System

Abstract

To provide a system.SOLUTION: A system includes: means for connecting corporate data including financial data, personnel data, and sales data to a database for storage; means for acquiring the corporate data from the database; means for preprocessing the acquired corporate data; means for calculating an achievement index on the basis of the preprocessed corporate data; means for outputting the calculated achievement index; means for totalizing a management index including an outputted achievement index; and means for using a generative AI model to generate a strategy proposal on the basis of the totalized management index.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Business decisions are often made based on personal input, making it difficult to have a multifaceted perspective and making effective business decisions. In addition, valuable data within the company is not fully utilized, resulting in missed opportunities for business expansion. [Means for solving the problem]

[0005] This invention aggregates management indicators and uses that information as an AI secretary, enabling a multifaceted perspective. It also aggregates corporate data and uses it to expand business. In particular, it automatically aggregates management indicators based on specific performance indicators and automatically proposes strategies based on the aggregated management indicators, enabling effective management decisions. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, the means for aggregating management indicators acquires financial data, personnel data, sales data, etc. of a company from a database and calculates performance indicators based on this data. For example, indicators such as sales, profit margin, and employee turnover rate can be considered.

[0029] "Example 2"

[0030] Another way to use AI as a secretary is to have AI make strategic proposals based on the collected management indicators. Specifically, AI analyzes the trends of management indicators and proposes management strategies based on the results. For example, if sales are declining, AI can propose a new marketing strategy.

[0031] "Example 3"

[0032] Furthermore, one way to aggregate corporate data and use it to expand business is to centrally manage various types of data within a company and use that data to explore new business opportunities. For example, analyzing customer purchasing data can provide hints for new product development.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Obtain the company's financial, personnel, sales, and other data from the database.

[0036] Step 2: Calculate performance indicators based on the acquired data, such as sales, profit margins, and employee turnover rates.

[0037] "Example 2"

[0038] Step 1: AI makes strategic proposals based on the collected management indicators.

[0039] Step 2: The AI ​​analyzes trends in management indicators and proposes management strategies based on the results. For example, if sales are declining, the AI ​​will propose a new marketing strategy.

[0040] "Example 3"

[0041] Step 1: Centrally manage various data within the company.

[0042] Step 2: Explore new business opportunities based on centrally managed data. For example, analyze customer purchasing data to gain insights into new product development.

[0043] Example 1

[0044] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] In conventional management indicator aggregation systems, the entire process from acquiring corporate data to cleaning it, calculating performance indicators, and generating and providing reports is often done manually, resulting in inefficiency. Furthermore, strategic proposals based on the aggregated management indicators are not automated, making it difficult to make quick and accurate management decisions. Furthermore, there is insufficient data aggregation to gain perspectives based on multiple angles and to lead to business expansion.

[0046] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0047] In this invention, the server includes means for acquiring corporate data, means for cleaning the acquired corporate data, means for calculating performance indicators based on the cleaned data, means for generating reports from the calculated performance indicators, means for providing the generated reports to users, means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, means for gaining perspectives based on multiple perspectives, means for making effective management decisions, and means for aggregating corporate data and using the resulting data to expand business. This automates the process from acquiring corporate data to providing reports, enabling quick and accurate management decisions. It also enables efficient data aggregation to gain perspectives based on multiple perspectives and expand business.

[0048] "Corporate data" is a general term for various data related to the operation of a company, such as financial data, personnel data, and sales data.

[0049] "Cleaning" is the process of removing missing values ​​and outliers from the acquired data and normalizing the data.

[0050] "Performance indicators" are indicators used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[0051] A "report" is a document or graph that visually represents calculated performance indicators.

[0052] "User" refers to a person such as a company manager or administrator who uses the system.

[0053] "Management indicators" are indicators used to evaluate a company's business situation, and refer to a wide range of indicators including performance indicators.

[0054] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators and make strategic proposals.

[0055] A "multifaceted perspective" is a perspective that analyzes corporate data from multiple different perspectives and allows for comprehensive judgment.

[0056] A "perspective" refers to the viewpoint or position that forms the basis for making management decisions.

[0057] "Effective business decisions" refer to appropriate decisions that contribute to corporate growth and increased profits.

[0058] "Data aggregation" is the process of centrally collecting and integrating distributed data.

[0059] "Business expansion" refers to activities aimed at increasing a company's market share or sales.

[0060] MODE FOR CARRYING OUT THE INVENTION

[0061] The present invention is a system for acquiring corporate data, cleaning it, calculating performance indicators, generating reports, and providing them to users. A specific embodiment of this system will be described below.

[0062] Data Acquisition

[0063] Subject: Server

[0064] The server retrieves a company's financial data, personnel data, sales data, etc. from a database. Specifically, it executes SQL queries using a database management system (DBMS) to extract the necessary data. Possible database servers used are MySQL (registered trademark) and PostgreSQL.

[0065] Data Cleaning

[0066] Subject: Server

[0067] The server cleans the acquired data. Specifically, it uses the Python (registered trademark) Pandas library to complement missing values, remove outliers, and normalize the data, thereby improving the quality of the data.

[0068] Calculation of performance indicators

[0069] Subject: Server

[0070] The server calculates performance indicators based on the cleaned data, such as sales, profit margins, and employee turnover rates. It uses Python and R to analyze the data and calculate the necessary indicators.

[0071] Generate reports

[0072] Subject: Server

[0073] The server generates reports based on the calculated performance indicators. Specifically, it uses BI (Business Intelligence) tools to create graphs and tables, making it possible to provide reports that are visually easy to understand.

[0074] Providing reports

[0075] Subject: Terminal

[0076] The terminal provides the generated report to the user. Specifically, the report is displayed through a web browser. The report is provided to the user using a web framework such as Flask.

[0077] Specific examples

[0078] Example 1: Calculating sales

[0079] The server retrieves sales data from the database for the past year. Next, it cleans the data using Python and aggregates monthly sales figures. Finally, it uses a BI tool to graph sales trends and output them as a report.

[0080] Example 2: Calculating employee turnover

[0081] The server retrieves employee data from the database for the past year. Next, it uses R to filter the data and calculate the number of employees who left and stayed each month. Finally, it uses a BI tool to graph the turnover rate over time and output it as a report.

[0082] Example prompts for generative AI models

[0083] "Write a program that uses a company's financial, HR, and sales data to calculate performance indicators such as sales, profit margins, and employee turnover. Retrieve the data from a database, clean and analyze it using Python or R, generate reports using a BI tool, and display the reports in a web browser using Flask."

[0084] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0085] Step 1:

[0086] Retrieving data from a database

[0087] Subject: Server

[0088] The server retrieves a company's financial data, personnel data, sales data, etc. from a database. Specifically, it extracts the required data using an SQL query. The inputs are database connection information and an SQL query, and the output is the retrieved data. For example, to retrieve sales data, the following SQL query is executed:

[0089] sql

[0090] SELECT FROM sales_data WHERE date BETWEEN(R) '2022-01-01' AND '2022-12-31';

[0091] This query retrieves sales data for the specified time period.

[0092] Step 2:

[0093] Clean the data

[0094] Subject: Server

[0095] The server cleans the acquired data. Specifically, it completes missing values, removes outliers, and normalizes the data. The acquired data is required as input, and the cleaned data is obtained as output. The data is cleaned using the Python Pandas library as follows:

[0096] python

[0097] import pandas as pd

[0098] Loading data

[0099] data = pd.read_sql(query, connection)

[0100] Missing value imputation

[0101] data.fillna(0, inplace=True)

[0102] Outlier removal

[0103] data = data[data['sales'] >= 0]

[0104] This process improves the quality of the data.

[0105] Step 3:

[0106] Calculate performance indicators

[0107] Subject: Server

[0108] The server calculates performance indicators based on the cleaned data. The cleaned data is required as input, and the calculated performance indicators are obtained as output. For example, sales, profit margin, employee turnover rate, etc. are calculated. Below is an example of calculating sales.

[0109] python

[0110] Aggregate monthly sales

[0111] monthly_sales = data.groupby('month')['sales'].sum()

[0112] This process calculates the monthly sales amount.

[0113] Step 4:

[0114] Generate a report

[0115] Subject: Server

[0116] The server generates a report based on the calculated performance indicators. The calculated performance indicators are required as input, and the generated report is obtained as output. Specifically, graphs and tables are created using BI (Business Intelligence) tools. Below is an example of graphing sales trends using Python's Matplotlib.

[0117] python

[0118] import matplotlib.pyplot as plt

[0119] Creating a graph

[0120] plt.plot(monthly_sales.index, monthly_sales.values)

[0121] plt.title('Monthly Sales')

[0122] plt.xlabel('Month')

[0123] plt.ylabel('Sales')

[0124] plt.savefig('monthly_sales_report.png')

[0125] This process generates a report that visually depicts sales trends.

[0126] Step 5:

[0127] Providing reports to users

[0128] Subject: Terminal

[0129] The terminal provides the generated report to the user. The input is the generated report, and the output is a state where the user can view the report. Specifically, the report is displayed through a web browser. Below is an example of displaying a report using Flask.

[0130] python

[0131] from flask import Flask, send_file

[0132] app = Flask(__name__)

[0133] @app.route(' / report')

[0134] def report():

[0135] return send_file('monthly_sales_report.png', mimetype='image / png')

[0136] if __name__ == '__main__':

[0137] app.run(debug=True)

[0138] This process allows users to view the report through a web browser.

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] Conventional management indicator aggregation systems took time to acquire and aggregate data, making it difficult to monitor performance in real time. Furthermore, there was a lack of feedback for managers and administrators to make quick management decisions, hindering efficient operations within the factory. Furthermore, strategic proposals based on the aggregated data were not automated, making it time-consuming and labor-intensive to formulate management strategies.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, means for obtaining perspectives based on multiple viewpoints, means for making effective management decisions, means for aggregating corporate data and using it to expand business, means that is installed on robots in the factory and acquires financial data, personnel data, and sales data in real time and calculates and displays performance indicators, and means for providing feedback to managers and administrators based on the calculated performance indicators. This enables real-time performance monitoring, resulting in quick management decisions and efficient factory operations.

[0144] "Management indicators" are indicators used to evaluate a company's financial situation and performance, and include sales, profit margins, employee turnover rates, etc.

[0145] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators, propose corporate strategies, and support management decisions.

[0146] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives, and is used to gain a more comprehensive understanding.

[0147] "Effective business decisions" refer to making optimal decisions to achieve a company's goals and solve problems.

[0148] "Corporate data" refers to various information related to corporate operations, such as a company's financial data, personnel data, and sales data.

[0149] "Business expansion" refers to activities and strategies aimed at increasing a company's market share or sales.

[0150] "Factory robots" refer to mechanical devices that perform automated tasks on factory production lines and work processes.

[0151] "Real-time" means that data is acquired and processed immediately, and the results are reflected without delay.

[0152] "Financial data" refers to data that shows a company's financial status, such as revenue, expenses, and profits.

[0153] "Human resources data" refers to data related to a company's personnel, such as the number of employees, turnover rate, and working hours.

[0154] "Sales data" refers to data related to a company's sales activities, such as sales performance, customer information, and return rates.

[0155] "Performance indicators" are specific numbers or indicators used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[0156] "Feedback" refers to information and advice provided to managers and administrators based on performance indicators and business decisions.

[0157] As an embodiment of the present invention, a system installed on a robot in a factory will be described as an example. This system has the function of aggregating management indicators and calculating and displaying performance indicators in real time. It also provides feedback to managers and administrators based on the calculated performance indicators.

[0158] 1. System Program

[0159] The system includes a program with the following main functions:

[0160] Ability to retrieve financial, personnel, and sales data from databases

[0161] Function to calculate performance indicators based on acquired data

[0162] Ability to display calculated performance indicators in real time

[0163] Ability to provide feedback to management and administrators

[0164] 2. Program processing explanation

[0165] Hardware used

[0166] Factory Robots

[0167] Software used

[0168] Python 3.x

[0169] Pandas library

[0170] NumPy library

[0171] Data processing and calculation

[0172] The server first retrieves financial, human resources, and sales data from the database. To do this, it uses SQL queries to extract the necessary information from the database. Next, it calculates performance indicators based on the retrieved data. Specifically, it calculates indicators such as sales revenue, profit margin, employee turnover rate, number of units sold, and return rate. These indicators are processed as data frames or arrays using Pandas or NumPy.

[0173] The calculated performance indicators are displayed in real time on the factory robot's display, allowing managers and executives to instantly understand the factory's performance. Feedback is also provided to managers and executives based on the calculated performance indicators. The feedback is automatically generated using a generative AI model and includes suggestions for management strategies and suggestions for improvement.

[0174] 3. Examples and prompts

[0175] For example, if a factory manager wants to use the "Factory Performance Monitoring Robot" to check monthly performance indicators, the results shown below will be displayed.

[0176] Performance indicators:

[0177] average_revenue: 150000.0

[0178] average_profit_margin: 0.2

[0179] average_turnover_rate: 0.04

[0180] average_units_sold: 1500.0

[0181] average_return_rate: 0.015

[0182] An example of a prompt sentence to input to the generative AI model is as follows:

[0183] Use the "Factory Performance Monitoring Robot" to calculate performance indicators based on the latest financial, personnel, and sales data, such as average sales, average profit margin, average turnover rate, average number of units sold, and average return rate, and display the results.

[0184] In this way, factory robots can monitor performance indicators in real time and provide feedback to managers and administrators, enabling faster business decisions and more efficient factory operations.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The server retrieves financial data, personnel data, and sales data from the database.

[0188] Input: Database connection information, SQL query

[0189] Output: Data frames of financial data, HR data, and sales data

[0190] What happens: The server connects to the database and executes SQL queries to extract the required data, which is then stored as a Pandas dataframe.

[0191] Step 2:

[0192] The server calculates performance indicators based on the acquired data.

[0193] Input: Data frames of financial data, human resources data, and sales data

[0194] Output: Performance indicators (average sales, average profit margin, average turnover rate, average units sold, average return rate)

[0195] What happens: The server uses Pandas or NumPy to calculate each item in the data frame. For example, to calculate the average sales value, it averages the sales column in the financial data. Other metrics are calculated similarly.

[0196] Step 3:

[0197] The server displays the calculated performance indicators in real time.

[0198] Input: Performance Indicators

[0199] Output: Performance indicators displayed on the screen

[0200] How it works: The server sends the calculated performance indicators to the factory robot's display and displays them in real time, allowing managers and executives to instantly check performance.

[0201] Step 4:

[0202] The server provides feedback to managers and administrators based on the calculated performance indicators.

[0203] Input: Performance Indicators

[0204] Output: Feedback message

[0205] Specific operation: The server analyzes performance indicators using the generative AI model and generates feedback messages including business strategy proposals and suggestions for improvement. The generated feedback messages are sent to managers and administrators.

[0206] Step 5:

[0207] The user inputs a prompt statement into the generative AI model and requests it to calculate performance indicators.

[0208] Input: prompt statement

[0209] Output: Calculated performance indicators

[0210] Specific operation: The user inputs a prompt statement to the generative AI model. For example, "Please use the 'Factory Performance Monitoring Robot' to calculate performance indicators based on the latest financial, personnel, and sales data. Specifically, please calculate average sales, average profit margin, average turnover rate, average number of units sold, and average return rate, and display the results." The server calculates the performance indicators based on this prompt statement and displays the results.

[0211] Example 2

[0212] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0213] In modern corporate management, making fast and accurate business decisions requires efficiently compiling and analyzing large amounts of management indicator data and making strategic proposals based on that data. However, with conventional systems, data compilation and analysis took time, and the quality of strategic proposals often lacked consistency. Furthermore, there was a lack of a way for users to input specific questions and receive appropriate strategic proposals. This often led to delayed business decisions and the adoption of incorrect strategies, which reduced a company's competitiveness.

[0214] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0215] In this invention, the server includes a means for aggregating management indicators, a means for making a strategic proposal using a generative AI model based on the aggregated management indicators, a means for sending a prompt sentence input by a user to the server, and a means for displaying the strategic proposal returned from the server to the user, thereby enabling quick and accurate management decisions.

[0216] "Management indicators" are numerical data used to evaluate a company's performance and business situation, and include sales, costs, profit margins, etc.

[0217] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze data and make predictions and suggestions based on specific purposes.

[0218] A "prompt sentence" is a question or instruction sentence that a user inputs to a system, and is input data that the system uses to generate responses or suggestions based on.

[0219] A "server" is a computer system that processes and stores data on a network, and provides services in response to requests from clients.

[0220] "User" refers to an individual or organization that uses the system to receive analysis of management indicators and strategic proposals.

[0221] "Aggregation" is the process of organizing multiple data sets and calculating statistical values ​​such as totals and averages.

[0222] A "strategic proposal" is a proposal that indicates the specific actions and policies that a company should take based on the results of an analysis of management indicators.

[0223] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives, and is a method for gaining a more comprehensive understanding.

[0224] "Perspective" refers to the position or viewpoint from which one observes and judges things.

[0225] "Management decisions" refer to making important decisions regarding the management of a company, including the formulation and implementation of strategies.

[0226] "Corporate data" means all data related to a company's business or activities, including sales, costs, customer information, etc.

[0227] "Business expansion" refers to activities and strategies aimed at increasing a company's size or market share.

[0228] This invention is a system that aggregates management indicators and uses a generative AI model to make strategic proposals based on the aggregated data. The system consists of three main elements: a server, a terminal, and a user.

[0229] server

[0230] The server first collects management indicator data and stores it in a database. Specifically, it obtains key management indicator data such as sales, costs, and profit margins from each department. A relational database management system such as MySQL or PostgreSQL is used as the database.

[0231] The server then uses the Python Pandas library to aggregate the data retrieved from the database, for example, to calculate monthly sales or cost totals, and the aggregated results are temporarily stored in memory.

[0232] The server then uses machine learning frameworks such as TENSORFLOW® and PyTorch to analyze trends in the aggregated data. For example, it models trends in sales increases and decreases and patterns of cost fluctuations. Based on the analysis results, a generative AI model generates business strategy proposals.

[0233] Terminal

[0234] The terminal is responsible for sending prompt sentences entered by the user to the server. For example, if the user enters "Sales are declining this month. What strategy should we take?", this prompt sentence is sent to the server. The terminal also displays strategy suggestions returned by the server to the user.

[0235] User

[0236] The user inputs a question about management indicators through the terminal. For example, "If sales are declining, what marketing strategy should we adopt?" The user checks the strategy proposals returned by the server and decides on a management strategy based on them.

[0237] Specific examples

[0238] Hardware and software used

[0239] Hardware: Server (equipped with high-performance CPU and GPU)

[0240] Software: Python, Pandas, TensorFlow, PyTorch, MySQL

[0241] Prompt Sentence Examples

[0242] "Sales are down this month, what strategy should we use?"

[0243] Specific actions

[0244] 1. The server collects the data

[0245] The server retrieves data from the sales management system and accounting system via API.

[0246] The acquired data is stored in a MySQL database.

[0247] 2. The server aggregates the data

[0248] The server uses Pandas to run queries like "SELECT FROM sales_data WHERE month = '2023-10'" to aggregate the data.

[0249] The aggregation results are stored in an in-memory DataFrame.

[0250] 3. The server analyzes the data

[0251] The server uses TensorFlow to run a model that predicts "sales trends."

[0252] Based on the analysis results, the generative AI model generates strategic proposals such as "implementing a new marketing campaign."

[0253] 4. The terminal sends a prompt

[0254] The terminal transmits the prompt sentence input by the user, "Sales are declining this month. What strategy should we adopt?" to the server.

[0255] Notify the user that the submission was successful.

[0256] 5. The device receives the strategy proposal.

[0257] The terminal receives the strategy proposal "to launch a new marketing campaign" sent back from the server.

[0258] Display received suggestions to the user.

[0259] 6. The user enters a prompt

[0260] The user inputs "If sales are declining, what marketing strategy should we adopt?" into an input field on the terminal.

[0261] Check the information you entered and press the send button.

[0262] 7. User confirms the strategy proposal

[0263] The user checks in detail the proposal "Launch a new marketing campaign" displayed on the terminal.

[0264] Based on the proposal, we will decide on a specific marketing strategy and put it into action.

[0265] In this way, the system helps users to quickly and effectively develop business strategies.

[0266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0267] Step 1:

[0268] The server collects management indicator data. Specifically, it obtains data such as sales, costs, and profit margins from the sales management system and accounting system via API. The input is management indicator data from each department, and the output is data stored in the database. The server stores the data using a relational database management system such as MySQL or PostgreSQL.

[0269] Step 2:

[0270] The server aggregates data retrieved from the database using Python's Pandas library. For example, to calculate the total sales or costs by month. The input is the raw data retrieved from the database, and the output is the aggregated data. The server aggregates the data using a Pandas DataFrame and stores it in memory.

[0271] Step 3:

[0272] The server uses machine learning frameworks such as TensorFlow and PyTorch to analyze trends in the aggregated data. For example, it models trends in sales increases and decreases or patterns of cost fluctuations. The input is the aggregated data, and the output is the analysis results. The server runs the machine learning model and generates the analysis results.

[0273] Step 4:

[0274] The server uses the generative AI model to generate strategic proposals based on the analysis results. The input is the analysis results, and the output is strategic proposals. The server uses the generative AI model to generate specific strategic proposals, such as "implement a new marketing campaign."

[0275] Step 5:

[0276] The terminal sends the prompt text entered by the user to the server. For example, it sends a prompt text such as "Sales are declining this month. What strategy should we adopt?" The input is the prompt text entered by the user, and the output is a notification of successful transmission to the server. The terminal sends the prompt text to the server and notifies the user that the transmission was successful.

[0277] Step 6:

[0278] The server receives the prompt sentence sent by the user and generates strategy proposals based on it using a generative AI model. The input is the prompt sentence from the user and the output is the strategy proposal. The server generates an appropriate strategy proposal for the prompt sentence and sends it back to the terminal.

[0279] Step 7:

[0280] The terminal receives the strategy proposal returned from the server and displays it to the user. The input is the strategy proposal from the server, and the output is the display to the user. The terminal displays the received strategy proposal to the user so that the user can confirm it.

[0281] Step 8:

[0282] The user checks the strategic proposal displayed on the terminal and decides on a business strategy based on it. The input is the strategic proposal displayed on the terminal, and the output is the user's business decision. The user decides on a specific marketing strategy based on the proposal and puts it into action.

[0283] (Application example 2)

[0284] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0285] Conventional systems for compiling and analyzing management indicators do not adequately aggregate data or propose strategies in real time, leading to a demand for faster and more accurate management decisions. Furthermore, there is a lack of automated methods for analyzing trends in management indicators and proposing specific strategies, making it difficult for managers and marketing personnel to quickly implement appropriate strategies.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0287] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, a means for analyzing trends in the management indicators using a generative AI model and generating prompt sentences to make strategic proposals, and a means for aggregating management indicators in real time and saving them in a database on the cloud. This enables real-time aggregation and analysis of management indicators, allowing for quick and specific strategic proposals to be made automatically.

[0288] "Management indicators" are numerical data used to evaluate a company's performance and business situation, and include sales, inventory, number of customers, repeat business rate, etc.

[0289] "Aggregation" means organizing multiple data sets and calculating statistical information such as totals and averages.

[0290] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators, propose management strategies, and provide business support.

[0291] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals, and includes marketing strategies and business expansion plans.

[0292] A "multifaceted perspective" means evaluating things from multiple different perspectives and making a comprehensive judgment.

[0293] A "perspective" is the position or viewpoint from which one observes and judges things.

[0294] "Effective business decisions" mean making appropriate and effective decisions to achieve a company's goals and solve problems.

[0295] "Corporate data" refers to various data related to the business and management of a company, including sales data, customer data, inventory data, and the like.

[0296] "Business expansion" refers to the activities and strategies used to increase a company's business scale and market share.

[0297] A "generative AI model" is a type of artificial intelligence that is used to generate natural language and analyze data for specific tasks.

[0298] A "prompt" is an instruction or question entered into a generative AI model to make it perform a specific task.

[0299] "Real-time" means that data and information are processed and reflected immediately the moment they are generated.

[0300] A "cloud database" is a database built on a remote server accessible via the Internet.

[0301] The system for implementing this invention is composed of three main elements: a server, a terminal, and a user. The specific functions and processing of each element will be explained below.

[0302] Server Functions and Processing

[0303] The server plays a central role in aggregating management indicators, analyzing them using a generative AI model, generating prompts, and making strategic proposals. The server uses the following hardware and software:

[0304] Hardware: A server machine with a powerful processor, sufficient memory, and storage.

[0305] Software: Database management systems, generative AI models (e.g., OpenAI's GPT-4), and data analysis tools (e.g., Pandas).

[0306] The server first aggregates management indicator data stored in a cloud database in real time. Next, it uses a generative AI model based on the aggregated data to analyze trends in management indicators. Finally, it generates prompt statements based on the analysis results and makes specific strategic proposals.

[0307] Terminal functions and processing

[0308] The terminal is a device that allows the user to receive and display information provided by the server. The terminal uses the following hardware and software:

[0309] Hardware: Smartphones, tablets, computers, etc.

[0310] Software: Web browser, dedicated application.

[0311] The terminal receives the aggregated results of management indicators and strategic proposals sent from the server and visually displays them to the user, allowing the user to check management indicators in real time and make appropriate management decisions.

[0312] User operations

[0313] The user checks the information provided by the server through the terminal and executes business strategies as necessary. The user performs the following operations.

[0314] Checking management indicators

[0315] Acceptance or revision of strategic proposals

[0316] Entering new data

[0317] Examples of concrete examples and prompts

[0318] For example, if sales are down 10%, the server will generate the following prompt:

[0319] "Sales are down 10%. Please suggest a new marketing strategy."

[0320] Based on this prompt, the generative AI model suggests specific strategies such as "launch a new social media campaign."

[0321] In this way, the server, terminals, and users can work together to aggregate and analyze management indicators in real time, enabling quick and automatic specific strategic proposals to be made.

[0322] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0323] Step 1:

[0324] The server retrieves management indicator data from a database on the cloud.

[0325] Input: Management indicator data stored in a database on the cloud.

[0326] Output: Obtained management indicator data.

[0327] Specific operation: The server uses a database management system to query business indicator data such as sales, inventory, number of customers, and repeat customer rate, and stores the data in a data frame.

[0328] Step 2:

[0329] The server aggregates the acquired management index data in real time.

[0330] Input: Obtained management indicator data.

[0331] Output: Aggregated business indicator data.

[0332] Specific operation: The server uses data analysis tools to calculate totals and averages of sales, changes in inventory, increases or decreases in the number of customers, etc., and generates summary results.

[0333] Step 3:

[0334] The server analyzes trends using a generative AI model based on the aggregated management indicator data.

[0335] Input: Aggregated business indicator data.

[0336] Output: Trend analysis results of management indicators.

[0337] Specific operation: The server inputs aggregated data into the generative AI model and analyzes trends in sales increases and decreases, patterns of customer behavior, etc.

[0338] Step 4:

[0339] The server generates prompt sentences based on the trend analysis results and makes strategy suggestions.

[0340] Input: Trend analysis results of management indicators.

[0341] Output: Generated prompt statements and strategy suggestions.

[0342] Specific behavior: The server uses the generative AI model to generate prompt statements such as, "Sales have fallen by 10%. Please suggest a new marketing strategy." and create specific strategic proposals.

[0343] Step 5:

[0344] The server transmits the generated strategy proposal to the terminal.

[0345] Input: Generated prompt statements and strategy suggestions.

[0346] Output: Strategy proposal sent to the terminal.

[0347] Specific operation: The server sends a strategy proposal to the terminal via the network so that the user can confirm it.

[0348] Step 6:

[0349] The terminal displays the strategy proposal sent from the server to the user.

[0350] Input: Strategy proposal sent by the server.

[0351] Output: Strategy proposals displayed to the user.

[0352] Specific operation: The terminal uses a dedicated application or a web browser to visually display the strategy proposal so that the user can check it.

[0353] Step 7:

[0354] The user checks the strategy proposal through the terminal and implements the business strategy as necessary.

[0355] Input: Strategy proposal displayed on the terminal.

[0356] Output: Implemented business strategy.

[0357] Specific operations: The user checks the strategy proposals displayed on the terminal and executes business strategies such as launching new marketing campaigns as necessary.

[0358] Example 3

[0359] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0360] In conventional corporate data management systems, data collection, preprocessing, analysis, and display of results were all carried out separately, making integrated data management and analysis difficult. Furthermore, there was a lack of concrete methods for utilizing the results of data analysis in business strategies, making it difficult to make quick and effective management decisions. This could result in companies missing out on new business opportunities.

[0361] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0362] In this invention, the server includes a means for collecting and aggregating corporate data, a means for preprocessing the aggregated data, a means for analyzing the preprocessed data using a generative AI model, a means for displaying the analysis results to the user, and a means for using the results to expand the business. This enables centralized management of corporate data and advanced data analysis, leading to rapid and effective management decisions.

[0363] "Corporate data" refers to various data generated by a company in the course of its business, including customer purchase data, inventory data, sales data, and the like.

[0364] "Means of collection and aggregation" refers to the means of automatically acquiring various data within a company and storing it centrally in a database.

[0365] "Preprocessing means" refers to means for imputing missing values, normalizing data, and detecting and correcting outliers in order to convert the collected data into a format suitable for analysis.

[0366] A "generative AI model" is a model that uses artificial intelligence to analyze data and extract patterns and make predictions, and includes, for example, natural language processing models.

[0367] The "means of analysis" refers to the means of inputting preprocessed data into a generative AI model, analyzing the data, and outputting the results.

[0368] The "display means" refers to a means for visually presenting the analysis results to the user, and includes displays in the form of dashboards, graphs, and tables.

[0369] "Means to expand business" refers to the means of exploring new business opportunities based on the analysis results and planning and implementing specific strategies.

[0370] This invention is a system that centrally manages corporate data, analyzes the data using a generative AI model, and plans and executes business strategies based on the results. Specific embodiments of this system are described below.

[0371] Hardware and software used

[0372] Server: A central server for data aggregation and management, for example, using a virtual server from a cloud service provider.

[0373] Database: A database for storing and querying data, for example using a relational database management system.

[0374] Generative AI models: AI models for analyzing data and exploring business opportunities, for example, using natural language processing models.

[0375] Terminal: A device where the user enters data and checks the results, for example, a personal computer or tablet.

[0376] Program processing

[0377] Data collection and aggregation

[0378] The server collects various data within the company (customer purchase data, inventory data, sales data, etc.) and stores it in a database. Specifically, it periodically retrieves data from POS systems and CRM systems. For example, the server calls the POS system's API to retrieve the latest purchase data and stores it in a relational database.

[0379] Data Preprocessing

[0380] The server preprocesses the collected data and converts it into a format suitable for analysis. Specifically, it performs tasks such as filling in missing values, normalizing the data, and detecting and correcting outliers. For example, the server fills in missing values ​​in purchasing data with the average value and restores the data to the database.

[0381] Analyzing the data

[0382] The server then uses the generative AI model to analyze the preprocessed data. Specifically, it clusters customer purchasing patterns and obtains hints for new product development. For example, the server inputs the following prompt sentence into the natural language processing model:

[0383] Prompt: "Analyze the following customer purchase data and provide ideas for new product development. The data is in the format of customer ID, product ID, purchase date, purchase quantity, and purchase amount."

[0384] The generative AI model analyzes the input data and returns the results.

[0385] Displaying the results

[0386] The device displays the analysis results to the user, who can then use them to explore new business opportunities. For example, the device displays the analysis results received from the server on a dashboard. Specifically, it displays a list of "products that sell well in the summer" and the clustering results of customer purchasing patterns in graphs and tables.

[0387] In this way, it becomes possible to centrally manage corporate data and perform advanced data analysis, thereby enabling quick and effective management decisions. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0388] Step 1:

[0389] The server collects and aggregates various data within the company. Specifically, it periodically obtains customer purchasing data, inventory data, sales data, etc. from the POS system and CRM system. As input, it calls the API of the POS system or CRM system to obtain the latest data. As output, it saves the obtained data in a relational database. Specifically, the server calls the API, obtains data in JSON format, and inserts it into the database.

[0390] Step 2:

[0391] The server preprocesses the collected data. Specifically, it completes missing values, normalizes data, and detects and corrects outliers. As input, it receives raw data stored in a relational database. As output, it stores the preprocessed data back in the database. Specifically, the server completes items with missing values ​​with average values, detects data with abnormally high purchase quantities, and corrects them to within an appropriate range.

[0392] Step 3:

[0393] The server analyzes the preprocessed data using a generative AI model. Specifically, it clusters customer purchasing patterns and obtains hints for new product development. The preprocessed data is input into the generative AI model. The analysis results are obtained as output. Specifically, the server inputs the following prompt into the natural language processing model: "Please analyze the following customer purchasing data and provide hints for new product development. The data is in the format of customer ID, product ID, purchase date, purchase quantity, and purchase amount." The generative AI model performs analysis based on the input data and returns the results.

[0394] Step 4:

[0395] The terminal displays the analysis results to the user. As input, it receives the analysis results received from the server. As output, it presents the results visually to the user. Specifically, the terminal displays the analysis results received from the server on a dashboard. For example, it displays a list of "products that sell well in the summer" or the clustering results of customer purchasing patterns in graph or table format.

[0396] This enables centralized management of corporate data and advanced data analysis, enabling quick and effective management decisions.

[0397] (Application example 3)

[0398] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0399] Modern companies need to collect a wide variety of data and use it effectively to increase their competitiveness. However, there is a lack of concrete methods for centrally managing this data and exploring business opportunities. There is also a need for efficient methods for analyzing customer purchasing data and obtaining ideas for new product development. This makes it difficult for companies to make quick and accurate management decisions.

[0400] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0401] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple viewpoints, and a means for analyzing customer data and proposing new product development ideas using a generative AI model. This allows companies to centrally manage collected data and explore business opportunities. Furthermore, by obtaining new product development ideas based on customer purchasing data, companies can make quick and accurate management decisions.

[0402] "Management indicators" are numbers or indicators used to evaluate a company's management situation.

[0403] "Aggregation" means calculating multiple data together to find a total or average.

[0404] "Artificial Intelligence Secretary" is a system that uses artificial intelligence technology to support management decisions and strategic proposals.

[0405] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals.

[0406] A "multifaceted perspective" means analyzing and evaluating things from multiple different perspectives.

[0407] A "perspective" is the position or viewpoint from which one observes and judges things.

[0408] "Effective business judgment" means making effective decisions to achieve a company's goals.

[0409] "Corporate data" refers to various types of information collected and generated in the course of a company's business operations.

[0410] "Business expansion" means increasing a company's business scale and market share.

[0411] "Customer data" refers to information about customers, including purchase history, behavioral history, etc.

[0412] A "generative AI model" is a model that uses artificial intelligence technology to generate new information and ideas from data.

[0413] "Product development" is the process of planning, designing, and manufacturing new products.

[0414] An idea is a new concept or idea.

[0415] The system for carrying out this invention operates in cooperation with a server, a terminal, and a user. The server includes a means for aggregating corporate data and aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple viewpoints, and a means for analyzing customer data and proposing new product development ideas using a generative AI model.

[0416] Hardware and software used

[0417] Hardware: Servers, smartphones

[0418] Software: Python, Pandas, Scikit-learn, Matplotlib, Transformers library

[0419] Data processing and calculation flow

[0420] 1. Data loading: The server loads various data, including the company's customer data, in formats such as CSV files.

[0421] 2. Data preprocessing: Use the Pandas library to standardize data such as purchase amount, visit frequency, and number of items added to cart.

[0422] 3. Clustering: Using the KMeans algorithm from the Scikit-learn library, we cluster customers and visualize the characteristics of each cluster.

[0423] 4. Initialize the generative AI model: Initialize the generative AI model (e.g., GPT-3®) using the Transformers library.

[0424] 5. Propose business opportunities: Generative AI models based on customer data suggest new product development ideas.

[0425] Specific examples

[0426] For example, by inputting the following prompt sentences into a generative AI model, ideas for new product development can be obtained.

[0427] Prompt Sentence Examples

[0428] "Please propose new product development ideas based on customer purchasing data. For example, products that match the latest trends or the season."

[0429] Given this prompt, a generative AI model might suggest ideas like:

[0430] "Based on current trends, we can develop new fashion items using eco-friendly materials. We can also offer seasonally appropriate products using cooling materials in the summer and warmer materials in the winter."

[0431] In this way, the server can centrally manage the collected data and explore business opportunities. It can also obtain ideas for new product development based on customer purchasing data, allowing for quick and accurate management decisions.

[0432] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0433] Step 1:

[0434] The server reads various data, including the company's customer data, in formats such as CSV files. The input data includes customer purchase history, visit frequency, and the number of items added to the cart. The output data is the read data frame. Specifically, the CSV file is converted into a data frame using the Pandas library.

[0435] Step 2:

[0436] The server preprocesses the loaded data. The input is the data frame obtained in step 1. The output is a standardized data frame. Specifically, it uses the Pandas library to handle missing values ​​and clean the data, and uses the StandardScaler library in the Scikit-learn library to standardize the data.

[0437] Step 3:

[0438] The server performs clustering using the standardized data. The input is the standardized data obtained in step 2. The output is a data frame containing the clustering results. Specifically, it clusters customers using the KMeans algorithm from the Scikit-learn library and adds the features of each cluster to the data frame.

[0439] Step 4:

[0440] The server visualizes the clustering results. The input is a data frame containing the clustering results obtained in step 3. The output is a graph showing the clustering results. Specifically, the clustering results are displayed as a scatter plot using the Matplotlib library.

[0441] Step 5:

[0442] The server initializes the generative AI model. The input is the configuration information for the generative AI model. The output is the initialized generative AI model. Specifically, the GPT-3 model is initialized using the Transformers library.

[0443] Step 6:

[0444] The server uses a generative AI model based on customer data to propose new product development ideas. The input is a data frame containing clustering results and a prompt. The output is a new product development idea proposed by the generative AI model. Specifically, the following prompt is input into the generative AI model and the generated text is obtained.

[0445] Prompt Sentence Examples

[0446] "Please propose new product development ideas based on customer purchasing data. For example, products that match the latest trends or the season."

[0447] Step 7:

[0448] The server creates a report to reflect the generated ideas in corporate strategy. The input is the ideas proposed by the generative AI model. The output is a report to reflect the ideas in corporate strategy. Specifically, the server automatically generates a report based on the generated ideas and provides it to the company's decision makers.

[0449] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0450] "Example 1"

[0451] In one embodiment of the present invention, the means for aggregating management indicators is to obtain a company's financial data, personnel data, sales data, etc. from a database and calculate performance indicators based on this data. Specifically, indicators such as sales, profit margins, and employee turnover rates are calculated.

[0452] "Example 2"

[0453] In addition, the AI ​​will use the collected management indicators as an AI secretary to make strategic proposals. The AI ​​will analyze trends in management indicators and propose management strategies based on the results. For example, if sales are declining, the AI ​​will propose a new marketing strategy.

[0454] "Example 3"

[0455] Furthermore, it combines an emotion engine that recognizes the user's emotions. This emotion engine automatically recognizes emotions from the user's voice, facial expressions, and text. For example, if a user says, "I'm frustrated that business performance didn't improve today," the emotion engine will recognize the user's emotion as "frustration." The AI ​​secretary uses this recognition result to propose strategies based on the user's emotions.

[0456] The processing flow of each embodiment will be described below.

[0457] "Example 1"

[0458] Step 1: Obtain the company's financial, personnel, sales, and other data from the database.

[0459] Step 2: Calculate performance indicators based on the acquired data, such as sales, profit margins, and employee turnover rates.

[0460] "Example 2"

[0461] Step 1: AI analyzes trends in management indicators.

[0462] Step 2: Propose a business strategy based on the analysis results. For example, if sales are declining, the AI ​​will propose a new marketing strategy.

[0463] "Example 3"

[0464] Step 1: Automatically recognize emotions from the user's voice, facial expressions, and text. For example, if a user says, "I'm frustrated that my business performance didn't improve today," the emotion engine will recognize the user's emotion as "frustration."

[0465] Step 2: The AI ​​secretary uses the recognition results to propose strategies based on the user's emotions.

[0466] Example 1

[0467] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0468] In conventional management indicator aggregation systems, data acquisition, preprocessing, and calculation of performance indicators are often done manually, resulting in inefficiency. Furthermore, because strategic proposals based on aggregated management indicators are not automated, it is difficult to make quick management decisions. Furthermore, it is difficult to gain a perspective based on multiple angles and to make effective management decisions. To solve these issues, a system is needed that automates everything from data acquisition to calculation of performance indicators and strategic proposals, allowing for efficient and quick management decisions.

[0469] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0470] In this invention, the server includes means for connecting to a database storing corporate data, means for acquiring financial data, personnel data, and sales data from the database, means for preprocessing the acquired data, means for calculating performance indicators based on the preprocessed data, means for outputting the calculated performance indicators, means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, means for obtaining perspectives based on multiple viewpoints, means for making effective management decisions, and means for aggregating corporate data and using the same to expand business. This automates the process from data acquisition to calculation of performance indicators and strategic proposals, enabling efficient and rapid management decisions.

[0471] "Corporate data" refers to information related to a company's business activities, such as financial data, personnel data, and sales data.

[0472] A "database" refers to an information management system that stores corporate data and allows it to be searched and retrieved as needed.

[0473] "Financial data" refers to information about a company's financial situation, such as revenues, expenses, and profits.

[0474] "Human Resources Data" refers to information related to human resources management, such as employee joining and leaving information, job titles, and salaries.

[0475] "Sales data" refers to information related to sales activities, such as sales, customer information, and transaction history.

[0476] "Preprocessing" refers to the data preparation work required before data analysis or calculations can be performed, such as processing missing values ​​in the acquired data and converting data types.

[0477] "Performance indicators" refer to indicators used to evaluate a company's performance, such as sales, profit margins, and employee turnover rates.

[0478] "Artificial intelligence secretary" refers to an artificial intelligence system that automatically makes strategic proposals based on aggregated management indicators.

[0479] A "multifaceted perspective" refers to a perspective that allows one to evaluate and analyze things from different viewpoints and standpoints.

[0480] "Perspective" refers to the position or viewpoint from which things are observed and evaluated.

[0481] "Management decisions" refer to making important decisions in a company's management activities.

[0482] "Business expansion" refers to activities aimed at expanding a company's business scale and market share.

[0483] MODE FOR CARRYING OUT THE INVENTION

[0484] The present invention relates to a system for efficiently aggregating management indicators of a company and automating strategic proposals. A specific embodiment of this system will be described below.

[0485] System Overview

[0486] The user connects to a database that stores corporate data and generates a program to retrieve the necessary data. This program runs on the server, retrieves the company's financial, personnel, and sales data from the database, performs preprocessing, and then calculates performance indicators. The calculated performance indicators are used as an AI secretary and are useful for corporate strategy.

[0487] Hardware and software used

[0488] The server uses database connectivity libraries such as MySQL Connector to connect to the database. The Python programming language and data processing libraries such as Pandas and NumPy are used to retrieve and preprocess the data. Python is also used to calculate performance indicators and output the results.

[0489] Data acquisition and preprocessing

[0490] The server retrieves financial, personnel, and sales data from the database. The retrieved data undergoes preprocessing, such as processing missing values ​​and converting data types, to maintain data integrity and enable accurate calculation of performance indicators.

[0491] Calculating performance indicators

[0492] Based on the pre-processed data, the server calculates performance indicators such as sales, profit margins, employee turnover, etc. These indicators are important data for evaluating the business status of a company.

[0493] Output of results and strategy proposals

[0494] The calculated performance indicators are output by the server. The output data is used as an AI secretary to propose corporate strategies, enabling quick and efficient management decisions.

[0495] Examples and prompts

[0496] As a concrete example, the following prompt sentence is input to the generative AI model:

[0497] Example prompt sentence:

[0498] "Create a program to calculate sales, profit margins, and employee turnover rates based on a company's financial, personnel, and sales data. Use MySQL as the database and Python as the programming language."

[0499] Based on this prompt, the generative AI model outputs the necessary program code, which the user can then run on the server to efficiently aggregate a company's management indicators and automate strategic proposals.

[0500] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0501] Step 1:

[0502] The server connects to the database that stores the company data. Specifically, it uses a database connection library such as MySQL Connector to connect to the database. The input required is the database hostname, username, password, and database name, and the output is a database connection object.

[0503] Step 2:

[0504] The server retrieves financial, HR, and sales data from the database by executing SQL queries to retrieve the required data. The inputs are a database connection object and an SQL query, and the output is the retrieved data.

[0505] Step 3:

[0506] The server preprocesses the acquired data. Specifically, it handles missing values ​​and converts data types. The acquired data is required as input, and the preprocessed data is obtained as output. Specific operations include removing missing values ​​and converting data types to the appropriate format.

[0507] Step 4:

[0508] The server calculates performance indicators based on the preprocessed data. Specifically, it calculates indicators such as sales, profit margins, and employee turnover rates. The preprocessed data is required as input, and the calculated performance indicators are obtained as output. Specific operations include calculating total sales, profit margins, and employee turnover rates.

[0509] Step 5:

[0510] The server outputs the calculated performance indicators. Specifically, it outputs them to the console or saves them to a file. The input requires the calculated performance indicators, and the output is the displayed performance indicators or a saved file. Specifically, it displays the performance indicators on the console or saves them to a text file.

[0511] Step 6:

[0512] The server uses the aggregated management indicators as an AI secretary to implement corporate strategy. Specifically, it uses a generative AI model to make strategic proposals. The input is the aggregated management indicators, and the output is a strategic proposal. Specifically, it inputs a prompt statement into the generative AI model to generate a strategic proposal.

[0513] (Application example 1)

[0514] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0515] In modern corporate management, it is important to efficiently aggregate a wide range of data, including financial, human resources, and sales data, and calculate and display performance indicators in real time. However, manually compiling and analyzing this data takes time and effort, which can lead to delays and errors in management decisions. Furthermore, there is no system in place that allows in-factory robots to collect this data and display performance indicators in real time, making it difficult to maximize factory operational efficiency.

[0516] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0517] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, and a means for obtaining perspectives based on multiple viewpoints. This makes it possible for the server to be installed in a robot in a factory to collect financial data, personnel data, and sales data in real time and calculate and display performance indicators.

[0518] "Management indicators" are numerical data used to evaluate a company's financial situation and performance.

[0519] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators and support corporate strategy proposals and decision-making.

[0520] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives.

[0521] "Effective business decisions" refer to making optimal decisions to achieve a company's goals and solve problems.

[0522] "Corporate data" refers to data that includes various information related to corporate operations, such as corporate financial data, personnel data, and sales data.

[0523] A "factory robot" is an automated mechanical device installed to perform work in a factory.

[0524] "Real-time collection" means that data is acquired immediately and processed without delay.

[0525] "Performance indicators" are specific numerical data used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[0526] To implement this invention, a system installed on a robot in a factory is required. This system has the function of collecting a company's financial data, personnel data, and sales data in real time and calculating and displaying performance indicators. A specific embodiment of this system is described below.

[0527] System Configuration

[0528] The server includes the following means:

[0529] 1. Means of aggregating management indicators: Obtain a company's financial data, personnel data, and sales data from a database and calculate performance indicators based on this data.

[0530] 2. How to use it as an AI secretary: Automatically make strategic proposals based on aggregated management indicators.

[0531] 3. A means of gaining perspectives from multiple angles: Analyzing data from different angles and perspectives to support management decisions.

[0532] Hardware and software used

[0533] Hardware: Factory robots

[0534] Software: Python, Pandas, NumPy

[0535] Data processing and calculation

[0536] The server processes the data as follows:

[0537] 1. Data Acquisition: Retrieving financial, HR, and sales data from databases using SQL queries and APIs.

[0538] 2. Data processing: Convert the acquired data into a Pandas data frame and perform any necessary preprocessing, such as imputing missing values ​​and normalizing the data.

[0539] 3. Performance indicator calculation: Calculate performance indicators such as sales, profit margin, turnover rate, etc. This involves numerical calculations using NumPy.

[0540] 4. Displaying results: The calculated performance indicators are displayed on the robot's display. Since real-time updates are required, technologies such as WebSocket are used.

[0541] Specific examples

[0542] For example, using data like this:

[0543] Sales: 100,000 yen, 150,000 yen, 200,000 yen

[0544] Profit: 20,000 yen, 30,000 yen, 40,000 yen

[0545] Number of employees: 50, 55, 60

[0546] Turnover rate: 5%, 4%, 3%

[0547] Number of new customers: 10, 15, 20

[0548] Number of repeat customers: 5, 10, 15

[0549] Prompt Sentence Examples

[0550] "Design a system to calculate and monitor performance indicators such as sales, profit margins, and employee turnover based on the factory's financial, personnel, and sales data in real time."

[0551] In this way, robots in factories will be able to calculate and display performance indicators in real time, which is expected to speed up and improve the accuracy of management decisions.

[0552] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0553] Step 1:

[0554] The server retrieves financial, HR, and sales data from the database. The input is the database connection information, and the output is the retrieved data. Specifically, the server retrieves the data using SQL queries or APIs. For example, it executes an SQL query such as "SELECT FROM financial_data;".

[0555] Step 2:

[0556] The server converts the acquired data into a Pandas data frame. The input is the acquired data, and the output is a Pandas data frame. Specifically, the data is converted into a data frame using the Pandas read_sql function. For example, execute "pd.read_sql(query, connection)".

[0557] Step 3:

[0558] The server performs preprocessing on the data frame. The input is a Pandas data frame, and the output is a preprocessed data frame. Specifically, it performs missing value imputation and data normalization. For example, it executes "df.fillna(0)" or "df = (df - df.mean()) / df.std()".

[0559] Step 4:

[0560] The server calculates performance indicators based on the preprocessed data. The input is the preprocessed data frame, and the output is the performance indicators. Specifically, it calculates sales, profit margin, turnover rate, etc. For example, it executes "total_sales = df['Sales'].sum()" or "profit_margin = df['Profit'].sum() / total_sales".

[0561] Step 5:

[0562] The server displays the calculated performance indicators on the robot's display. The input is the performance indicators, and the output is the information displayed on the display. Specifically, data is sent in real time using technologies such as WebSocket. For example, execute "ws.send(json.dumps(kpis))".

[0563] Step 6:

[0564] The server automatically makes strategic proposals based on the aggregated management indicators. The input is performance indicators, and the output is strategic proposals. Specifically, it uses a generative AI model to generate prompts and makes strategic proposals. For example, execute "strategy = ai_model.generate(prompt)".

[0565] Step 7:

[0566] The server displays the strategy proposal on the robot's display. The input is the strategy proposal, and the output is the information displayed on the display. Specifically, data is sent in real time using technologies such as WebSocket. For example, execute "ws.send(json.dumps(strategy))".

[0567] Example 2

[0568] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0569] In conventional systems for compiling management indicators and proposing strategies, the compilation and analysis of management indicators is often done manually, which takes time and effort. In addition, because strategic proposals are also dependent on human judgment, there is a problem that subjective elements are easily introduced, making it difficult to obtain objective and effective proposals. Furthermore, because the specific procedures and methods for implementing the proposed strategies are not clear, it can take a long time to put them into practice. There is a need for a system that can solve these problems and enable the proposal and implementation of efficient and effective management strategies.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0571] In this invention, the server includes a means for aggregating management indicators, a means for making strategic proposals using a generative AI model based on the aggregated management indicators, and a means for presenting the generated strategic proposals to the user. This automates the aggregation and analysis of management indicators, and the use of the generative AI model enables objective and effective strategic proposals. Furthermore, presenting the proposals to the user allows the strategy to be quickly put into action.

[0572] "Management indicators" are numerical data used to evaluate a company's performance and financial situation, and include sales, profits, costs, etc.

[0573] "Aggregation" is the process of collecting and organizing multiple pieces of data to obtain statistical information.

[0574] A "generative AI model" is a model trained to perform a specific task using artificial intelligence techniques, and is used here to generate strategic recommendations.

[0575] A "strategic proposal" is a concrete action plan for improving a company's business strategy based on the results of an analysis of management indicators.

[0576] "Users" are entities that use this system to collect management indicators and receive strategic proposals, and refer to company managers and staff.

[0577] A "server" is a computer system that performs processes such as data aggregation, analysis, and running generative AI models.

[0578] A "prompt" is an instruction to be input into a generative AI model, and includes trends in business indicators and specific problems.

[0579] "Automation" refers to a system that performs processing autonomously without human intervention.

[0580] "Presentation" refers to the act of showing the generated strategic proposal to the user, and is done in the form of a screen display or report.

[0581] This invention is a system that aggregates management indicators and uses a generative AI model based on the aggregated management indicators to make strategic proposals. Specific embodiments of this system are described below.

[0582] First, the user inputs management indicator data into the terminal. Management indicator data is numerical data such as sales, profits, and costs, and is input as an Excel or CSV file. For example, sales data for the past three months can be input.

[0583] The device then sends the entered data to the server, which sends the data via an HTTP request and passes it to the server in JSON format.

[0584] The server reads the received data and performs aggregation and analysis using the Pandas library. Pandas is a Python data processing library that can efficiently aggregate data and perform statistical analysis. For example, it can calculate monthly sales trends and check whether sales are declining.

[0585] Next, the server generates a prompt based on the aggregated results. The prompt is an instruction to be input to the generative AI model, and includes trends in business indicators and specific problems. For example, the server generates the following prompt:

[0586] Current business indicator data shows that sales have dropped 10% over the past three months. This may be due to a decrease in marketing activities. Please suggest a new marketing strategy.

[0587] The server inputs the generated prompt sentence into a generative AI model (e.g., GPT-4) to generate a strategic proposal. The generative AI model proposes an appropriate business strategy based on the input prompt sentence. For example, it generates the following strategic proposal:

[0588] To improve sales, we suggest the following new marketing strategies:

[0589] 1. Strengthening social media advertising: In particular, utilize SNS (Social Networking Service) to reach the target demographic.

[0590] 2. Run email marketing campaigns: Reach out to existing customers with special offers and new product information.

[0591] 3. Collaborative Campaigns: Partner with companies in related industries and conduct joint promotions.

[0592] The server sends the generated strategy proposal to the terminal, where the data is again sent via an HTTP request and displayed on the terminal.

[0593] Finally, the user can review the strategy suggestions displayed on the device and implement them as needed, for example, by contacting an advertising agency to implement the suggested social media advertising enhancements.

[0594] In this way, the system automates the aggregation and analysis of management indicators and uses generative AI models to provide objective and effective strategic proposals, enabling users to make quick management decisions and expand their business.

[0595] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0596] Step 1:

[0597] The user inputs management index data into the terminal.

[0598] Users input management indicator data such as sales, profits, and costs into the terminal using Excel or CSV files. For example, they input sales data for the past three months. The input data is temporarily saved in the terminal.

[0599] Step 2:

[0600] The terminal transmits the input data to the server.

[0601] By clicking the "Send" button, the terminal sends the entered management indicator data to the server. The data is passed to the server in JSON format via an HTTP request. The input is the management indicator data, and the output is the data sent to the server.

[0602] Step 3:

[0603] The server receives the data and aggregates and analyzes it using the Pandas library.

[0604] The server converts the received JSON data into a Pandas data frame and calculates monthly sales trends. For example, it checks whether sales are declining by 10% each month. The input is management indicator data in JSON format, and the output is the aggregated data and its analysis results.

[0605] Step 4:

[0606] The server generates a prompt based on the results of the calculation.

[0607] Based on the aggregated results, the server generates a prompt to be input to the generative AI model. For example, if sales have dropped by 10%, a prompt is created to ask for the cause and improvement measures. The input is the aggregated results, and the output is the generated prompt. Specific examples of prompts are as follows:

[0608] Current business indicator data shows that sales have dropped 10% over the past three months. This may be due to a decrease in marketing activities. Please suggest a new marketing strategy.

[0609] Step 5:

[0610] The server inputs a prompt sentence into the generative AI model (GPT-4) and generates a strategy proposal.

[0611] The server inputs the generated prompt into GPT-4 to generate a strategic proposal. The generative AI model proposes an appropriate business strategy based on the input prompt. The input is the prompt, and the output is the generated strategic proposal. Specific examples of strategic proposals are as follows:

[0612] To improve sales, we suggest the following new marketing strategies:

[0613] 1. Strengthening social media advertising: Reaching target audiences, especially through social media.

[0614] 2. Run email marketing campaigns: Reach out to existing customers with special offers and new product information.

[0615] 3. Collaborative Campaigns: Partner with companies in related industries and conduct joint promotions.

[0616] Step 6:

[0617] The server sends the generated proposal to the terminal.

[0618] The server sends the generated strategy proposal to the terminal in JSON format. The data is again sent via HTTP request. The input is the generated strategy proposal, and the output is the data sent to the terminal.

[0619] Step 7:

[0620] The terminal displays the suggestions to the user.

[0621] The terminal displays the received strategy proposal on the screen. The user can check the proposal content. The input is the strategy proposal received from the server, and the output is the display to the user.

[0622] Step 8:

[0623] The user reviews the proposal and implements the strategy as needed.

[0624] The user reviews the proposal and, for example, contacts an advertising agency to implement enhanced social media advertising. The input is the displayed strategy proposal, and the output is the specific action to be taken.

[0625] (Application example 2)

[0626] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0627] In modern corporate management, the aggregation and analysis of management indicators is extremely important, but it is difficult to do this efficiently and automatically generate strategic proposals based on the results. Furthermore, while a multifaceted perspective is required when making management decisions, there is a lack of systems that can achieve this. Furthermore, there is no means of utilizing generative AI models to make specific strategic proposals, making it difficult for managers to make quick and accurate decisions.

[0628] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0629] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, a means for obtaining perspectives based on multifaceted viewpoints, a means for generating strategic proposals using a generative AI model, and a means for generating prompt sentences to make strategic proposals. This enables efficient aggregation and analysis of management indicators, and enables the automatic generation of specific and multifaceted strategic proposals based on the results.

[0630] "Management indicators" are numerical data used to evaluate a company's management situation, and include specific performance indicators such as sales, profit margins, and number of customers.

[0631] "Means of aggregation" refers to a method or device for collecting multiple data and summarizing them statistically.

[0632] "AI secretary" refers to a system or program that uses artificial intelligence to analyze management indicators and make strategic proposals.

[0633] "Corporate strategy" refers to the plans and policies a company adopts to achieve its long-term goals.

[0634] A "multifaceted perspective" refers to a method of evaluating and analyzing things from multiple different perspectives.

[0635] A "means of gaining perspective" refers to a method or device for understanding things from a particular point of view or position.

[0636] "Effective business judgment" refers to judgment that allows for making optimal decisions in the management of a company.

[0637] "Corporate data" refers to all information related to the operation of a company, including sales data, customer information, inventory data, and the like.

[0638] "Business expansion" refers to activities and strategies aimed at increasing a company's size and market share.

[0639] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[0640] A "prompt" is text that is used to input specific instructions or questions to a generative AI model.

[0641] The "means for generating strategic proposals" refers to a method or device for automatically proposing corporate strategies based on management indicators and other data.

[0642] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The following explains how each element functions.

[0643] server

[0644] The server plays a central role in aggregating and analyzing management indicators. Specifically, the following hardware and software are used:

[0645] Hardware: A server machine with a powerful processor, sufficient memory, and storage.

[0646] Software: Database management systems (e.g., MySQL), data analysis tools (e.g., Pandas, Scikit-learn), generative AI models (e.g., OpenAI GPT-3).

[0647] The server first stores the management indicator data provided by the company in a database. It then uses data analysis tools to aggregate this data and analyze trends. For example, it can predict future sales based on indicators such as sales data, advertising expenses, and number of customer visits.

[0648] Based on the prediction results, a generative AI model is used to generate strategy proposals. Specifically, a prompt sentence is generated and input into the generative AI model to obtain appropriate strategy proposals.

[0649] Terminal

[0650] A terminal is a device that allows a user to receive and display information provided by a server. Specifically, the terminal uses the following hardware and software:

[0651] Hardware: Smartphones, tablets, computers, etc.

[0652] Software: Web browser, dedicated application.

[0653] The terminal receives the strategic proposal sent from the server and displays it to the user, who can then make business decisions based on it.

[0654] User

[0655] Users are company executives and managers who make business decisions based on strategic proposals provided by the server. Users access the server through their terminals to input necessary data and check strategic proposals.

[0656] Specific examples

[0657] For example, if advertising expenditures for October were $5,000 and the number of customer visits was 2,000, the server would predict sales based on this data. If the prediction results indicate a decline in sales, the following prompt sentence would be input into the generative AI model:

[0658] Sales are declining. Please suggest a new marketing strategy.

[0659] Based on this prompt, the generative AI model will propose specific marketing strategies, which will be displayed on the device, allowing users to make business decisions based on them.

[0660] In this way, the collaboration between the server, terminal, and user makes it possible to propose and implement efficient and effective management strategies.

[0661] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0662] Step 1:

[0663] The server receives management indicator data provided by the company and stores it in a database. Input data includes sales data, advertising expenses, number of customer visits, etc., and stores this in a database management system (e.g., MySQL). The output is management indicator data stored in the database.

[0664] Step 2:

[0665] The server aggregates the management indicator data stored in the database using data analysis tools (e.g., Pandas, Scikit-learn) and analyzes trends. The input is the management indicator data obtained from the database, and the output is the aggregated data and the results of its trend analysis. Specific operations include data cleaning, aggregation, and regression analysis.

[0666] Step 3:

[0667] The server predicts future sales based on the trend analysis results. The input is the trend analysis results, and the output is predicted sales data. Specifically, it predicts future sales using a regression model.

[0668] Step 4:

[0669] The server generates strategic proposals using a generative AI model (e.g., OpenAI GPT-3) based on the prediction results. The input is the predicted sales data, and the output is the generated strategic proposals. Specifically, it generates a prompt sentence and inputs it into the generative AI model.

[0670] Step 5:

[0671] The server transmits the generated strategic proposal to the terminal. The input is the generated strategic proposal, and the output is the strategic proposal transmitted to the terminal. Specifically, the server transmits the strategic proposal to the terminal via the network.

[0672] Step 6:

[0673] The terminal receives the strategic proposal sent from the server and displays it to the user. The input is the strategic proposal sent from the server, and the output is the strategic proposal displayed to the user. Specifically, the terminal displays the strategic proposal using a dedicated application or a web browser.

[0674] Step 7:

[0675] The user makes a business decision based on the strategic proposal displayed on the terminal. The input is the strategic proposal displayed on the terminal, and the output is the user's business decision. Specifically, the user checks the strategic proposal and makes the necessary business decision.

[0676] Example 3

[0677] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0678] Conventional corporate strategy systems had the problem that it took a long time to compile and analyze management indicators, making it difficult to make quick management decisions. In addition, because strategy proposals did not take into account the user's emotions, it was not possible to provide the optimal strategy for each individual situation. Furthermore, data management was insufficient, which meant that new opportunities for business expansion could be missed.

[0679] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0680] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple viewpoints, a means for making effective management decisions, a means for aggregating corporate data and using it to expand business, a means for recognizing user emotions, a means for proposing strategies based on the recognized emotions, and a means for generating strategies using a generative AI model. This enables quick and accurate management decisions and optimal strategic proposals that take user emotions into consideration. Furthermore, centralized data management makes it possible to capture new business opportunities without missing them.

[0681] "Management indicators" are numerical data used to evaluate the management status of a company, and include sales, profit margins, inventory turnover, and the like.

[0682] "Artificial Intelligence Secretary" is a system that uses artificial intelligence technology to analyze management indicators and propose and support corporate strategies.

[0683] A "multifaceted perspective" is a method of evaluating and analyzing things from multiple different perspectives, and is used to improve the accuracy of management decisions.

[0684] "Effective business decisions" mean making optimal decisions to achieve a company's goals and solve problems.

[0685] "Corporate data" refers to data that companies generate and collect on a daily basis, including customer information, sales data, inventory data, etc.

[0686] "User emotions" refer to the feelings or emotions that a user has about a particular situation or piece of information, and include joy, sadness, regret, and the like.

[0687] "Emotion recognition" is a technology that analyzes a user's emotions from voice, facial expressions, text, etc., and identifies specific emotions.

[0688] "Strategic proposal" means proposing a specific action plan to achieve a company's goals or solve problems based on the analysis results and perceived emotions.

[0689] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate new information and strategies.

[0690] The present invention relates to a system that aggregates corporate data, recognizes user emotions, and proposes business strategies. A specific embodiment of this system will be described below.

[0691] The server collects various data from within the company and stores it in a MySQL database. Data is collected by retrieving it from each department's system via API. The collected data includes customer purchasing data, inventory data, sales data, etc.

[0692] The server then analyzes the data using Python's Pandas library. The purpose of the analysis is to explore new business opportunities. For example, analyzing customer purchasing data can extract purchasing patterns and trends. This can then be used to generate ideas for new product development and marketing strategies.

[0693] Additionally, the device captures the user's voice, facial expressions, and text using a microphone, camera, and text input field. The captured data is sent to a server and analyzed using an emotion recognition engine such as the Microsoft® Azure® Emotion API. The emotion recognition engine classifies the user's emotions into categories such as "happiness," "sadness," and "regret."

[0694] The server generates a strategy based on the user's emotions based on the results of the emotion recognition engine. This strategy is created using a generative AI model, which analyzes data and generates new information and strategies.

[0695] Users can access the system through a terminal and input prompt sentences such as the following into the generative AI model:

[0696] Examples:

[0697] The user inputs, "I would like you to analyze recent sales data and suggest ideas for new product development."

[0698] The server retrieves recent sales data from a MySQL database and analyzes the data using the Pandas library, generating new product development ideas and presenting them to the user.

[0699] Example prompt sentence:

[0700] "Analyze recent customer purchase data and propose new marketing strategies."

[0701] "Recognize user emotions and suggest business strategies based on those emotions."

[0702] In this way, a system is realized in which the server, terminals, and users work together to aggregate and analyze corporate data and recognize users' emotions, thereby proposing strategies that will lead to business expansion. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[0703] Step 1:

[0704] The server collects various data from within the company. Specifically, it obtains data from each department's system via API. Input includes customer purchase data, inventory data, sales data, etc. This data is stored in a MySQL database. The output is company data that is centrally managed in the database.

[0705] Step 2:

[0706] The server retrieves the required data from the MySQL database using a query. The input includes specific query conditions. The server analyzes the data using Python's Pandas library. Specifically, it creates a data frame, cleans and aggregates the data, and outputs the analysis results. For example, customer purchasing patterns and trends are extracted.

[0707] Step 3:

[0708] The device captures the user's voice, facial expressions, and text. The input includes the user's speech and facial expressions. The device transmits these data to the server. The output is the captured data transmitted to the server.

[0709] Step 4:

[0710] The server uses an emotion recognition engine such as the Microsoft Azure Emotion API to recognize the user's emotions from the captured data. Input includes voice, facial expressions, and text data. Specifically, the emotion recognition engine analyzes the data and classifies the user's emotions into categories such as "joy," "sadness," and "regret." The output is the recognized emotion data.

[0711] Step 5:

[0712] The server generates a strategy using a generative AI model based on the results of the emotion recognition engine. The input includes the recognized emotion data and the analysis results of company data. Specifically, the server inputs a prompt statement into the generative AI model to generate a strategy. The output is a strategy proposal based on the user's emotion.

[0713] Step 6:

[0714] The user checks the proposed strategy through the terminal. The input includes the strategy proposal sent from the server. The user decides whether to execute the proposed strategy. The output is the user's decision.

[0715] In this way, a system is realized in which servers, terminals, and users work together to aggregate and analyze corporate data and recognize users' emotions, proposing strategies that will lead to business expansion.

[0716] (Application example 3)

[0717] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0718] While conventional management support systems can aggregate and analyze corporate data, they lack the ability to recognize customer emotions and propose appropriate responses based on those emotions. This makes it difficult to improve customer experience or respond quickly, which can result in missed business opportunities. Furthermore, while utilizing emotional data would enable more accurate proposals when compiling management indicators or proposing strategies, current systems lack such functionality.

[0719] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0720] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple perspectives, a means for making effective management decisions, a means for aggregating corporate data and using it to expand business, a means for aggregating and analyzing customer purchase history and behavioral data, a means for recognizing emotions from customer voice, facial expressions, and text, and a means for proposing appropriate responses based on the recognized emotions. This makes it possible to improve the customer experience by recognizing customer emotions in real time and proposing appropriate responses. Furthermore, utilizing emotion data makes it possible to propose more accurate management strategies, preventing missed business opportunities.

[0721] "Management indicators" are numbers or indicators used to evaluate a company's management situation.

[0722] "Aggregation" means collecting and organizing data to calculate statistical values ​​such as totals and averages.

[0723] "AI Secretary" is a system that uses artificial intelligence to provide management support and strategic proposals.

[0724] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals.

[0725] A "multifaceted perspective" means analyzing and evaluating things from multiple different perspectives.

[0726] A "perspective" is the position or viewpoint from which one observes and judges things.

[0727] "Business judgment" refers to making important decisions regarding the management of a company.

[0728] "Corporate data" refers to various data related to the business and management of a company.

[0729] "Business expansion" means increasing a company's business scale and market share.

[0730] "Purchase history" is a record of products that a customer has purchased in the past.

[0731] "Behavioral data" is data about customer behavior.

[0732] "Analysis" means examining data in detail to find meaning and trends.

[0733] "Speech" is a means of communication using the human voice.

[0734] "Facial expression" refers to emotions and intentions expressed through the movement of facial muscles.

[0735] "Text" is information written in text.

[0736] "Emotion recognition" is a technology that identifies human emotions from voice, facial expressions, and text.

[0737] A "solution" is a means or method for dealing with a particular situation or problem.

[0738] The system for implementing this invention uses the following hardware and software: a server, a smartphone, and smart glasses. The software uses Python, TensorFlow, OpenCV, and Google® Cloud Natural Language API.

[0739] System configuration

[0740] 1. Server:

[0741] The server aggregates corporate data and compiles management indicators.

[0742] The server acts as an AI secretary and makes strategic proposals based on the collected management indicators.

[0743] The server aggregates and analyzes customer purchasing history and behavioral data.

[0744] 2. Smartphones and smart glasses:

[0745] Smartphones and smart glasses capture customers' voices and facial expressions.

[0746] The smartphone and smart glasses transmit the captured data to a server.

[0747] Data processing and calculation

[0748] 1. Data Collection:

[0749] The server collects management indicators and customer data from the company database (MySQL).

[0750] The smartphone and smart glasses capture the customer's voice and facial expressions and transmit them to a server in real time.

[0751] 2. Emotion recognition:

[0752] The server uses OpenCV to analyze facial expression data and TensorFlow to classify emotions.

[0753] The server uses the Google Cloud Natural Language API to recognize emotions from speech and text.

[0754] 3. Strategic proposal:

[0755] Based on the recognized emotion data, the server uses a generative AI model (GPT-3) to suggest an appropriate response.

[0756] Specific examples

[0757] For example, imagine a customer wearing smart glasses walking around a store looking for a product. If the customer says, "I'm having trouble finding this product," the microphone in the smart glasses will capture the voice and the emotion engine will recognize the "confused" feeling. The smart glasses' display will ask, "Would you like to contact a staff member?" If the customer answers "yes," a notification will be sent to the staff member.

[0758] Prompt Sentence Examples

[0759] If a customer says, "I'm having trouble finding this product," the emotion engine recognizes this and notifies staff to act quickly.

[0760] In this way, the system can recognize customer emotions in real time and suggest appropriate responses, improving the customer experience. Utilizing emotion data also makes it possible to propose more accurate business strategies, helping to prevent missed business opportunities.

[0761] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0762] Step 1:

[0763] Data collection

[0764] The server collects management indicators and customer data from the company database (MySQL). Specifically, the server executes SQL queries to retrieve purchase history and behavioral data. The input is the database connection information and query, and the output is the retrieved data.

[0765] Step 2:

[0766] Voice and facial expression capture

[0767] The devices (smartphones and smart glasses) capture the customer's voice and facial expressions. Specifically, the device's microphone and camera are used to capture audio and video in real time. The input is the customer's voice and facial expressions, and the output is the captured audio and video data.

[0768] Step 3:

[0769] Data transmission

[0770] The device sends the captured audio and video data to the server. Specifically, the device uploads the data to the server via the Internet. The input is the captured audio and video data, and the output is the data sent to the server.

[0771] Step 4:

[0772] emotion recognition

[0773] The server analyzes video data using OpenCV and classifies emotions using TensorFlow. It also recognizes emotions from audio data using the Google Cloud Natural Language API. The input is audio and video data, and the output is the recognized emotion data.

[0774] Step 5:

[0775] Strategic proposal

[0776] The server uses a generative AI model (GPT-3) to propose an appropriate response based on the recognized emotion data. Specifically, the emotion data is input as a prompt into the generative AI model, which then generates a response. The input is the recognized emotion data, and the output is a proposed response.

[0777] Step 6:

[0778] notification

[0779] The server notifies the terminal of the proposed action. Specifically, the server generates a notification message and sends it to the terminal. The input is the proposed action, and the output is the notification message displayed on the terminal.

[0780] Step 7:

[0781] Response execution

[0782] The user (store staff) takes appropriate action based on the notification message displayed on the terminal. Specifically, the user approaches the customer and responds quickly to confused customers. The input is the notification message, and the output is improved customer satisfaction.

[0783] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0784] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0785] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0786] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0787] [Second embodiment]

[0788] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0789] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0790] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0791] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0792] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0793] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0794] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0795] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0796] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0797] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0798] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0799] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0800] "Example 1"

[0801] In one embodiment of the present invention, the means for aggregating management indicators acquires financial data, personnel data, sales data, etc. of a company from a database and calculates performance indicators based on this data. For example, indicators such as sales, profit margin, and employee turnover rate can be considered.

[0802] "Example 2"

[0803] Another way to use AI as a secretary is to have AI make strategic proposals based on the collected management indicators. Specifically, AI analyzes the trends of management indicators and proposes management strategies based on the results. For example, if sales are declining, AI can propose a new marketing strategy.

[0804] "Example 3"

[0805] Furthermore, one way to aggregate corporate data and use it to expand business is to centrally manage various types of data within a company and use that data to explore new business opportunities. For example, analyzing customer purchasing data can provide hints for new product development.

[0806] The processing flow of each embodiment will be described below.

[0807] "Example 1"

[0808] Step 1: Obtain the company's financial, personnel, sales, and other data from the database.

[0809] Step 2: Calculate performance indicators based on the acquired data, such as sales, profit margins, and employee turnover rates.

[0810] "Example 2"

[0811] Step 1: AI makes strategic proposals based on the collected management indicators.

[0812] Step 2: The AI ​​analyzes trends in management indicators and proposes management strategies based on the results. For example, if sales are declining, the AI ​​will propose a new marketing strategy.

[0813] "Example 3"

[0814] Step 1: Centrally manage various data within the company.

[0815] Step 2: Explore new business opportunities based on centrally managed data. For example, analyze customer purchasing data to gain insights into new product development.

[0816] Example 1

[0817] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0818] In conventional management indicator aggregation systems, the entire process from acquiring corporate data to cleaning it, calculating performance indicators, and generating and providing reports is often done manually, resulting in inefficiency. Furthermore, strategic proposals based on the aggregated management indicators are not automated, making it difficult to make quick and accurate management decisions. Furthermore, there is insufficient data aggregation to gain perspectives based on multiple angles and to lead to business expansion.

[0819] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0820] In this invention, the server includes means for acquiring corporate data, means for cleaning the acquired corporate data, means for calculating performance indicators based on the cleaned data, means for generating reports from the calculated performance indicators, means for providing the generated reports to users, means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, means for gaining perspectives based on multiple perspectives, means for making effective management decisions, and means for aggregating corporate data and using the resulting data to expand business. This automates the process from acquiring corporate data to providing reports, enabling quick and accurate management decisions. It also enables efficient data aggregation to gain perspectives based on multiple perspectives and expand business.

[0821] "Corporate data" is a general term for various data related to the operation of a company, such as financial data, personnel data, and sales data.

[0822] "Cleaning" is the process of removing missing values ​​and outliers from the acquired data and normalizing the data.

[0823] "Performance indicators" are indicators used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[0824] A "report" is a document or graph that visually represents calculated performance indicators.

[0825] "User" refers to a person such as a company manager or administrator who uses the system.

[0826] "Management indicators" are indicators used to evaluate a company's business situation, and refer to a wide range of indicators including performance indicators.

[0827] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators and make strategic proposals.

[0828] A "multifaceted perspective" is a perspective that analyzes corporate data from multiple different perspectives and allows for comprehensive judgment.

[0829] A "perspective" refers to the viewpoint or position that forms the basis for making management decisions.

[0830] "Effective business decisions" refer to appropriate decisions that contribute to corporate growth and increased profits.

[0831] "Data aggregation" is the process of centrally collecting and integrating distributed data.

[0832] "Business expansion" refers to activities aimed at increasing a company's market share or sales.

[0833] MODE FOR CARRYING OUT THE INVENTION

[0834] The present invention is a system for acquiring corporate data, cleaning it, calculating performance indicators, generating reports, and providing them to users. A specific embodiment of this system will be described below.

[0835] Data Acquisition

[0836] Subject: Server

[0837] The server retrieves the company's financial data, personnel data, sales data, etc. from a database. Specifically, it executes SQL queries using a database management system (DBMS) to extract the required data. The database server used could be MySQL or PostgreSQL.

[0838] Data Cleaning

[0839] Subject: Server

[0840] The server cleans the acquired data by using the Python Pandas library to impute missing values, remove outliers, and normalize the data, thereby improving the quality of the data.

[0841] Calculation of performance indicators

[0842] Subject: Server

[0843] The server calculates performance indicators based on the cleaned data, such as sales, profit margins, and employee turnover rates. It uses Python and R to analyze the data and calculate the necessary indicators.

[0844] Generate reports

[0845] Subject: Server

[0846] The server generates reports based on the calculated performance indicators. Specifically, it uses BI (Business Intelligence) tools to create graphs and tables, making it possible to provide reports that are visually easy to understand.

[0847] Providing reports

[0848] Subject: Terminal

[0849] The terminal provides the generated report to the user. Specifically, the report is displayed through a web browser. The report is provided to the user using a web framework such as Flask.

[0850] Specific examples

[0851] Example 1: Calculating sales

[0852] The server retrieves sales data from the database for the past year. Next, it cleans the data using Python and aggregates monthly sales figures. Finally, it uses a BI tool to graph sales trends and output them as a report.

[0853] Example 2: Calculating employee turnover

[0854] The server retrieves employee data from the database for the past year. Next, it uses R to filter the data and calculate the number of employees who left and stayed each month. Finally, it uses a BI tool to graph the turnover rate over time and output it as a report.

[0855] Example prompts for generative AI models

[0856] "Write a program that uses a company's financial, HR, and sales data to calculate performance indicators such as sales, profit margins, and employee turnover. Retrieve the data from a database, clean and analyze it using Python or R, generate reports using a BI tool, and display the reports in a web browser using Flask."

[0857] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0858] Step 1:

[0859] Retrieving data from a database

[0860] Subject: Server

[0861] The server retrieves a company's financial data, personnel data, sales data, etc. from a database. Specifically, it extracts the required data using an SQL query. The inputs are database connection information and an SQL query, and the output is the retrieved data. For example, to retrieve sales data, the following SQL query is executed:

[0862] sql

[0863] SELECT FROM sales_data WHERE date BETWEEN '2022-01-01' AND '2022-12-31';

[0864] This query retrieves sales data for the specified time period.

[0865] Step 2:

[0866] Clean the data

[0867] Subject: Server

[0868] The server cleans the acquired data. Specifically, it completes missing values, removes outliers, and normalizes the data. The acquired data is required as input, and the cleaned data is obtained as output. The data is cleaned using the Python Pandas library as follows:

[0869] python

[0870] import pandas as pd

[0871] Loading data

[0872] data = pd.read_sql(query, connection)

[0873] Missing value imputation

[0874] data.fillna(0, inplace=True)

[0875] Outlier removal

[0876] data = data[data['sales'] >= 0]

[0877] This process improves the quality of the data.

[0878] Step 3:

[0879] Calculate performance indicators

[0880] Subject: Server

[0881] The server calculates performance indicators based on the cleaned data. The cleaned data is required as input, and the calculated performance indicators are obtained as output. For example, sales, profit margin, employee turnover rate, etc. are calculated. Below is an example of calculating sales.

[0882] python

[0883] Aggregate monthly sales

[0884] monthly_sales = data.groupby('month')['sales'].sum()

[0885] This process calculates the monthly sales amount.

[0886] Step 4:

[0887] Generate a report

[0888] Subject: Server

[0889] The server generates a report based on the calculated performance indicators. The calculated performance indicators are required as input, and the generated report is obtained as output. Specifically, graphs and tables are created using BI (Business Intelligence) tools. Below is an example of graphing sales trends using Python's Matplotlib.

[0890] python

[0891] import matplotlib.pyplot as plt

[0892] Creating a graph

[0893] plt.plot(monthly_sales.index, monthly_sales.values)

[0894] plt.title('Monthly Sales')

[0895] plt.xlabel('Month')

[0896] plt.ylabel('Sales')

[0897] plt.savefig('monthly_sales_report.png')

[0898] This process generates a report that visually depicts sales trends.

[0899] Step 5:

[0900] Providing reports to users

[0901] Subject: Terminal

[0902] The terminal provides the generated report to the user. The input is the generated report, and the output is a state where the user can view the report. Specifically, the report is displayed through a web browser. Below is an example of displaying a report using Flask.

[0903] python

[0904] from flask import Flask, send_file

[0905] app = Flask(__name__)

[0906] @app.route(' / report')

[0907] def report():

[0908] return send_file('monthly_sales_report.png', mimetype='image / png')

[0909] if __name__ == '__main__':

[0910] app.run(debug=True)

[0911] This process allows users to view the report through a web browser.

[0912] (Application example 1)

[0913] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0914] Conventional management indicator aggregation systems took time to acquire and aggregate data, making it difficult to monitor performance in real time. Furthermore, there was a lack of feedback for managers and administrators to make quick management decisions, hindering efficient operations within the factory. Furthermore, strategic proposals based on the aggregated data were not automated, making it time-consuming and labor-intensive to formulate management strategies.

[0915] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0916] In this invention, the server includes means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, means for obtaining perspectives based on multiple viewpoints, means for making effective management decisions, means for aggregating corporate data and using it to expand business, means that is installed on robots in the factory and acquires financial data, personnel data, and sales data in real time and calculates and displays performance indicators, and means for providing feedback to managers and administrators based on the calculated performance indicators. This enables real-time performance monitoring, resulting in quick management decisions and efficient factory operations.

[0917] "Management indicators" are indicators used to evaluate a company's financial situation and performance, and include sales, profit margins, employee turnover rates, etc.

[0918] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators, propose corporate strategies, and support management decisions.

[0919] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives, and is used to gain a more comprehensive understanding.

[0920] "Effective business decisions" refer to making optimal decisions to achieve a company's goals and solve problems.

[0921] "Corporate data" refers to various information related to corporate operations, such as a company's financial data, personnel data, and sales data.

[0922] "Business expansion" refers to activities and strategies aimed at increasing a company's market share or sales.

[0923] "Factory robots" refer to mechanical devices that perform automated tasks on factory production lines and work processes.

[0924] "Real-time" means that data is acquired and processed immediately, and the results are reflected without delay.

[0925] "Financial data" refers to data that shows a company's financial status, such as revenue, expenses, and profits.

[0926] "Human resources data" refers to data related to a company's personnel, such as the number of employees, turnover rate, and working hours.

[0927] "Sales data" refers to data related to a company's sales activities, such as sales performance, customer information, and return rates.

[0928] "Performance indicators" are specific numbers or indicators used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[0929] "Feedback" refers to information and advice provided to managers and administrators based on performance indicators and business decisions.

[0930] As an embodiment of the present invention, a system installed on a robot in a factory will be described as an example. This system has the function of aggregating management indicators and calculating and displaying performance indicators in real time. It also provides feedback to managers and administrators based on the calculated performance indicators.

[0931] 1. System Program

[0932] The system includes a program with the following main functions:

[0933] Ability to retrieve financial, personnel, and sales data from databases

[0934] Function to calculate performance indicators based on acquired data

[0935] Ability to display calculated performance indicators in real time

[0936] Ability to provide feedback to management and administrators

[0937] 2. Program processing explanation

[0938] Hardware used

[0939] Factory Robots

[0940] Software used

[0941] Python 3.x

[0942] Pandas library

[0943] NumPy library

[0944] Data processing and calculation

[0945] The server first retrieves financial, human resources, and sales data from the database. To do this, it uses SQL queries to extract the necessary information from the database. Next, it calculates performance indicators based on the retrieved data. Specifically, it calculates indicators such as sales revenue, profit margin, employee turnover rate, number of units sold, and return rate. These indicators are processed as data frames or arrays using Pandas or NumPy.

[0946] The calculated performance indicators are displayed in real time on the factory robot's display, allowing managers and executives to instantly understand the factory's performance. Feedback is also provided to managers and executives based on the calculated performance indicators. The feedback is automatically generated using a generative AI model and includes suggestions for management strategies and suggestions for improvement.

[0947] 3. Examples and prompts

[0948] For example, if a factory manager wants to use the "Factory Performance Monitoring Robot" to check monthly performance indicators, the results shown below will be displayed.

[0949] Performance indicators:

[0950] average_revenue: 150000.0

[0951] average_profit_margin: 0.2

[0952] average_turnover_rate: 0.04

[0953] average_units_sold: 1500.0

[0954] average_return_rate: 0.015

[0955] An example of a prompt sentence to input to the generative AI model is as follows:

[0956] Use the "Factory Performance Monitoring Robot" to calculate performance indicators based on the latest financial, personnel, and sales data, such as average sales, average profit margin, average turnover rate, average number of units sold, and average return rate, and display the results.

[0957] In this way, factory robots can monitor performance indicators in real time and provide feedback to managers and administrators, enabling faster business decisions and more efficient factory operations.

[0958] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0959] Step 1:

[0960] The server retrieves financial data, personnel data, and sales data from the database.

[0961] Input: Database connection information, SQL query

[0962] Output: Data frames of financial data, human resources data, and sales data

[0963] What happens: The server connects to the database and executes SQL queries to extract the required data, which is then stored as a Pandas dataframe.

[0964] Step 2:

[0965] The server calculates performance indicators based on the acquired data.

[0966] Input: Data frames of financial data, human resources data, and sales data

[0967] Output: Performance indicators (average sales, average profit margin, average turnover rate, average units sold, average return rate)

[0968] What happens: The server uses Pandas or NumPy to calculate each item in the data frame. For example, to calculate the average sales value, it averages the sales column in the financial data. Other metrics are calculated similarly.

[0969] Step 3:

[0970] The server displays the calculated performance indicators in real time.

[0971] Input: Performance Indicators

[0972] Output: Performance indicators displayed on the screen

[0973] How it works: The server sends the calculated performance indicators to the factory robot's display and displays them in real time, allowing managers and executives to instantly check performance.

[0974] Step 4:

[0975] The server provides feedback to managers and administrators based on the calculated performance indicators.

[0976] Input: Performance Indicators

[0977] Output: Feedback message

[0978] Specific operation: The server analyzes performance indicators using the generative AI model and generates feedback messages including business strategy proposals and suggestions for improvement. The generated feedback messages are sent to managers and administrators.

[0979] Step 5:

[0980] The user inputs a prompt statement into the generative AI model and requests it to calculate performance indicators.

[0981] Input: prompt statement

[0982] Output: Calculated performance indicators

[0983] Specific operation: The user inputs a prompt statement to the generative AI model. For example, "Please use the 'Factory Performance Monitoring Robot' to calculate performance indicators based on the latest financial, personnel, and sales data. Specifically, please calculate average sales, average profit margin, average turnover rate, average number of units sold, and average return rate, and display the results." The server calculates the performance indicators based on this prompt statement and displays the results.

[0984] Example 2

[0985] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0986] In modern corporate management, making fast and accurate business decisions requires efficiently compiling and analyzing large amounts of management indicator data and making strategic proposals based on that data. However, with conventional systems, data compilation and analysis took time, and the quality of strategic proposals often lacked consistency. Furthermore, there was a lack of a way for users to input specific questions and receive appropriate strategic proposals. This often led to delayed business decisions and the adoption of incorrect strategies, which reduced a company's competitiveness.

[0987] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0988] In this invention, the server includes a means for aggregating management indicators, a means for making a strategic proposal using a generative AI model based on the aggregated management indicators, a means for sending a prompt sentence input by a user to the server, and a means for displaying the strategic proposal returned from the server to the user, thereby enabling quick and accurate management decisions.

[0989] "Management indicators" are numerical data used to evaluate a company's performance and business situation, and include sales, costs, profit margins, etc.

[0990] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze data and make predictions and suggestions based on specific purposes.

[0991] A "prompt sentence" is a question or instruction sentence that a user inputs to a system, and is input data that the system uses to generate responses or suggestions based on.

[0992] A "server" is a computer system that processes and stores data on a network, and provides services in response to requests from clients.

[0993] "User" refers to an individual or organization that uses the system to receive analysis of management indicators and strategic proposals.

[0994] "Aggregation" is the process of organizing multiple data sets and calculating statistical values ​​such as totals and averages.

[0995] A "strategic proposal" is a proposal that indicates the specific actions and policies that a company should take based on the results of an analysis of management indicators.

[0996] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives, and is a method for gaining a more comprehensive understanding.

[0997] "Perspective" refers to the position or viewpoint from which one observes and judges things.

[0998] "Management decisions" refer to making important decisions regarding the management of a company, including the formulation and implementation of strategies.

[0999] "Corporate data" means all data related to a company's business or activities, including sales, costs, customer information, etc.

[1000] "Business expansion" refers to activities and strategies aimed at increasing a company's size or market share.

[1001] This invention is a system that aggregates management indicators and uses a generative AI model to make strategic proposals based on the aggregated data. The system consists of three main elements: a server, a terminal, and a user.

[1002] server

[1003] The server first collects management indicator data and stores it in a database. Specifically, it obtains key management indicator data such as sales, costs, and profit margins from each department. A relational database management system such as MySQL or PostgreSQL is used as the database.

[1004] The server then uses the Python Pandas library to aggregate the data retrieved from the database, for example, to calculate monthly sales or cost totals, and the aggregated results are temporarily stored in memory.

[1005] The server then uses machine learning frameworks such as TensorFlow and PyTorch to analyze trends in the aggregated data, for example, to model trends in sales increases and decreases or patterns of cost fluctuations. Based on the analysis results, a generative AI model generates business strategy proposals.

[1006] Terminal

[1007] The terminal is responsible for sending prompt sentences entered by the user to the server. For example, if the user enters "Sales are declining this month. What strategy should we take?", this prompt sentence is sent to the server. The terminal also displays strategy suggestions returned by the server to the user.

[1008] User

[1009] The user inputs a question about management indicators through the terminal. For example, "If sales are declining, what marketing strategy should we adopt?" The user checks the strategy proposals returned by the server and decides on a management strategy based on them.

[1010] Specific examples

[1011] Hardware and software used

[1012] Hardware: Server (equipped with high-performance CPU and GPU)

[1013] Software: Python, Pandas, TensorFlow, PyTorch, MySQL

[1014] Prompt Sentence Examples

[1015] "Sales are down this month, what strategy should we use?"

[1016] Specific actions

[1017] 1. The server collects the data

[1018] The server retrieves data from the sales management system and accounting system via API.

[1019] The acquired data is stored in a MySQL database.

[1020] 2. The server aggregates the data

[1021] The server uses Pandas to run queries like "SELECT FROM sales_data WHERE month = '2023-10'" to aggregate the data.

[1022] The aggregation results are stored in an in-memory DataFrame.

[1023] 3. The server analyzes the data

[1024] The server uses TensorFlow to run a model that predicts "sales trends."

[1025] Based on the analysis results, the generative AI model generates strategic proposals such as "implementing a new marketing campaign."

[1026] 4. The terminal sends a prompt

[1027] The terminal transmits the prompt sentence input by the user, "Sales are declining this month. What strategy should we adopt?" to the server.

[1028] Notify the user that the submission was successful.

[1029] 5. The device receives the strategy proposal.

[1030] The terminal receives the strategy proposal "to launch a new marketing campaign" sent back from the server.

[1031] Display received suggestions to the user.

[1032] 6. The user enters a prompt

[1033] The user inputs "If sales are declining, what marketing strategy should we adopt?" into an input field on the terminal.

[1034] Check the information you entered and press the send button.

[1035] 7. User confirms the strategy proposal

[1036] The user checks in detail the proposal "Launch a new marketing campaign" displayed on the terminal.

[1037] Based on the proposal, we will decide on a specific marketing strategy and put it into action.

[1038] In this way, the system helps users to quickly and effectively develop business strategies.

[1039] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1040] Step 1:

[1041] The server collects management indicator data. Specifically, it obtains data such as sales, costs, and profit margins from the sales management system and accounting system via API. The input is management indicator data from each department, and the output is data stored in the database. The server stores the data using a relational database management system such as MySQL or PostgreSQL.

[1042] Step 2:

[1043] The server aggregates data retrieved from the database using Python's Pandas library. For example, to calculate the total sales or costs by month. The input is the raw data retrieved from the database, and the output is the aggregated data. The server aggregates the data using a Pandas DataFrame and stores it in memory.

[1044] Step 3:

[1045] The server uses machine learning frameworks such as TensorFlow and PyTorch to analyze trends in the aggregated data. For example, it models trends in sales increases and decreases or patterns of cost fluctuations. The input is the aggregated data, and the output is the analysis results. The server runs the machine learning model and generates the analysis results.

[1046] Step 4:

[1047] The server uses the generative AI model to generate strategic proposals based on the analysis results. The input is the analysis results, and the output is strategic proposals. The server uses the generative AI model to generate specific strategic proposals, such as "implement a new marketing campaign."

[1048] Step 5:

[1049] The terminal sends the prompt text entered by the user to the server. For example, it sends a prompt text such as "Sales are declining this month. What strategy should we adopt?" The input is the prompt text entered by the user, and the output is a notification of successful transmission to the server. The terminal sends the prompt text to the server and notifies the user that the transmission was successful.

[1050] Step 6:

[1051] The server receives the prompt sentence sent by the user and generates strategy proposals based on it using a generative AI model. The input is the prompt sentence from the user and the output is the strategy proposal. The server generates an appropriate strategy proposal for the prompt sentence and sends it back to the terminal.

[1052] Step 7:

[1053] The terminal receives the strategy proposal returned from the server and displays it to the user. The input is the strategy proposal from the server, and the output is the display to the user. The terminal displays the received strategy proposal to the user so that the user can confirm it.

[1054] Step 8:

[1055] The user checks the strategic proposal displayed on the terminal and decides on a business strategy based on it. The input is the strategic proposal displayed on the terminal, and the output is the user's business decision. The user decides on a specific marketing strategy based on the proposal and puts it into action.

[1056] (Application example 2)

[1057] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1058] Conventional systems for compiling and analyzing management indicators do not adequately aggregate data or propose strategies in real time, leading to a demand for faster and more accurate management decisions. Furthermore, there is a lack of automated methods for analyzing trends in management indicators and proposing specific strategies, making it difficult for managers and marketing personnel to quickly implement appropriate strategies.

[1059] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1060] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, a means for analyzing trends in the management indicators using a generative AI model and generating prompt sentences to make strategic proposals, and a means for aggregating management indicators in real time and saving them in a database on the cloud. This enables real-time aggregation and analysis of management indicators, allowing for quick and specific strategic proposals to be made automatically.

[1061] "Management indicators" are numerical data used to evaluate a company's performance and business situation, and include sales, inventory, number of customers, repeat business rate, etc.

[1062] "Aggregation" means organizing multiple data sets and calculating statistical information such as totals and averages.

[1063] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators, propose management strategies, and provide business support.

[1064] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals, and includes marketing strategies and business expansion plans.

[1065] A "multifaceted perspective" means evaluating things from multiple different perspectives and making a comprehensive judgment.

[1066] A "perspective" is the position or viewpoint from which one observes and judges things.

[1067] "Effective business decisions" mean making appropriate and effective decisions to achieve a company's goals and solve problems.

[1068] "Corporate data" refers to various data related to the business and management of a company, including sales data, customer data, inventory data, and the like.

[1069] "Business expansion" refers to the activities and strategies used to increase a company's business scale and market share.

[1070] A "generative AI model" is a type of artificial intelligence that is used to generate natural language and analyze data for specific tasks.

[1071] A "prompt" is an instruction or question entered into a generative AI model to make it perform a specific task.

[1072] "Real-time" means that data and information are processed and reflected immediately the moment they are generated.

[1073] A "cloud database" is a database built on a remote server accessible via the Internet.

[1074] The system for implementing this invention is composed of three main elements: a server, a terminal, and a user. The specific functions and processing of each element will be explained below.

[1075] Server Functions and Processing

[1076] The server plays a central role in aggregating management indicators, analyzing them using a generative AI model, generating prompts, and making strategic proposals. The server uses the following hardware and software:

[1077] Hardware: A server machine with a powerful processor, sufficient memory, and storage.

[1078] Software: Database management systems, generative AI models (e.g., OpenAI's GPT-4), data analysis tools (e.g., Pandas).

[1079] The server first aggregates management indicator data stored in a cloud database in real time. Next, it uses a generative AI model based on the aggregated data to analyze trends in management indicators. Finally, it generates prompt statements based on the analysis results and makes specific strategic proposals.

[1080] Terminal functions and processing

[1081] The terminal is a device that allows the user to receive and display information provided by the server. The terminal uses the following hardware and software:

[1082] Hardware: Smartphones, tablets, computers, etc.

[1083] Software: Web browser, dedicated application.

[1084] The terminal receives the aggregated results of management indicators and strategic proposals sent from the server and visually displays them to the user, allowing the user to check management indicators in real time and make appropriate management decisions.

[1085] User operations

[1086] The user checks the information provided by the server through the terminal and executes business strategies as necessary. The user performs the following operations.

[1087] Checking management indicators

[1088] Acceptance or revision of strategic proposals

[1089] Entering new data

[1090] Examples of concrete examples and prompts

[1091] For example, if sales are down 10%, the server will generate the following prompt:

[1092] "Sales are down 10%. Please suggest a new marketing strategy."

[1093] Based on this prompt, the generative AI model suggests specific strategies such as "launch a new social media campaign."

[1094] In this way, the server, terminals, and users can work together to aggregate and analyze management indicators in real time, enabling quick and automatic specific strategic proposals to be made.

[1095] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1096] Step 1:

[1097] The server retrieves management indicator data from a database on the cloud.

[1098] Input: Management indicator data stored in a database on the cloud.

[1099] Output: Obtained management indicator data.

[1100] Specific operation: The server uses a database management system to query business indicator data such as sales, inventory, number of customers, and repeat customer rate, and stores the data in a data frame.

[1101] Step 2:

[1102] The server aggregates the acquired management index data in real time.

[1103] Input: Obtained management indicator data.

[1104] Output: Aggregated business indicator data.

[1105] Specific operation: The server uses data analysis tools to calculate totals and averages of sales, changes in inventory, increases or decreases in the number of customers, etc., and generates summary results.

[1106] Step 3:

[1107] The server analyzes trends using a generative AI model based on the aggregated management indicator data.

[1108] Input: Aggregated business indicator data.

[1109] Output: Trend analysis results of management indicators.

[1110] Specific operation: The server inputs aggregated data into the generative AI model and analyzes trends in sales increases and decreases, patterns of customer behavior, etc.

[1111] Step 4:

[1112] The server generates prompt sentences based on the trend analysis results and makes strategy suggestions.

[1113] Input: Trend analysis results of management indicators.

[1114] Output: Generated prompt statements and strategy suggestions.

[1115] Specific behavior: The server uses the generative AI model to generate prompt statements such as, "Sales have fallen by 10%. Please suggest a new marketing strategy." and create specific strategic proposals.

[1116] Step 5:

[1117] The server transmits the generated strategy proposal to the terminal.

[1118] Input: Generated prompt statements and strategy suggestions.

[1119] Output: Strategy proposal sent to the terminal.

[1120] Specific operation: The server sends a strategy proposal to the terminal via the network so that the user can confirm it.

[1121] Step 6:

[1122] The terminal displays the strategy proposal sent from the server to the user.

[1123] Input: Strategy proposal sent by the server.

[1124] Output: Strategy proposals displayed to the user.

[1125] Specific operation: The terminal uses a dedicated application or a web browser to visually display the strategy proposal so that the user can check it.

[1126] Step 7:

[1127] The user checks the strategy proposal through the terminal and implements the business strategy as necessary.

[1128] Input: Strategy proposal displayed on the terminal.

[1129] Output: Implemented business strategy.

[1130] Specific operations: The user checks the strategy proposals displayed on the terminal and executes business strategies such as launching new marketing campaigns as necessary.

[1131] Example 3

[1132] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1133] In conventional corporate data management systems, data collection, preprocessing, analysis, and display of results were all carried out separately, making integrated data management and analysis difficult. Furthermore, there was a lack of concrete methods for utilizing the results of data analysis in business strategies, making it difficult to make quick and effective management decisions. This could result in companies missing out on new business opportunities.

[1134] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1135] In this invention, the server includes a means for collecting and aggregating corporate data, a means for preprocessing the aggregated data, a means for analyzing the preprocessed data using a generative AI model, a means for displaying the analysis results to the user, and a means for using the results to expand the business. This enables centralized management of corporate data and advanced data analysis, leading to rapid and effective management decisions.

[1136] "Corporate data" refers to various data generated by a company in the course of its business, including customer purchase data, inventory data, sales data, and the like.

[1137] "Means of collection and aggregation" refers to the means of automatically acquiring various data within a company and storing it centrally in a database.

[1138] "Preprocessing means" refers to means for imputing missing values, normalizing data, and detecting and correcting outliers in order to convert the collected data into a format suitable for analysis.

[1139] A "generative AI model" is a model that uses artificial intelligence to analyze data and extract patterns and make predictions, and includes, for example, natural language processing models.

[1140] The "means of analysis" refers to the means of inputting preprocessed data into a generative AI model, analyzing the data, and outputting the results.

[1141] The "display means" refers to a means for visually presenting the analysis results to the user, and includes displays in the form of dashboards, graphs, and tables.

[1142] "Means to expand business" refers to the means of exploring new business opportunities based on the analysis results and planning and implementing specific strategies.

[1143] This invention is a system that centrally manages corporate data, analyzes the data using a generative AI model, and plans and executes business strategies based on the results. Specific embodiments of this system are described below.

[1144] Hardware and software used

[1145] Server: A central server for data aggregation and management, for example, using a virtual server from a cloud service provider.

[1146] Database: A database for storing and querying data, for example using a relational database management system.

[1147] Generative AI models: AI models for analyzing data and exploring business opportunities, for example, using natural language processing models.

[1148] Terminal: A device where the user enters data and checks the results, for example, a personal computer or tablet.

[1149] Program processing

[1150] Data collection and aggregation

[1151] The server collects various data within the company (customer purchase data, inventory data, sales data, etc.) and stores it in a database. Specifically, it periodically retrieves data from POS systems and CRM systems. For example, the server calls the POS system's API to retrieve the latest purchase data and stores it in a relational database.

[1152] Data Preprocessing

[1153] The server preprocesses the collected data and converts it into a format suitable for analysis. Specifically, it performs tasks such as filling in missing values, normalizing the data, and detecting and correcting outliers. For example, the server fills in missing values ​​in purchasing data with the average value and restores the data to the database.

[1154] Analyzing the data

[1155] The server then uses the generative AI model to analyze the preprocessed data. Specifically, it clusters customer purchasing patterns and obtains hints for new product development. For example, the server inputs the following prompt sentence into the natural language processing model:

[1156] Prompt: "Analyze the following customer purchase data and provide ideas for new product development. The data is in the format of customer ID, product ID, purchase date, purchase quantity, and purchase amount."

[1157] The generative AI model analyzes the input data and returns the results.

[1158] Displaying the results

[1159] The device displays the analysis results to the user, who can then use them to explore new business opportunities. For example, the device displays the analysis results received from the server on a dashboard. Specifically, it displays a list of "products that sell well in the summer" and the clustering results of customer purchasing patterns in graphs and tables.

[1160] In this way, it becomes possible to centrally manage corporate data and perform advanced data analysis, thereby enabling quick and effective management decisions. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1161] Step 1:

[1162] The server collects and aggregates various data within the company. Specifically, it periodically obtains customer purchasing data, inventory data, sales data, etc. from the POS system and CRM system. As input, it calls the API of the POS system or CRM system to obtain the latest data. As output, it saves the obtained data in a relational database. Specifically, the server calls the API, obtains data in JSON format, and inserts it into the database.

[1163] Step 2:

[1164] The server preprocesses the collected data. Specifically, it completes missing values, normalizes data, and detects and corrects outliers. As input, it receives raw data stored in a relational database. As output, it stores the preprocessed data back in the database. Specifically, the server completes items with missing values ​​with average values, detects data with abnormally high purchase quantities, and corrects them to within an appropriate range.

[1165] Step 3:

[1166] The server analyzes the preprocessed data using a generative AI model. Specifically, it clusters customer purchasing patterns and obtains hints for new product development. The preprocessed data is input into the generative AI model. The analysis results are obtained as output. Specifically, the server inputs the following prompt into the natural language processing model: "Please analyze the following customer purchasing data and provide hints for new product development. The data is in the format of customer ID, product ID, purchase date, purchase quantity, and purchase amount." The generative AI model performs analysis based on the input data and returns the results.

[1167] Step 4:

[1168] The terminal displays the analysis results to the user. As input, it receives the analysis results received from the server. As output, it presents the results visually to the user. Specifically, the terminal displays the analysis results received from the server on a dashboard. For example, it displays a list of "products that sell well in the summer" or the clustering results of customer purchasing patterns in graph or table format.

[1169] This enables centralized management of corporate data and advanced data analysis, enabling quick and effective management decisions.

[1170] (Application example 3)

[1171] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1172] Modern companies need to collect a wide variety of data and use it effectively to increase their competitiveness. However, there is a lack of concrete methods for centrally managing this data and exploring business opportunities. There is also a need for efficient methods for analyzing customer purchasing data and obtaining ideas for new product development. This makes it difficult for companies to make quick and accurate management decisions.

[1173] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1174] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple viewpoints, and a means for analyzing customer data and proposing new product development ideas using a generative AI model. This allows companies to centrally manage collected data and explore business opportunities. Furthermore, by obtaining new product development ideas based on customer purchasing data, companies can make quick and accurate management decisions.

[1175] "Management indicators" are numbers or indicators used to evaluate a company's management situation.

[1176] "Aggregation" means calculating multiple data together to find a total or average.

[1177] "Artificial Intelligence Secretary" is a system that uses artificial intelligence technology to support management decisions and strategic proposals.

[1178] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals.

[1179] A "multifaceted perspective" means analyzing and evaluating things from multiple different perspectives.

[1180] A "perspective" is the position or viewpoint from which one observes and judges things.

[1181] "Effective business judgment" means making effective decisions to achieve a company's goals.

[1182] "Corporate data" refers to various types of information collected and generated in the course of a company's business operations.

[1183] "Business expansion" means increasing a company's business scale and market share.

[1184] "Customer data" refers to information about customers, including purchase history, behavioral history, etc.

[1185] A "generative AI model" is a model that uses artificial intelligence technology to generate new information and ideas from data.

[1186] "Product development" is the process of planning, designing, and manufacturing new products.

[1187] An idea is a new concept or idea.

[1188] The system for carrying out this invention operates in cooperation with a server, a terminal, and a user. The server includes a means for aggregating corporate data and aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple viewpoints, and a means for analyzing customer data and proposing new product development ideas using a generative AI model.

[1189] Hardware and software used

[1190] Hardware: Servers, smartphones

[1191] Software: Python, Pandas, Scikit-learn, Matplotlib, Transformers library

[1192] Data processing and calculation flow

[1193] 1. Data loading: The server loads various data, including the company's customer data, in formats such as CSV files.

[1194] 2. Data preprocessing: Use the Pandas library to standardize data such as purchase amount, visit frequency, and number of items added to cart.

[1195] 3. Clustering: Using the KMeans algorithm from the Scikit-learn library, we cluster customers and visualize the characteristics of each cluster.

[1196] 4. Initialize the generative AI model: Initialize the generative AI model (e.g., GPT-3) using the Transformers library.

[1197] 5. Propose business opportunities: Generative AI models based on customer data suggest new product development ideas.

[1198] Specific examples

[1199] For example, by inputting the following prompt sentences into a generative AI model, ideas for new product development can be obtained.

[1200] Prompt Sentence Examples

[1201] "Please propose new product development ideas based on customer purchasing data. For example, products that match the latest trends or the season."

[1202] Given this prompt, a generative AI model might suggest ideas like:

[1203] "Based on current trends, we can develop new fashion items using eco-friendly materials. We can also offer seasonally appropriate products using cooling materials in the summer and warmer materials in the winter."

[1204] In this way, the server can centrally manage the collected data and explore business opportunities. It can also obtain ideas for new product development based on customer purchasing data, allowing for quick and accurate management decisions.

[1205] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1206] Step 1:

[1207] The server reads various data, including the company's customer data, in formats such as CSV files. The input data includes customer purchase history, visit frequency, and the number of items added to the cart. The output data is the read data frame. Specifically, the CSV file is converted into a data frame using the Pandas library.

[1208] Step 2:

[1209] The server preprocesses the loaded data. The input is the data frame obtained in step 1. The output is a standardized data frame. Specifically, it uses the Pandas library to handle missing values ​​and clean the data, and uses the StandardScaler library in the Scikit-learn library to standardize the data.

[1210] Step 3:

[1211] The server performs clustering using the standardized data. The input is the standardized data obtained in step 2. The output is a data frame containing the clustering results. Specifically, it clusters customers using the KMeans algorithm from the Scikit-learn library and adds the features of each cluster to the data frame.

[1212] Step 4:

[1213] The server visualizes the clustering results. The input is a data frame containing the clustering results obtained in step 3. The output is a graph showing the clustering results. Specifically, the clustering results are displayed as a scatter plot using the Matplotlib library.

[1214] Step 5:

[1215] The server initializes the generative AI model. The input is the configuration information for the generative AI model. The output is the initialized generative AI model. Specifically, the GPT-3 model is initialized using the Transformers library.

[1216] Step 6:

[1217] The server uses a generative AI model based on customer data to propose new product development ideas. The input is a data frame containing clustering results and a prompt. The output is a new product development idea proposed by the generative AI model. Specifically, the following prompt is input into the generative AI model and the generated text is obtained.

[1218] Prompt Sentence Examples

[1219] "Please propose new product development ideas based on customer purchasing data. For example, products that match the latest trends or the season."

[1220] Step 7:

[1221] The server creates a report to reflect the generated ideas in corporate strategy. The input is the ideas proposed by the generative AI model. The output is a report to reflect the ideas in corporate strategy. Specifically, the server automatically generates a report based on the generated ideas and provides it to the company's decision makers.

[1222] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1223] "Example 1"

[1224] In one embodiment of the present invention, the means for aggregating management indicators is to obtain a company's financial data, personnel data, sales data, etc. from a database and calculate performance indicators based on this data. Specifically, indicators such as sales, profit margins, and employee turnover rates are calculated.

[1225] "Example 2"

[1226] In addition, the AI ​​will use the collected management indicators as an AI secretary to make strategic proposals. The AI ​​will analyze trends in management indicators and propose management strategies based on the results. For example, if sales are declining, the AI ​​will propose a new marketing strategy.

[1227] "Example 3"

[1228] Furthermore, it combines an emotion engine that recognizes the user's emotions. This emotion engine automatically recognizes emotions from the user's voice, facial expressions, and text. For example, if a user says, "I'm frustrated that business performance didn't improve today," the emotion engine will recognize the user's emotion as "frustration." The AI ​​secretary uses this recognition result to propose strategies based on the user's emotions.

[1229] The processing flow of each embodiment will be described below.

[1230] "Example 1"

[1231] Step 1: Obtain the company's financial, personnel, sales, and other data from the database.

[1232] Step 2: Calculate performance indicators based on the acquired data, such as sales, profit margins, and employee turnover rates.

[1233] "Example 2"

[1234] Step 1: AI analyzes trends in management indicators.

[1235] Step 2: Propose a business strategy based on the analysis results. For example, if sales are declining, the AI ​​will propose a new marketing strategy.

[1236] "Example 3"

[1237] Step 1: Automatically recognize emotions from the user's voice, facial expressions, and text. For example, if a user says, "I'm frustrated that my business performance didn't improve today," the emotion engine will recognize the user's emotion as "frustration."

[1238] Step 2: The AI ​​secretary uses the recognition results to propose strategies based on the user's emotions.

[1239] Example 1

[1240] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1241] In conventional management indicator aggregation systems, data acquisition, preprocessing, and calculation of performance indicators are often done manually, resulting in inefficiency. Furthermore, because strategic proposals based on aggregated management indicators are not automated, it is difficult to make quick management decisions. Furthermore, it is difficult to gain a perspective based on multiple angles and to make effective management decisions. To solve these issues, a system is needed that automates everything from data acquisition to calculation of performance indicators and strategic proposals, allowing for efficient and quick management decisions.

[1242] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1243] In this invention, the server includes means for connecting to a database storing corporate data, means for acquiring financial data, personnel data, and sales data from the database, means for preprocessing the acquired data, means for calculating performance indicators based on the preprocessed data, means for outputting the calculated performance indicators, means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, means for obtaining perspectives based on multiple viewpoints, means for making effective management decisions, and means for aggregating corporate data and using the same to expand business. This automates the process from data acquisition to calculation of performance indicators and strategic proposals, enabling efficient and rapid management decisions.

[1244] "Corporate data" refers to information related to a company's business activities, such as financial data, personnel data, and sales data.

[1245] A "database" refers to an information management system that stores corporate data and allows it to be searched and retrieved as needed.

[1246] "Financial data" refers to information about a company's financial situation, such as revenues, expenses, and profits.

[1247] "Human Resources Data" refers to information related to human resources management, such as employee joining and leaving information, job titles, and salaries.

[1248] "Sales data" refers to information related to sales activities, such as sales, customer information, and transaction history.

[1249] "Preprocessing" refers to the data preparation work required before data analysis or calculations can be performed, such as processing missing values ​​in the acquired data and converting data types.

[1250] "Performance indicators" refer to indicators used to evaluate a company's performance, such as sales, profit margins, and employee turnover rates.

[1251] "Artificial intelligence secretary" refers to an artificial intelligence system that automatically makes strategic proposals based on aggregated management indicators.

[1252] A "multifaceted perspective" refers to a perspective that allows one to evaluate and analyze things from different viewpoints and standpoints.

[1253] "Perspective" refers to the position or viewpoint from which things are observed and evaluated.

[1254] "Management decisions" refer to making important decisions in a company's management activities.

[1255] "Business expansion" refers to activities aimed at expanding a company's business scale and market share.

[1256] MODE FOR CARRYING OUT THE INVENTION

[1257] The present invention relates to a system for efficiently aggregating management indicators of a company and automating strategic proposals. A specific embodiment of this system will be described below.

[1258] System Overview

[1259] The user connects to a database that stores corporate data and generates a program to retrieve the necessary data. This program runs on the server, retrieves the company's financial, personnel, and sales data from the database, performs preprocessing, and then calculates performance indicators. The calculated performance indicators are used as an AI secretary and are useful for corporate strategy.

[1260] Hardware and software used

[1261] The server uses database connectivity libraries such as MySQL Connector to connect to the database. The Python programming language and data processing libraries such as Pandas and NumPy are used to retrieve and preprocess the data. Python is also used to calculate performance indicators and output the results.

[1262] Data acquisition and preprocessing

[1263] The server retrieves financial, personnel, and sales data from the database. The retrieved data undergoes preprocessing, such as processing missing values ​​and converting data types, to maintain data integrity and enable accurate calculation of performance indicators.

[1264] Calculating performance indicators

[1265] Based on the pre-processed data, the server calculates performance indicators such as sales, profit margins, employee turnover, etc. These indicators are important data for evaluating the business status of a company.

[1266] Output of results and strategy proposals

[1267] The calculated performance indicators are output by the server. The output data is used as an AI secretary to propose corporate strategies, enabling quick and efficient management decisions.

[1268] Examples and prompts

[1269] As a concrete example, the following prompt sentence is input to the generative AI model:

[1270] Example prompt sentence:

[1271] "Create a program to calculate sales, profit margins, and employee turnover rates based on a company's financial, personnel, and sales data. Use MySQL as the database and Python as the programming language."

[1272] Based on this prompt, the generative AI model outputs the necessary program code, which the user can then run on the server to efficiently aggregate a company's management indicators and automate strategic proposals.

[1273] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1274] Step 1:

[1275] The server connects to the database that stores the company data. Specifically, it uses a database connection library such as MySQL Connector to connect to the database. The input required is the database hostname, username, password, and database name, and the output is a database connection object.

[1276] Step 2:

[1277] The server retrieves financial, HR, and sales data from the database by executing SQL queries to retrieve the required data. The inputs are a database connection object and an SQL query, and the output is the retrieved data.

[1278] Step 3:

[1279] The server preprocesses the acquired data. Specifically, it handles missing values ​​and converts data types. The acquired data is required as input, and the preprocessed data is obtained as output. Specific operations include removing missing values ​​and converting data types to the appropriate format.

[1280] Step 4:

[1281] The server calculates performance indicators based on the preprocessed data. Specifically, it calculates indicators such as sales, profit margins, and employee turnover rates. The preprocessed data is required as input, and the calculated performance indicators are obtained as output. Specific operations include calculating total sales, profit margins, and employee turnover rates.

[1282] Step 5:

[1283] The server outputs the calculated performance indicators. Specifically, it outputs them to the console or saves them to a file. The input requires the calculated performance indicators, and the output is the displayed performance indicators or a saved file. Specifically, it displays the performance indicators on the console or saves them to a text file.

[1284] Step 6:

[1285] The server uses the aggregated management indicators as an AI secretary to implement corporate strategy. Specifically, it uses a generative AI model to make strategic proposals. The input is the aggregated management indicators, and the output is a strategic proposal. Specifically, it inputs a prompt statement into the generative AI model to generate a strategic proposal.

[1286] (Application example 1)

[1287] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1288] In modern corporate management, it is important to efficiently aggregate a wide range of data, including financial, human resources, and sales data, and calculate and display performance indicators in real time. However, manually compiling and analyzing this data takes time and effort, which can lead to delays and errors in management decisions. Furthermore, there is no system in place that allows in-factory robots to collect this data and display performance indicators in real time, making it difficult to maximize factory operational efficiency.

[1289] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1290] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, and a means for obtaining perspectives based on multiple viewpoints. This makes it possible for the server to be installed in a robot in a factory to collect financial data, personnel data, and sales data in real time and calculate and display performance indicators.

[1291] "Management indicators" are numerical data used to evaluate a company's financial situation and performance.

[1292] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators and support corporate strategy proposals and decision-making.

[1293] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives.

[1294] "Effective business decisions" refer to making optimal decisions to achieve a company's goals and solve problems.

[1295] "Corporate data" refers to data that includes various information related to corporate operations, such as corporate financial data, personnel data, and sales data.

[1296] A "factory robot" is an automated mechanical device installed to perform work in a factory.

[1297] "Real-time collection" means that data is acquired immediately and processed without delay.

[1298] "Performance indicators" are specific numerical data used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[1299] To implement this invention, a system installed on a robot in a factory is required. This system has the function of collecting a company's financial data, personnel data, and sales data in real time and calculating and displaying performance indicators. A specific embodiment of this system is described below.

[1300] System Configuration

[1301] The server includes the following means:

[1302] 1. Means of aggregating management indicators: Obtain a company's financial data, personnel data, and sales data from a database and calculate performance indicators based on this data.

[1303] 2. How to use it as an AI secretary: Automatically make strategic proposals based on aggregated management indicators.

[1304] 3. A means of gaining perspectives from multiple angles: Analyzing data from different angles and perspectives to support management decisions.

[1305] Hardware and software used

[1306] Hardware: Factory robots

[1307] Software: Python, Pandas, NumPy

[1308] Data processing and calculation

[1309] The server processes the data as follows:

[1310] 1. Data Acquisition: Retrieving financial, HR, and sales data from databases using SQL queries and APIs.

[1311] 2. Data processing: Convert the acquired data into a Pandas data frame and perform any necessary preprocessing, such as imputing missing values ​​and normalizing the data.

[1312] 3. Performance indicator calculation: Calculate performance indicators such as sales, profit margin, turnover rate, etc. This involves numerical calculations using NumPy.

[1313] 4. Displaying results: The calculated performance indicators are displayed on the robot's display. Since real-time updates are required, technologies such as WebSocket are used.

[1314] Specific examples

[1315] For example, using data like this:

[1316] Sales: 100,000 yen, 150,000 yen, 200,000 yen

[1317] Profit: 20,000 yen, 30,000 yen, 40,000 yen

[1318] Number of employees: 50, 55, 60

[1319] Turnover rate: 5%, 4%, 3%

[1320] Number of new customers: 10, 15, 20

[1321] Number of repeat customers: 5, 10, 15

[1322] Prompt Sentence Examples

[1323] "Design a system to calculate and monitor performance indicators such as sales, profit margins, and employee turnover based on the factory's financial, personnel, and sales data in real time."

[1324] In this way, robots in factories will be able to calculate and display performance indicators in real time, which is expected to speed up and improve the accuracy of management decisions.

[1325] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1326] Step 1:

[1327] The server retrieves financial, HR, and sales data from the database. The input is the database connection information, and the output is the retrieved data. Specifically, the server retrieves the data using SQL queries or APIs. For example, it executes an SQL query such as "SELECT FROM financial_data;".

[1328] Step 2:

[1329] The server converts the acquired data into a Pandas data frame. The input is the acquired data, and the output is a Pandas data frame. Specifically, the data is converted into a data frame using the Pandas read_sql function. For example, execute "pd.read_sql(query, connection)".

[1330] Step 3:

[1331] The server performs preprocessing on the data frame. The input is a Pandas data frame, and the output is a preprocessed data frame. Specifically, it performs missing value imputation and data normalization. For example, it executes "df.fillna(0)" or "df = (df - df.mean()) / df.std()".

[1332] Step 4:

[1333] The server calculates performance indicators based on the preprocessed data. The input is the preprocessed data frame, and the output is the performance indicators. Specifically, it calculates sales, profit margin, turnover rate, etc. For example, it executes "total_sales = df['Sales'].sum()" or "profit_margin = df['Profit'].sum() / total_sales".

[1334] Step 5:

[1335] The server displays the calculated performance indicators on the robot's display. The input is the performance indicators, and the output is the information displayed on the display. Specifically, data is sent in real time using technologies such as WebSocket. For example, execute "ws.send(json.dumps(kpis))".

[1336] Step 6:

[1337] The server automatically makes strategic proposals based on the aggregated management indicators. The input is performance indicators, and the output is strategic proposals. Specifically, it uses a generative AI model to generate prompts and makes strategic proposals. For example, execute "strategy = ai_model.generate(prompt)".

[1338] Step 7:

[1339] The server displays the strategy proposal on the robot's display. The input is the strategy proposal, and the output is the information displayed on the display. Specifically, data is sent in real time using technologies such as WebSocket. For example, execute "ws.send(json.dumps(strategy))".

[1340] Example 2

[1341] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1342] In conventional systems for compiling management indicators and proposing strategies, the compilation and analysis of management indicators is often done manually, which takes time and effort. In addition, because strategic proposals are also dependent on human judgment, there is a problem that subjective elements are easily introduced, making it difficult to obtain objective and effective proposals. Furthermore, because the specific procedures and methods for implementing the proposed strategies are not clear, it can take a long time to put them into practice. There is a need for a system that can solve these problems and enable the proposal and implementation of efficient and effective management strategies.

[1343] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1344] In this invention, the server includes a means for aggregating management indicators, a means for making strategic proposals using a generative AI model based on the aggregated management indicators, and a means for presenting the generated strategic proposals to the user. This automates the aggregation and analysis of management indicators, and the use of the generative AI model enables objective and effective strategic proposals. Furthermore, presenting the proposals to the user allows the strategy to be quickly put into action.

[1345] "Management indicators" are numerical data used to evaluate a company's performance and financial situation, and include sales, profits, costs, etc.

[1346] "Aggregation" is the process of collecting and organizing multiple pieces of data to obtain statistical information.

[1347] A "generative AI model" is a model trained to perform a specific task using artificial intelligence techniques, and is used here to generate strategic recommendations.

[1348] A "strategic proposal" is a concrete action plan for improving a company's business strategy based on the results of an analysis of management indicators.

[1349] "Users" are entities that use this system to collect management indicators and receive strategic proposals, and refer to company managers and staff.

[1350] A "server" is a computer system that performs processes such as data aggregation, analysis, and running generative AI models.

[1351] A "prompt" is an instruction to be input into a generative AI model, and includes trends in business indicators and specific problems.

[1352] "Automation" refers to a system that performs processing autonomously without human intervention.

[1353] "Presentation" refers to the act of showing the generated strategic proposal to the user, and is done in the form of a screen display or report.

[1354] This invention is a system that aggregates management indicators and uses a generative AI model based on the aggregated management indicators to make strategic proposals. Specific embodiments of this system are described below.

[1355] First, the user inputs management indicator data into the terminal. Management indicator data is numerical data such as sales, profits, and costs, and is input as an Excel or CSV file. For example, sales data for the past three months can be input.

[1356] The device then sends the entered data to the server, which sends the data via an HTTP request and passes it to the server in JSON format.

[1357] The server reads the received data and performs aggregation and analysis using the Pandas library. Pandas is a Python data processing library that can efficiently aggregate data and perform statistical analysis. For example, it can calculate monthly sales trends and check whether sales are declining.

[1358] Next, the server generates a prompt based on the aggregated results. The prompt is an instruction to be input to the generative AI model, and includes trends in business indicators and specific problems. For example, the server generates the following prompt:

[1359] Current business indicator data shows that sales have dropped 10% over the past three months. This may be due to a decrease in marketing activities. Please suggest a new marketing strategy.

[1360] The server inputs the generated prompt sentence into a generative AI model (e.g., GPT-4) to generate a strategic proposal. The generative AI model proposes an appropriate business strategy based on the input prompt sentence. For example, it generates the following strategic proposal:

[1361] To improve sales, we suggest the following new marketing strategies:

[1362] 1. Strengthening social media advertising: Reaching target audiences, especially through social media.

[1363] 2. Run email marketing campaigns: Reach out to existing customers with special offers and new product information.

[1364] 3. Collaborative Campaigns: Partner with companies in related industries and conduct joint promotions.

[1365] The server sends the generated strategy proposal to the terminal, where the data is again sent via an HTTP request and displayed on the terminal.

[1366] Finally, the user can review the strategy suggestions displayed on the device and implement them as needed, for example, by contacting an advertising agency to implement the suggested social media advertising enhancements.

[1367] In this way, the system automates the aggregation and analysis of management indicators and uses generative AI models to provide objective and effective strategic proposals, enabling users to make quick management decisions and expand their business.

[1368] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1369] Step 1:

[1370] The user inputs management index data into the terminal.

[1371] Users input management indicator data such as sales, profits, and costs into the terminal using Excel or CSV files. For example, they input sales data for the past three months. The input data is temporarily saved in the terminal.

[1372] Step 2:

[1373] The terminal transmits the input data to the server.

[1374] By clicking the "Send" button, the terminal sends the entered management indicator data to the server. The data is passed to the server in JSON format via an HTTP request. The input is the management indicator data, and the output is the data sent to the server.

[1375] Step 3:

[1376] The server receives the data and aggregates and analyzes it using the Pandas library.

[1377] The server converts the received JSON data into a Pandas data frame and calculates monthly sales trends. For example, it checks whether sales are declining by 10% each month. The input is management indicator data in JSON format, and the output is the aggregated data and its analysis results.

[1378] Step 4:

[1379] The server generates a prompt based on the results of the calculation.

[1380] Based on the aggregated results, the server generates a prompt to be input to the generative AI model. For example, if sales have dropped by 10%, a prompt is created to ask for the cause and improvement measures. The input is the aggregated results, and the output is the generated prompt. Specific examples of prompts are as follows:

[1381] Current business indicator data shows that sales have dropped 10% over the past three months. This may be due to a decrease in marketing activities. Please suggest a new marketing strategy.

[1382] Step 5:

[1383] The server inputs a prompt sentence into the generative AI model (GPT-4) and generates a strategy proposal.

[1384] The server inputs the generated prompt into GPT-4 to generate a strategic proposal. The generative AI model proposes an appropriate business strategy based on the input prompt. The input is the prompt, and the output is the generated strategic proposal. Specific examples of strategic proposals are as follows:

[1385] To improve sales, we suggest the following new marketing strategies:

[1386] 1. Strengthening social media advertising: Reaching target audiences, especially through social media.

[1387] 2. Run email marketing campaigns: Reach out to existing customers with special offers and new product information.

[1388] 3. Collaborative Campaigns: Partner with companies in related industries and conduct joint promotions.

[1389] Step 6:

[1390] The server sends the generated proposal to the terminal.

[1391] The server sends the generated strategy proposal to the terminal in JSON format. The data is again sent via HTTP request. The input is the generated strategy proposal, and the output is the data sent to the terminal.

[1392] Step 7:

[1393] The terminal displays the suggestions to the user.

[1394] The terminal displays the received strategy proposal on the screen. The user can check the proposal content. The input is the strategy proposal received from the server, and the output is the display to the user.

[1395] Step 8:

[1396] The user reviews the proposal and implements the strategy as needed.

[1397] The user reviews the proposal and, for example, contacts an advertising agency to implement enhanced social media advertising. The input is the displayed strategy proposal, and the output is the specific action to be taken.

[1398] (Application example 2)

[1399] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1400] In modern corporate management, the aggregation and analysis of management indicators is extremely important, but it is difficult to do this efficiently and automatically generate strategic proposals based on the results. Furthermore, while a multifaceted perspective is required when making management decisions, there is a lack of systems that can achieve this. Furthermore, there is no means of utilizing generative AI models to make specific strategic proposals, making it difficult for managers to make quick and accurate decisions.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1402] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, a means for obtaining perspectives based on multifaceted viewpoints, a means for generating strategic proposals using a generative AI model, and a means for generating prompt sentences to make strategic proposals. This enables efficient aggregation and analysis of management indicators, and enables the automatic generation of specific and multifaceted strategic proposals based on the results.

[1403] "Management indicators" are numerical data used to evaluate a company's management situation, and include specific performance indicators such as sales, profit margins, and number of customers.

[1404] "Means of aggregation" refers to a method or device for collecting multiple data and summarizing them statistically.

[1405] "AI secretary" refers to a system or program that uses artificial intelligence to analyze management indicators and make strategic proposals.

[1406] "Corporate strategy" refers to the plans and policies a company adopts to achieve its long-term goals.

[1407] A "multifaceted perspective" refers to a method of evaluating and analyzing things from multiple different perspectives.

[1408] A "means of gaining perspective" refers to a method or device for understanding things from a particular point of view or position.

[1409] "Effective business judgment" refers to judgment that allows for making optimal decisions in the management of a company.

[1410] "Corporate data" refers to all information related to the operation of a company, including sales data, customer information, inventory data, and the like.

[1411] "Business expansion" refers to activities and strategies aimed at increasing a company's size and market share.

[1412] A "generative AI model" refers to a model that has been trained using artificial intelligence to perform a specific task.

[1413] A "prompt" is text that is used to input specific instructions or questions to a generative AI model.

[1414] The "means for generating strategic proposals" refers to a method or device for automatically proposing corporate strategies based on management indicators and other data.

[1415] The system for implementing this invention consists of three main elements: a server, a terminal, and a user. The following explains how each element functions.

[1416] server

[1417] The server plays a central role in aggregating and analyzing management indicators. Specifically, the following hardware and software are used:

[1418] Hardware: A server machine with a powerful processor, sufficient memory, and storage.

[1419] Software: Database management systems (e.g., MySQL), data analysis tools (e.g., Pandas, Scikit-learn), generative AI models (e.g., OpenAI GPT-3).

[1420] The server first stores the management indicator data provided by the company in a database. It then uses data analysis tools to aggregate this data and analyze trends. For example, it can predict future sales based on indicators such as sales data, advertising expenses, and number of customer visits.

[1421] Based on the prediction results, a generative AI model is used to generate strategy proposals. Specifically, a prompt sentence is generated and input into the generative AI model to obtain appropriate strategy proposals.

[1422] Terminal

[1423] A terminal is a device that allows a user to receive and display information provided by a server. Specifically, the terminal uses the following hardware and software:

[1424] Hardware: Smartphones, tablets, computers, etc.

[1425] Software: Web browser, dedicated application.

[1426] The terminal receives the strategic proposal sent from the server and displays it to the user, who can then make business decisions based on it.

[1427] User

[1428] Users are company executives and managers who make business decisions based on strategic proposals provided by the server. Users access the server through their terminals to input necessary data and check strategic proposals.

[1429] Specific examples

[1430] For example, if advertising expenditures for October were $5,000 and the number of customer visits was 2,000, the server would predict sales based on this data. If the prediction results indicate a decline in sales, the following prompt sentence would be input into the generative AI model:

[1431] Sales are declining. Please suggest a new marketing strategy.

[1432] Based on this prompt, the generative AI model will propose specific marketing strategies, which will be displayed on the device, allowing users to make business decisions based on them.

[1433] In this way, the collaboration between the server, terminal, and user makes it possible to propose and implement efficient and effective management strategies.

[1434] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1435] Step 1:

[1436] The server receives management indicator data provided by the company and stores it in a database. Input data includes sales data, advertising expenses, number of customer visits, etc., and stores this in a database management system (e.g., MySQL). The output is management indicator data stored in the database.

[1437] Step 2:

[1438] The server aggregates the management indicator data stored in the database using data analysis tools (e.g., Pandas, Scikit-learn) and analyzes trends. The input is the management indicator data obtained from the database, and the output is the aggregated data and the results of its trend analysis. Specific operations include data cleaning, aggregation, and regression analysis.

[1439] Step 3:

[1440] The server predicts future sales based on the trend analysis results. The input is the trend analysis results, and the output is predicted sales data. Specifically, it predicts future sales using a regression model.

[1441] Step 4:

[1442] The server generates strategic proposals using a generative AI model (e.g., OpenAI GPT-3) based on the prediction results. The input is the predicted sales data, and the output is the generated strategic proposals. Specifically, it generates a prompt sentence and inputs it into the generative AI model.

[1443] Step 5:

[1444] The server transmits the generated strategic proposal to the terminal. The input is the generated strategic proposal, and the output is the strategic proposal transmitted to the terminal. Specifically, the server transmits the strategic proposal to the terminal via the network.

[1445] Step 6:

[1446] The terminal receives the strategic proposal sent from the server and displays it to the user. The input is the strategic proposal sent from the server, and the output is the strategic proposal displayed to the user. Specifically, the terminal displays the strategic proposal using a dedicated application or a web browser.

[1447] Step 7:

[1448] The user makes a business decision based on the strategic proposal displayed on the terminal. The input is the strategic proposal displayed on the terminal, and the output is the user's business decision. Specifically, the user checks the strategic proposal and makes the necessary business decision.

[1449] Example 3

[1450] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1451] Conventional corporate strategy systems had the problem that it took a long time to compile and analyze management indicators, making it difficult to make quick management decisions. In addition, because strategy proposals did not take into account the user's emotions, it was not possible to provide the optimal strategy for each individual situation. Furthermore, data management was insufficient, which meant that new opportunities for business expansion could be missed.

[1452] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1453] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple viewpoints, a means for making effective management decisions, a means for aggregating corporate data and using it to expand business, a means for recognizing user emotions, a means for proposing strategies based on the recognized emotions, and a means for generating strategies using a generative AI model. This enables quick and accurate management decisions and optimal strategic proposals that take user emotions into consideration. Furthermore, centralized data management makes it possible to capture new business opportunities without missing them.

[1454] "Management indicators" are numerical data used to evaluate the management status of a company, and include sales, profit margins, inventory turnover, and the like.

[1455] "Artificial Intelligence Secretary" is a system that uses artificial intelligence technology to analyze management indicators and propose and support corporate strategies.

[1456] A "multifaceted perspective" is a method of evaluating and analyzing things from multiple different perspectives, and is used to improve the accuracy of management decisions.

[1457] "Effective business decisions" mean making optimal decisions to achieve a company's goals and solve problems.

[1458] "Corporate data" refers to data that companies generate and collect on a daily basis, including customer information, sales data, inventory data, etc.

[1459] "User emotions" refer to the feelings or emotions that a user has about a particular situation or piece of information, and include joy, sadness, regret, and the like.

[1460] "Emotion recognition" is a technology that analyzes a user's emotions from voice, facial expressions, text, etc., and identifies specific emotions.

[1461] "Strategic proposal" means proposing a specific action plan to achieve a company's goals or solve problems based on the analysis results and perceived emotions.

[1462] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate new information and strategies.

[1463] The present invention relates to a system that aggregates corporate data, recognizes user emotions, and proposes business strategies. Specific embodiments of this system will be described below.

[1464] The server collects various data from within the company and stores it in a MySQL database. Data is collected by retrieving it from each department's system via API. The collected data includes customer purchasing data, inventory data, sales data, etc.

[1465] The server then analyzes the data using Python's Pandas library. The purpose of the analysis is to explore new business opportunities. For example, analyzing customer purchasing data can extract purchasing patterns and trends. This can then be used to generate ideas for new product development and marketing strategies.

[1466] Additionally, the device captures the user's voice, facial expressions, and text using a microphone, camera, and text input field. The captured data is sent to a server and analyzed using an emotion recognition engine such as the Microsoft Azure Emotion API. The emotion recognition engine classifies the user's emotions into categories such as "happiness," "sadness," and "regret."

[1467] The server generates a strategy based on the user's emotions based on the results of the emotion recognition engine. This strategy is created using a generative AI model, which analyzes data and generates new information and strategies.

[1468] Users can access the system through a terminal and input prompt sentences such as the following into the generative AI model:

[1469] Examples:

[1470] The user inputs, "I would like you to analyze recent sales data and suggest ideas for new product development."

[1471] The server retrieves recent sales data from a MySQL database and analyzes the data using the Pandas library, generating new product development ideas and presenting them to the user.

[1472] Example prompt sentence:

[1473] "Analyze recent customer purchase data and propose new marketing strategies."

[1474] "Recognize user emotions and suggest business strategies based on those emotions."

[1475] In this way, a system is realized in which the server, terminals, and users work together to aggregate and analyze corporate data and recognize users' emotions, thereby proposing strategies that lead to business expansion. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1476] Step 1:

[1477] The server collects various data from within the company. Specifically, it obtains data from each department's system via API. Input includes customer purchase data, inventory data, sales data, etc. This data is stored in a MySQL database. The output is company data that is centrally managed in the database.

[1478] Step 2:

[1479] The server retrieves the required data from the MySQL database using a query. The input includes specific query conditions. The server analyzes the data using Python's Pandas library. Specifically, it creates a data frame, cleans and aggregates the data, and outputs the analysis results. For example, customer purchasing patterns and trends are extracted.

[1480] Step 3:

[1481] The device captures the user's voice, facial expressions, and text. The input includes the user's speech and facial expressions. The device transmits these data to the server. The output is the captured data transmitted to the server.

[1482] Step 4:

[1483] The server uses an emotion recognition engine such as the Microsoft Azure Emotion API to recognize the user's emotions from the captured data. Input includes voice, facial expressions, and text data. Specifically, the emotion recognition engine analyzes the data and classifies the user's emotions into categories such as "joy," "sadness," and "regret." The output is the recognized emotion data.

[1484] Step 5:

[1485] The server generates a strategy using a generative AI model based on the results of the emotion recognition engine. The input includes the recognized emotion data and the analysis results of company data. Specifically, the server inputs a prompt statement into the generative AI model to generate a strategy. The output is a strategy proposal based on the user's emotion.

[1486] Step 6:

[1487] The user checks the proposed strategy through the terminal. The input includes the strategy proposal sent from the server. The user decides whether to execute the proposed strategy. The output is the user's decision.

[1488] In this way, a system is realized in which servers, terminals, and users work together to aggregate and analyze corporate data and recognize user emotions, proposing strategies that will lead to business expansion.

[1489] (Application example 3)

[1490] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1491] While conventional management support systems can aggregate and analyze corporate data, they lack the ability to recognize customer emotions and propose appropriate responses based on those emotions. This makes it difficult to improve customer experience or respond quickly, which can result in missed business opportunities. Furthermore, while utilizing emotional data would enable more accurate proposals when compiling management indicators or proposing strategies, current systems lack such functionality.

[1492] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1493] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple perspectives, a means for making effective management decisions, a means for aggregating corporate data and using it to expand business, a means for aggregating and analyzing customer purchase history and behavioral data, a means for recognizing emotions from customer voice, facial expressions, and text, and a means for proposing appropriate responses based on the recognized emotions. This makes it possible to improve the customer experience by recognizing customer emotions in real time and proposing appropriate responses. Furthermore, utilizing emotion data makes it possible to propose more accurate management strategies, preventing missed business opportunities.

[1494] "Management indicators" are numbers or indicators used to evaluate a company's management situation.

[1495] "Aggregation" means collecting and organizing data to calculate statistical values ​​such as totals and averages.

[1496] "AI Secretary" is a system that uses artificial intelligence to provide management support and strategic proposals.

[1497] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals.

[1498] A "multifaceted perspective" means analyzing and evaluating things from multiple different perspectives.

[1499] A "perspective" is the position or viewpoint from which one observes and judges things.

[1500] "Business judgment" refers to making important decisions regarding the management of a company.

[1501] "Corporate data" refers to various data related to the business and management of a company.

[1502] "Business expansion" means increasing a company's business scale and market share.

[1503] "Purchase history" is a record of products that a customer has purchased in the past.

[1504] "Behavioral data" is data about customer behavior.

[1505] "Analysis" means examining data in detail to find meaning and trends.

[1506] "Speech" is a means of communication using the human voice.

[1507] "Facial expression" refers to emotions and intentions expressed through the movement of facial muscles.

[1508] "Text" is information written in text.

[1509] "Emotion recognition" is a technology that identifies human emotions from voice, facial expressions, and text.

[1510] A "solution" is a means or method for dealing with a particular situation or problem.

[1511] The system for implementing this invention uses the following hardware and software: a server, a smartphone, and smart glasses. The software uses Python, TensorFlow, OpenCV, and the Google Cloud Natural Language API.

[1512] System configuration

[1513] 1. Server:

[1514] The server aggregates corporate data and compiles management indicators.

[1515] The server acts as an AI secretary and makes strategic proposals based on the collected management indicators.

[1516] The server aggregates and analyzes customer purchasing history and behavioral data.

[1517] 2. Smartphones and smart glasses:

[1518] Smartphones and smart glasses capture customers' voices and facial expressions.

[1519] The smartphone and smart glasses transmit the captured data to a server.

[1520] Data processing and calculation

[1521] 1. Data Collection:

[1522] The server collects management indicators and customer data from the company database (MySQL).

[1523] The smartphones and smart glasses capture the customer's voice and facial expressions and transmit them to a server in real time.

[1524] 2. Emotion recognition:

[1525] The server uses OpenCV to analyze facial expression data and TensorFlow to classify emotions.

[1526] The server uses the Google Cloud Natural Language API to recognize emotions from speech and text.

[1527] 3. Strategic proposal:

[1528] Based on the recognized emotion data, the server uses a generative AI model (GPT-3) to suggest an appropriate response.

[1529] Specific examples

[1530] For example, imagine a customer wearing smart glasses walking around a store looking for a product. If the customer says, "I'm having trouble finding this product," the microphone in the smart glasses will capture the voice and the emotion engine will recognize the "confused" feeling. The smart glasses' display will ask, "Would you like to contact a staff member?" If the customer answers "yes," a notification will be sent to the staff member.

[1531] Prompt Sentence Examples

[1532] If a customer says, "I'm having trouble finding this product," the emotion engine recognizes this and notifies staff to act quickly.

[1533] In this way, the system can recognize customer emotions in real time and suggest appropriate responses, improving the customer experience. Utilizing emotion data also makes it possible to propose more accurate business strategies, helping to prevent missed business opportunities.

[1534] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1535] Step 1:

[1536] Data collection

[1537] The server collects management indicators and customer data from the company database (MySQL). Specifically, the server executes SQL queries to retrieve purchase history and behavioral data. The input is the database connection information and query, and the output is the retrieved data.

[1538] Step 2:

[1539] Voice and facial expression capture

[1540] The devices (smartphones and smart glasses) capture the customer's voice and facial expressions. Specifically, the device's microphone and camera are used to capture audio and video in real time. The input is the customer's voice and facial expressions, and the output is the captured audio and video data.

[1541] Step 3:

[1542] Data transmission

[1543] The device sends the captured audio and video data to the server. Specifically, the device uploads the data to the server via the Internet. The input is the captured audio and video data, and the output is the data sent to the server.

[1544] Step 4:

[1545] emotion recognition

[1546] The server analyzes video data using OpenCV and classifies emotions using TensorFlow. It also recognizes emotions from audio data using the Google Cloud Natural Language API. The input is audio and video data, and the output is the recognized emotion data.

[1547] Step 5:

[1548] Strategic proposals

[1549] The server uses a generative AI model (GPT-3) to propose an appropriate response based on the recognized emotion data. Specifically, the emotion data is input as a prompt into the generative AI model, which then generates a response. The input is the recognized emotion data, and the output is a proposed response.

[1550] Step 6:

[1551] notification

[1552] The server notifies the terminal of the proposed action. Specifically, the server generates a notification message and sends it to the terminal. The input is the proposed action, and the output is the notification message displayed on the terminal.

[1553] Step 7:

[1554] Response execution

[1555] The user (store staff) takes appropriate action based on the notification message displayed on the terminal. Specifically, the user approaches the customer and responds quickly to confused customers. The input is the notification message, and the output is improved customer satisfaction.

[1556] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1557] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1558] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1559] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1560] [Third embodiment]

[1561] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1562] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1563] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1564] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1565] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1566] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1567] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1568] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1569] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1570] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1571] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1572] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1573] "Example 1"

[1574] In one embodiment of the present invention, the means for aggregating management indicators acquires financial data, personnel data, sales data, etc. of a company from a database and calculates performance indicators based on this data. For example, indicators such as sales, profit margin, and employee turnover rate can be considered.

[1575] "Example 2"

[1576] Another way to use AI as a secretary is to have AI make strategic proposals based on the collected management indicators. Specifically, AI analyzes the trends of management indicators and proposes management strategies based on the results. For example, if sales are declining, AI can propose a new marketing strategy.

[1577] "Example 3"

[1578] Furthermore, one way to aggregate corporate data and use it to expand business is to centrally manage various types of data within a company and use that data to explore new business opportunities. For example, analyzing customer purchasing data can provide hints for new product development.

[1579] The processing flow of each embodiment will be described below.

[1580] "Example 1"

[1581] Step 1: Obtain the company's financial, personnel, sales, and other data from the database.

[1582] Step 2: Calculate performance indicators based on the acquired data, such as sales, profit margins, and employee turnover rates.

[1583] "Example 2"

[1584] Step 1: AI makes strategic proposals based on the collected management indicators.

[1585] Step 2: The AI ​​analyzes trends in management indicators and proposes management strategies based on the results. For example, if sales are declining, the AI ​​will propose a new marketing strategy.

[1586] "Example 3"

[1587] Step 1: Centrally manage various data within the company.

[1588] Step 2: Explore new business opportunities based on centrally managed data. For example, analyze customer purchasing data to gain insights into new product development.

[1589] Example 1

[1590] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1591] In conventional management indicator aggregation systems, the entire process from acquiring corporate data to cleaning it, calculating performance indicators, and generating and providing reports is often done manually, resulting in inefficiency. Furthermore, strategic proposals based on the aggregated management indicators are not automated, making it difficult to make quick and accurate management decisions. Furthermore, there is insufficient data aggregation to gain perspectives based on multiple angles and to lead to business expansion.

[1592] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1593] In this invention, the server includes means for acquiring corporate data, means for cleaning the acquired corporate data, means for calculating performance indicators based on the cleaned data, means for generating reports from the calculated performance indicators, means for providing the generated reports to users, means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, means for gaining perspectives based on multiple perspectives, means for making effective management decisions, and means for aggregating corporate data and using the resulting data to expand business. This automates the process from acquiring corporate data to providing reports, enabling quick and accurate management decisions. It also enables efficient data aggregation to gain perspectives based on multiple perspectives and expand business.

[1594] "Corporate data" is a general term for various data related to the operation of a company, such as financial data, personnel data, and sales data.

[1595] "Cleaning" is the process of removing missing values ​​and outliers from the acquired data and normalizing the data.

[1596] "Performance indicators" are indicators used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[1597] A "report" is a document or graph that visually represents calculated performance indicators.

[1598] "User" refers to a person such as a company manager or administrator who uses the system.

[1599] "Management indicators" are indicators used to evaluate a company's business situation, and refer to a wide range of indicators including performance indicators.

[1600] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators and make strategic proposals.

[1601] A "multifaceted perspective" is a perspective that analyzes corporate data from multiple different perspectives and allows for comprehensive judgment.

[1602] A "perspective" refers to the viewpoint or position that forms the basis for making management decisions.

[1603] "Effective business decisions" refer to appropriate decisions that contribute to corporate growth and increased profits.

[1604] "Data aggregation" is the process of centrally collecting and integrating distributed data.

[1605] "Business expansion" refers to activities aimed at increasing a company's market share or sales.

[1606] MODE FOR CARRYING OUT THE INVENTION

[1607] The present invention is a system for acquiring corporate data, cleaning it, calculating performance indicators, generating reports, and providing them to users. A specific embodiment of this system will be described below.

[1608] Data Acquisition

[1609] Subject: Server

[1610] The server retrieves the company's financial data, personnel data, sales data, etc. from a database. Specifically, it executes SQL queries using a database management system (DBMS) to extract the required data. The database server used could be MySQL or PostgreSQL.

[1611] Data Cleaning

[1612] Subject: Server

[1613] The server cleans the acquired data by using the Python Pandas library to impute missing values, remove outliers, and normalize the data, thereby improving the quality of the data.

[1614] Calculation of performance indicators

[1615] Subject: Server

[1616] The server calculates performance indicators based on the cleaned data, such as sales, profit margins, and employee turnover rates. It uses Python and R to analyze the data and calculate the necessary indicators.

[1617] Generate reports

[1618] Subject: Server

[1619] The server generates reports based on the calculated performance indicators. Specifically, it uses BI (Business Intelligence) tools to create graphs and tables, making it possible to provide reports that are visually easy to understand.

[1620] Providing reports

[1621] Subject: Terminal

[1622] The terminal provides the generated report to the user. Specifically, the report is displayed through a web browser. The report is provided to the user using a web framework such as Flask.

[1623] Specific examples

[1624] Example 1: Calculating sales

[1625] The server retrieves sales data from the database for the past year. Next, it cleans the data using Python and aggregates monthly sales figures. Finally, it uses a BI tool to graph sales trends and output them as a report.

[1626] Example 2: Calculating employee turnover

[1627] The server retrieves employee data from the database for the past year. Next, it uses R to filter the data and calculate the number of employees who left and stayed each month. Finally, it uses a BI tool to graph the turnover rate over time and output it as a report.

[1628] Example prompts for generative AI models

[1629] "Write a program that uses a company's financial, HR, and sales data to calculate performance indicators such as sales, profit margins, and employee turnover. Retrieve the data from a database, clean and analyze it using Python or R, generate reports using a BI tool, and display the reports in a web browser using Flask."

[1630] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1631] Step 1:

[1632] Retrieving data from a database

[1633] Subject: Server

[1634] The server retrieves a company's financial data, personnel data, sales data, etc. from a database. Specifically, it extracts the required data using an SQL query. The inputs are database connection information and an SQL query, and the output is the retrieved data. For example, to retrieve sales data, the following SQL query is executed:

[1635] sql

[1636] SELECT FROM sales_data WHERE date BETWEEN '2022-01-01' AND '2022-12-31';

[1637] This query retrieves sales data for the specified time period.

[1638] Step 2:

[1639] Clean the data

[1640] Subject: Server

[1641] The server cleans the acquired data. Specifically, it completes missing values, removes outliers, and normalizes the data. The acquired data is required as input, and the cleaned data is obtained as output. The data is cleaned using the Python Pandas library as follows:

[1642] python

[1643] import pandas as pd

[1644] Loading data

[1645] data = pd.read_sql(query, connection)

[1646] Missing value imputation

[1647] data.fillna(0, inplace=True)

[1648] Outlier removal

[1649] data = data[data['sales'] >= 0]

[1650] This process improves the quality of the data.

[1651] Step 3:

[1652] Calculate performance indicators

[1653] Subject: Server

[1654] The server calculates performance indicators based on the cleaned data. The cleaned data is required as input, and the calculated performance indicators are obtained as output. For example, sales, profit margin, employee turnover rate, etc. are calculated. Below is an example of calculating sales.

[1655] python

[1656] Aggregate monthly sales

[1657] monthly_sales = data.groupby('month')['sales'].sum()

[1658] This process calculates the monthly sales amount.

[1659] Step 4:

[1660] Generate a report

[1661] Subject: Server

[1662] The server generates a report based on the calculated performance indicators. The calculated performance indicators are required as input, and the generated report is obtained as output. Specifically, graphs and tables are created using BI (Business Intelligence) tools. Below is an example of graphing sales trends using Python's Matplotlib.

[1663] python

[1664] import matplotlib.pyplot as plt

[1665] Creating a graph

[1666] plt.plot(monthly_sales.index, monthly_sales.values)

[1667] plt.title('Monthly Sales')

[1668] plt.xlabel('Month')

[1669] plt.ylabel('Sales')

[1670] plt.savefig('monthly_sales_report.png')

[1671] This process generates a report that visually depicts sales trends.

[1672] Step 5:

[1673] Providing reports to users

[1674] Subject: Terminal

[1675] The terminal provides the generated report to the user. The input is the generated report, and the output is a state where the user can view the report. Specifically, the report is displayed through a web browser. Below is an example of displaying a report using Flask.

[1676] python

[1677] from flask import Flask, send_file

[1678] app = Flask(__name__)

[1679] @app.route(' / report')

[1680] def report():

[1681] return send_file('monthly_sales_report.png', mimetype='image / png')

[1682] if __name__ == '__main__':

[1683] app.run(debug=True)

[1684] This process allows users to view the report through a web browser.

[1685] (Application example 1)

[1686] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1687] Conventional management indicator aggregation systems took time to acquire and aggregate data, making it difficult to monitor performance in real time. Furthermore, there was a lack of feedback for managers and administrators to make quick management decisions, hindering efficient operations within the factory. Furthermore, strategic proposals based on the aggregated data were not automated, making it time-consuming and labor-intensive to formulate management strategies.

[1688] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1689] In this invention, the server includes means for aggregating management indicators, means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, means for obtaining perspectives based on multiple viewpoints, means for making effective management decisions, means for aggregating corporate data and using it to expand business, means that is installed on robots in the factory and acquires financial data, personnel data, and sales data in real time and calculates and displays performance indicators, and means for providing feedback to managers and administrators based on the calculated performance indicators. This enables real-time performance monitoring, resulting in quick management decisions and efficient factory operations.

[1690] "Management indicators" are indicators used to evaluate a company's financial situation and performance, and include sales, profit margins, employee turnover rates, etc.

[1691] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators, propose corporate strategies, and support management decisions.

[1692] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives, and is used to gain a more comprehensive understanding.

[1693] "Effective business decisions" refer to making optimal decisions to achieve a company's goals and solve problems.

[1694] "Corporate data" refers to various information related to corporate operations, such as a company's financial data, personnel data, and sales data.

[1695] "Business expansion" refers to activities and strategies aimed at increasing a company's market share or sales.

[1696] "Factory robots" refer to mechanical devices that perform automated tasks on factory production lines and work processes.

[1697] "Real-time" means that data is acquired and processed immediately, and the results are reflected without delay.

[1698] "Financial data" refers to data that shows a company's financial status, such as revenue, expenses, and profits.

[1699] "Human resources data" refers to data related to a company's personnel, such as the number of employees, turnover rate, and working hours.

[1700] "Sales data" refers to data related to a company's sales activities, such as sales performance, customer information, and return rates.

[1701] "Performance indicators" are specific numbers or indicators used to evaluate a company's performance, and include sales, profit margins, employee turnover rates, etc.

[1702] "Feedback" refers to information and advice provided to managers and administrators based on performance indicators and business decisions.

[1703] As an embodiment of the present invention, a system installed on a robot in a factory will be described as an example. This system has the function of aggregating management indicators and calculating and displaying performance indicators in real time. It also provides feedback to managers and administrators based on the calculated performance indicators.

[1704] 1. System Program

[1705] The system includes a program with the following main functions:

[1706] Ability to retrieve financial, personnel, and sales data from databases

[1707] Function to calculate performance indicators based on acquired data

[1708] Ability to display calculated performance indicators in real time

[1709] Ability to provide feedback to management and administrators

[1710] 2. Program processing explanation

[1711] Hardware used

[1712] Factory Robots

[1713] Software used

[1714] Python 3.x

[1715] Pandas library

[1716] NumPy library

[1717] Data processing and calculation

[1718] The server first retrieves financial, human resources, and sales data from the database. To do this, it uses SQL queries to extract the necessary information from the database. Next, it calculates performance indicators based on the retrieved data. Specifically, it calculates indicators such as sales revenue, profit margin, employee turnover rate, number of units sold, and return rate. These indicators are processed as data frames or arrays using Pandas or NumPy.

[1719] The calculated performance indicators are displayed in real time on the factory robot's display, allowing managers and executives to instantly understand the factory's performance. Feedback is also provided to managers and executives based on the calculated performance indicators. The feedback is automatically generated using a generative AI model and includes suggestions for management strategies and suggestions for improvement.

[1720] 3. Examples and prompts

[1721] For example, if a factory manager wants to use the "Factory Performance Monitoring Robot" to check monthly performance indicators, the results shown below will be displayed.

[1722] Performance indicators:

[1723] average_revenue: 150000.0

[1724] average_profit_margin: 0.2

[1725] average_turnover_rate: 0.04

[1726] average_units_sold: 1500.0

[1727] average_return_rate: 0.015

[1728] An example of a prompt sentence to input to the generative AI model is as follows:

[1729] Use the "Factory Performance Monitoring Robot" to calculate performance indicators based on the latest financial, personnel, and sales data, such as average sales, average profit margin, average turnover rate, average number of units sold, and average return rate, and display the results.

[1730] In this way, factory robots can monitor performance indicators in real time and provide feedback to managers and administrators, enabling faster business decisions and more efficient factory operations.

[1731] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1732] Step 1:

[1733] The server retrieves financial data, personnel data, and sales data from the database.

[1734] Input: Database connection information, SQL query

[1735] Output: Data frames of financial data, human resources data, and sales data

[1736] What happens: The server connects to the database and executes SQL queries to extract the required data, which is then stored as a Pandas dataframe.

[1737] Step 2:

[1738] The server calculates performance indicators based on the acquired data.

[1739] Input: Data frames of financial data, human resources data, and sales data

[1740] Output: Performance indicators (average sales, average profit margin, average turnover rate, average units sold, average return rate)

[1741] What happens: The server uses Pandas or NumPy to calculate each item in the data frame. For example, to calculate the average sales value, it averages the sales column in the financial data. Other metrics are calculated similarly.

[1742] Step 3:

[1743] The server displays the calculated performance indicators in real time.

[1744] Input: Performance Indicators

[1745] Output: Performance indicators displayed on the screen

[1746] How it works: The server sends the calculated performance indicators to the factory robot's display and displays them in real time, allowing managers and executives to instantly check performance.

[1747] Step 4:

[1748] The server provides feedback to managers and administrators based on the calculated performance indicators.

[1749] Input: Performance Indicators

[1750] Output: Feedback message

[1751] Specific operation: The server analyzes performance indicators using the generative AI model and generates feedback messages including business strategy proposals and suggestions for improvement. The generated feedback messages are sent to managers and administrators.

[1752] Step 5:

[1753] The user inputs a prompt statement into the generative AI model and requests it to calculate performance indicators.

[1754] Input: prompt statement

[1755] Output: Calculated performance indicators

[1756] Specific operation: The user inputs a prompt statement to the generative AI model. For example, "Please use the 'Factory Performance Monitoring Robot' to calculate performance indicators based on the latest financial, personnel, and sales data. Specifically, please calculate average sales, average profit margin, average turnover rate, average number of units sold, and average return rate, and display the results." The server calculates the performance indicators based on this prompt statement and displays the results.

[1757] Example 2

[1758] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1759] In modern corporate management, making fast and accurate business decisions requires efficiently compiling and analyzing large amounts of management indicator data and making strategic proposals based on that data. However, with conventional systems, data compilation and analysis took time, and the quality of strategic proposals often lacked consistency. Furthermore, there was a lack of a way for users to input specific questions and receive appropriate strategic proposals. This often led to delayed business decisions and the adoption of incorrect strategies, which reduced a company's competitiveness.

[1760] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1761] In this invention, the server includes a means for aggregating management indicators, a means for making a strategic proposal using a generative AI model based on the aggregated management indicators, a means for sending a prompt sentence input by a user to the server, and a means for displaying the strategic proposal returned from the server to the user, thereby enabling quick and accurate management decisions.

[1762] "Management indicators" are numerical data used to evaluate a company's performance and business situation, and include sales, costs, profit margins, etc.

[1763] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning to analyze data and make predictions and suggestions based on specific purposes.

[1764] A "prompt sentence" is a question or instruction sentence that a user inputs to a system, and is input data that the system uses to generate responses or suggestions based on.

[1765] A "server" is a computer system that processes and stores data on a network, and provides services in response to requests from clients.

[1766] "User" refers to an individual or organization that uses the system to receive analysis of management indicators and strategic proposals.

[1767] "Aggregation" is the process of organizing multiple data sets and calculating statistical values ​​such as totals and averages.

[1768] A "strategic proposal" is a proposal that indicates the specific actions and policies that a company should take based on the results of an analysis of management indicators.

[1769] A "multifaceted perspective" is a way of evaluating and analyzing things from different angles and perspectives, and is a method for gaining a more comprehensive understanding.

[1770] "Perspective" refers to the position or viewpoint from which one observes and judges things.

[1771] "Management decisions" refer to making important decisions regarding the management of a company, including the formulation and implementation of strategies.

[1772] "Corporate data" means all data related to a company's business or activities, including sales, costs, customer information, etc.

[1773] "Business expansion" refers to activities and strategies aimed at increasing a company's size or market share.

[1774] This invention is a system that aggregates management indicators and uses a generative AI model to make strategic proposals based on the aggregated data. The system consists of three main elements: a server, a terminal, and a user.

[1775] server

[1776] The server first collects management indicator data and stores it in a database. Specifically, it obtains key management indicator data such as sales, costs, and profit margins from each department. A relational database management system such as MySQL or PostgreSQL is used as the database.

[1777] The server then uses the Python Pandas library to aggregate the data retrieved from the database, for example, to calculate monthly sales or cost totals, and the aggregated results are temporarily stored in memory.

[1778] The server then uses machine learning frameworks such as TensorFlow and PyTorch to analyze trends in the aggregated data, for example, to model trends in sales increases and decreases or patterns of cost fluctuations. Based on the analysis results, a generative AI model generates business strategy proposals.

[1779] Terminal

[1780] The terminal is responsible for sending prompt sentences entered by the user to the server. For example, if the user enters "Sales are declining this month. What strategy should we take?", this prompt sentence is sent to the server. The terminal also displays strategy suggestions returned by the server to the user.

[1781] User

[1782] The user inputs a question about management indicators through the terminal. For example, "If sales are declining, what marketing strategy should we adopt?" The user checks the strategy proposals returned by the server and decides on a management strategy based on them.

[1783] Specific examples

[1784] Hardware and software used

[1785] Hardware: Server (equipped with high-performance CPU and GPU)

[1786] Software: Python, Pandas, TensorFlow, PyTorch, MySQL

[1787] Prompt Sentence Examples

[1788] "Sales are down this month, what strategy should we use?"

[1789] Specific actions

[1790] 1. The server collects the data

[1791] The server retrieves data from the sales management system and accounting system via API.

[1792] The acquired data is stored in a MySQL database.

[1793] 2. The server aggregates the data

[1794] The server uses Pandas to run queries like "SELECT FROM sales_data WHERE month = '2023-10'" to aggregate the data.

[1795] The aggregation results are stored in an in-memory DataFrame.

[1796] 3. The server analyzes the data

[1797] The server uses TensorFlow to run a model that predicts "sales trends."

[1798] Based on the analysis results, the generative AI model generates strategic proposals such as "implementing a new marketing campaign."

[1799] 4. The terminal sends a prompt

[1800] The terminal transmits the prompt sentence input by the user, "Sales are declining this month. What strategy should we adopt?" to the server.

[1801] Notify the user that the submission was successful.

[1802] 5. The device receives the strategy proposal.

[1803] The terminal receives the strategy proposal "to launch a new marketing campaign" sent back from the server.

[1804] Display received suggestions to the user.

[1805] 6. The user enters a prompt

[1806] The user inputs "If sales are declining, what marketing strategy should we adopt?" into an input field on the terminal.

[1807] Check the information you entered and press the send button.

[1808] 7. User confirms the strategy proposal

[1809] The user checks in detail the proposal "Launch a new marketing campaign" displayed on the terminal.

[1810] Based on the proposal, we will decide on a specific marketing strategy and put it into action.

[1811] In this way, the system helps users to quickly and effectively develop business strategies.

[1812] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1813] Step 1:

[1814] The server collects management indicator data. Specifically, it obtains data such as sales, costs, and profit margins from the sales management system and accounting system via API. The input is management indicator data from each department, and the output is data stored in the database. The server stores the data using a relational database management system such as MySQL or PostgreSQL.

[1815] Step 2:

[1816] The server aggregates data retrieved from the database using Python's Pandas library. For example, to calculate the total sales or costs by month. The input is the raw data retrieved from the database, and the output is the aggregated data. The server aggregates the data using a Pandas DataFrame and stores it in memory.

[1817] Step 3:

[1818] The server uses machine learning frameworks such as TensorFlow and PyTorch to analyze trends in the aggregated data. For example, it models trends in sales increases and decreases or patterns of cost fluctuations. The input is the aggregated data, and the output is the analysis results. The server runs the machine learning model and generates the analysis results.

[1819] Step 4:

[1820] The server uses the generative AI model to generate strategic proposals based on the analysis results. The input is the analysis results, and the output is strategic proposals. The server uses the generative AI model to generate specific strategic proposals, such as "implement a new marketing campaign."

[1821] Step 5:

[1822] The terminal sends the prompt text entered by the user to the server. For example, it sends a prompt text such as "Sales are declining this month. What strategy should we adopt?" The input is the prompt text entered by the user, and the output is a notification of successful transmission to the server. The terminal sends the prompt text to the server and notifies the user that the transmission was successful.

[1823] Step 6:

[1824] The server receives the prompt sentence sent by the user and generates strategy proposals based on it using a generative AI model. The input is the prompt sentence from the user and the output is the strategy proposal. The server generates an appropriate strategy proposal for the prompt sentence and sends it back to the terminal.

[1825] Step 7:

[1826] The terminal receives the strategy proposal returned from the server and displays it to the user. The input is the strategy proposal from the server, and the output is the display to the user. The terminal displays the received strategy proposal to the user so that the user can confirm it.

[1827] Step 8:

[1828] The user checks the strategic proposal displayed on the terminal and decides on a business strategy based on it. The input is the strategic proposal displayed on the terminal, and the output is the user's business decision. The user decides on a specific marketing strategy based on the proposal and puts it into action.

[1829] (Application example 2)

[1830] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1831] Conventional systems for compiling and analyzing management indicators do not adequately aggregate data or propose strategies in real time, leading to a demand for faster and more accurate management decisions. Furthermore, there is a lack of automated methods for analyzing trends in management indicators and proposing specific strategies, making it difficult for managers and marketing personnel to quickly implement appropriate strategies.

[1832] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1833] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary for corporate strategy, a means for analyzing trends in the management indicators using a generative AI model and generating prompt sentences to make strategic proposals, and a means for aggregating management indicators in real time and saving them in a database on the cloud. This enables real-time aggregation and analysis of management indicators, allowing for quick and specific strategic proposals to be made automatically.

[1834] "Management indicators" are numerical data used to evaluate a company's performance and business situation, and include sales, inventory, number of customers, repeat business rate, etc.

[1835] "Aggregation" means organizing multiple data sets and calculating statistical information such as totals and averages.

[1836] "AI Secretary" is a system that uses artificial intelligence to analyze management indicators, propose management strategies, and provide business support.

[1837] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals, and includes marketing strategies and business expansion plans.

[1838] A "multifaceted perspective" means evaluating things from multiple different perspectives and making a comprehensive judgment.

[1839] A "perspective" is the position or viewpoint from which one observes and judges things.

[1840] "Effective business decisions" mean making appropriate and effective decisions to achieve a company's goals and solve problems.

[1841] "Corporate data" refers to various data related to the business and management of a company, including sales data, customer data, inventory data, and the like.

[1842] "Business expansion" refers to the activities and strategies used to increase a company's business scale and market share.

[1843] A "generative AI model" is a type of artificial intelligence that is used to generate natural language and analyze data for specific tasks.

[1844] A "prompt" is an instruction or question entered into a generative AI model to make it perform a specific task.

[1845] "Real-time" means that data and information are processed and reflected immediately the moment they are generated.

[1846] A "cloud database" is a database built on a remote server accessible via the Internet.

[1847] The system for implementing this invention is composed of three main elements: a server, a terminal, and a user. The specific functions and processing of each element will be explained below.

[1848] Server Functions and Processing

[1849] The server plays a central role in aggregating management indicators, analyzing them using a generative AI model, and generating prompts to propose strategies. The server uses the following hardware and software:

[1850] Hardware: A server machine with a powerful processor, sufficient memory, and storage.

[1851] Software: Database management systems, generative AI models (e.g., OpenAI's GPT-4), data analysis tools (e.g., Pandas).

[1852] The server first aggregates management indicator data stored in a cloud database in real time. Next, it uses a generative AI model based on the aggregated data to analyze trends in management indicators. Finally, it generates prompt statements based on the analysis results and makes specific strategic proposals.

[1853] Terminal functions and processing

[1854] The terminal is a device that allows the user to receive and display information provided by the server. The terminal uses the following hardware and software:

[1855] Hardware: Smartphones, tablets, computers, etc.

[1856] Software: Web browser, dedicated application.

[1857] The terminal receives the aggregated results of management indicators and strategic proposals sent from the server and visually displays them to the user, allowing the user to check management indicators in real time and make appropriate management decisions.

[1858] User operations

[1859] The user checks the information provided by the server through the terminal and executes business strategies as necessary. The user performs the following operations.

[1860] Checking management indicators

[1861] Acceptance or revision of strategic proposals

[1862] Entering new data

[1863] Examples of specific examples and prompts

[1864] For example, if sales are down 10%, the server generates the following prompt:

[1865] "Sales are down 10%. Please suggest a new marketing strategy."

[1866] Based on this prompt, the generative AI model suggests specific strategies such as "launch a new social media campaign."

[1867] In this way, the server, terminals, and users can work together to aggregate and analyze management indicators in real time, enabling quick and automatic specific strategic proposals to be made.

[1868] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1869] Step 1:

[1870] The server retrieves management indicator data from a database on the cloud.

[1871] Input: Management indicator data stored in a database on the cloud.

[1872] Output: Obtained management indicator data.

[1873] Specific operation: The server uses a database management system to query business indicator data such as sales, inventory, number of customers, and repeat customer rate, and stores the data in a data frame.

[1874] Step 2:

[1875] The server aggregates the acquired management index data in real time.

[1876] Input: Obtained management indicator data.

[1877] Output: Aggregated business indicator data.

[1878] Specific operation: The server uses data analysis tools to calculate totals and averages of sales, changes in inventory, increases or decreases in the number of customers, etc., and generates summary results.

[1879] Step 3:

[1880] The server analyzes trends using a generative AI model based on the aggregated management indicator data.

[1881] Input: Aggregated business indicator data.

[1882] Output: Trend analysis results of management indicators.

[1883] Specific operation: The server inputs aggregated data into the generative AI model and analyzes trends in sales increases and decreases, patterns of customer behavior, etc.

[1884] Step 4:

[1885] The server generates prompt sentences based on the trend analysis results and makes strategy suggestions.

[1886] Input: Trend analysis results of management indicators.

[1887] Output: Generated prompt statements and strategy suggestions.

[1888] Specific behavior: The server uses a generative AI model to generate prompt statements such as, "Sales have fallen by 10%. Please suggest a new marketing strategy." and create specific strategic proposals.

[1889] Step 5:

[1890] The server transmits the generated strategy proposal to the terminal.

[1891] Input: Generated prompt statements and strategy suggestions.

[1892] Output: Strategy proposal sent to the terminal.

[1893] Specific operation: The server sends a strategy proposal to the terminal via the network so that the user can confirm it.

[1894] Step 6:

[1895] The terminal displays the strategy proposal sent from the server to the user.

[1896] Input: Strategy proposal sent by the server.

[1897] Output: Strategy proposals displayed to the user.

[1898] Specific operation: The terminal uses a dedicated application or a web browser to visually display the strategy proposal so that the user can check it.

[1899] Step 7:

[1900] The user checks the strategy proposal through the terminal and implements the business strategy as necessary.

[1901] Input: Strategy proposal displayed on the terminal.

[1902] Output: Implemented business strategy.

[1903] Specific operations: The user checks the strategy proposals displayed on the terminal and executes business strategies such as launching new marketing campaigns as necessary.

[1904] Example 3

[1905] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1906] In conventional corporate data management systems, data collection, preprocessing, analysis, and display of results were all carried out separately, making integrated data management and analysis difficult. Furthermore, there was a lack of concrete methods for utilizing the results of data analysis in business strategies, making it difficult to make quick and effective management decisions. This could result in companies missing out on new business opportunities.

[1907] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1908] In this invention, the server includes a means for collecting and aggregating corporate data, a means for preprocessing the aggregated data, a means for analyzing the preprocessed data using a generative AI model, a means for displaying the analysis results to the user, and a means for using the results to expand the business. This enables centralized management of corporate data and advanced data analysis, leading to rapid and effective management decisions.

[1909] "Corporate data" refers to various data generated by a company in the course of its business, including customer purchase data, inventory data, sales data, and the like.

[1910] "Means of collection and aggregation" refers to the means of automatically acquiring various data within a company and storing it centrally in a database.

[1911] "Preprocessing means" refers to means for imputing missing values, normalizing data, and detecting and correcting outliers in order to convert the collected data into a format suitable for analysis.

[1912] A "generative AI model" is a model that uses artificial intelligence to analyze data and extract patterns and make predictions, and includes, for example, natural language processing models.

[1913] The "means of analysis" refers to the means of inputting preprocessed data into a generative AI model, analyzing the data, and outputting the results.

[1914] The "display means" refers to a means for visually presenting the analysis results to the user, and includes displays in the form of dashboards, graphs, and tables.

[1915] "Means to expand business" refers to the means of exploring new business opportunities based on the analysis results and planning and implementing specific strategies.

[1916] This invention is a system that centrally manages corporate data, analyzes the data using a generative AI model, and plans and executes business strategies based on the results. Specific embodiments of this system are described below.

[1917] Hardware and software used

[1918] Server: A central server for data aggregation and management, for example, using a virtual server from a cloud service provider.

[1919] Database: A database for storing and querying data, for example using a relational database management system.

[1920] Generative AI models: AI models for analyzing data and exploring business opportunities, for example, using natural language processing models.

[1921] Terminal: A device where the user enters data and checks the results, for example, a personal computer or tablet.

[1922] Program processing

[1923] Data collection and aggregation

[1924] The server collects various data within the company (customer purchase data, inventory data, sales data, etc.) and stores it in a database. Specifically, it periodically retrieves data from POS systems and CRM systems. For example, the server calls the POS system's API to retrieve the latest purchase data and stores it in a relational database.

[1925] Data Preprocessing

[1926] The server preprocesses the collected data and converts it into a format suitable for analysis. Specifically, it performs tasks such as filling in missing values, normalizing the data, and detecting and correcting outliers. For example, the server fills in missing values ​​in purchasing data with the average value and restores the data to the database.

[1927] Analyzing the data

[1928] The server then uses the generative AI model to analyze the preprocessed data. Specifically, it clusters customer purchasing patterns and obtains hints for new product development. For example, the server inputs the following prompt sentence into the natural language processing model:

[1929] Prompt: "Analyze the following customer purchase data and provide ideas for new product development. The data is in the format of customer ID, product ID, purchase date, purchase quantity, and purchase amount."

[1930] The generative AI model analyzes the input data and returns the results.

[1931] Displaying the results

[1932] The device displays the analysis results to the user, who can then use them to explore new business opportunities. For example, the device displays the analysis results received from the server on a dashboard. Specifically, it displays a list of "products that sell well in the summer" and the clustering results of customer purchasing patterns in graphs and tables.

[1933] In this way, it becomes possible to centrally manage corporate data and perform advanced data analysis, thereby enabling quick and effective management decisions. The flow of the specific processing in the third embodiment will be described with reference to FIG.

[1934] Step 1:

[1935] The server collects and aggregates various data within the company. Specifically, it periodically obtains customer purchasing data, inventory data, sales data, etc. from the POS system and CRM system. As input, it calls the API of the POS system or CRM system to obtain the latest data. As output, it saves the obtained data in a relational database. Specifically, the server calls the API, obtains data in JSON format, and inserts it into the database.

[1936] Step 2:

[1937] The server preprocesses the collected data. Specifically, it completes missing values, normalizes data, and detects and corrects outliers. As input, it receives raw data stored in a relational database. As output, it stores the preprocessed data back in the database. Specifically, the server completes items with missing values ​​with average values, detects data with abnormally high purchase quantities, and corrects them to within an appropriate range.

[1938] Step 3:

[1939] The server analyzes the preprocessed data using a generative AI model. Specifically, it clusters customer purchasing patterns and obtains hints for new product development. The preprocessed data is input into the generative AI model. The analysis results are obtained as output. Specifically, the server inputs the following prompt into the natural language processing model: "Please analyze the following customer purchasing data and provide hints for new product development. The data is in the format of customer ID, product ID, purchase date, purchase quantity, and purchase amount." The generative AI model performs analysis based on the input data and returns the results.

[1940] Step 4:

[1941] The terminal displays the analysis results to the user. As input, it receives the analysis results received from the server. As output, it presents the results visually to the user. Specifically, the terminal displays the analysis results received from the server on a dashboard. For example, it displays a list of "products that sell well in the summer" or the clustering results of customer purchasing patterns in graph or table format.

[1942] This enables centralized management of corporate data and advanced data analysis, enabling quick and effective management decisions.

[1943] (Application example 3)

[1944] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1945] Modern companies need to collect a wide variety of data and use it effectively to increase their competitiveness. However, there is a lack of concrete methods for centrally managing this data and exploring business opportunities. There is also a need for efficient methods for analyzing customer purchasing data and obtaining ideas for new product development. This makes it difficult for companies to make quick and accurate management decisions.

[1946] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1947] In this invention, the server includes a means for aggregating management indicators, a means for utilizing the aggregated management indicators as an AI secretary and for utilizing them in corporate strategy, a means for obtaining perspectives based on multiple viewpoints, and a means for analyzing customer data and proposing new product development ideas using a generative AI model. This allows companies to centrally manage collected data and explore business opportunities. Furthermore, by obtaining new product development ideas based on customer purchasing data, companies can make quick and accurate management decisions.

[1948] "Management indicators" are numbers or indicators used to evaluate a company's management situation.

[1949] "Aggregation" means calculating multiple data together to find a total or average.

[1950] "Artificial Intelligence Secretary" is a system that uses artificial intelligence technology to support management decisions and strategic proposals.

[1951] A "corporate strategy" is a plan or policy that a company formulates to achieve its long-term goals.

[1952] A "multifaceted perspective" means analyzing and evaluating things from multiple different perspectives.

[1953] A "perspective" is the position or viewpoint from which one observes and judges things.

[1954] "Effective business judgment" means making effective decisions to achieve a company's goals.

[1955] "Corporate data" refers to various types of information collected and generated in the course of a company's business operations.

[1956] "Business expansion" means increasing a company's business scale and market share.

[1957] "Customer data" refers to information about customers, including purchase history, behavioral history, etc.

[1958] A "generative AI model" is a model that uses artificial intelligence technology to generate new information and ideas from data.

[1959] "Product development" is the process...

Claims

1. means for connecting to a database storing company data including financial data, personnel data, and sales data; means for acquiring said company data from said database; A means for pre-processing the acquired enterprise data; means for calculating performance indicators based on the pre-processed company data; means for outputting the calculated performance indicators; A means for aggregating management indicators including the output business performance indicators; A means for generating a strategic proposal using a generation AI model based on the aggregated management indicators; A system including:

2. The means for aggregating the management indicators aggregates the management indicators based on specific performance indicators. The system of claim 1 .

3. means for recognizing an emotion of a user; The means for generating a strategy proposal generates the strategy proposal based on the recognized emotion of the user. The system of claim 1 .

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