the net pay.

[0103] Next, the server uses AI algorithms to analyze salary data and historical spending data to propose a financial plan tailored to each individual employee. This plan includes savings and investment suggestions, aligning with the employee's lifestyle and future plans. These calculations and suggestions are performed using cloud-based databases and data processing tools such as Python, NumPy, and pandas.

[0104] Employees, as users, can access their pay stubs and proposed financial plans using their own devices (smartphones or PCs). The on-device interface is designed for iOS and Android®, and data is shared with the server via APIs. Employees can also ask questions to a generative AI model using natural language processing and receive real-time feedback on their salaries.

[0105] Specifically, users can receive an optimal financial plan from the AI ​​by entering a prompt such as, "I'm planning a big trip next year; please give me a savings plan for it." In this way, employees are supported in managing their daily finances and can make smarter financial decisions.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server automatically collects work information and operation data from each employee's terminal via the company network. Inputs include login times and operation logs of applications used, which are sent from the terminals. Outputs are stored in a database and used for subsequent processing.

[0109] Step 2:

[0110] The server calculates payroll data based on collected work information and operational data. Inputs include time allocation data and parameters necessary for payroll calculation, and the output calculates each employee's base salary and deductions. Various public insurance and tax information registered in the database are referenced here.

[0111] Step 3:

[0112] The server calculates the net pay by applying deductions based on the payroll data. The inputs for this step are base salary, tax information, and insurance information, and the output is the net pay amount. A Python calculation library is used for the calculations.

[0113] Step 4:

[0114] The server analyzes past salary data and spending history to create a financial plan. An AI algorithm is used, taking salary data, user spending history, and future goal data as input. The output is a suggested savings and investment plan based on this analysis.

[0115] Step 5:

[0116] The terminal displays the user's received pay stubs and financial plan suggestions. The user can access this information through the interface and input specific prompts into an AI model. Based on these prompts, the user receives real-time advice from the AI ​​model as output.

[0117] Step 6:

[0118] The device analyzes user prompts using natural language processing and passes them to a generative AI model. The input is a user question, and the output is an answer from the AI. For example, using the prompt "How much should I save each month?", the user can receive suggestions regarding specific savings goals.

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

[0120] This invention is implemented as a system that automatically collects employee work data and computer operation information, and automatically calculates salary information based on this data. Furthermore, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. It also has a function to evaluate work efficiency and automatically respond to user input using natural language processing. In addition, this system incorporates an emotion engine that recognizes user emotions, enabling flexible responses that take into account the user's psychological state.

[0121] In a specific implementation, a server acts as a central management device, periodically collecting work data and operation logs from each employee's terminal. The terminal generates login information when the user starts work and sends it to the server. The server centrally manages this data and analyzes work hours and work efficiency using an AI algorithm. The results of this analysis are directly reflected in payroll calculations, and the server calculates each employee's pay information based on the payroll components.

[0122] Simultaneously with payroll calculation, the emotion engine analyzes the user's emotional state based on their input and device usage patterns. This includes analyzing user interaction data using natural language processing. For example, if the server determines that a user is experiencing stress, it will propose financial plans for stress relief and measures to reduce workload. Such responses are then presented to the user on their device.

[0123] For example, if an employee expresses dissatisfaction with the system during work, the emotion engine recognizes a specific emotional state (e.g., stress, anxiety) based on that information. The server then takes this state into consideration and suggests to the user more flexible working hours or participation in relaxing programs. This entire process not only improves work efficiency but also contributes to increased employee satisfaction.

[0124] Thus, the system of the present invention achieves improved organizational productivity and addresses the individual needs of employees through the coordinated and autonomous operation of each step.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The terminal records the start time of work when the user logs in and sends this information to the server. It also periodically generates operation logs showing which applications the user is using and for how long.

[0128] Step 2:

[0129] The server stores received login information and operation logs in a database. This database is used to track working hours and analyze work efficiency.

[0130] Step 3:

[0131] The server analyzes the stored data and calculates the user's base salary based on their working hours. Furthermore, it calculates social security contributions and taxes, automatically determining the net salary after deductions.

[0132] Step 4:

[0133] The server uses an emotion engine to analyze input data from the terminal and identify the user's emotional state. For example, it infers emotions from frequent input patterns and the strength of key presses.

[0134] Step 5:

[0135] The server uses the results of the emotion engine analysis to propose an optimal financial plan to the user. This proposal is flexible and adapts to the user's emotional state.

[0136] Step 6:

[0137] The terminal notifies the user of salary information and financial plan proposals sent from the server. The user can view details on the screen and enter questions about the content.

[0138] Step 7:

[0139] The server analyzes user inquiries and feedback using natural language processing and automatically generates responses based on an existing database. These responses are also tailored to take user sentiment into consideration.

[0140] Step 8:

[0141] The server aggregates data over a certain period and generates reports on changes in each user's work efficiency and emotional state. This helps in formulating strategies to improve the overall performance of the organization.

[0142] (Example 2)

[0143] 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".

[0144] Improving employee work efficiency, providing an effective work environment tailored to individual psychological states, and accurately and efficiently handling public burdens and taxes are crucial for business operations. However, currently, collecting and analyzing work data is cumbersome, and there is a lack of systems that automatically propose appropriate countermeasures based on emotional states. In addition, there is a need for a system that generates sophisticated responses using generative AI models.

[0145] 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.

[0146] In this invention, the server includes means for automatically collecting work-related information and data on electronic device operation, means for automatically calculating reward information, and means for evaluating input data using an emotion analysis engine. This enables the analysis of worker efficiency, the provision of appropriate response measures based on emotions, and the generation of advanced responses by a generative AI model.

[0147] "Work-related information" refers to data concerning an employee's work, including working hours, break times, and job duties.

[0148] "Electronic device operation data" refers to log information and operation history generated when a user operates a computer or other digital device.

[0149] "Compensation information" refers to information regarding wages and salaries paid to employees, including elements such as base pay, overtime pay, and deductions.

[0150] "Public burdens and taxes" refer to legally mandated financial burdens such as social security contributions and income tax deducted from salaries.

[0151] "Individualized financial planning" refers to proposals and plans for financial management based on the income, expenses, and savings plans of individual employees.

[0152] An "emotion analysis engine" is a system that analyzes a user's psychological state based on their input and behavioral patterns, and is implemented using technologies such as natural language processing.

[0153] A "generative AI model" is an artificial intelligence technology that generates intelligent responses or results from input requests or data.

[0154] "Means of generating responses" refers to processes and mechanisms for providing appropriate feedback in response to user input.

[0155] This invention relates to a system that collects employee work data and electronic device operation logs, and uses this data to calculate and automatically process salary information and other data. Specifically, a server functions as a central management device and performs various analyses using data received from employee terminals.

[0156] The terminal generates and sends information to the server from the moment the employee logs in, enabling them to begin work. This allows the server to track work hours and detailed operations. The server then feeds the collected data into an AI algorithm to analyze each employee's work efficiency and hours, and calculates compensation based on the results. This process utilizes statistical analysis and machine learning techniques.

[0157] Furthermore, the server equipped with an emotion analysis engine has the function of analyzing user input and terminal usage to determine the user's emotional state. For example, if a user expresses dissatisfaction, the emotion analysis engine recognizes that the user is feeling stressed and proposes appropriate financial plans and measures to reduce workload. This proposal contributes to reducing the user's psychological burden and increasing their sense of well-being in the workplace.

[0158] Furthermore, this system uses a generative AI model to generate appropriate responses based on prompt messages. For example, by sending the prompt message "Employee A appears to be experiencing stress. What kind of support can be provided?" to the server, the server can utilize the AI ​​model to present appropriate support measures to the user.

[0159] Thus, the present invention is a system that improves organizational productivity by enhancing the work efficiency of workers and enabling flexible responses tailored to individual emotional states.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The terminal generates login information and creates a session for the start of work when the user begins work. This login information is sent to the server. User authentication data is used as input, and the success / failure status of authentication is provided to the server as output.

[0163] Step 2:

[0164] The server collects work data and operation logs that are periodically sent from terminals. This includes recordings of active windows, keystrokes, and application usage history. The collected data is centrally managed on the server. Input is raw log data, and output is a statistical work history report.

[0165] Step 3:

[0166] The server feeds collected work data into an AI algorithm to analyze work hours and work efficiency. Using a machine learning model, it learns patterns and evaluates whether work is being performed efficiently. Work data is used as input, and the output is an analyzed work efficiency score.

[0167] Step 4:

[0168] The server calculates each employee's salary based on the analysis results. Salary calculation combines working hours, work efficiency, and base pay elements. The input is the analyzed data, and the output is individual salary information.

[0169] Step 5:

[0170] The server uses an emotion analysis engine to analyze user input and operation logs to determine the user's emotional state. Natural language processing techniques are used to analyze the sentiment of the text entered by the user. The input is user behavior data, and the output is an emotional state score.

[0171] Step 6:

[0172] The server proposes individualized financial plans and workload reduction measures based on the user's emotional state. These proposals are generated using an AI model from several options and presented to the user. Inputs are emotional state scores and business data, while output is the specific proposed content.

[0173] Step 7:

[0174] The server uses a generative AI model to generate responses based on the prompt. For example, if the prompt is "Suggest stress relief measures," the AI ​​model generates an appropriate response and presents it to the user. The input is the prompt, and the output is the generated response.

[0175] (Application Example 2)

[0176] 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".

[0177] In today's work environment, not only employee working hours and work efficiency, but also their emotional state can significantly impact productivity and satisfaction. However, comprehensively managing these factors and providing optimal support to individual employees is not easy. Standard payroll and performance evaluation systems function without considering emotional states, limiting their ability to mitigate employee stress and dissatisfaction. As a result, there is a challenge in that individual employee needs cannot be adequately addressed, leading to decreased work efficiency and employee satisfaction.

[0178] 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.

[0179] In this invention, the server includes means for automatically collecting work data and computer operation information, means for automatically calculating salary information based on the collected information, and means for performing sentiment analysis and proposing flexible responses based on the user's emotional state. This enables individualized optimization that takes into account the emotional state of employees.

[0180] "Work data" refers to information related to employees' working hours, attendance, and working time.

[0181] "Computer operation information" refers to log information related to the operation of equipment used by employees to perform their work.

[0182] "Salary information" refers to data related to the wages and compensation that should be paid to employees.

[0183] "Means for automatically processing social security and taxation" refers to a system equipped with the function to automatically calculate necessary social insurance contributions and tax deductions based on calculated salary information.

[0184] "Means of proposing individualized financial plans" refers to a function that presents optimal ways of using funds and savings plans based on each employee's salary information and personal circumstances.

[0185] "Emotional analysis" refers to a technique that analyzes a user's words, actions, and behavioral patterns to infer their emotional state.

[0186] "Means of proposing flexible responses" refers to a system that provides actions and support tailored to the user based on the results of emotion analysis.

[0187] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human language.

[0188] The system that implements this application is designed as a smart management system that takes into account the emotional state of employees in a factory environment. This system consists of a server, individual employee terminals, and a data analysis engine.

[0189] The server is responsible for centrally managing employee work data and equipment operation information. Terminals are distributed to each employee, and at the start of their workday, each employee's attendance is recorded through biometric authentication (e.g., facial recognition or fingerprint recognition). Furthermore, daily work information is collected through the operation and feedback input methods used during work.

[0190] In data analysis, software using natural language processing technology (e.g., NaturalLanguageProcessor) is used to analyze text input from employees. Next, EmotionEngine grasps the emotional state from the input and identifies emotions such as stress and dissatisfaction. If an employee sends feedback such as "I feel tired" or "The workload is heavy," the system quickly provides appropriate feedback and resources accordingly (e.g., scheduling time for relaxation, suggesting refreshment programs). It also utilizes prompt sentences generated by a generative AI model to guide specific suggestions.

[0191] For example, if an employee sends feedback such as, "The complexity of the work is increasing, so I need more support," the system can recognize this as an emotional issue and suggest educational content or workload adjustments.

[0192] An example of a prompt for the generating AI model would be: "Identify the specific cause of the stress the employee is experiencing and propose solutions. Feedback: 'The complexity of the work is increasing, so I need a little more support.'" This ensures that the feedback is tailored to the employee's feelings and needs.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The terminal uses biometric authentication to identify employees when they start work. Input includes employee facial data and fingerprint data. This data is used to accurately record attendance times and transmit them to the server.

[0196] Step 2:

[0197] The server uses work information received from terminals and employee equipment operation logs to store employee work data in a central database. The input is work hours and operation logs, and the output is integrated employee work data. This data forms the basis for salary calculation.

[0198] Step 3:

[0199] The server analyzes work data using an AI algorithm and automatically calculates salaries. Inputs include work hours, operation logs, and data on the company's salary components. The analysis outputs salary information, and based on this information, automatic processing of social security and taxation is performed.

[0200] Step 4:

[0201] Users (employees) provide feedback using natural language via their terminals during their daily work. This feedback is text-based and the data is sent to the server.

[0202] Step 5:

[0203] The server analyzes the collected feedback using natural language processing software (e.g., NaturalLanguageProcessor) and analyzes the employee's emotional state using an emotion analysis engine (e.g., EmotionEngine). The input is text feedback, and the output is an evaluation of the emotional state. This allows the server to understand the stress and dissatisfaction that employees are experiencing.

[0204] Step 6:

[0205] The server uses emotion analysis results and a generative AI model to propose appropriate responses to employees. Input includes emotional state assessment results and internal resource information, and output generates personalized support suggestions. These suggestions are presented to the user via a terminal. For example, they might suggest appropriate break times or introduce training programs.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention is implemented as a system that automatically collects employee work data and computer operation information, and automatically calculates salary information based on this data. In addition, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. Furthermore, it evaluates operational efficiency and has the functionality to automatically respond to operations and questions in human language using natural language processing.

[0223] In a specific implementation, a server acts as a central administrator, periodically collecting work data and computer operation logs from each employee's terminal. When an employee logs in at the start of their workday, the terminal generates and sends this information to the server. The server aggregates and manages this data, and uses AI algorithms to analyze work hours and work efficiency.

[0224] In payroll calculations, the server calculates the base salary based on each employee's pre-configured salary components. Furthermore, it calculates various deductions based on the latest tax and social security laws to determine the employee's final take-home pay. This result is automatically delivered to terminals, allowing employees to easily check their individual pay stubs.

[0225] Furthermore, regarding financial planning proposals, the server analyzes each employee's salary history and past payment data to create individualized financial strategies. These proposals are optimized to each employee's lifestyle and future plans and are presented to them via their terminals. Employees can review the proposed plans in detail and provide feedback to the server in natural language.

[0226] For example, if an employee is considering purchasing a home, the system simulates future income and expenses and proposes an optimal loan plan based on their current income. This allows the employee to gain a deeper understanding of financial product selection. Through this process, the server improves operational efficiency and automatically provides each employee with useful financial advice.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The terminal generates login information when an employee starts work and sends it to the server. This includes the login time and identification information.

[0230] Step 2:

[0231] The terminal generates PC operation logs at set time intervals and periodically sends this data to the server. The operation logs record the names of the applications used and the duration of their use.

[0232] Step 3:

[0233] The server stores received login information and operation logs in a centralized database. This database serves as foundational information for use in subsequent processing steps.

[0234] Step 4:

[0235] The server uses AI algorithms to analyze collected work data and evaluate each employee's working hours and work efficiency. This evaluation result is then reflected in subsequent payroll calculations.

[0236] Step 5:

[0237] The server starts the payroll module and calculates each employee's base salary based on pre-configured payroll rules. Then, taking into account the latest tax laws and social security regulations, it deducts the necessary amounts to calculate the final take-home pay.

[0238] Step 6:

[0239] The server sends the generated payroll information to each employee's terminal and displays their payslip. This information helps users accurately understand their own salary structure.

[0240] Step 7:

[0241] The server analyzes users' payroll history and payment data to generate individualized financial plans. These plans present optimal strategies to meet employees' future financial needs.

[0242] Step 8:

[0243] The device displays the generated financial plan to the user. The user can then provide feedback and questions in natural language.

[0244] Step 9:

[0245] The server analyzes user input using natural language processing and generates automated responses. This allows for immediate addressing of user questions and requests.

[0246] (Example 1)

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

[0248] The challenge lies in providing a system that efficiently manages employee work information and computer operation history, accurately calculates compensation information, automates social welfare and tax processing, and optimizes and presents individual asset plans, thereby improving employee work efficiency and supporting enhanced asset management capabilities.

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

[0250] In this invention, the server includes means for automatically collecting employee work information and computer operation history, means for automatically calculating compensation information based on the collected information, means for automatically processing social welfare and taxation based on the calculated compensation information, and means for analyzing each employee's salary history and settlement history to optimize individual asset plans. This makes it possible to simplify employee salary management and tax procedures, and further supports employees in making financially meaningful decisions by proposing individually optimized asset plans.

[0251] "Employee work information" refers to information related to work, including employees' arrival and departure times, working hours, and break times.

[0252] "Computer operation history" refers to information that shows a record of operations performed on computers and other digital devices.

[0253] "Compensation information" refers to information that shows monetary compensation for an employee's work, such as salary, wages, and bonuses.

[0254] "Social welfare" is a term that refers to public welfare services and benefits provided under the social security system.

[0255] "Taxation" refers to taxes that the government collects from individuals and corporations in accordance with the law.

[0256] An "individualized asset plan" is a savings, investment, and spending strategy optimized based on the financial situation of an individual or organization.

[0257] "Means of automatic data collection" refers to functions or devices in which a machine or software collects data without human intervention.

[0258] "Means of calculation" refers to a device or algorithm that performs calculations based on data or input information and derives a result.

[0259] "Means of processing" refers to the technology or system used to manage, modify, or analyze data for a specific purpose.

[0260] "Means of analysis and optimization" refers to methods or techniques for analyzing data to derive the most efficient solutions or results.

[0261] This system automatically collects each employee's work information and computer operation history, and calculates compensation information based on that data. Specifically, it manages information by linking the server and terminals, and is designed to allow users to efficiently check and operate the information.

[0262] The server acts as a central management system, periodically collecting work information and computer operation history transmitted from each employee's terminal. The server stores this data in a database and analyzes and processes it using AI algorithms. These AI algorithms are useful for analyzing work hours and calculating various legal deductions. The server also automatically performs tax processing in accordance with the latest tax and social welfare laws.

[0263] Each employee is provided with a terminal, which generates work information when they log in at the start of their daily workday. The terminal sends this information to the server and also has the function of displaying compensation information and individual asset plans received from the server to the user. Through the terminal, users can check asset plan suggestions in real time and provide feedback in natural language as needed.

[0264] For example, if a user is considering purchasing a new home, the server will perform a future income and expenditure simulation based on collected salary history and payment information, and propose the optimal loan plan. The user can receive an immediate response from the server by entering a prompt message on their terminal such as, "I would like to purchase a home in the future, so please propose a financial plan."

[0265] In this way, this system utilizes a generative AI model and natural language processing technology to enable accurate and rapid responses, thereby achieving efficient business operations and user support.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] When an employee logs into the terminal at the start of their workday, it generates their work start time and employee ID. This becomes the input information. The terminal periodically sends this information to the server, forming the basic data for work information. The server receives this information and records it in the database.

[0269] Step 2:

[0270] The server inputs received work information and operation history into an AI algorithm to analyze work hours and work efficiency. This analysis outputs work efficiency patterns for each employee. Specifically, the server calculates how much time was spent on each task and adds the results to the profile as an indicator of work efficiency.

[0271] Step 3:

[0272] The server initiates a process to calculate compensation information based on the analysis results. This calculation involves data processing that takes into account working hours, salary components (basic salary, allowances, etc.), and legal deductions (taxes, social insurance contributions). This results in the output of accurate net compensation amounts, which are then saved as customized salary data for each employee.

[0273] Step 4:

[0274] The server uses the calculated compensation information to analyze each employee's current and past salary and payment history, and generates asset planning suggestions. The AI ​​model derives the optimal savings plan and investment strategy, which is then provided as output. Specifically, it proposes appropriate financial products and asset management methods based on the employee's lifestyle and future plans.

[0275] Step 5:

[0276] Users can view asset plans and reward information provided by the server through their terminal. They can provide feedback by entering prompt messages. For example, by entering "I would like to buy a house in the future, so please propose a financial plan" into the terminal, they will receive a specific proposal from the server. Based on this feedback, the server will further modify the plan or provide additional information.

[0277] (Application Example 1)

[0278] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0279] There is a demand to automate accurate and efficient salary calculation and fund management from the work information of employees and the data of information processing device operations. Also, there is a demand for providing information to realize a richer life by proposing savings and investment strategies optimized for each individual. Conventional methods are time-consuming and difficult to cope with the latest tax and public insurance.

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

[0281] In this invention, the server includes means for automatically collecting work information and data of information processing device operations, means for automatically calculating salary data based on the collected data, and means for automatically processing public insurance and tax. Thereby, accurate and efficient salary calculation and fund management become possible.

[0282] "Work information" is data related to an employee's working hours, job content, and activities during work, and is the basis for salary calculation and work efficiency analysis.

[0283] "Data of information processing device operations" is an operation log, input history, and record related to the used application accompanying the use of the information processing device, and is information used to evaluate an employee's work activities.

[0284] "Salary data" is wage information calculated for each individual employee, and indicates the final take-home amount reflecting work information, various deductions, tax obligations, etc.

[0285] "Public insurance" is insurance based on the social security system, including health insurance and pension insurance, etc., and is borne by employees and employers.

[0286] "Taxation" refers to the obligation of taxes calculated in accordance with laws and regulations based on employees' income, and refers to levies including income tax and resident tax.

[0287] "Financial plan" is a financial strategy created based on the income, expenses, lifestyle, and future goals of individual employees, indicating the direction of savings and investment.

[0288] "Means" refers to the processes or mechanical / technical methods adopted to achieve a specific purpose, and is a necessary component in the execution of the system.

[0289] In the system that realizes this application example, first, the server operates as the center. The server automatically collects the work information of each employee and the operation data of the information processing device, and stores them in the database. The server calculates the salary data using the collected data, and further applies deductions based on the latest public insurance and tax information to calculate the take-home pay.

[0290] Next, in order to propose a financial plan tailored to each individual employee, the server analyzes the salary data and past expenditure data using an AI algorithm. The financial plan also includes proposals for savings and investment, and is tailored to the employee's lifestyle and future plans. These calculations and proposals are performed using cloud-based databases and data processing tools such as Python, NumPy, and pandas.

[0291] The employee, who is the user, can access their salary details and the proposed financial plan using their terminal (smartphone or PC). The interface on the terminal is designed for iOS and Android, and the data is linked to the server through an API. In addition, the employee can use natural language processing to ask questions to the generative AI model and receive real-time feedback regarding their salary.

[0292] Specifically, users can receive an optimal financial plan from the AI ​​by entering a prompt such as, "I'm planning a big trip next year; please give me a savings plan for it." In this way, employees are supported in managing their daily finances and can make smarter financial decisions.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The server automatically collects work information and operation data from each employee's terminal via the company network. Inputs include login times and operation logs of applications used, which are sent from the terminals. Outputs are stored in a database and used for subsequent processing.

[0296] Step 2:

[0297] The server calculates payroll data based on collected work information and operational data. Inputs include time allocation data and parameters necessary for payroll calculation, and the output calculates each employee's base salary and deductions. Various public insurance and tax information registered in the database are referenced here.

[0298] Step 3:

[0299] The server calculates the net pay by applying deductions based on the payroll data. The inputs for this step are base salary, tax information, and insurance information, and the output is the net pay amount. A Python calculation library is used for the calculations.

[0300] Step 4:

[0301] The server analyzes past salary data and spending history to create a financial plan. An AI algorithm is used, taking salary data, user spending history, and future goal data as input. The output is a suggested savings and investment plan based on this analysis.

[0302] Step 5:

[0303] The terminal displays the salary statement and the proposed financial plan received by the user. The user can access this information through the interface and input specific prompt sentences into the generative AI model. Based on the input of the prompt sentences, the user receives real-time advice from the AI model as output.

[0304] Step 6:

[0305] The terminal analyzes the prompt sentences from the user using natural language processing and passes them to the generative AI model. There is a user's question as input, and an answer from the AI is returned as output. For example, by using the prompt sentence "How much should I save monthly?", the user can obtain a proposal regarding a specific savings goal.

[0306] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0307] The present invention is implemented as a system that automatically collects employees' work data and computer operation information, and automatically calculates salary information based on these data. Furthermore, social security and taxation are automatically processed based on the calculated salary information, and an individual financial plan is proposed. In addition, it has a function of evaluating work efficiency and automatically responding to user input by utilizing natural language processing. Moreover, this system incorporates an emotion engine that recognizes the user's emotion, enabling flexible responses considering the user's psychological state.

[0308] In a specific implementation, a server acts as a central management device, periodically collecting work data and operation logs from each employee's terminal. The terminal generates login information when the user starts work and sends it to the server. The server centrally manages this data and analyzes work hours and work efficiency using an AI algorithm. The results of this analysis are directly reflected in payroll calculations, and the server calculates each employee's pay information based on the payroll components.

[0309] Simultaneously with payroll calculation, the emotion engine analyzes the user's emotional state based on their input and device usage patterns. This includes analyzing user interaction data using natural language processing. For example, if the server determines that a user is experiencing stress, it will propose financial plans for stress relief and measures to reduce workload. Such responses are then presented to the user on their device.

[0310] For example, if an employee expresses dissatisfaction with the system during work, the emotion engine recognizes a specific emotional state (e.g., stress, anxiety) based on that information. The server then takes this state into consideration and suggests to the user more flexible working hours or participation in relaxing programs. This entire process not only improves work efficiency but also contributes to increased employee satisfaction.

[0311] Thus, the system of the present invention achieves improved organizational productivity and addresses the individual needs of employees through the coordinated and autonomous operation of each step.

[0312] The following describes the processing flow.

[0313] Step 1:

[0314] The terminal records the start time of work when the user logs in and sends this information to the server. It also periodically generates operation logs showing which applications the user is using and for how long.

[0315] Step 2:

[0316] The server stores received login information and operation logs in a database. This database is used to track working hours and analyze work efficiency.

[0317] Step 3:

[0318] The server analyzes the stored data and calculates the user's base salary based on their working hours. Furthermore, it calculates social security contributions and taxes, automatically determining the net salary after deductions.

[0319] Step 4:

[0320] The server uses an emotion engine to analyze input data from the terminal and identify the user's emotional state. For example, it infers emotions from frequent input patterns and the strength of key presses.

[0321] Step 5:

[0322] The server uses the results of the emotion engine analysis to propose an optimal financial plan to the user. This proposal is flexible and adapts to the user's emotional state.

[0323] Step 6:

[0324] The terminal notifies the user of salary information and financial plan proposals sent from the server. The user can view details on the screen and enter questions about the content.

[0325] Step 7:

[0326] The server analyzes user inquiries and feedback using natural language processing and automatically generates responses based on an existing database. These responses are also tailored to take user sentiment into consideration.

[0327] Step 8:

[0328] The server aggregates data over a certain period and generates reports on changes in each user's work efficiency and emotional state. This helps in formulating strategies to improve the overall performance of the organization.

[0329] (Example 2)

[0330] 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".

[0331] Improving employee work efficiency, providing an effective work environment tailored to individual psychological states, and accurately and efficiently handling public burdens and taxes are crucial for business operations. However, currently, collecting and analyzing work data is cumbersome, and there is a lack of systems that automatically propose appropriate countermeasures based on emotional states. In addition, there is a need for a system that generates sophisticated responses using generative AI models.

[0332] 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.

[0333] In this invention, the server includes means for automatically collecting work-related information and data on electronic device operation, means for automatically calculating reward information, and means for evaluating input data using an emotion analysis engine. This enables the analysis of worker efficiency, the provision of appropriate response measures based on emotions, and the generation of advanced responses by a generative AI model.

[0334] "Work-related information" refers to data concerning an employee's work, including working hours, break times, and job duties.

[0335] "Electronic device operation data" refers to log information and operation history generated when a user operates a computer or other digital device.

[0336] "Compensation information" refers to information regarding wages and salaries paid to employees, including elements such as base pay, overtime pay, and deductions.

[0337] "Public burdens and taxes" refer to legally mandated financial burdens such as social security contributions and income tax deducted from salaries.

[0338] "Individualized financial planning" refers to proposals and plans for financial management based on the income, expenses, and savings plans of individual employees.

[0339] An "emotion analysis engine" is a system that analyzes a user's psychological state based on their input and behavioral patterns, and is implemented using technologies such as natural language processing.

[0340] A "generative AI model" is an artificial intelligence technology that generates intelligent responses or results from input requests or data.

[0341] "Means of generating responses" refers to processes and mechanisms for providing appropriate feedback in response to user input.

[0342] This invention relates to a system that collects employee work data and electronic device operation logs, and uses this data to calculate and automatically process salary information and other data. Specifically, a server functions as a central management device and performs various analyses using data received from employee terminals.

[0343] The terminal generates and sends information to the server from the moment the employee logs in, enabling them to begin work. This allows the server to track work hours and detailed operations. The server then feeds the collected data into an AI algorithm to analyze each employee's work efficiency and hours, and calculates compensation based on the results. This process utilizes statistical analysis and machine learning techniques.

[0344] Furthermore, the server equipped with an emotion analysis engine has the function of analyzing user input and terminal usage to determine the user's emotional state. For example, if a user expresses dissatisfaction, the emotion analysis engine recognizes that the user is feeling stressed and proposes appropriate financial plans and measures to reduce workload. This proposal contributes to reducing the user's psychological burden and increasing their sense of well-being in the workplace.

[0345] Furthermore, this system uses a generative AI model to generate appropriate responses based on prompt messages. For example, by sending the prompt message "Employee A appears to be experiencing stress. What kind of support can be provided?" to the server, the server can utilize the AI ​​model to present appropriate support measures to the user.

[0346] Thus, the present invention is a system that improves organizational productivity by enhancing the work efficiency of workers and enabling flexible responses tailored to individual emotional states.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The terminal generates login information and creates a session for the start of work when the user begins work. This login information is sent to the server. User authentication data is used as input, and the success / failure status of authentication is provided to the server as output.

[0350] Step 2:

[0351] The server collects work data and operation logs that are periodically sent from terminals. This includes recordings of active windows, keystrokes, and application usage history. The collected data is centrally managed on the server. Input is raw log data, and output is a statistical work history report.

[0352] Step 3:

[0353] The server feeds collected work data into an AI algorithm to analyze work hours and work efficiency. Using a machine learning model, it learns patterns and evaluates whether work is being performed efficiently. Work data is used as input, and the output is an analyzed work efficiency score.

[0354] Step 4:

[0355] The server calculates each employee's salary based on the analysis results. Salary calculation combines working hours, work efficiency, and base pay elements. The input is the analyzed data, and the output is individual salary information.

[0356] Step 5:

[0357] The server uses an emotion analysis engine to analyze user input and operation logs to determine the user's emotional state. Natural language processing techniques are used to analyze the sentiment of the text entered by the user. The input is user behavior data, and the output is an emotional state score.

[0358] Step 6:

[0359] The server proposes individualized financial plans and workload reduction measures based on the user's emotional state. These proposals are generated using an AI model from several options and presented to the user. Inputs are emotional state scores and business data, while output is the specific proposed content.

[0360] Step 7:

[0361] The server uses a generative AI model to generate responses based on the prompt. For example, if the prompt is "Suggest stress relief measures," the AI ​​model generates an appropriate response and presents it to the user. The input is the prompt, and the output is the generated response.

[0362] (Application Example 2)

[0363] 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."

[0364] In today's work environment, not only employee working hours and work efficiency, but also their emotional state can significantly impact productivity and satisfaction. However, comprehensively managing these factors and providing optimal support to individual employees is not easy. Standard payroll and performance evaluation systems function without considering emotional states, limiting their ability to mitigate employee stress and dissatisfaction. As a result, there is a challenge in that individual employee needs cannot be adequately addressed, leading to decreased work efficiency and employee satisfaction.

[0365] 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.

[0366] In this invention, the server includes means for automatically collecting work data and computer operation information, means for automatically calculating salary information based on the collected information, and means for performing sentiment analysis and proposing flexible responses based on the user's emotional state. This enables individualized optimization that takes into account the emotional state of employees.

[0367] "Work data" refers to information related to employees' working hours, attendance, and working time.

[0368] "Computer operation information" refers to log information related to the operation of equipment used by employees to perform their work.

[0369] "Salary information" refers to data related to the wages and compensation that should be paid to employees.

[0370] "Means for automatically processing social security and taxation" refers to a system equipped with the function to automatically calculate necessary social insurance contributions and tax deductions based on calculated salary information.

[0371] "Means of proposing individualized financial plans" refers to a function that presents optimal ways of using funds and savings plans based on each employee's salary information and personal circumstances.

[0372] "Emotional analysis" refers to a technique that analyzes a user's words, actions, and behavioral patterns to infer their emotional state.

[0373] "Means of proposing flexible responses" refers to a system that provides actions and support tailored to the user based on the results of emotion analysis.

[0374] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human language.

[0375] The system that implements this application is designed as a smart management system that takes into account the emotional state of employees in a factory environment. This system consists of a server, individual employee terminals, and a data analysis engine.

[0376] The server is responsible for centrally managing employee work data and equipment operation information. Terminals are distributed to each employee, and at the start of their workday, each employee's attendance is recorded through biometric authentication (e.g., facial recognition or fingerprint recognition). Furthermore, daily work information is collected through the operation and feedback input methods used during work.

[0377] In data analysis, software using natural language processing technology (e.g., NaturalLanguageProcessor) is used to analyze text input from employees. Next, EmotionEngine grasps the emotional state from the input and identifies emotions such as stress and dissatisfaction. If an employee sends feedback such as "I feel tired" or "The workload is heavy," the system quickly provides appropriate feedback and resources accordingly (e.g., scheduling time for relaxation, suggesting refreshment programs). It also utilizes prompt sentences generated by a generative AI model to guide specific suggestions.

[0378] For example, if an employee sends feedback such as, "The complexity of the work is increasing, so I need more support," the system can recognize this as an emotional issue and suggest educational content or workload adjustments.

[0379] An example of a prompt for the generating AI model would be: "Identify the specific cause of the stress the employee is experiencing and propose solutions. Feedback: 'The complexity of the work is increasing, so I need a little more support.'" This ensures that the feedback is tailored to the employee's feelings and needs.

[0380] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0381] Step 1:

[0382] The terminal uses biometric authentication to identify employees when they start work. Input includes employee facial data and fingerprint data. This data is used to accurately record attendance times and transmit them to the server.

[0383] Step 2:

[0384] The server uses work information received from terminals and employee equipment operation logs to store employee work data in a central database. The input is work hours and operation logs, and the output is integrated employee work data. This data forms the basis for salary calculation.

[0385] Step 3:

[0386] The server analyzes work data using an AI algorithm and automatically calculates salaries. Inputs include work hours, operation logs, and data on the company's salary components. The analysis outputs salary information, and based on this information, automatic processing of social security and taxation is performed.

[0387] Step 4:

[0388] Users (employees) provide feedback using natural language via their terminals during their daily work. This feedback is text-based and the data is sent to the server.

[0389] Step 5:

[0390] The server analyzes the collected feedback using natural language processing software (e.g., NaturalLanguageProcessor) and analyzes the employee's emotional state using an emotion analysis engine (e.g., EmotionEngine). The input is text feedback, and the output is an evaluation of the emotional state. This allows the server to understand the stress and dissatisfaction that employees are experiencing.

[0391] Step 6:

[0392] The server uses emotion analysis results and a generative AI model to propose appropriate responses to employees. Input includes emotional state assessment results and internal resource information, and output generates personalized support suggestions. These suggestions are presented to the user via a terminal. For example, they might suggest appropriate break times or introduce training programs.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Third Embodiment]

[0397] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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".

[0409] This invention is implemented as a system that automatically collects employee work data and computer operation information, and automatically calculates salary information based on this data. In addition, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. Furthermore, it evaluates operational efficiency and has the functionality to automatically respond to operations and questions in human language using natural language processing.

[0410] In a specific implementation, a server acts as a central administrator, periodically collecting work data and computer operation logs from each employee's terminal. When an employee logs in at the start of their workday, the terminal generates and sends this information to the server. The server aggregates and manages this data, and uses AI algorithms to analyze work hours and work efficiency.

[0411] In payroll calculations, the server calculates the base salary based on each employee's pre-configured salary components. Furthermore, it calculates various deductions based on the latest tax and social security laws to determine the employee's final take-home pay. This result is automatically delivered to terminals, allowing employees to easily check their individual pay stubs.

[0412] Furthermore, regarding financial planning proposals, the server analyzes each employee's salary history and past payment data to create individualized financial strategies. These proposals are optimized to each employee's lifestyle and future plans and are presented to them via their terminals. Employees can review the proposed plans in detail and provide feedback to the server in natural language.

[0413] For example, if an employee is considering purchasing a home, the system simulates future income and expenses and proposes an optimal loan plan based on their current income. This allows the employee to gain a deeper understanding of financial product selection. Through this process, the server improves operational efficiency and automatically provides each employee with useful financial advice.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The terminal generates login information when an employee starts work and sends it to the server. This includes the login time and identification information.

[0417] Step 2:

[0418] The terminal generates PC operation logs at set time intervals and periodically sends this data to the server. The operation logs record the names of the applications used and the duration of their use.

[0419] Step 3:

[0420] The server stores received login information and operation logs in a centralized database. This database serves as foundational information for use in subsequent processing steps.

[0421] Step 4:

[0422] The server uses AI algorithms to analyze collected work data and evaluate each employee's working hours and work efficiency. This evaluation result is then reflected in subsequent payroll calculations.

[0423] Step 5:

[0424] The server starts the payroll module and calculates each employee's base salary based on pre-configured payroll rules. Then, taking into account the latest tax laws and social security regulations, it deducts the necessary amounts to calculate the final take-home pay.

[0425] Step 6:

[0426] The server sends the generated payroll information to each employee's terminal and displays their payslip. This information helps users accurately understand their own salary structure.

[0427] Step 7:

[0428] The server analyzes users' payroll history and payment data to generate individualized financial plans. These plans present optimal strategies to meet employees' future financial needs.

[0429] Step 8:

[0430] The device displays the generated financial plan to the user. The user can then provide feedback and questions in natural language.

[0431] Step 9:

[0432] The server analyzes user input using natural language processing and generates automated responses. This allows for immediate addressing of user questions and requests.

[0433] (Example 1)

[0434] 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."

[0435] The challenge lies in providing a system that efficiently manages employee work information and computer operation history, accurately calculates compensation information, automates social welfare and tax processing, and optimizes and presents individual asset plans, thereby improving employee work efficiency and supporting enhanced asset management capabilities.

[0436] 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.

[0437] In this invention, the server includes means for automatically collecting employee work information and computer operation history, means for automatically calculating compensation information based on the collected information, means for automatically processing social welfare and taxation based on the calculated compensation information, and means for analyzing each employee's salary history and settlement history to optimize individual asset plans. This makes it possible to simplify employee salary management and tax procedures, and further supports employees in making financially meaningful decisions by proposing individually optimized asset plans.

[0438] "Employee work information" refers to information related to work, including employees' arrival and departure times, working hours, and break times.

[0439] "Computer operation history" refers to information that shows a record of operations performed on computers and other digital devices.

[0440] "Compensation information" refers to information that shows monetary compensation for an employee's work, such as salary, wages, and bonuses.

[0441] "Social welfare" is a term that refers to public welfare services and benefits provided under the social security system.

[0442] "Taxation" refers to taxes that the government collects from individuals and corporations in accordance with the law.

[0443] An "individualized asset plan" is a savings, investment, and spending strategy optimized based on the financial situation of an individual or organization.

[0444] "Means of automatic data collection" refers to functions or devices in which a machine or software collects data without human intervention.

[0445] "Means of calculation" refers to a device or algorithm that performs calculations based on data or input information and derives a result.

[0446] "Means of processing" refers to the technology or system used to manage, modify, or analyze data for a specific purpose.

[0447] "Means of analysis and optimization" refers to methods or techniques for analyzing data to derive the most efficient solutions or results.

[0448] This system automatically collects each employee's work information and computer operation history, and calculates compensation information based on that data. Specifically, it manages information by linking the server and terminals, and is designed to allow users to efficiently check and operate the information.

[0449] The server acts as a central management system, periodically collecting work information and computer operation history transmitted from each employee's terminal. The server stores this data in a database and analyzes and processes it using AI algorithms. These AI algorithms are useful for analyzing work hours and calculating various legal deductions. The server also automatically performs tax processing in accordance with the latest tax and social welfare laws.

[0450] Each employee is provided with a terminal, which generates work information when they log in at the start of their daily workday. The terminal sends this information to the server and also has the function of displaying compensation information and individual asset plans received from the server to the user. Through the terminal, users can check asset plan suggestions in real time and provide feedback in natural language as needed.

[0451] For example, if a user is considering purchasing a new home, the server will perform a future income and expenditure simulation based on collected salary history and payment information, and propose the optimal loan plan. The user can receive an immediate response from the server by entering a prompt message on their terminal such as, "I would like to purchase a home in the future, so please propose a financial plan."

[0452] In this way, this system utilizes a generative AI model and natural language processing technology to enable accurate and rapid responses, thereby achieving efficient business operations and user support.

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

[0454] Step 1:

[0455] When an employee logs into the terminal at the start of their workday, it generates their work start time and employee ID. This becomes the input information. The terminal periodically sends this information to the server, forming the basic data for work information. The server receives this information and records it in the database.

[0456] Step 2:

[0457] The server inputs received work information and operation history into an AI algorithm to analyze work hours and work efficiency. This analysis outputs work efficiency patterns for each employee. Specifically, the server calculates how much time was spent on each task and adds the results to the profile as an indicator of work efficiency.

[0458] Step 3:

[0459] The server initiates a process to calculate compensation information based on the analysis results. This calculation involves data processing that takes into account working hours, salary components (basic salary, allowances, etc.), and legal deductions (taxes, social insurance contributions). This results in the output of accurate net compensation amounts, which are then saved as customized salary data for each employee.

[0460] Step 4:

[0461] The server uses the calculated compensation information to analyze each employee's current and past salary and payment history, and generates asset planning suggestions. The AI ​​model derives the optimal savings plan and investment strategy, which is then provided as output. Specifically, it proposes appropriate financial products and asset management methods based on the employee's lifestyle and future plans.

[0462] Step 5:

[0463] Users can view asset plans and reward information provided by the server through their terminal. They can provide feedback by entering prompt messages. For example, by entering "I would like to buy a house in the future, so please propose a financial plan" into the terminal, they will receive a specific proposal from the server. Based on this feedback, the server will further modify the plan or provide additional information.

[0464] (Application Example 1)

[0465] 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."

[0466] There is a need to automate accurate and efficient payroll calculation and fund management using employee work information and data from information processing equipment operations. Furthermore, there is a demand for information that helps individuals achieve a more prosperous life by proposing savings and investment strategies optimized for each individual. Traditional methods are time-consuming and cumbersome, and struggle to keep up with the latest tax and public insurance regulations.

[0467] 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.

[0468] In this invention, the server includes means for automatically collecting work information and data on the operation of the information processing device, means for automatically calculating salary data based on the collected data, and means for automatically processing public insurance and taxes. This enables accurate and efficient payroll calculation and fund management.

[0469] "Work information" refers to data on employees' working hours, job duties, and activities during work, and forms the basis for payroll calculation and work efficiency analysis.

[0470] "Data from information processing device operations" refers to records of operation logs, input history, and applications used in connection with the use of information processing devices, and is information used to evaluate employees' work activities.

[0471] "Salary data" refers to wage information calculated for each individual employee, reflecting work information, various deductions, tax obligations, etc., to show the final take-home pay.

[0472] "Public insurance" refers to insurance based on the social security system, including health insurance and pension insurance, which are paid for by both employees and employers.

[0473] "Taxation" refers to the obligation to pay taxes based on an employee's income in accordance with the law, and includes taxation such as income tax and local inhabitant tax.

[0474] A "financial plan" is a financial strategy created based on an individual employee's income, expenses, lifestyle, and future goals, outlining their savings and investment strategies.

[0475] "Means" refer to the processes or mechanical / technical methods employed to achieve a specific objective, and are essential components in the execution of a system.

[0476] In the system that implements this application example, the server is the central component. The server automatically collects each employee's work information and information processing device operation data, and stores it in a database. The server uses the collected data to calculate salary data, and then applies deductions based on the latest public insurance and tax information to calculate the net pay.

[0477] Next, the server uses AI algorithms to analyze salary data and historical spending data to propose a financial plan tailored to each individual employee. This plan includes savings and investment suggestions, aligning with the employee's lifestyle and future plans. These calculations and suggestions are performed using cloud-based databases and data processing tools such as Python, NumPy, and pandas.

[0478] Employees, as users, can access their pay stubs and proposed financial plans using their own devices (smartphones or PCs). The on-device interface is designed for iOS and Android, and data is shared with the server via an API. Employees can also ask questions to a generative AI model using natural language processing and receive real-time feedback on their salary.

[0479] Specifically, users can receive an optimal financial plan from the AI ​​by entering a prompt such as, "I'm planning a big trip next year; please give me a savings plan for it." In this way, employees are supported in managing their daily finances and can make smarter financial decisions.

[0480] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0481] Step 1:

[0482] The server automatically collects work information and operation data from each employee's terminal via the company network. Inputs include login times and operation logs of applications used, which are sent from the terminals. Outputs are stored in a database and used for subsequent processing.

[0483] Step 2:

[0484] The server calculates payroll data based on collected work information and operational data. Inputs include time allocation data and parameters necessary for payroll calculation, and the output calculates each employee's base salary and deductions. Various public insurance and tax information registered in the database are referenced here.

[0485] Step 3:

[0486] The server calculates the net pay by applying deductions based on the payroll data. The inputs for this step are base salary, tax information, and insurance information, and the output is the net pay amount. A Python calculation library is used for the calculations.

[0487] Step 4:

[0488] The server analyzes past salary data and spending history to create a financial plan. An AI algorithm is used, taking salary data, user spending history, and future goal data as input. The output is a suggested savings and investment plan based on this analysis.

[0489] Step 5:

[0490] The terminal displays the user's received pay stubs and financial plan suggestions. The user can access this information through the interface and input specific prompts into an AI model. Based on these prompts, the user receives real-time advice from the AI ​​model as output.

[0491] Step 6:

[0492] The device analyzes user prompts using natural language processing and passes them to a generative AI model. The input is a user question, and the output is an answer from the AI. For example, using the prompt "How much should I save each month?", the user can receive suggestions regarding specific savings goals.

[0493] 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.

[0494] This invention is implemented as a system that automatically collects employee work data and computer operation information, and automatically calculates salary information based on this data. Furthermore, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. It also has a function to evaluate work efficiency and automatically respond to user input using natural language processing. In addition, this system incorporates an emotion engine that recognizes user emotions, enabling flexible responses that take into account the user's psychological state.

[0495] In a specific implementation, a server acts as a central management device, periodically collecting work data and operation logs from each employee's terminal. The terminal generates login information when the user starts work and sends it to the server. The server centrally manages this data and analyzes work hours and work efficiency using an AI algorithm. The results of this analysis are directly reflected in payroll calculations, and the server calculates each employee's pay information based on the payroll components.

[0496] Simultaneously with payroll calculation, the emotion engine analyzes the user's emotional state based on their input and device usage patterns. This includes analyzing user interaction data using natural language processing. For example, if the server determines that a user is experiencing stress, it will propose financial plans for stress relief and measures to reduce workload. Such responses are then presented to the user on their device.

[0497] For example, if an employee expresses dissatisfaction with the system during work, the emotion engine recognizes a specific emotional state (e.g., stress, anxiety) based on that information. The server then takes this state into consideration and suggests to the user more flexible working hours or participation in relaxing programs. This entire process not only improves work efficiency but also contributes to increased employee satisfaction.

[0498] Thus, the system of the present invention achieves improved organizational productivity and addresses the individual needs of employees through the coordinated and autonomous operation of each step.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The terminal records the start time of work when the user logs in and sends this information to the server. It also periodically generates operation logs showing which applications the user is using and for how long.

[0502] Step 2:

[0503] The server stores received login information and operation logs in a database. This database is used to track working hours and analyze work efficiency.

[0504] Step 3:

[0505] The server analyzes the stored data and calculates the user's base salary based on their working hours. Furthermore, it calculates social security contributions and taxes, automatically determining the net salary after deductions.

[0506] Step 4:

[0507] The server uses an emotion engine to analyze input data from the terminal and identify the user's emotional state. For example, it infers emotions from frequent input patterns and the strength of key presses.

[0508] Step 5:

[0509] The server uses the results of the emotion engine analysis to propose an optimal financial plan to the user. This proposal is flexible and adapts to the user's emotional state.

[0510] Step 6:

[0511] The terminal notifies the user of salary information and financial plan proposals sent from the server. The user can view details on the screen and enter questions about the content.

[0512] Step 7:

[0513] The server analyzes user inquiries and feedback using natural language processing and automatically generates responses based on an existing database. These responses are also tailored to take user sentiment into consideration.

[0514] Step 8:

[0515] The server aggregates data over a certain period and generates reports on changes in each user's work efficiency and emotional state. This helps in formulating strategies to improve the overall performance of the organization.

[0516] (Example 2)

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

[0518] Improving employee work efficiency, providing an effective work environment tailored to individual psychological states, and accurately and efficiently handling public burdens and taxes are crucial for business operations. However, currently, collecting and analyzing work data is cumbersome, and there is a lack of systems that automatically propose appropriate countermeasures based on emotional states. In addition, there is a need for a system that generates sophisticated responses using generative AI models.

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

[0520] In this invention, the server includes means for automatically collecting work-related information and data on electronic device operation, means for automatically calculating reward information, and means for evaluating input data using an emotion analysis engine. This enables the analysis of worker efficiency, the provision of appropriate response measures based on emotions, and the generation of advanced responses by a generative AI model.

[0521] "Work-related information" refers to data concerning an employee's work, including working hours, break times, and job duties.

[0522] "Electronic device operation data" refers to log information and operation history generated when a user operates a computer or other digital device.

[0523] "Compensation information" refers to information regarding wages and salaries paid to employees, including elements such as base pay, overtime pay, and deductions.

[0524] "Public burdens and taxes" refer to legally mandated financial burdens such as social security contributions and income tax deducted from salaries.

[0525] "Individualized financial planning" refers to proposals and plans for financial management based on the income, expenses, and savings plans of individual employees.

[0526] An "emotion analysis engine" is a system that analyzes a user's psychological state based on their input and behavioral patterns, and is implemented using technologies such as natural language processing.

[0527] A "generative AI model" is an artificial intelligence technology that generates intelligent responses or results from input requests or data.

[0528] "Means of generating responses" refers to processes and mechanisms for providing appropriate feedback in response to user input.

[0529] This invention relates to a system that collects employee work data and electronic device operation logs, and uses this data to calculate and automatically process salary information and other data. Specifically, a server functions as a central management device and performs various analyses using data received from employee terminals.

[0530] The terminal generates and sends information to the server from the moment the employee logs in, enabling them to begin work. This allows the server to track work hours and detailed operations. The server then feeds the collected data into an AI algorithm to analyze each employee's work efficiency and hours, and calculates compensation based on the results. This process utilizes statistical analysis and machine learning techniques.

[0531] Furthermore, the server equipped with an emotion analysis engine has the function of analyzing user input and terminal usage to determine the user's emotional state. For example, if a user expresses dissatisfaction, the emotion analysis engine recognizes that the user is feeling stressed and proposes appropriate financial plans and measures to reduce workload. This proposal contributes to reducing the user's psychological burden and increasing their sense of well-being in the workplace.

[0532] Furthermore, this system uses a generative AI model to generate appropriate responses based on prompt messages. For example, by sending the prompt message "Employee A appears to be experiencing stress. What kind of support can be provided?" to the server, the server can utilize the AI ​​model to present appropriate support measures to the user.

[0533] Thus, the present invention is a system that improves organizational productivity by enhancing the work efficiency of workers and enabling flexible responses tailored to individual emotional states.

[0534] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0535] Step 1:

[0536] The terminal generates login information and creates a session for the start of work when the user begins work. This login information is sent to the server. User authentication data is used as input, and the success / failure status of authentication is provided to the server as output.

[0537] Step 2:

[0538] The server collects work data and operation logs that are periodically sent from terminals. This includes recordings of active windows, keystrokes, and application usage history. The collected data is centrally managed on the server. Input is raw log data, and output is a statistical work history report.

[0539] Step 3:

[0540] The server feeds collected work data into an AI algorithm to analyze work hours and work efficiency. Using a machine learning model, it learns patterns and evaluates whether work is being performed efficiently. Work data is used as input, and the output is an analyzed work efficiency score.

[0541] Step 4:

[0542] The server calculates each employee's salary based on the analysis results. Salary calculation combines working hours, work efficiency, and base pay elements. The input is the analyzed data, and the output is individual salary information.

[0543] Step 5:

[0544] The server uses an emotion analysis engine to analyze user input and operation logs to determine the user's emotional state. Natural language processing techniques are used to analyze the sentiment of the text entered by the user. The input is user behavior data, and the output is an emotional state score.

[0545] Step 6:

[0546] The server proposes individualized financial plans and workload reduction measures based on the user's emotional state. These proposals are generated using an AI model from several options and presented to the user. Inputs are emotional state scores and business data, while output is the specific proposed content.

[0547] Step 7:

[0548] The server uses a generative AI model to generate responses based on the prompt. For example, if the prompt is "Suggest stress relief measures," the AI ​​model generates an appropriate response and presents it to the user. The input is the prompt, and the output is the generated response.

[0549] (Application Example 2)

[0550] 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."

[0551] In today's work environment, not only employee working hours and work efficiency, but also their emotional state can significantly impact productivity and satisfaction. However, comprehensively managing these factors and providing optimal support to individual employees is not easy. Standard payroll and performance evaluation systems function without considering emotional states, limiting their ability to mitigate employee stress and dissatisfaction. As a result, there is a challenge in that individual employee needs cannot be adequately addressed, leading to decreased work efficiency and employee satisfaction.

[0552] 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.

[0553] In this invention, the server includes means for automatically collecting work data and computer operation information, means for automatically calculating salary information based on the collected information, and means for performing sentiment analysis and proposing flexible responses based on the user's emotional state. This enables individualized optimization that takes into account the emotional state of employees.

[0554] "Work data" refers to information related to employees' working hours, attendance, and working time.

[0555] "Computer operation information" refers to log information related to the operation of equipment used by employees to perform their work.

[0556] "Salary information" refers to data related to the wages and compensation that should be paid to employees.

[0557] "Means for automatically processing social security and taxation" refers to a system equipped with the function to automatically calculate necessary social insurance contributions and tax deductions based on calculated salary information.

[0558] "Means of proposing individualized financial plans" refers to a function that presents optimal ways of using funds and savings plans based on each employee's salary information and personal circumstances.

[0559] "Emotional analysis" refers to a technique that analyzes a user's words, actions, and behavioral patterns to infer their emotional state.

[0560] "Means of proposing flexible responses" refers to a system that provides actions and support tailored to the user based on the results of emotion analysis.

[0561] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human language.

[0562] The system that implements this application is designed as a smart management system that takes into account the emotional state of employees in a factory environment. This system consists of a server, individual employee terminals, and a data analysis engine.

[0563] The server is responsible for centrally managing employee work data and equipment operation information. Terminals are distributed to each employee, and at the start of their workday, each employee's attendance is recorded through biometric authentication (e.g., facial recognition or fingerprint recognition). Furthermore, daily work information is collected through the operation and feedback input methods used during work.

[0564] In data analysis, software using natural language processing technology (e.g., NaturalLanguageProcessor) is used to analyze text input from employees. Next, EmotionEngine grasps the emotional state from the input and identifies emotions such as stress and dissatisfaction. If an employee sends feedback such as "I feel tired" or "The workload is heavy," the system quickly provides appropriate feedback and resources accordingly (e.g., scheduling time for relaxation, suggesting refreshment programs). It also utilizes prompt sentences generated by a generative AI model to guide specific suggestions.

[0565] For example, if an employee sends feedback such as, "The complexity of the work is increasing, so I need more support," the system can recognize this as an emotional issue and suggest educational content or workload adjustments.

[0566] An example of a prompt for the generating AI model would be: "Identify the specific cause of the stress the employee is experiencing and propose solutions. Feedback: 'The complexity of the work is increasing, so I need a little more support.'" This ensures that the feedback is tailored to the employee's feelings and needs.

[0567] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0568] Step 1:

[0569] The terminal uses biometric authentication to identify employees when they start work. Input includes employee facial data and fingerprint data. This data is used to accurately record attendance times and transmit them to the server.

[0570] Step 2:

[0571] The server uses work information received from terminals and employee equipment operation logs to store employee work data in a central database. The input is work hours and operation logs, and the output is integrated employee work data. This data forms the basis for salary calculation.

[0572] Step 3:

[0573] The server analyzes work data using an AI algorithm and automatically calculates salaries. Inputs include work hours, operation logs, and data on the company's salary components. The analysis outputs salary information, and based on this information, automatic processing of social security and taxation is performed.

[0574] Step 4:

[0575] Users (employees) provide feedback using natural language via their terminals during their daily work. This feedback is text-based and the data is sent to the server.

[0576] Step 5:

[0577] The server analyzes the collected feedback using natural language processing software (e.g., NaturalLanguageProcessor) and analyzes the employee's emotional state using an emotion analysis engine (e.g., EmotionEngine). The input is text feedback, and the output is an evaluation of the emotional state. This allows the server to understand the stress and dissatisfaction that employees are experiencing.

[0578] Step 6:

[0579] The server uses emotion analysis results and a generative AI model to propose appropriate responses to employees. Input includes emotional state assessment results and internal resource information, and output generates personalized support suggestions. These suggestions are presented to the user via a terminal. For example, they might suggest appropriate break times or introduce training programs.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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".

[0597] This invention is implemented as a system that automatically collects employee work data and computer operation information, and automatically calculates salary information based on this data. In addition, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. Furthermore, it evaluates operational efficiency and has the functionality to automatically respond to operations and questions in human language using natural language processing.

[0598] In a specific implementation, a server acts as a central administrator, periodically collecting work data and computer operation logs from each employee's terminal. When an employee logs in at the start of their workday, the terminal generates and sends this information to the server. The server aggregates and manages this data, and uses AI algorithms to analyze work hours and work efficiency.

[0599] In payroll calculations, the server calculates the base salary based on each employee's pre-configured salary components. Furthermore, it calculates various deductions based on the latest tax and social security laws to determine the employee's final take-home pay. This result is automatically delivered to terminals, allowing employees to easily check their individual pay stubs.

[0600] Furthermore, regarding financial planning proposals, the server analyzes each employee's salary history and past payment data to create individualized financial strategies. These proposals are optimized to each employee's lifestyle and future plans and are presented to them via their terminals. Employees can review the proposed plans in detail and provide feedback to the server in natural language.

[0601] For example, if an employee is considering purchasing a home, the system simulates future income and expenses and proposes an optimal loan plan based on their current income. This allows the employee to gain a deeper understanding of financial product selection. Through this process, the server improves operational efficiency and automatically provides each employee with useful financial advice.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] The terminal generates login information when an employee starts work and sends it to the server. This includes the login time and identification information.

[0605] Step 2:

[0606] The terminal generates PC operation logs at set time intervals and periodically sends this data to the server. The operation logs record the names of the applications used and the duration of their use.

[0607] Step 3:

[0608] The server stores received login information and operation logs in a centralized database. This database serves as foundational information for use in subsequent processing steps.

[0609] Step 4:

[0610] The server uses AI algorithms to analyze collected work data and evaluate each employee's working hours and work efficiency. This evaluation result is then reflected in subsequent payroll calculations.

[0611] Step 5:

[0612] The server starts the payroll module and calculates each employee's base salary based on pre-configured payroll rules. Then, taking into account the latest tax laws and social security regulations, it deducts the necessary amounts to calculate the final take-home pay.

[0613] Step 6:

[0614] The server sends the generated payroll information to each employee's terminal and displays their payslip. This information helps users accurately understand their own salary structure.

[0615] Step 7:

[0616] The server analyzes users' payroll history and payment data to generate individualized financial plans. These plans present optimal strategies to meet employees' future financial needs.

[0617] Step 8:

[0618] The device displays the generated financial plan to the user. The user can then provide feedback and questions in natural language.

[0619] Step 9:

[0620] The server analyzes user input using natural language processing and generates automated responses. This allows for immediate addressing of user questions and requests.

[0621] (Example 1)

[0622] 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".

[0623] The challenge lies in providing a system that efficiently manages employee work information and computer operation history, accurately calculates compensation information, automates social welfare and tax processing, and optimizes and presents individual asset plans, thereby improving employee work efficiency and supporting enhanced asset management capabilities.

[0624] 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.

[0625] In this invention, the server includes means for automatically collecting employee work information and computer operation history, means for automatically calculating compensation information based on the collected information, means for automatically processing social welfare and taxation based on the calculated compensation information, and means for analyzing each employee's salary history and settlement history to optimize individual asset plans. This makes it possible to simplify employee salary management and tax procedures, and further supports employees in making financially meaningful decisions by proposing individually optimized asset plans.

[0626] "Employee work information" refers to information related to work, including employees' arrival and departure times, working hours, and break times.

[0627] "Computer operation history" refers to information that shows a record of operations performed on computers and other digital devices.

[0628] "Compensation information" refers to information that shows monetary compensation for an employee's work, such as salary, wages, and bonuses.

[0629] "Social welfare" is a term that refers to public welfare services and benefits provided under the social security system.

[0630] "Taxation" refers to taxes that the government collects from individuals and corporations in accordance with the law.

[0631] An "individualized asset plan" is a savings, investment, and spending strategy optimized based on the financial situation of an individual or organization.

[0632] "Means of automatic data collection" refers to functions or devices in which a machine or software collects data without human intervention.

[0633] "Means of calculation" refers to a device or algorithm that performs calculations based on data or input information and derives a result.

[0634] "Means of processing" refers to the technology or system used to manage, modify, or analyze data for a specific purpose.

[0635] "Means of analysis and optimization" refers to methods or techniques for analyzing data to derive the most efficient solutions or results.

[0636] This system automatically collects each employee's work information and computer operation history, and calculates compensation information based on that data. Specifically, it manages information by linking the server and terminals, and is designed to allow users to efficiently check and operate the information.

[0637] The server acts as a central management system, periodically collecting work information and computer operation history transmitted from each employee's terminal. The server stores this data in a database and analyzes and processes it using AI algorithms. These AI algorithms are useful for analyzing work hours and calculating various legal deductions. The server also automatically performs tax processing in accordance with the latest tax and social welfare laws.

[0638] Each employee is provided with a terminal, which generates work information when they log in at the start of their daily workday. The terminal sends this information to the server and also has the function of displaying compensation information and individual asset plans received from the server to the user. Through the terminal, users can check asset plan suggestions in real time and provide feedback in natural language as needed.

[0639] For example, if a user is considering purchasing a new home, the server will perform a future income and expenditure simulation based on collected salary history and payment information, and propose the optimal loan plan. The user can receive an immediate response from the server by entering a prompt message on their terminal such as, "I would like to purchase a home in the future, so please propose a financial plan."

[0640] In this way, this system utilizes a generative AI model and natural language processing technology to enable accurate and rapid responses, thereby achieving efficient business operations and user support.

[0641] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0642] Step 1:

[0643] When an employee logs into the terminal at the start of their workday, it generates their work start time and employee ID. This becomes the input information. The terminal periodically sends this information to the server, forming the basic data for work information. The server receives this information and records it in the database.

[0644] Step 2:

[0645] The server inputs received work information and operation history into an AI algorithm to analyze work hours and work efficiency. This analysis outputs work efficiency patterns for each employee. Specifically, the server calculates how much time was spent on each task and adds the results to the profile as an indicator of work efficiency.

[0646] Step 3:

[0647] The server initiates a process to calculate compensation information based on the analysis results. This calculation involves data processing that takes into account working hours, salary components (basic salary, allowances, etc.), and legal deductions (taxes, social insurance contributions). This results in the output of accurate net compensation amounts, which are then saved as customized salary data for each employee.

[0648] Step 4:

[0649] The server uses the calculated compensation information to analyze each employee's current and past salary and payment history, and generates asset planning suggestions. The AI ​​model derives the optimal savings plan and investment strategy, which is then provided as output. Specifically, it proposes appropriate financial products and asset management methods based on the employee's lifestyle and future plans.

[0650] Step 5:

[0651] Users can view asset plans and reward information provided by the server through their terminal. They can provide feedback by entering prompt messages. For example, by entering "I would like to buy a house in the future, so please propose a financial plan" into the terminal, they will receive a specific proposal from the server. Based on this feedback, the server will further modify the plan or provide additional information.

[0652] (Application Example 1)

[0653] 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".

[0654] There is a need to automate accurate and efficient payroll calculation and fund management using employee work information and data from information processing equipment operations. Furthermore, there is a demand for information that helps individuals achieve a more prosperous life by proposing savings and investment strategies optimized for each individual. Traditional methods are time-consuming and cumbersome, and struggle to keep up with the latest tax and public insurance regulations.

[0655] 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.

[0656] In this invention, the server includes means for automatically collecting work information and data on the operation of the information processing device, means for automatically calculating salary data based on the collected data, and means for automatically processing public insurance and taxes. This enables accurate and efficient payroll calculation and fund management.

[0657] "Work information" refers to data on employees' working hours, job duties, and activities during work, and forms the basis for payroll calculation and work efficiency analysis.

[0658] "Data from information processing device operations" refers to records of operation logs, input history, and applications used in connection with the use of information processing devices, and is information used to evaluate employees' work activities.

[0659] "Salary data" refers to wage information calculated for each individual employee, reflecting work information, various deductions, tax obligations, etc., to show the final take-home pay.

[0660] "Public insurance" refers to insurance based on the social security system, including health insurance and pension insurance, which are paid for by both employees and employers.

[0661] "Taxation" refers to the obligation to pay taxes based on an employee's income in accordance with the law, and includes taxation such as income tax and local inhabitant tax.

[0662] A "financial plan" is a financial strategy created based on an individual employee's income, expenses, lifestyle, and future goals, outlining their savings and investment strategies.

[0663] "Means" refer to the processes or mechanical / technical methods employed to achieve a specific objective, and are essential components in the execution of a system.

[0664] In the system that implements this application example, the server is the central component. The server automatically collects each employee's work information and information processing device operation data, and stores it in a database. The server uses the collected data to calculate salary data, and then applies deductions based on the latest public insurance and tax information to calculate the net pay.

[0665] Next, the server uses AI algorithms to analyze salary data and historical spending data to propose a financial plan tailored to each individual employee. This plan includes savings and investment suggestions, aligning with the employee's lifestyle and future plans. These calculations and suggestions are performed using cloud-based databases and data processing tools such as Python, NumPy, and pandas.

[0666] Employees, as users, can access their pay stubs and proposed financial plans using their own devices (smartphones or PCs). The on-device interface is designed for iOS and Android, and data is shared with the server via an API. Employees can also ask questions to a generative AI model using natural language processing and receive real-time feedback on their salary.

[0667] Specifically, users can receive an optimal financial plan from the AI ​​by entering a prompt such as, "I'm planning a big trip next year; please give me a savings plan for it." In this way, employees are supported in managing their daily finances and can make smarter financial decisions.

[0668] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0669] Step 1:

[0670] The server automatically collects work information and operation data from each employee's terminal via the company network. Inputs include login times and operation logs of applications used, which are sent from the terminals. Outputs are stored in a database and used for subsequent processing.

[0671] Step 2:

[0672] The server calculates payroll data based on collected work information and operational data. Inputs include time allocation data and parameters necessary for payroll calculation, and the output calculates each employee's base salary and deductions. Various public insurance and tax information registered in the database are referenced here.

[0673] Step 3:

[0674] The server calculates the net pay by applying deductions based on the payroll data. The inputs for this step are base salary, tax information, and insurance information, and the output is the net pay amount. A Python calculation library is used for the calculations.

[0675] Step 4:

[0676] The server analyzes past salary data and spending history to create a financial plan. An AI algorithm is used, taking salary data, user spending history, and future goal data as input. The output is a suggested savings and investment plan based on this analysis.

[0677] Step 5:

[0678] The terminal displays the user's received pay stubs and financial plan suggestions. The user can access this information through the interface and input specific prompts into an AI model. Based on these prompts, the user receives real-time advice from the AI ​​model as output.

[0679] Step 6:

[0680] The device analyzes user prompts using natural language processing and passes them to a generative AI model. The input is a user question, and the output is an answer from the AI. For example, using the prompt "How much should I save each month?", the user can receive suggestions regarding specific savings goals.

[0681] 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.

[0682] This invention is implemented as a system that automatically collects employee work data and computer operation information, and automatically calculates salary information based on this data. Furthermore, it automatically processes social security and taxation based on the calculated salary information and proposes individual financial plans. It also has a function to evaluate work efficiency and automatically respond to user input using natural language processing. In addition, this system incorporates an emotion engine that recognizes user emotions, enabling flexible responses that take into account the user's psychological state.

[0683] In a specific implementation, a server acts as a central management device, periodically collecting work data and operation logs from each employee's terminal. The terminal generates login information when the user starts work and sends it to the server. The server centrally manages this data and analyzes work hours and work efficiency using an AI algorithm. The results of this analysis are directly reflected in payroll calculations, and the server calculates each employee's pay information based on the payroll components.

[0684] Simultaneously with payroll calculation, the emotion engine analyzes the user's emotional state based on their input and device usage patterns. This includes analyzing user interaction data using natural language processing. For example, if the server determines that a user is experiencing stress, it will propose financial plans for stress relief and measures to reduce workload. Such responses are then presented to the user on their device.

[0685] For example, if an employee expresses dissatisfaction with the system during work, the emotion engine recognizes a specific emotional state (e.g., stress, anxiety) based on that information. The server then takes this state into consideration and suggests to the user more flexible working hours or participation in relaxing programs. This entire process not only improves work efficiency but also contributes to increased employee satisfaction.

[0686] Thus, the system of the present invention achieves improved organizational productivity and addresses the individual needs of employees through the coordinated and autonomous operation of each step.

[0687] The following describes the processing flow.

[0688] Step 1:

[0689] The terminal records the start time of work when the user logs in and sends this information to the server. It also periodically generates operation logs showing which applications the user is using and for how long.

[0690] Step 2:

[0691] The server stores received login information and operation logs in a database. This database is used to track working hours and analyze work efficiency.

[0692] Step 3:

[0693] The server analyzes the stored data and calculates the user's base salary based on their working hours. Furthermore, it calculates social security contributions and taxes, automatically determining the net salary after deductions.

[0694] Step 4:

[0695] The server uses an emotion engine to analyze input data from the terminal and identify the user's emotional state. For example, it infers emotions from frequent input patterns and the strength of key presses.

[0696] Step 5:

[0697] The server uses the results of the emotion engine analysis to propose an optimal financial plan to the user. This proposal is flexible and adapts to the user's emotional state.

[0698] Step 6:

[0699] The terminal notifies the user of salary information and financial plan proposals sent from the server. The user can view details on the screen and enter questions about the content.

[0700] Step 7:

[0701] The server analyzes user inquiries and feedback using natural language processing and automatically generates responses based on an existing database. These responses are also tailored to take user sentiment into consideration.

[0702] Step 8:

[0703] The server aggregates data over a certain period and generates reports on changes in each user's work efficiency and emotional state. This helps in formulating strategies to improve the overall performance of the organization.

[0704] (Example 2)

[0705] 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".

[0706] Improving employee work efficiency, providing an effective work environment tailored to individual psychological states, and accurately and efficiently handling public burdens and taxes are crucial for business operations. However, currently, collecting and analyzing work data is cumbersome, and there is a lack of systems that automatically propose appropriate countermeasures based on emotional states. In addition, there is a need for a system that generates sophisticated responses using generative AI models.

[0707] 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.

[0708] In this invention, the server includes means for automatically collecting work-related information and data on electronic device operation, means for automatically calculating reward information, and means for evaluating input data using an emotion analysis engine. This enables the analysis of worker efficiency, the provision of appropriate response measures based on emotions, and the generation of advanced responses by a generative AI model.

[0709] "Work-related information" refers to data concerning an employee's work, including working hours, break times, and job duties.

[0710] "Electronic device operation data" refers to log information and operation history generated when a user operates a computer or other digital device.

[0711] "Compensation information" refers to information regarding wages and salaries paid to employees, including elements such as base pay, overtime pay, and deductions.

[0712] "Public burdens and taxes" refer to legally mandated financial burdens such as social security contributions and income tax deducted from salaries.

[0713] "Individualized financial planning" refers to proposals and plans for financial management based on the income, expenses, and savings plans of individual employees.

[0714] An "emotion analysis engine" is a system that analyzes a user's psychological state based on their input and behavioral patterns, and is implemented using technologies such as natural language processing.

[0715] A "generative AI model" is an artificial intelligence technology that generates intelligent responses or results from input requests or data.

[0716] "Means of generating responses" refers to processes and mechanisms for providing appropriate feedback in response to user input.

[0717] This invention relates to a system that collects employee work data and electronic device operation logs, and uses this data to calculate and automatically process salary information and other data. Specifically, a server functions as a central management device and performs various analyses using data received from employee terminals.

[0718] The terminal generates and sends information to the server from the moment the employee logs in, enabling them to begin work. This allows the server to track work hours and detailed operations. The server then feeds the collected data into an AI algorithm to analyze each employee's work efficiency and hours, and calculates compensation based on the results. This process utilizes statistical analysis and machine learning techniques.

[0719] Furthermore, the server equipped with an emotion analysis engine has the function of analyzing user input and terminal usage to determine the user's emotional state. For example, if a user expresses dissatisfaction, the emotion analysis engine recognizes that the user is feeling stressed and proposes appropriate financial plans and measures to reduce workload. This proposal contributes to reducing the user's psychological burden and increasing their sense of well-being in the workplace.

[0720] Furthermore, this system uses a generative AI model to generate appropriate responses based on prompt messages. For example, by sending the prompt message "Employee A appears to be experiencing stress. What kind of support can be provided?" to the server, the server can utilize the AI ​​model to present appropriate support measures to the user.

[0721] Thus, the present invention is a system that improves organizational productivity by enhancing the work efficiency of workers and enabling flexible responses tailored to individual emotional states.

[0722] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0723] Step 1:

[0724] The terminal generates login information and creates a session for the start of work when the user begins work. This login information is sent to the server. User authentication data is used as input, and the success / failure status of authentication is provided to the server as output.

[0725] Step 2:

[0726] The server collects work data and operation logs that are periodically sent from terminals. This includes recordings of active windows, keystrokes, and application usage history. The collected data is centrally managed on the server. Input is raw log data, and output is a statistical work history report.

[0727] Step 3:

[0728] The server feeds collected work data into an AI algorithm to analyze work hours and work efficiency. Using a machine learning model, it learns patterns and evaluates whether work is being performed efficiently. Work data is used as input, and the output is an analyzed work efficiency score.

[0729] Step 4:

[0730] The server calculates each employee's salary based on the analysis results. Salary calculation combines working hours, work efficiency, and base pay elements. The input is the analyzed data, and the output is individual salary information.

[0731] Step 5:

[0732] The server uses an emotion analysis engine to analyze user input and operation logs to determine the user's emotional state. Natural language processing techniques are used to analyze the sentiment of the text entered by the user. The input is user behavior data, and the output is an emotional state score.

[0733] Step 6:

[0734] The server proposes individualized financial plans and workload reduction measures based on the user's emotional state. These proposals are generated using an AI model from several options and presented to the user. Inputs are emotional state scores and business data, while output is the specific proposed content.

[0735] Step 7:

[0736] The server uses a generative AI model to generate responses based on the prompt. For example, if the prompt is "Suggest stress relief measures," the AI ​​model generates an appropriate response and presents it to the user. The input is the prompt, and the output is the generated response.

[0737] (Application Example 2)

[0738] 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".

[0739] In today's work environment, not only employee working hours and work efficiency, but also their emotional state can significantly impact productivity and satisfaction. However, comprehensively managing these factors and providing optimal support to individual employees is not easy. Standard payroll and performance evaluation systems function without considering emotional states, limiting their ability to mitigate employee stress and dissatisfaction. As a result, there is a challenge in that individual employee needs cannot be adequately addressed, leading to decreased work efficiency and employee satisfaction.

[0740] 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.

[0741] In this invention, the server includes means for automatically collecting work data and computer operation information, means for automatically calculating salary information based on the collected information, and means for performing sentiment analysis and proposing flexible responses based on the user's emotional state. This enables individualized optimization that takes into account the emotional state of employees.

[0742] "Work data" refers to information related to employees' working hours, attendance, and working time.

[0743] "Computer operation information" refers to log information related to the operation of equipment used by employees to perform their work.

[0744] "Salary information" refers to data related to the wages and compensation that should be paid to employees.

[0745] "Means for automatically processing social security and taxation" refers to a system equipped with the function to automatically calculate necessary social insurance contributions and tax deductions based on calculated salary information.

[0746] "Means of proposing individualized financial plans" refers to a function that presents optimal ways of using funds and savings plans based on each employee's salary information and personal circumstances.

[0747] "Emotional analysis" refers to a technique that analyzes a user's words, actions, and behavioral patterns to infer their emotional state.

[0748] "Means of proposing flexible responses" refers to a system that provides actions and support tailored to the user based on the results of emotion analysis.

[0749] "Natural language processing" refers to the technology that enables computers to understand, interpret, and generate human language.

[0750] The system that implements this application is designed as a smart management system that takes into account the emotional state of employees in a factory environment. This system consists of a server, individual employee terminals, and a data analysis engine.

[0751] The server is responsible for centrally managing employee work data and equipment operation information. Terminals are distributed to each employee, and at the start of their workday, each employee's attendance is recorded through biometric authentication (e.g., facial recognition or fingerprint recognition). Furthermore, daily work information is collected through the operation and feedback input methods used during work.

[0752] In data analysis, software using natural language processing technology (e.g., NaturalLanguageProcessor) is used to analyze text input from employees. Next, EmotionEngine grasps the emotional state from the input and identifies emotions such as stress and dissatisfaction. If an employee sends feedback such as "I feel tired" or "The workload is heavy," the system quickly provides appropriate feedback and resources accordingly (e.g., scheduling time for relaxation, suggesting refreshment programs). It also utilizes prompt sentences generated by a generative AI model to guide specific suggestions.

[0753] For example, if an employee sends feedback such as, "The complexity of the work is increasing, so I need more support," the system can recognize this as an emotional issue and suggest educational content or workload adjustments.

[0754] An example of a prompt for the generating AI model would be: "Identify the specific cause of the stress the employee is experiencing and propose solutions. Feedback: 'The complexity of the work is increasing, so I need a little more support.'" This ensures that the feedback is tailored to the employee's feelings and needs.

[0755] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0756] Step 1:

[0757] The terminal uses biometric authentication to identify employees when they start work. Input includes employee facial data and fingerprint data. This data is used to accurately record attendance times and transmit them to the server.

[0758] Step 2:

[0759] The server uses work information received from terminals and employee equipment operation logs to store employee work data in a central database. The input is work hours and operation logs, and the output is integrated employee work data. This data forms the basis for salary calculation.

[0760] Step 3:

[0761] The server analyzes work data using an AI algorithm and automatically calculates salaries. Inputs include work hours, operation logs, and data on the company's salary components. The analysis outputs salary information, and based on this information, automatic processing of social security and taxation is performed.

[0762] Step 4:

[0763] Users (employees) provide feedback using natural language via their terminals during their daily work. This feedback is text-based and the data is sent to the server.

[0764] Step 5:

[0765] The server analyzes the collected feedback using natural language processing software (e.g., NaturalLanguageProcessor) and analyzes the employee's emotional state using an emotion analysis engine (e.g., EmotionEngine). The input is text feedback, and the output is an evaluation of the emotional state. This allows the server to understand the stress and dissatisfaction that employees are experiencing.

[0766] Step 6:

[0767] The server uses emotion analysis results and a generative AI model to propose appropriate responses to employees. Input includes emotional state assessment results and internal resource information, and output generates personalized support suggestions. These suggestions are presented to the user via a terminal. For example, they might suggest appropriate break times or introduce training programs.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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."

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] The following is further disclosed regarding the embodiments described above.

[0790] (Claim 1)

[0791] A means of automatically collecting work data and computer operation information,

[0792] A means for automatically calculating salary information based on the collected information,

[0793] A means of automatically processing social security and taxation based on the calculated salary information,

[0794] A means of proposing individual financial plans based on the salary information,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, characterized in that it includes means for analyzing working hours and evaluating work efficiency.

[0798] (Claim 3)

[0799] The system according to claim 1, characterized in that it includes means for automatically responding to user input using natural language processing.

[0800] "Example 1"

[0801] (Claim 1)

[0802] A means for automatically collecting employee work information and computer operation history,

[0803] A means for automatically calculating reward information based on the collected information,

[0804] A means for automatically processing social welfare and taxation based on the calculated compensation information,

[0805] A means of presenting individual asset plans based on the compensation information,

[0806] A means of optimizing asset planning by analyzing the salary history and payment history of each employee,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, characterized in that it includes means for analyzing working hours to evaluate work efficiency and recording the evaluation results in a profile.

[0810] (Claim 3)

[0811] The system according to claim 1, characterized in that it includes means for using a generative AI model to automatically respond to human language input and process inquiries.

[0812] "Application Example 1"

[0813] (Claim 1)

[0814] A means for automatically collecting work information and data on the operation of information processing equipment,

[0815] A means for automatically calculating salary data based on the collected data,

[0816] A means for automatically processing public insurance and taxes based on the calculated salary data,

[0817] A means of proposing individual financial plans based on the salary data,

[0818] A means of automatically making savings and investment suggestions using the user's financial information,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, comprising means for analyzing working hours and evaluating work efficiency.

[0822] (Claim 3)

[0823] The system according to claim 1, comprising means for automatically responding to user input using natural language processing.

[0824] "Example 2 of combining an emotion engine"

[0825] (Claim 1)

[0826] A means of automatically collecting work-related information and data on the operation of electronic devices,

[0827] A means for automatically calculating reward information based on the collected data,

[0828] A means of automatically processing public burdens and taxes based on the calculated compensation information,

[0829] A means of proposing individual financial plans based on the compensation information,

[0830] A means for evaluating input data using an emotion analysis engine to analyze the emotional state of the user,

[0831] A means of generating a response to a given request sentence using a generative AI model,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, characterized in that it includes means for analyzing working hours and evaluating work efficiency.

[0835] (Claim 3)

[0836] The system according to claim 1, characterized in that it includes means for automatically responding to user input using language analysis technology.

[0837] "Application example 2 when combining with an emotional engine"

[0838] (Claim 1)

[0839] A means of automatically collecting work data and computer operation information,

[0840] A means for automatically calculating salary information based on the collected information,

[0841] A means of automatically processing social security and taxation based on the calculated salary information,

[0842] A means of proposing individual financial plans based on the salary information,

[0843] A means of performing emotion analysis and proposing flexible responses based on the user's emotional state,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, characterized in that it includes means for analyzing working hours and evaluating work efficiency.

[0847] (Claim 3)

[0848] The system according to claim 1, characterized in that it includes means for automatically responding to user input using natural language processing, and means for analyzing the user's emotional state from the user's input information and providing appropriate support. [Explanation of Symbols]

[0849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of automatically collecting work data and computer operation information, A means for automatically calculating salary information based on the collected information, A means of automatically processing social security and taxation based on the calculated salary information, A means of proposing individual financial plans based on the salary information, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for analyzing working hours and evaluating work efficiency.

3. The system according to claim 1, characterized in that it includes means for automatically responding to user input using natural language processing.

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