system

Abstract

The system according to this embodiment aims to optimize financial efficiency by allocating budgets based on scientific data. [Solution] The system according to the embodiment comprises a collection unit, an organization unit, a forecasting unit, a proposal unit, and a reporting unit. The collection unit collects data from each department. The organization unit organizes the data collected by the collection unit. The forecasting unit forecasts profits based on the data organized by the organization unit. The proposal unit proposes investment targets based on the profits forecasted by the forecasting unit. The reporting unit provides monthly reports based on the investment targets proposed by the proposal unit.

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] In the prior art, the criteria for budget allocation are subjective and lack a scientific approach, so there is a problem that it is difficult to optimize the fund efficiency.

[0005] The system according to the embodiment aims to perform budget allocation based on scientific data and optimize the fund efficiency.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a data organization unit, a forecasting unit, a proposal unit, and a reporting unit. The data collection unit collects data from each department. The data organization unit organizes the data collected by the data collection unit. The forecasting unit forecasts profits based on the data organized by the data organization unit. The proposal unit proposes investment targets based on the profits forecasted by the forecasting unit. The reporting unit provides monthly reports based on the investment targets proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can optimize funding efficiency by allocating budgets based on scientific data. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Budget Innovator System according to an embodiment of the present invention is a system that dramatically improves management accounting operations, maximizes the power of compound interest by utilizing generative AI technology, and optimizes a company's capital efficiency. The Budget Innovator System, centered on the management accounting team, effectively manages profits and supports the sustainable growth of the entire company. A current problem is that budgeting has become merely a "task" of taking measures within the budget according to sales requests, and the criteria for budget allocation decisions are subjective and lack a scientific approach. What management accounting should do is determine where and how to invest surplus profits. The Budget Innovator System utilizes generative AI to consider the current month's budget and profits up to last month to make the optimal budget allocation. It also builds a mechanism to maximize profits on a monthly basis by utilizing the effect of compound interest. This optimizes the capital efficiency of the entire company and enhances the importance of profit management. For example, the Budget Innovator System automatically collects and organizes operational data, revenue data, and budget data from each department and performs data cleansing. This reduces the time spent on manual data collection and minimizes human error. Next, the Budget Innovator System uses this month's budget and profit data up to last month to simulate the compounding effect, with the management accounting team taking the lead in calculating projected profits. This enables future revenue forecasting and supports optimal investment decisions. Furthermore, the Budget Innovator System automatically analyzes and proposes optimal investment priorities for each department, allowing the management accounting team to maximize ROI (Return on Investment). This maximizes the effectiveness of budget allocation and supports objective decision-making. Finally, the Budget Innovator System reflects the profits earned each month into the budget for the following month, and the management accounting team reports rapid feedback and an evaluation of investment effectiveness. This enables flexible budget allocation and real-time operational improvements. In this way, the Budget Innovator System maximizes ROI through budget allocation based on scientific data, communicates the value of management accounting to the entire company, and supports strategic management decisions. In addition, the utilization of compounding effects using generational AI is expected to lead to sustainable profit growth. In this way, the Budget Innovator System can optimize the company's capital efficiency and enhance the importance of profit management.

[0029] The budget innovator system according to this embodiment comprises a collection unit, an organization unit, a forecasting unit, a proposal unit, and a reporting unit. The collection unit collects data from each department. Data from each department includes, but is not limited to, operational data, revenue data, budget data, etc. The collection unit automatically collects data from each department using, for example, an API. The collection unit can also collect data using sensors. For example, the collection unit connects to the systems of each department and collects data in real time. The organization unit organizes the data collected by the collection unit. Organization is performed by, for example, data cleansing, classification, and aggregation, but is not limited to these methods. The organization unit imputes missing values ​​and removes outliers, for example. The organization unit can also classify and aggregate data by category. For example, the organization unit organizes data stored in a database using queries. The forecasting unit forecasts profits based on the data organized by the organization unit. Forecasts are made based on, for example, the algorithm used and the period covered by the forecast, but is not limited to these methods. The forecasting unit predicts profits using, for example, machine learning algorithms. The forecasting unit can also predict future profits based on historical data. For example, the forecasting unit predicts profit trends using time-series data. The proposal unit proposes investment targets based on the profits predicted by the forecasting unit. Proposals are made based on, for example, investment selection criteria and proposal format, but are not limited to these examples. The proposal unit proposes investment targets based on, for example, ROI (Return on Investment). The proposal unit can also propose investment targets based on risk assessment. For example, the proposal unit evaluates the risk and return of investment targets and proposes the optimal investment target. The reporting unit provides monthly reports based on the investment targets proposed by the proposal unit. Monthly reports are made based on, for example, the format of the report and the type of data reported, but are not limited to these examples. The reporting unit prepares monthly reports and submits them to the management accounting team. The reporting unit can also send monthly reports via email. For example, the reporting unit provides monthly reports through a web application. This allows the budget innovator system according to the embodiment to optimize the company's financial efficiency.

[0030] The data collection unit collects data from each department. This data includes, but is not limited to, operational data, revenue data, and budget data. The unit can automatically collect data from each department using APIs, for example. Specifically, it sends API requests to each department's system to retrieve the necessary data. Even if the data format or structure differs, the unit has the capability to convert it into a unified format. The unit can also collect data using sensors. For example, it can collect data from environmental sensors and energy consumption sensors within the office to aid in operational cost analysis. Furthermore, the unit connects to each department's system to collect data in real time. This ensures that the latest data is always available, supporting rapid decision-making. The unit can flexibly configure the frequency and timing of data collection, enabling data collection tailored to specific events or periods. For example, during month-end closing, it can collect data more frequently to provide foundational data for accurate monthly reports. This allows the unit to efficiently collect data across the entire company and quickly provide information needed by other departments.

[0031] The organization department organizes the data collected by the collection department. Organization is carried out by methods such as data cleansing, classification, and aggregation, but is not limited to these examples. Specifically, in data cleansing, missing values ​​and outliers are detected in the collected data and then filled in or removed. For example, if there are missing values, they are filled in based on past data or related data, and if outliers are detected, that data is removed or corrected. The organization department can also classify and aggregate data by category. For example, operational data, revenue data, and budget data are classified into their respective categories, and aggregation is performed for each category. This makes it possible to understand the data for each category at a glance. The organization department organizes data stored in the database using queries. Specifically, SQL queries are used to extract the necessary data from the database and perform aggregation and analysis. Furthermore, the organization department performs data validation to ensure the accuracy and consistency of the data. For example, the format and scope of the data are checked to ensure that no invalid data is included. This allows the organization department to organize the collected data accurately and efficiently and make it easy for other departments to use.

[0032] The forecasting unit predicts profits based on data organized by the data processing unit. Predictions are made based on, for example, the algorithm used and the time period covered, but are not limited to these examples. Specifically, machine learning algorithms are used to predict profits. For example, regression analysis and time series analysis are used to predict future profits from historical data. The forecasting unit can also predict future profits based on historical data. For example, revenue data from the past several years is used to predict future revenue trends. Furthermore, the forecasting unit can make more accurate predictions by taking into account external economic indicators and market trends. For example, data such as economic growth rates and consumer confidence indices are incorporated to evaluate their impact on corporate profits. The forecasting unit can visualize the prediction results and display them clearly using graphs and charts. This allows management and other departments to easily understand the prediction results and use them to aid in decision-making. Furthermore, the forecasting unit continuously evaluates the accuracy of the prediction model and improves it as needed. For example, the model is retrained each time new data is collected to improve prediction accuracy. This allows the forecasting unit to always provide highly accurate profit forecasts based on the latest information, supporting the company's strategic decision-making.

[0033] The Proposal Department proposes investment targets based on the profits predicted by the Forecasting Department. Proposals are made based on, for example, investment selection criteria and proposal format, but are not limited to these examples. Specifically, investment targets are proposed based on ROI (Return on Investment). For example, the investment target with the highest ROI relative to the predicted profits is selected and proposed. The Proposal Department can also propose investment targets based on risk assessment. For example, the risk and return of an investment target are evaluated, and investment targets with low risk and high return are selected. In selecting investment targets, the Proposal Department takes into account the company's strategy and objectives. For example, if a company is aiming to expand into new businesses, investment targets related to that area will be prioritized. Furthermore, the Proposal Department makes the investment selection process transparent and reports the selection criteria and evaluation results in detail. This makes it easier for management and investment committees to understand the proposals and improves the quality of decision-making. The Proposal Department provides proposals in a presentation format, explaining them clearly using visual materials and data. This makes the proposals more effective and increases the likelihood of them being adopted. Furthermore, the proposal department can continuously improve the quality of its proposals by collecting feedback on the proposals and incorporating it into future proposals. This allows the proposal department to effectively support companies' investment strategies and propose optimal investment targets.

[0034] The reporting department prepares monthly reports based on investments proposed by the proposal department. These monthly reports may vary depending on the format and type of data presented, but are not limited to these examples. Specifically, the reporting department prepares monthly reports and submits them to the management accounting team. These reports include detailed data such as investment performance, ROI, and risk assessments. The reporting department can also send monthly reports via email. For example, the reporting department can provide monthly reports through a web application, allowing stakeholders to access the reports anytime, anywhere. Furthermore, the reporting department can visually present the monthly reports using graphs and charts to enhance clarity, making the reports easier to understand and aiding in decision-making. The reporting department verifies and reviews data to ensure accuracy and consistency. For example, they check data integrity and ensure there are no errors before preparing a report. Additionally, the reporting department can continuously improve the quality of reports by collecting feedback and incorporating it into future reports. This allows the reporting department to accurately understand the company's investment performance and provide crucial information to support strategic decision-making.

[0035] The data collection unit can automatically collect operational data, revenue data, and budget data from each department. For example, the data collection unit can automatically collect data from each department using APIs. For example, the data collection unit can connect to each department's system and collect data in real time. The data collection unit can also collect data using sensors. For example, the data collection unit can collect operational data from each department using sensors and store it in a database. Furthermore, the data collection unit can automatically collect revenue data from each department and perform data cleansing. For example, the data collection unit can collect revenue data via APIs and impute missing data values. This reduces the time spent on manual data collection and minimizes human error by automatically collecting data from each department. Automatic collection includes, but is not limited to, data collection using APIs and data collection using sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data from each department into a generating AI and have the generating AI perform data collection and organization.

[0036] The data cleansing unit can cleanse the collected data. For example, the data cleansing unit can impute missing values ​​and remove outliers. For example, the data cleansing unit can organize data stored in a database using queries. The data cleansing unit can also classify and aggregate data by category. For example, the data cleansing unit can remove duplicate data and maintain data consistency. Furthermore, the data cleansing unit can standardize data formats and improve data quality. For example, the data cleansing unit can standardize data formats and ensure data integrity. Thus, by cleansing the collected data, the quality of the data can be improved. Cleansing includes, but is not limited to, imputing missing values ​​and removing outliers. Some or all of the above processes in the data cleansing unit may be performed using, for example, AI, or not using AI. For example, the data cleansing unit can input the collected data into a generating AI and have the generating AI perform the data cleansing.

[0037] The forecasting unit can simulate the compounding effect using this month's budget and profit data up to last month. The forecasting unit can predict profits using, for example, machine learning algorithms. For example, the forecasting unit can predict future profits based on historical data. Furthermore, the forecasting unit can predict profit trends using time-series data. For example, the forecasting unit can simulate the compounding effect and predict future returns. In addition, the forecasting unit can support optimal investment decisions based on the results of the compounding effect simulation. For example, the forecasting unit can suggest investment targets based on the results of the compounding effect simulation. Thus, simulating the compounding effect enables support for future return predictions and optimal investment decisions. Simulating the compounding effect includes, but is not limited to, the formulas used and the simulation period. Some or all of the above-described processes in the forecasting unit may be performed using, for example, AI, or not. For example, the forecasting unit can input this month's budget and profit data up to last month into a generating AI and have the generating AI perform a compounding effect simulation.

[0038] The proposal department can automatically analyze and propose investment priorities for each department. For example, the proposal department can propose investment targets based on ROI (Return on Investment). For example, the proposal department can propose investment targets based on risk assessment. Furthermore, the proposal department can evaluate the risk and return of investment targets and propose the optimal investment targets. For example, the proposal department can evaluate the growth potential of investment targets and determine investment priorities. In addition, the proposal department can analyze market trends of investment targets and propose the optimal investment targets. For example, the proposal department can select investment targets and propose investment priorities based on market data. By automatically analyzing and proposing the optimal investment priorities, the effectiveness of budget allocation can be maximized and objective decision-making can be supported. Investment priorities include, but are not limited to, ROI (Return on Investment) and risk assessment. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data from each department into a generating AI and have the generating AI perform the analysis and proposal of investment priorities.

[0039] The reporting department can incorporate monthly profits into the budget for the following month. For example, the reporting department can prepare monthly reports and submit them to the management accounting team. For example, the reporting department can send monthly reports via email. The reporting department can also provide monthly reports through a web application. For example, the reporting department can share monthly reports on an online platform and make them accessible in real time. Furthermore, the reporting department can provide feedback to incorporate monthly profits into the budget for the following month. For example, the reporting department can analyze profit data and adjust the budget allocation for the following month. This allows for flexible budget allocation and real-time operational improvements by incorporating monthly profits into the budget for the following month. Incorporation into the budget for the following month may include, but is not limited to, determining what percentage of profits to include in the budget for the following month. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can input monthly profit data into a generating AI and have the generating AI perform the task of incorporating it into the budget for the following month.

[0040] The reporting department can provide feedback and evaluate the effectiveness of investments. For example, the reporting department can prepare monthly reports and submit them to the management accounting team. For example, the reporting department can send monthly reports via email. The reporting department can also provide monthly reports through a web application. For example, the reporting department can share monthly reports on an online platform and make them accessible in real time. Furthermore, the reporting department can evaluate the effectiveness of investments and provide feedback. For example, the reporting department can evaluate the performance of investment targets and adjust the investment strategy for the following month. This enables real-time business improvement through rapid feedback and evaluation of the effectiveness of investments. Feedback and evaluation of the effectiveness of investments include, but are not limited to, KPIs (Key Performance Indicators) and ROI (Return on Investment). Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input evaluation data for effectiveness into a generating AI and have the generating AI generate feedback.

[0041] The data collection unit can analyze the past data collection history of each department and select a collection method. For example, the data collection unit can select the most efficient collection method based on the past data collection history of each department. For example, the data collection unit can analyze the past data collection history of each department and select a collection method to improve data quality. The data collection unit can also optimize the timing of data collection by referring to the past data collection history of each department. For example, the data collection unit can adjust the frequency of data collection based on the past data collection history. In this way, by analyzing the past data collection history, the optimal collection method can be selected and data quality can be improved. The analysis of past data collection history includes, but is not limited to, collection frequency and the type of data collected. Some or all of the above processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0042] The data collection unit can filter data based on the current projects and work status of each department during data collection. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the current project status of each department. For example, the data collection unit can filter and collect data of high importance based on the work status of each department. The data collection unit can also collect only the necessary data, taking into account the project progress of each department. For example, the data collection unit can determine the priority of data collection based on the project progress. This allows for the priority collection of highly relevant data by filtering based on the current projects and work status of each department. Filtering includes, but is not limited to, project importance and work progress. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input project data from each department into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each department during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the geographical location information of each department. For example, the data collection unit optimizes the data collection range by considering the geographical location information of each department. The data collection unit can also improve the efficiency of data collection by referring to the geographical location information of each department. For example, the data collection unit determines the priority of data collection based on geographical location information. This allows for the priority collection of highly relevant data and improved data collection efficiency by considering the geographical location information of each department. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the geographical location information of each department into a generating AI and have the generating AI perform the collection of highly relevant data.

[0044] The data collection unit can analyze each department's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze each department's social media activities and collect highly relevant data. For example, the data collection unit can determine data collection priorities based on each department's social media activities. The data collection unit can also optimize the scope of data collection based on each department's social media activities. For example, the data collection unit can determine data collection priorities based on social media posting frequency and engagement rates. This allows for the collection of highly relevant data and optimization of the scope of data collection by analyzing each department's social media activities. Social media activities include, but are not limited to, posting frequency and engagement rates. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each department's social media data into a generating AI and have the generating AI collect relevant data.

[0045] The data sorting unit can adjust the degree of sorting based on the importance of the data during data sorting. For example, the sorting unit can prioritize sorting highly important data and perform detailed cleansing. For example, the sorting unit can simplify sorting of less important data. The sorting unit can also adjust the sorting procedure according to the importance of the data. For example, the sorting unit can evaluate the importance of the data and determine the sorting priority. This allows for efficient data sorting by adjusting the level of detail based on the importance of the data. Data importance includes, but is not limited to, the frequency of data use and the impact on business operations. Some or all of the above processes in the sorting unit may be performed using AI, for example, or not using AI. For example, the sorting unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the degree of sorting.

[0046] The data cleansing unit can apply different cleansing algorithms depending on the data category during data cleansing. For example, the data cleansing unit can select the optimal cleansing algorithm depending on the data category. For example, the data cleansing unit can apply different cleansing methods for each data category. The data cleansing unit can also adjust the level of detail of cleansing based on the data category. For example, the data cleansing unit can evaluate the data category and apply the optimal cleansing algorithm. This enables efficient data cleansing by applying different cleansing algorithms depending on the data category. Cleansing algorithms include, but are not limited to, differences in algorithms for each category. Some or all of the above processing in the data cleansing unit may be performed using AI, for example, or without AI. For example, the data cleansing unit can input the data category into a generating AI and have the generating AI execute the application of the cleansing algorithm.

[0047] The data sorting unit can determine sorting priorities based on the data submission date during data sorting. For example, the sorting unit may prioritize sorting the most recent data and perform a quick cleansing. For example, the sorting unit may sort older data later. The sorting unit can also adjust the sorting procedure based on the data submission date. For example, the sorting unit may evaluate the data submission date and determine the sorting priority. This enables efficient data sorting by determining sorting priorities based on the data submission date. The data submission date includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processes in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the data submission date into a generating AI and have the generating AI determine the sorting priority.

[0048] The data sorting unit can determine the sorting order based on the relevance of the data during data sorting. For example, the sorting unit may prioritize sorting and cleanse highly relevant data. For example, it may sort less relevant data later. The sorting unit can also adjust the sorting procedure based on the relevance of the data. For example, it may evaluate the relevance of the data and determine the sorting priority. This allows for efficient data sorting by adjusting the sorting order based on the relevance of the data. Data relevance includes, but is not limited to, data correlation and co-occurrence relationships. Some or all of the above processes in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the data relevance into a generating AI and have the generating AI determine the sorting order.

[0049] The prediction unit can adjust its prediction algorithm by referring to past profit data when making profit predictions. For example, the prediction unit can select the optimal prediction algorithm based on past profit data. For example, the prediction unit can analyze past profit data and optimize the prediction algorithm. The prediction unit can also improve the accuracy of predictions by referring to past profit data. For example, the prediction unit adjusts the prediction algorithm based on past profit data. This allows the prediction algorithm to be optimized and the accuracy of predictions to be improved by referring to past profit data. The prediction algorithm includes, but is not limited to, examples such as algorithm optimization using past data. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or without using AI. For example, the prediction unit can input past profit data into a generating AI and have the generating AI perform the adjustment of the prediction algorithm.

[0050] The forecasting unit can apply different forecasting methods to each data category when forecasting profits. For example, the forecasting unit can select the optimal forecasting method according to the data category. For example, the forecasting unit can apply different forecasting algorithms to each data category. The forecasting unit can also adjust the level of detail of the forecast based on the data category. For example, the forecasting unit can evaluate the data category and apply the optimal forecasting method. This enables efficient profit forecasting by applying different forecasting methods to each data category. Fortune-telling methods include, but are not limited to, differences in forecasting methods for each category. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the data category into a generating AI and have the generating AI perform the application of the forecasting method.

[0051] The forecasting unit can determine forecast priorities based on data submission timing when forecasting profits. For example, the forecasting unit may prioritize forecasting the most recent data to quickly forecast profits. For example, the forecasting unit may postpone forecasting older data. The forecasting unit can also adjust the forecasting procedure based on data submission timing. For example, the forecasting unit may evaluate data submission timing and determine forecast priorities. This enables efficient profit forecasting by determining forecast priorities based on data submission timing. Data submission timing includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the forecasting unit may input data submission timing into a generating AI and have the generating AI determine forecast priorities.

[0052] The forecasting unit can improve the accuracy of its forecasts by referring to relevant market data when forecasting profits. For example, the forecasting unit can make optimal profit forecasts based on relevant market data. For example, the forecasting unit can optimize its forecasting algorithm by referring to relevant market data. The forecasting unit can also improve the accuracy of its forecasts by referring to relevant market data. For example, the forecasting unit can adjust its forecasting algorithm based on relevant market data. In this way, the accuracy of the forecast can be improved by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and competitive analysis data. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the forecasting unit can input relevant market data into a generating AI and have the generating AI perform the improvement of forecast accuracy.

[0053] The proposal unit can adjust the level of detail in investment proposals based on the importance of the investment target. For example, the proposal unit can provide detailed proposals for highly important investment targets, and simplified proposals for less important investment targets. The proposal unit can also adjust the procedure of the proposal according to the importance of the investment target. For example, the proposal unit can evaluate the importance of the investment target and determine the priority of the proposal. This allows for efficient investment proposals by adjusting the level of detail based on the importance of the investment target. The importance of an investment target includes, but is not limited to, the growth potential and risk assessment of the investment target. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the investment target into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0054] The proposal unit can apply different proposal algorithms depending on the investment category when making investment proposals. For example, the proposal unit can select the optimal proposal algorithm depending on the investment category. For example, the proposal unit can apply different proposal methods for each investment category. The proposal unit can also adjust the level of detail of the proposal based on the investment category. For example, the proposal unit can evaluate the investment category and apply the optimal proposal algorithm. This makes it possible to make efficient investment proposals by applying different proposal algorithms depending on the investment category. Proposal algorithms include, but are not limited to, differences in proposal algorithms for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the investment category into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0055] The proposal department can prioritize investment proposals based on the submission timing of the investment targets. For example, the proposal department may prioritize proposals for the most recent investment targets and submit investment proposals quickly. For example, the proposal department may postpone proposals for investment targets with older submission dates. The proposal department can also adjust the proposal procedure based on the submission timing of the investment targets. For example, the proposal department may evaluate the submission timing of investment targets and determine the priority of proposals. This enables efficient investment proposals by prioritizing proposals based on the submission timing of investment targets. The submission timing of investment targets includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department may input the submission timing of investment targets into a generating AI and have the generating AI perform the determination of proposal priority.

[0056] The proposal department can determine the order of investment proposals based on the relevance of the investment targets when making investment proposals. For example, the proposal department may prioritize proposals for highly relevant investment targets and provide detailed proposals. For example, the proposal department may postpone proposals for less relevant investment targets. The proposal department can also adjust the procedure of proposals based on the relevance of the investment targets. For example, the proposal department may evaluate the relevance of investment targets and determine the priority of proposals. This allows for efficient investment proposals by adjusting the order of proposals based on the relevance of the investment targets. The relevance of investment targets includes, but is not limited to, the business content of the investment target and industry relevance. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the relevance of investment targets into a generating AI and have the generating AI determine the order of proposals.

[0057] The reporting unit can adjust its reporting algorithm by referring to past reporting data when submitting a report. For example, the reporting unit can select the optimal reporting algorithm based on past reporting data. For example, the reporting unit can analyze past reporting data and optimize the reporting algorithm. The reporting unit can also improve the accuracy of reports by referring to past reporting data. For example, the reporting unit adjusts the reporting algorithm based on past reporting data. This allows the reporting algorithm to be optimized and the accuracy of reports to be improved by referring to past reporting data. The reporting algorithm includes, but is not limited to, examples such as algorithm optimization using past data. Some or all of the above processes in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input past reporting data into a generating AI and have the generating AI perform the adjustment of the reporting algorithm.

[0058] The reporting unit can apply different reporting methods to each data category when reporting. For example, the reporting unit can select the optimal reporting method according to the data category. For example, the reporting unit can apply different reporting algorithms to each data category. The reporting unit can also adjust the level of detail in the report based on the data category. For example, the reporting unit can evaluate the data category and apply the optimal reporting method. This enables efficient reporting by applying different reporting methods to each data category. Reporting methods include, but are not limited to, differences in reporting methods for each category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the data category into a generating AI and have the generating AI perform the application of the reporting method.

[0059] The reporting department can prioritize reports based on the timing of data submission. For example, the reporting department may prioritize reporting the most recent data and report quickly. For example, it may postpone reporting older data. The reporting department can also adjust its reporting procedures based on the timing of data submission. For example, it may evaluate the timing of data submission and determine the reporting priority. This enables efficient reporting by prioritizing reports based on the timing of data submission. The timing of data submission includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processes in the reporting department may be performed using, for example, AI, or not using AI. For example, the reporting department may input the timing of data submission into a generating AI and have the generating AI determine the reporting priority.

[0060] The reporting unit can adjust the accuracy of its reports by referring to relevant market data during the reporting process. For example, the reporting unit can provide optimal reports based on relevant market data. For example, the reporting unit can optimize its reporting algorithm by referring to relevant market data. The reporting unit can also improve the accuracy of its reports by referring to relevant market data. For example, the reporting unit can adjust its reporting algorithm based on relevant market data. This allows for improved reporting accuracy by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and competitive analysis data. Some or all of the above processing in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input relevant market data into a generating AI and have the generating AI perform improvements to the accuracy of its reports.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The Budget Innovator System can analyze each department's past budget usage history and propose the optimal budget allocation method. For example, it can evaluate each department's budget utilization rate based on past budget usage history and adjust for budget surpluses or deficits. It can also determine the priority of budget allocation for each department based on past budget usage history. Furthermore, by analyzing past budget usage history, it is possible to reduce budget waste and achieve efficient budget allocation. This makes it possible to utilize past data to enable more scientific and objective budget allocation.

[0063] The budget innovator system allows for budget allocation while considering the current project status of each department. For example, it can determine budget priorities based on the progress of each department's projects. It can also evaluate the importance of each department's projects and allocate budget preferentially to important projects. Furthermore, it can monitor the progress of each department's projects and reallocate budgets as needed. This enables flexible budget allocation tailored to the project status of each department, thereby improving the success rate of projects.

[0064] The budget innovator system can allocate budgets while considering the geographical location of each department. For example, it optimizes the scope of budget allocation based on the geographical location of each department. It can also promote budget sharing between geographically close departments, enabling efficient budget utilization. Furthermore, it can determine budget allocation priorities based on geographical location. This allows for flexible budget allocation that takes geographical factors into account, reducing budget waste.

[0065] The budget innovator system can analyze each department's social media activities and propose budget allocations. For example, it can determine budget allocation priorities based on each department's social media activities. It can also evaluate the response on social media and adjust budget allocations accordingly. Furthermore, it can measure the effectiveness of the budget based on social media activities and reflect this in future budget allocations. This enables flexible budget allocation using social media data, maximizing budget effectiveness.

[0066] The budget innovator system can analyze the data collection history of each department and propose the optimal data collection method. For example, it can select the most efficient data collection method based on past data collection history. It can also optimize the timing of data collection based on the data collection history. Furthermore, by analyzing the data collection history, it can propose collection methods to improve data quality. This makes it possible to efficiently collect high-quality data by utilizing past data collection history.

[0067] The Budget Innovator System allows for adjusting the level of data organization based on data importance. For example, it can prioritize organizing high-importance data and perform detailed cleansing. Conversely, it can simplify the organization of less important data. Furthermore, it can adjust the organization procedure according to data importance. This enables efficient data organization based on data importance, thereby improving data quality.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects data from each department. This data includes operational data, revenue data, budget data, etc. The data collection unit automatically collects data from each department using APIs. It can also collect data using sensors. For example, the data collection unit connects to each department's system and collects data in real time. Step 2: The sorting unit sorts the data collected by the collection unit. Sorting is done by methods such as data cleansing, classification, and aggregation. The sorting unit imputes missing data and removes outliers. It can also classify and aggregate data by category. For example, the sorting unit sorts data stored in a database using queries. Step 3: The forecasting unit predicts profits based on the data organized by the organizing unit. The prediction is made based on the algorithm used and the time period for which the prediction is taken. The forecasting unit uses machine learning algorithms to predict profits. It can also predict future profits based on historical data. For example, the forecasting unit uses time series data to predict profit trends. Step 4: The proposal team proposes investment targets based on the profits predicted by the forecasting team. The proposals are made based on the investment selection criteria and proposal format. The proposal team proposes investment targets based on ROI (Return on Investment). They can also propose investment targets based on risk assessment. For example, the proposal team evaluates the risk and return of investment targets and proposes the optimal investment targets. Step 5: The reporting department prepares monthly reports based on the investment targets proposed by the proposal department. The monthly reports are prepared according to the format of the report and the type of data to be reported. The reporting department prepares the monthly report and submits it to the management accounting team. The monthly report can also be sent by email. For example, the reporting department provides the monthly report through a web application.

[0070] (Example of form 2) The Budget Innovator System according to an embodiment of the present invention is a system that dramatically improves management accounting operations, maximizes the power of compound interest by utilizing generative AI technology, and optimizes a company's capital efficiency. The Budget Innovator System, centered on the management accounting team, effectively manages profits and supports the sustainable growth of the entire company. A current problem is that budgeting has become merely a "task" of taking measures within the budget according to sales requests, and the criteria for budget allocation decisions are subjective and lack a scientific approach. What management accounting should do is determine where and how to invest surplus profits. The Budget Innovator System utilizes generative AI to consider the current month's budget and profits up to last month to make the optimal budget allocation. It also builds a mechanism to maximize profits on a monthly basis by utilizing the effect of compound interest. This optimizes the capital efficiency of the entire company and enhances the importance of profit management. For example, the Budget Innovator System automatically collects and organizes operational data, revenue data, and budget data from each department and performs data cleansing. This reduces the time spent on manual data collection and minimizes human error. Next, the Budget Innovator System uses this month's budget and profit data up to last month to simulate the compounding effect, with the management accounting team taking the lead in calculating projected profits. This enables future revenue forecasting and supports optimal investment decisions. Furthermore, the Budget Innovator System automatically analyzes and proposes optimal investment priorities for each department, allowing the management accounting team to maximize ROI (Return on Investment). This maximizes the effectiveness of budget allocation and supports objective decision-making. Finally, the Budget Innovator System reflects the profits earned each month into the budget for the following month, and the management accounting team reports rapid feedback and an evaluation of investment effectiveness. This enables flexible budget allocation and real-time operational improvements. In this way, the Budget Innovator System maximizes ROI through budget allocation based on scientific data, communicates the value of management accounting to the entire company, and supports strategic management decisions. In addition, the utilization of compounding effects using generational AI is expected to lead to sustainable profit growth. In this way, the Budget Innovator System can optimize the company's capital efficiency and enhance the importance of profit management.

[0071] The budget innovator system according to this embodiment comprises a collection unit, an organization unit, a forecasting unit, a proposal unit, and a reporting unit. The collection unit collects data from each department. Data from each department includes, but is not limited to, operational data, revenue data, budget data, etc. The collection unit automatically collects data from each department using, for example, an API. The collection unit can also collect data using sensors. For example, the collection unit connects to the systems of each department and collects data in real time. The organization unit organizes the data collected by the collection unit. Organization is performed by, for example, data cleansing, classification, and aggregation, but is not limited to these methods. The organization unit imputes missing values ​​and removes outliers, for example. The organization unit can also classify and aggregate data by category. For example, the organization unit organizes data stored in a database using queries. The forecasting unit forecasts profits based on the data organized by the organization unit. Forecasts are made based on, for example, the algorithm used and the period covered by the forecast, but is not limited to these methods. The forecasting unit predicts profits using, for example, machine learning algorithms. The forecasting unit can also predict future profits based on historical data. For example, the forecasting unit predicts profit trends using time-series data. The proposal unit proposes investment targets based on the profits predicted by the forecasting unit. Proposals are made based on, for example, investment selection criteria and proposal format, but are not limited to these examples. The proposal unit proposes investment targets based on, for example, ROI (Return on Investment). The proposal unit can also propose investment targets based on risk assessment. For example, the proposal unit evaluates the risk and return of investment targets and proposes the optimal investment target. The reporting unit provides monthly reports based on the investment targets proposed by the proposal unit. Monthly reports are made based on, for example, the format of the report and the type of data reported, but are not limited to these examples. The reporting unit prepares monthly reports and submits them to the management accounting team. The reporting unit can also send monthly reports via email. For example, the reporting unit provides monthly reports through a web application. This allows the budget innovator system according to the embodiment to optimize the company's financial efficiency.

[0072] The data collection unit collects data from each department. This data includes, but is not limited to, operational data, revenue data, and budget data. The unit can automatically collect data from each department using APIs, for example. Specifically, it sends API requests to each department's system to retrieve the necessary data. Even if the data format or structure differs, the unit has the capability to convert it into a unified format. The unit can also collect data using sensors. For example, it can collect data from environmental sensors and energy consumption sensors within the office to aid in operational cost analysis. Furthermore, the unit connects to each department's system to collect data in real time. This ensures that the latest data is always available, supporting rapid decision-making. The unit can flexibly configure the frequency and timing of data collection, enabling data collection tailored to specific events or periods. For example, during month-end closing, it can collect data more frequently to provide foundational data for accurate monthly reports. This allows the unit to efficiently collect data across the entire company and quickly provide information needed by other departments.

[0073] The organization department organizes the data collected by the collection department. Organization is carried out by methods such as data cleansing, classification, and aggregation, but is not limited to these examples. Specifically, in data cleansing, missing values ​​and outliers are detected in the collected data and then filled in or removed. For example, if there are missing values, they are filled in based on past data or related data, and if outliers are detected, that data is removed or corrected. The organization department can also classify and aggregate data by category. For example, operational data, revenue data, and budget data are classified into their respective categories, and aggregation is performed for each category. This makes it possible to understand the data for each category at a glance. The organization department organizes data stored in the database using queries. Specifically, SQL queries are used to extract the necessary data from the database and perform aggregation and analysis. Furthermore, the organization department performs data validation to ensure the accuracy and consistency of the data. For example, the format and scope of the data are checked to ensure that no invalid data is included. This allows the organization department to organize the collected data accurately and efficiently and make it easy for other departments to use.

[0074] The forecasting unit predicts profits based on data organized by the data processing unit. Predictions are made based on, for example, the algorithm used and the time period covered, but are not limited to these examples. Specifically, machine learning algorithms are used to predict profits. For example, regression analysis and time series analysis are used to predict future profits from historical data. The forecasting unit can also predict future profits based on historical data. For example, revenue data from the past several years is used to predict future revenue trends. Furthermore, the forecasting unit can make more accurate predictions by taking into account external economic indicators and market trends. For example, data such as economic growth rates and consumer confidence indices are incorporated to evaluate their impact on corporate profits. The forecasting unit can visualize the prediction results and display them clearly using graphs and charts. This allows management and other departments to easily understand the prediction results and use them to aid in decision-making. Furthermore, the forecasting unit continuously evaluates the accuracy of the prediction model and improves it as needed. For example, the model is retrained each time new data is collected to improve prediction accuracy. This allows the forecasting unit to always provide highly accurate profit forecasts based on the latest information, supporting the company's strategic decision-making.

[0075] The Proposal Department proposes investment targets based on the profits predicted by the Forecasting Department. Proposals are made based on, for example, investment selection criteria and proposal format, but are not limited to these examples. Specifically, investment targets are proposed based on ROI (Return on Investment). For example, the investment target with the highest ROI relative to the predicted profits is selected and proposed. The Proposal Department can also propose investment targets based on risk assessment. For example, the risk and return of an investment target are evaluated, and investment targets with low risk and high return are selected. In selecting investment targets, the Proposal Department takes into account the company's strategy and objectives. For example, if a company is aiming to expand into new businesses, investment targets related to that area will be prioritized. Furthermore, the Proposal Department makes the investment selection process transparent and reports the selection criteria and evaluation results in detail. This makes it easier for management and investment committees to understand the proposals and improves the quality of decision-making. The Proposal Department provides proposals in a presentation format, explaining them clearly using visual materials and data. This makes the proposals more effective and increases the likelihood of them being adopted. Furthermore, the proposal department can continuously improve the quality of its proposals by collecting feedback on the proposals and incorporating it into future proposals. This allows the proposal department to effectively support companies' investment strategies and propose optimal investment targets.

[0076] The reporting department prepares monthly reports based on investments proposed by the proposal department. These monthly reports may vary depending on the format and type of data presented, but are not limited to these examples. Specifically, the reporting department prepares monthly reports and submits them to the management accounting team. These reports include detailed data such as investment performance, ROI, and risk assessments. The reporting department can also send monthly reports via email. For example, the reporting department can provide monthly reports through a web application, allowing stakeholders to access the reports anytime, anywhere. Furthermore, the reporting department can visually present the monthly reports using graphs and charts to enhance clarity, making the reports easier to understand and aiding in decision-making. The reporting department verifies and reviews data to ensure accuracy and consistency. For example, they check data integrity and ensure there are no errors before preparing a report. Additionally, the reporting department can continuously improve the quality of reports by collecting feedback and incorporating it into future reports. This allows the reporting department to accurately understand the company's investment performance and provide crucial information to support strategic decision-making.

[0077] The data collection unit can automatically collect operational data, revenue data, and budget data from each department. For example, the data collection unit can automatically collect data from each department using APIs. For example, the data collection unit can connect to each department's system and collect data in real time. The data collection unit can also collect data using sensors. For example, the data collection unit can collect operational data from each department using sensors and store it in a database. Furthermore, the data collection unit can automatically collect revenue data from each department and perform data cleansing. For example, the data collection unit can collect revenue data via APIs and impute missing data values. This reduces the time spent on manual data collection and minimizes human error by automatically collecting data from each department. Automatic collection includes, but is not limited to, data collection using APIs and data collection using sensors. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data from each department into a generating AI and have the generating AI perform data collection and organization.

[0078] The data cleansing unit can cleanse the collected data. For example, the data cleansing unit can impute missing values ​​and remove outliers. For example, the data cleansing unit can organize data stored in a database using queries. The data cleansing unit can also classify and aggregate data by category. For example, the data cleansing unit can remove duplicate data and maintain data consistency. Furthermore, the data cleansing unit can standardize data formats and improve data quality. For example, the data cleansing unit can standardize data formats and ensure data integrity. Thus, by cleansing the collected data, the quality of the data can be improved. Cleansing includes, but is not limited to, imputing missing values ​​and removing outliers. Some or all of the above processes in the data cleansing unit may be performed using, for example, AI, or not using AI. For example, the data cleansing unit can input the collected data into a generating AI and have the generating AI perform the data cleansing.

[0079] The forecasting unit can simulate the compounding effect using this month's budget and profit data up to last month. The forecasting unit can predict profits using, for example, machine learning algorithms. For example, the forecasting unit can predict future profits based on historical data. Furthermore, the forecasting unit can predict profit trends using time-series data. For example, the forecasting unit can simulate the compounding effect and predict future returns. In addition, the forecasting unit can support optimal investment decisions based on the results of the compounding effect simulation. For example, the forecasting unit can suggest investment targets based on the results of the compounding effect simulation. Thus, simulating the compounding effect enables support for future return predictions and optimal investment decisions. Simulating the compounding effect includes, but is not limited to, the formulas used and the simulation period. Some or all of the above-described processes in the forecasting unit may be performed using, for example, AI, or not. For example, the forecasting unit can input this month's budget and profit data up to last month into a generating AI and have the generating AI perform a compounding effect simulation.

[0080] The proposal department can automatically analyze and propose investment priorities for each department. For example, the proposal department can propose investment targets based on ROI (Return on Investment). For example, the proposal department can propose investment targets based on risk assessment. Furthermore, the proposal department can evaluate the risk and return of investment targets and propose the optimal investment targets. For example, the proposal department can evaluate the growth potential of investment targets and determine investment priorities. In addition, the proposal department can analyze market trends of investment targets and propose the optimal investment targets. For example, the proposal department can select investment targets and propose investment priorities based on market data. By automatically analyzing and proposing the optimal investment priorities, the effectiveness of budget allocation can be maximized and objective decision-making can be supported. Investment priorities include, but are not limited to, ROI (Return on Investment) and risk assessment. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input data from each department into a generating AI and have the generating AI perform the analysis and proposal of investment priorities.

[0081] The reporting department can incorporate monthly profits into the budget for the following month. For example, the reporting department can prepare monthly reports and submit them to the management accounting team. For example, the reporting department can send monthly reports via email. The reporting department can also provide monthly reports through a web application. For example, the reporting department can share monthly reports on an online platform and make them accessible in real time. Furthermore, the reporting department can provide feedback to incorporate monthly profits into the budget for the following month. For example, the reporting department can analyze profit data and adjust the budget allocation for the following month. This allows for flexible budget allocation and real-time operational improvements by incorporating monthly profits into the budget for the following month. Incorporation into the budget for the following month may include, but is not limited to, determining what percentage of profits to include in the budget for the following month. Some or all of the above processes in the reporting department may be performed using AI, or not. For example, the reporting department can input monthly profit data into a generating AI and have the generating AI perform the task of incorporating it into the budget for the following month.

[0082] The reporting department can provide feedback and evaluate the effectiveness of investments. For example, the reporting department can prepare monthly reports and submit them to the management accounting team. For example, the reporting department can send monthly reports via email. The reporting department can also provide monthly reports through a web application. For example, the reporting department can share monthly reports on an online platform and make them accessible in real time. Furthermore, the reporting department can evaluate the effectiveness of investments and provide feedback. For example, the reporting department can evaluate the performance of investment targets and adjust the investment strategy for the following month. This enables real-time business improvement through rapid feedback and evaluation of the effectiveness of investments. Feedback and evaluation of the effectiveness of investments include, but are not limited to, KPIs (Key Performance Indicators) and ROI (Return on Investment). Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input evaluation data for effectiveness into a generating AI and have the generating AI generate feedback.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. The data collection unit can also collect data quickly and only the minimum necessary data if the user is in a hurry. For example, the data collection unit can monitor the user's emotions in real time and adjust the timing of data collection according to changes in emotions. By adjusting the timing of data collection according to the user's emotions, the burden on the user is reduced and efficient data collection becomes possible. Estimating the user's emotions includes, but is not limited to, emotion analysis algorithms and acquisition of biometric information using sensors. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0084] The data collection unit can analyze the past data collection history of each department and select a collection method. For example, the data collection unit can select the most efficient collection method based on the past data collection history of each department. For example, the data collection unit can analyze the past data collection history of each department and select a collection method to improve data quality. The data collection unit can also optimize the timing of data collection by referring to the past data collection history of each department. For example, the data collection unit can adjust the frequency of data collection based on the past data collection history. In this way, by analyzing the past data collection history, the optimal collection method can be selected and data quality can be improved. The analysis of past data collection history includes, but is not limited to, collection frequency and the type of data collected. Some or all of the above processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0085] The data collection unit can filter data based on the current projects and work status of each department during data collection. For example, the data collection unit can prioritize the collection of highly relevant data, taking into account the current project status of each department. For example, the data collection unit can filter and collect data of high importance based on the work status of each department. The data collection unit can also collect only the necessary data, taking into account the project progress of each department. For example, the data collection unit can determine the priority of data collection based on the project progress. This allows for the priority collection of highly relevant data by filtering based on the current projects and work status of each department. Filtering includes, but is not limited to, project importance and work progress. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input project data from each department into a generating AI and have the generating AI perform the filtering.

[0086] The data collection unit can estimate the user's emotions and determine the order in which to collect data based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. Also, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, the data collection unit can monitor the user's emotions in real time and determine the order in which to collect data according to changes in emotions. This enables efficient data collection by prioritizing the data to be collected according to the user's emotions. The order in which to collect data may include, but is not limited to, data importance and urgency of collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the order in which the data to be collected.

[0087] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each department during data collection. For example, the data collection unit prioritizes the collection of highly relevant data based on the geographical location information of each department. For example, the data collection unit optimizes the data collection range by considering the geographical location information of each department. The data collection unit can also improve the efficiency of data collection by referring to the geographical location information of each department. For example, the data collection unit determines the priority of data collection based on geographical location information. This allows for the priority collection of highly relevant data and improved data collection efficiency by considering the geographical location information of each department. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the geographical location information of each department into a generating AI and have the generating AI perform the collection of highly relevant data.

[0088] The data collection unit can analyze each department's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze each department's social media activities and collect highly relevant data. For example, the data collection unit can determine data collection priorities based on each department's social media activities. The data collection unit can also optimize the scope of data collection based on each department's social media activities. For example, the data collection unit can determine data collection priorities based on social media posting frequency and engagement rates. This allows for the collection of highly relevant data and optimization of the scope of data collection by analyzing each department's social media activities. Social media activities include, but are not limited to, posting frequency and engagement rates. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each department's social media data into a generating AI and have the generating AI collect relevant data.

[0089] The data cleansing unit can estimate the user's emotions and adjust the data cleansing method based on the estimated emotions. For example, if the user is stressed, the unit can apply a simple data cleansing method. For example, if the user is relaxed, the unit can apply a detailed data cleansing method. The unit can also apply a method for rapid data cleansing if the user is in a hurry. For example, the unit can monitor the user's emotions in real time and adjust the data cleansing method according to changes in emotions. This allows for efficient data cleansing by adjusting the data cleansing method according to the user's emotions. The data cleansing method includes, but is not limited to, adjusting the cleansing method based on the results of emotion analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data cleansing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input user emotion data into a generating AI and have the generating AI adjust the data cleansing method.

[0090] The data sorting unit can adjust the degree of sorting based on the importance of the data during data sorting. For example, the sorting unit can prioritize sorting highly important data and perform detailed cleansing. For example, the sorting unit can simplify sorting of less important data. The sorting unit can also adjust the sorting procedure according to the importance of the data. For example, the sorting unit can evaluate the importance of the data and determine the sorting priority. This allows for efficient data sorting by adjusting the level of detail based on the importance of the data. Data importance includes, but is not limited to, the frequency of data use and the impact on business operations. Some or all of the above processes in the sorting unit may be performed using AI, for example, or not using AI. For example, the sorting unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the degree of sorting.

[0091] The data cleansing unit can apply different cleansing algorithms depending on the data category during data cleansing. For example, the data cleansing unit can select the optimal cleansing algorithm depending on the data category. For example, the data cleansing unit can apply different cleansing methods for each data category. The data cleansing unit can also adjust the level of detail of cleansing based on the data category. For example, the data cleansing unit can evaluate the data category and apply the optimal cleansing algorithm. This enables efficient data cleansing by applying different cleansing algorithms depending on the data category. Cleansing algorithms include, but are not limited to, differences in algorithms for each category. Some or all of the above processing in the data cleansing unit may be performed using AI, for example, or without AI. For example, the data cleansing unit can input the data category into a generating AI and have the generating AI execute the application of the cleansing algorithm.

[0092] The processing unit can estimate the user's emotions and determine the order of data cleansing based on the estimated emotions. For example, if the user is stressed, the processing unit will prioritize cleaning high-priority data. For example, if the user is relaxed, the processing unit will prioritize detailed data cleansing. Also, if the user is in a hurry, the processing unit can prioritize processing data that can be cleaned quickly. For example, the processing unit can monitor the user's emotions in real time and determine the order of data cleansing in response to changes in emotions. This enables efficient data cleansing by determining the priority of data cleansing according to the user's emotions. The order of data cleansing includes, but is not limited to, determining priorities based on the results of emotion analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input user emotion data into a generating AI and have the generating AI determine the order of data cleansing.

[0093] The data sorting unit can determine sorting priorities based on the data submission date during data sorting. For example, the sorting unit may prioritize sorting the most recent data and perform a quick cleansing. For example, the sorting unit may sort older data later. The sorting unit can also adjust the sorting procedure based on the data submission date. For example, the sorting unit may evaluate the data submission date and determine the sorting priority. This enables efficient data sorting by determining sorting priorities based on the data submission date. The data submission date includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processes in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the data submission date into a generating AI and have the generating AI determine the sorting priority.

[0094] The data sorting unit can determine the sorting order based on the relevance of the data during data sorting. For example, the sorting unit may prioritize sorting and cleanse highly relevant data. For example, it may sort less relevant data later. The sorting unit can also adjust the sorting procedure based on the relevance of the data. For example, it may evaluate the relevance of the data and determine the sorting priority. This allows for efficient data sorting by adjusting the sorting order based on the relevance of the data. Data relevance includes, but is not limited to, data correlation and co-occurrence relationships. Some or all of the above processes in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the data relevance into a generating AI and have the generating AI determine the sorting order.

[0095] The prediction unit can estimate the user's emotions and adjust the profit prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit applies a simple profit prediction method. For example, if the user is relaxed, the prediction unit applies a detailed profit prediction method. The prediction unit can also apply a method for rapid profit prediction if the user is in a hurry. For example, the prediction unit monitors the user's emotions in real time and adjusts the profit prediction method in response to changes in emotions. This allows for efficient profit prediction by adjusting the profit prediction method according to the user's emotions. The profit prediction method includes, but is not limited to, adjusting the prediction method based on the results of emotion analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generating AI and have the generating AI adjust the profit prediction method.

[0096] The prediction unit can adjust its prediction algorithm by referring to past profit data when making profit predictions. For example, the prediction unit can select the optimal prediction algorithm based on past profit data. For example, the prediction unit can analyze past profit data and optimize the prediction algorithm. The prediction unit can also improve the accuracy of predictions by referring to past profit data. For example, the prediction unit adjusts the prediction algorithm based on past profit data. This allows the prediction algorithm to be optimized and the accuracy of predictions to be improved by referring to past profit data. The prediction algorithm includes, but is not limited to, examples such as algorithm optimization using past data. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or without using AI. For example, the prediction unit can input past profit data into a generating AI and have the generating AI perform the adjustment of the prediction algorithm.

[0097] The forecasting unit can apply different forecasting methods to each data category when forecasting profits. For example, the forecasting unit can select the optimal forecasting method according to the data category. For example, the forecasting unit can apply different forecasting algorithms to each data category. The forecasting unit can also adjust the level of detail of the forecast based on the data category. For example, the forecasting unit can evaluate the data category and apply the optimal forecasting method. This enables efficient profit forecasting by applying different forecasting methods to each data category. Fortune-telling methods include, but are not limited to, differences in forecasting methods for each category. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input the data category into a generating AI and have the generating AI perform the application of the forecasting method.

[0098] The prediction unit can estimate the user's emotions and determine the order of profit predictions based on the estimated emotions. For example, if the user is stressed, the prediction unit will prioritize high-priority profit predictions. For example, if the user is relaxed, the prediction unit will prioritize detailed profit predictions. The prediction unit can also prioritize processing data that allows for quick profit predictions if the user is in a hurry. For example, the prediction unit can monitor the user's emotions in real time and determine the order of profit predictions according to changes in emotions. This enables efficient profit prediction by determining the priority of profit predictions according to the user's emotions. The order of profit predictions includes, but is not limited to, determining priorities based on the results of emotion analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input user emotion data into a generating AI and have the generating AI determine the order of profit predictions.

[0099] The forecasting unit can determine forecast priorities based on data submission timing when forecasting profits. For example, the forecasting unit may prioritize forecasting the most recent data to quickly forecast profits. For example, the forecasting unit may postpone forecasting older data. The forecasting unit can also adjust the forecasting procedure based on data submission timing. For example, the forecasting unit may evaluate data submission timing and determine forecast priorities. This enables efficient profit forecasting by determining forecast priorities based on data submission timing. Data submission timing includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the forecasting unit may input data submission timing into a generating AI and have the generating AI determine forecast priorities.

[0100] The forecasting unit can improve the accuracy of its forecasts by referring to relevant market data when forecasting profits. For example, the forecasting unit can make optimal profit forecasts based on relevant market data. For example, the forecasting unit can optimize its forecasting algorithm by referring to relevant market data. The forecasting unit can also improve the accuracy of its forecasts by referring to relevant market data. For example, the forecasting unit can adjust its forecasting algorithm based on relevant market data. In this way, the accuracy of the forecast can be improved by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and competitive analysis data. Some or all of the above processing in the forecasting unit may be performed using, for example, AI, or not using AI. For example, the forecasting unit can input relevant market data into a generating AI and have the generating AI perform the improvement of forecast accuracy.

[0101] The proposal unit can estimate the user's emotions and adjust the way investment proposals are presented based on the estimated emotions. For example, if the user is stressed, the proposal unit will present investment proposals in a simple manner. For example, if the user is relaxed, the proposal unit will present investment proposals in a detailed manner. Furthermore, if the user is in a hurry, the proposal unit can present investment proposals in a manner that can be quickly understood. For example, the proposal unit can monitor the user's emotions in real time and adjust the way investment proposals are presented in response to changes in emotions. This allows for efficient investment proposals by adjusting the presentation of investment proposals according to the user's emotions. The presentation of investment proposals includes, but is not limited to, adjustments to the presentation based on the results of emotion analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generating AI and have the AI ​​adjust the way investment proposals are presented.

[0102] The proposal unit can adjust the level of detail in investment proposals based on the importance of the investment target. For example, the proposal unit can provide detailed proposals for highly important investment targets, and simplified proposals for less important investment targets. The proposal unit can also adjust the procedure of the proposal according to the importance of the investment target. For example, the proposal unit can evaluate the importance of the investment target and determine the priority of the proposal. This allows for efficient investment proposals by adjusting the level of detail based on the importance of the investment target. The importance of an investment target includes, but is not limited to, the growth potential and risk assessment of the investment target. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the importance of the investment target into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposal.

[0103] The proposal unit can apply different proposal algorithms depending on the investment category when making investment proposals. For example, the proposal unit can select the optimal proposal algorithm depending on the investment category. For example, the proposal unit can apply different proposal methods for each investment category. The proposal unit can also adjust the level of detail of the proposal based on the investment category. For example, the proposal unit can evaluate the investment category and apply the optimal proposal algorithm. This makes it possible to make efficient investment proposals by applying different proposal algorithms depending on the investment category. Proposal algorithms include, but are not limited to, differences in proposal algorithms for each category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the investment category into a generating AI and have the generating AI execute the application of the proposal algorithm.

[0104] The proposal unit can estimate the user's emotions and determine the order of investment proposals based on the estimated emotions. For example, if the user is stressed, the proposal unit will prioritize high-priority investment proposals. For example, if the user is relaxed, the proposal unit will prioritize detailed investment proposals. Also, if the user is in a hurry, the proposal unit can prioritize investment proposals that can be quickly understood. For example, the proposal unit can monitor the user's emotions in real time and determine the order of investment proposals in response to changes in emotions. This enables efficient investment proposals by prioritizing investment proposals according to the user's emotions. The order of investment proposals includes, but is not limited to, determining priorities based on the results of emotion analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generating AI and have the AI ​​determine the order of investment proposals.

[0105] The proposal department can prioritize investment proposals based on the submission timing of the investment targets. For example, the proposal department may prioritize proposals for the most recent investment targets and submit investment proposals quickly. For example, the proposal department may postpone proposals for investment targets with older submission dates. The proposal department can also adjust the proposal procedure based on the submission timing of the investment targets. For example, the proposal department may evaluate the submission timing of investment targets and determine the priority of proposals. This enables efficient investment proposals by prioritizing proposals based on the submission timing of investment targets. The submission timing of investment targets includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department may input the submission timing of investment targets into a generating AI and have the generating AI perform the determination of proposal priority.

[0106] The proposal department can determine the order of investment proposals based on the relevance of the investment targets when making investment proposals. For example, the proposal department may prioritize proposals for highly relevant investment targets and provide detailed proposals. For example, the proposal department may postpone proposals for less relevant investment targets. The proposal department can also adjust the procedure of proposals based on the relevance of the investment targets. For example, the proposal department may evaluate the relevance of investment targets and determine the priority of proposals. This allows for efficient investment proposals by adjusting the order of proposals based on the relevance of the investment targets. The relevance of investment targets includes, but is not limited to, the business content of the investment target and industry relevance. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the relevance of investment targets into a generating AI and have the generating AI determine the order of proposals.

[0107] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is stressed, the reporting unit may apply a simple reporting method. For example, if the user is relaxed, the reporting unit may apply a detailed reporting method. The reporting unit may also apply a method for rapid reporting if the user is in a hurry. For example, the reporting unit may monitor the user's emotions in real time and adjust the reporting method according to changes in emotions. This allows for efficient reporting by adjusting the reporting method according to the user's emotions. Reporting methods include, but are not limited to, adjusting the reporting method based on the results of emotion analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the reporting method.

[0108] The reporting unit can adjust its reporting algorithm by referring to past reporting data when submitting a report. For example, the reporting unit can select the optimal reporting algorithm based on past reporting data. For example, the reporting unit can analyze past reporting data and optimize the reporting algorithm. The reporting unit can also improve the accuracy of reports by referring to past reporting data. For example, the reporting unit adjusts the reporting algorithm based on past reporting data. This allows the reporting algorithm to be optimized and the accuracy of reports to be improved by referring to past reporting data. The reporting algorithm includes, but is not limited to, examples such as algorithm optimization using past data. Some or all of the above processes in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input past reporting data into a generating AI and have the generating AI perform the adjustment of the reporting algorithm.

[0109] The reporting unit can apply different reporting methods to each data category when reporting. For example, the reporting unit can select the optimal reporting method according to the data category. For example, the reporting unit can apply different reporting algorithms to each data category. The reporting unit can also adjust the level of detail in the report based on the data category. For example, the reporting unit can evaluate the data category and apply the optimal reporting method. This enables efficient reporting by applying different reporting methods to each data category. Reporting methods include, but are not limited to, differences in reporting methods for each category. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the data category into a generating AI and have the generating AI perform the application of the reporting method.

[0110] The reporting unit can estimate the user's emotions and determine the order of reports based on the estimated emotions. For example, if the user is stressed, the reporting unit will prioritize high-priority reports. For example, if the user is relaxed, the reporting unit will prioritize detailed reports. The reporting unit can also prioritize data that can be reported quickly if the user is in a hurry. For example, the reporting unit can monitor the user's emotions in real time and determine the order of reports according to changes in emotions. This enables efficient reporting by prioritizing reports according to the user's emotions. The order of reports includes, but is not limited to, determining priorities based on emotion analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting department can input user emotion data into a generating AI and have the AI ​​determine the order in which reports are presented.

[0111] The reporting department can prioritize reports based on the timing of data submission. For example, the reporting department may prioritize reporting the most recent data and report quickly. For example, it may postpone reporting older data. The reporting department can also adjust its reporting procedures based on the timing of data submission. For example, it may evaluate the timing of data submission and determine the reporting priority. This enables efficient reporting by prioritizing reports based on the timing of data submission. The timing of data submission includes, but is not limited to, submission deadlines and submission frequency. Some or all of the above processes in the reporting department may be performed using, for example, AI, or not using AI. For example, the reporting department may input the timing of data submission into a generating AI and have the generating AI determine the reporting priority.

[0112] The reporting unit can adjust the accuracy of its reports by referring to relevant market data during the reporting process. For example, the reporting unit can provide optimal reports based on relevant market data. For example, the reporting unit can optimize its reporting algorithm by referring to relevant market data. The reporting unit can also improve the accuracy of its reports by referring to relevant market data. For example, the reporting unit can adjust its reporting algorithm based on relevant market data. This allows for improved reporting accuracy by referring to relevant market data. Relevant market data includes, but is not limited to, market research data and competitive analysis data. Some or all of the above processing in the reporting unit may be performed using, for example, AI, or not using AI. For example, the reporting unit can input relevant market data into a generating AI and have the generating AI perform improvements to the accuracy of its reports.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The budget innovator system can further estimate user emotions and propose budget allocations based on those emotions. For example, if a user is stressed, the system can prioritize suggesting low-risk investments. Conversely, if a user is relaxed, it can suggest high-risk but high-return investments. Furthermore, if a user is in a hurry, it can provide concise suggestions to enable quick decision-making. This allows for flexible budget allocation tailored to user emotions, thereby improving user satisfaction.

[0115] The Budget Innovator System can analyze each department's past budget usage history and propose the optimal budget allocation method. For example, it can evaluate each department's budget utilization rate based on past budget usage history and adjust for budget surpluses or deficits. It can also determine the priority of budget allocation for each department based on past budget usage history. Furthermore, by analyzing past budget usage history, it is possible to reduce budget waste and achieve efficient budget allocation. This makes it possible to utilize past data to enable more scientific and objective budget allocation.

[0116] The budget innovator system allows for budget allocation while considering the current project status of each department. For example, it can determine budget priorities based on the progress of each department's projects. It can also evaluate the importance of each department's projects and allocate budget preferentially to important projects. Furthermore, it can monitor the progress of each department's projects and reallocate budgets as needed. This enables flexible budget allocation tailored to the project status of each department, thereby improving the success rate of projects.

[0117] The budget innovator system can allocate budgets while considering the geographical location of each department. For example, it optimizes the scope of budget allocation based on the geographical location of each department. It can also promote budget sharing between geographically close departments, enabling efficient budget utilization. Furthermore, it can determine budget allocation priorities based on geographical location. This allows for flexible budget allocation that takes geographical factors into account, reducing budget waste.

[0118] The budget innovator system can analyze each department's social media activities and propose budget allocations. For example, it can determine budget allocation priorities based on each department's social media activities. It can also evaluate the response on social media and adjust budget allocations accordingly. Furthermore, it can measure the effectiveness of the budget based on social media activities and reflect this in future budget allocations. This enables flexible budget allocation using social media data, maximizing budget effectiveness.

[0119] The budget innovator system can estimate user emotions and provide feedback on budget allocation based on those emotions. For example, if a user is stressed, the system provides concise feedback to reduce their burden. If the user is relaxed, it provides detailed feedback to deepen their understanding of the budget allocation. Furthermore, if the user is in a hurry, it can provide rapid feedback to enable immediate action. This allows for flexible feedback tailored to the user's emotions, thereby improving user satisfaction.

[0120] The budget innovator system can analyze the data collection history of each department and propose the optimal data collection method. For example, it can select the most efficient data collection method based on past data collection history. It can also optimize the timing of data collection based on the data collection history. Furthermore, by analyzing the data collection history, it can propose collection methods to improve data quality. This makes it possible to efficiently collect high-quality data by utilizing past data collection history.

[0121] The budget innovator system can estimate user emotions and adjust data cleansing methods based on those estimates. For example, if a user is stressed, the system applies a simple data cleansing method. If the user is relaxed, it can apply a more detailed data cleansing method. Furthermore, if the user is in a hurry, it can apply a method for rapid data cleansing. This enables flexible data cleansing tailored to user emotions, resulting in efficient data processing.

[0122] The Budget Innovator System allows for adjusting the level of data organization based on data importance. For example, it can prioritize organizing high-importance data and perform detailed cleansing. Conversely, it can simplify the organization of less important data. Furthermore, it can adjust the organization procedure according to data importance. This enables efficient data organization based on data importance, thereby improving data quality.

[0123] The budget innovator system can estimate a user's emotions and adjust the reporting method based on that estimation. For example, if a user is stressed, the system applies a simple reporting method. If the user is relaxed, it can apply a detailed reporting method. Furthermore, if the user is in a hurry, it can apply a method that provides quick reporting. This enables flexible reporting tailored to the user's emotions, resulting in efficient information dissemination.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The data collection unit collects data from each department. This data includes operational data, revenue data, budget data, etc. The data collection unit automatically collects data from each department using APIs. It can also collect data using sensors. For example, the data collection unit connects to each department's system and collects data in real time. Step 2: The sorting unit sorts the data collected by the collection unit. Sorting is done by methods such as data cleansing, classification, and aggregation. The sorting unit imputes missing data and removes outliers. It can also classify and aggregate data by category. For example, the sorting unit sorts data stored in a database using queries. Step 3: The forecasting unit predicts profits based on the data organized by the organizing unit. The prediction is made based on the algorithm used and the time period for which the prediction is taken. The forecasting unit uses machine learning algorithms to predict profits. It can also predict future profits based on historical data. For example, the forecasting unit uses time series data to predict profit trends. Step 4: The proposal team proposes investment targets based on the profits predicted by the forecasting team. The proposals are made based on the investment selection criteria and proposal format. The proposal team proposes investment targets based on ROI (Return on Investment). They can also propose investment targets based on risk assessment. For example, the proposal team evaluates the risk and return of investment targets and proposes the optimal investment targets. Step 5: The reporting department prepares monthly reports based on the investment targets proposed by the proposal department. The monthly reports are prepared according to the format of the report and the type of data to be reported. The reporting department prepares the monthly report and submits it to the management accounting team. The monthly report can also be sent by email. For example, the reporting department provides the monthly report through a web application.

[0126] 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.

[0127] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the data collection unit, data organization unit, forecasting unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data from each department using the sensors and APIs of the smart device 14 and organizes the data using the specific processing unit 290 of the data processing unit 12. The forecasting unit forecasts profits using the specific processing unit 290 of the data processing unit 12, and the proposal unit proposes investment targets using the specific processing unit 290 of the data processing unit 12. The reporting unit can provide monthly reports using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0133] 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.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0135] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] 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.

[0137] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the data collection unit, data organization unit, forecasting unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data from each department using the sensors and APIs of the smart glasses 214 and organizes the data using the specific processing unit 290 of the data processing unit 12. The forecasting unit forecasts profits using the specific processing unit 290 of the data processing unit 12, and the proposal unit proposes investment targets using the specific processing unit 290 of the data processing unit 12. The reporting unit can provide monthly reports using the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0149] 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.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0151] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] 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.

[0153] 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.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, data organization unit, forecasting unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data from each department using the sensors and APIs of the headset terminal 314 and organizes the data using the specific processing unit 290 of the data processing unit 12. The forecasting unit forecasts profits using the specific processing unit 290 of the data processing unit 12, and the proposal unit proposes investment targets using the specific processing unit 290 of the data processing unit 12. The reporting unit can provide monthly reports using the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0165] 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.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0167] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] 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.

[0169] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] 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.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the data collection unit, data organization unit, forecasting unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data from each department using the sensors and APIs of the robot 414 and organizes the data using the specific processing unit 290 of the data processing unit 12. The forecasting unit forecasts profits using the specific processing unit 290 of the data processing unit 12, and the proposal unit proposes investment targets using the specific processing unit 290 of the data processing unit 12. The reporting unit can provide monthly reports using the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0179] 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.

[0180] Figure 9 shows the 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.

[0181] 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.

[0182] 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.

[0183] 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, and motorcycles, 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 based, for example, 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.

[0184] 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."

[0185] 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.

[0186] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] 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 other things 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.

[0196] 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.

[0197] (Note 1) The data collection department collects data from each department, A sorting unit that sorts the data collected by the aforementioned collection unit, A forecasting unit that predicts profits based on the data organized by the aforementioned organizing unit, A proposal unit that proposes investment targets based on the profits predicted by the forecasting unit, The system includes a reporting unit that provides monthly reports based on the investment targets proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Automatically collects operational data, revenue data, and budget data from each department. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned editing unit, Cleanse the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, We will simulate the compounding effect using this month's budget and profit data from last month. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Automatically analyzes and proposes investment priorities for each department. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reporting department, The profits earned each month are reflected in the budget for the following month. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reporting department, We will provide feedback and evaluate the return on investment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the past data collection history of each department and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the current projects and work status of each department. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the order in which to collect data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the geographical location information of each department is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, analyze the social media activities of each department and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editing unit, We estimate user sentiment and adjust the data cleansing method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editing unit, When organizing data, adjust the level of organization based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editing unit, When organizing data, different cleansing algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editing unit, The system estimates user sentiment and determines the order of data cleansing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editing unit, When organizing data, prioritize the organization based on the data submission date. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editing unit, When organizing data, determine the order of organization based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, We estimate user sentiment and adjust profit forecasting methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When forecasting profits, the forecasting algorithm is adjusted by referring to past profit data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When forecasting profits, different forecasting methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, The system estimates user sentiment and determines the order of profit predictions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When forecasting profits, prioritize forecasts based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, When forecasting profits, we adjust the accuracy of the forecast by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the way investment proposals are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making an investment proposal, adjust the level of the proposal based on the importance of the investment target. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making investment proposals, different proposal algorithms are applied depending on the category of the investment target. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and determines the order of investment proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When submitting investment proposals, we prioritize proposals based on when the target companies submitted their proposals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making investment proposals, the order of proposals is determined based on the relevance of the investment targets. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reporting department, We estimate the user's emotions and adjust the reporting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reporting department, When reporting, the reporting algorithm is started by referring to past reporting data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reporting department, When reporting, apply different reporting methods for each data category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reporting department, The system estimates the user's emotions and determines the order of reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned reporting department, When reporting, prioritize reports based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned reporting department, When reporting, we adjust the accuracy of the report by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data from each department, A sorting unit that sorts the data collected by the aforementioned collection unit, A forecasting unit that predicts profits based on the data organized by the aforementioned organizing unit, A proposal unit that proposes investment targets based on the profits predicted by the forecasting unit, The system includes a reporting unit that provides monthly reports based on the investment targets proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Automatically collects operational data, revenue data, and budget data from each department. The system according to feature 1.

3. The aforementioned editing unit, Cleanse the collected data. The system according to feature 1.

4. The prediction unit, We will simulate the compounding effect using this month's budget and profit data from last month. The system according to feature 1.

5. The aforementioned proposal section is, Automatically analyzes and proposes investment priorities for each department. The system according to feature 1.

6. The aforementioned reporting department, The profits earned each month are reflected in the budget for the following month. The system according to feature 1.

7. The aforementioned reporting department, We will provide feedback and evaluate the return on investment. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the past data collection history of each department and select the appropriate data collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on the current projects and work status of each department. The system according to feature 1.

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