system

Abstract

We provide the system. [Solution] A means of collecting task data from employees' terminals, A means of preprocessing the collected data and correcting missing values ​​and outliers, A method for evaluating employee task progress and productivity using generative AI models, A means of visualizing and providing evaluation results to users, A means of automatically generating a report based on the analysis results and sending it to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the spread of telecommuting, it has become difficult for managers and leaders to grasp the progress of their subordinates' tasks and productivity. For this reason, there is a problem that performance evaluation becomes inaccurate and appropriate management becomes difficult. In addition, it is necessary to measure the impact of communication activities among employees and use it to improve the efficiency of the entire organization.

Means for Solving the Problems

[0005] This invention provides a system that collects task data from employees' terminals, preprocesses that data, and then uses a generated AI model to evaluate employees' task progress and productivity. By visualizing and providing the evaluation results to users, managers can intuitively grasp the status of their subordinates. Furthermore, by automatically generating reports based on the analysis results and sending them to users periodically, the system supports appropriate management and guidance. In addition, it includes means for analyzing the causes of abnormal delays using data from similar past projects with the generated AI model, and for visualizing the impact of employees' communication activities on task progress.

[0006] "Employee" refers to an employee who performs specific tasks within an organization.

[0007] A "terminal" refers to a computer device used for inputting and outputting data.

[0008] "Task data" refers to data that records the progress and related information of a specific task.

[0009] "Collection" refers to the process of gathering information and data from multiple sources.

[0010] A "generative AI model" refers to a mathematical or computational method designed to analyze and predict data using artificial intelligence.

[0011] "Task progress rate" refers to an indicator that shows how far along a particular task is in its completion.

[0012] "Productivity" refers to an indicator that shows the efficiency and effectiveness of work performance over a specific period.

[0013] "Evaluation" refers to the process of analyzing information and performance based on specific criteria to determine their value and usefulness.

[0014] "Visualization" refers to the process of representing data in visual forms such as graphs and charts to make it easier to understand.

[0015] "User" refers to an individual or an officer who uses this system, especially a manager.

[0016] "Report" refers to a collection of information that documents analysis results and proposals.

[0017] "Automatic generation" refers to a process in which the system autonomously processes data and creates deliverables without human intervention.

[0018] "Abnormal delay" refers to a state of being behind the planned progress, and its causes include unexpected events and failures.

[0019] "Communication activities" refer to the exchange of information and the transmission of intentions through oral, written, or digital media.

Brief Description of Drawings

[0020] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map where multiple emotions are mapped. [Figure 10] Shows an emotion map where multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0033] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0040] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0041] This invention is a system that streamlines organizational management in a work-from-home environment, aiming to visualize and evaluate employees' task progress and communication activities. The system includes a server and each employee's terminal, providing information through the collection, processing, and analysis of task data.

[0042] First, the server automatically collects data periodically from each employee's terminal via a task management tool. This data includes the task's start time, end time, progress rate, assigned person, and related communication history. For example, it also includes email and chat history data used by employees during the day.

[0043] Next, the server preprocesses the collected data. Preprocessing involves imputing missing values ​​and detecting outliers to ensure data integrity. This allows the generative AI model to process the data accurately.

[0044] Subsequently, the server uses a generative AI model to evaluate the employee's task progress and productivity. This evaluation process leverages past data patterns. For example, if an employee's productivity is declining on a "design task," the cause can be analyzed by comparing it with past data for similar tasks.

[0045] The analyzed results are visualized by the server and made easily accessible to managers through a dashboard. This includes Gantt charts showing progress and bar charts showing productivity metrics.

[0046] Finally, the server automatically generates reports based on the assessments and analyses and periodically sends them to users via email. These reports include an overview of employee activity and specific improvement suggestions, providing managers with the information they need to make rational decisions. Users can also access the reports in real time from the dashboard and obtain detailed information as needed.

[0047] For example, if an employee is in charge of a design project, the server monitors its progress and, if it detects that it is behind schedule, analyzes the cause through an AI model. For instance, it might be found that infrequent communication is a contributing factor to the delay. This information is then presented to the manager in a graph, and improvement suggestions, such as "recommend regular team meetings," are automatically generated.

[0048] In this way, even in a work-from-home environment, users can accurately grasp and efficiently manage the overall picture of employee progress and communication.

[0049] The following describes the processing flow.

[0050] Step 1:

[0051] The server periodically collects data from each employee's terminal via the task management tool's API. This data includes task identifiers, assignee names, start and end times, progress status, and communication logs. The server stores this data in a central database.

[0052] Step 2:

[0053] The server preprocesses the collected data. This includes checking data integrity and detecting and correcting missing or outlier values. For example, if the end time is not entered, it is assumed to be in progress. The data is also standardized and converted into an input format for the AI ​​model.

[0054] Step 3:

[0055] The server inputs pre-processed data into the generated AI model and evaluates the task's progress and productivity. The AI ​​model compares this data to past data patterns and analyzes the causes of unusual delays and inefficiencies. This process utilizes delay patterns and productivity metrics frequently observed in the design phase.

[0056] Step 4:

[0057] The server visualizes the analysis results. This includes Gantt charts showing task progress and bar charts displaying individual employee productivity. Users can view this visualized data through a dashboard.

[0058] Step 5:

[0059] The server automatically generates a report based on the evaluations it receives. This report includes information on the status of employees' tasks, the causes of unusual delays, and suggestions for improvement. The server periodically sends this report to users via email.

[0060] Step 6:

[0061] Users can access reports and visualized performance data provided through the dashboard in real time, and check the status of their subordinates in detail as needed. This allows them to make immediate decisions and take measures to improve organizational performance.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In a remote work environment, effectively managing employee work progress and productivity is a challenging task. In particular, understanding how individual employees communicate and how that impacts task progress and productivity is crucial. There is a need for efficient methods to visualize this information and use it for improvement.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for acquiring work data from an information processing device, means for preprocessing the acquired information and correcting missing or abnormal information, and means for evaluating the progress and efficiency of the work using a generated AI model. This enables accurate tracking and efficient management of employees' work progress and productivity, even in a teleworking environment.

[0067] An "information processing device" refers to a computer or terminal that can acquire, transmit, and analyze data.

[0068] "Work data" refers to information about the tasks performed by employees, including start time, end time, progress status, and information about the person in charge.

[0069] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and specifically includes imputing missing data and correcting outliers.

[0070] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to perform data analysis and pattern recognition.

[0071] "Progress status" refers to information that expresses the degree of progress of a task or project.

[0072] "Efficiency" is an indicator that shows how efficiently a task or work can be performed.

[0073] "User" refers to a person who uses the system to obtain or manage information, and in this context, it mainly refers to a manager.

[0074] "Visualization" refers to the process of representing analysis results in the form of graphs, charts, and other visual representations, making the information intuitively understandable.

[0075] A description of embodiments for carrying out this invention will be given.

[0076] The system for managing employee work progress and productivity in a remote work environment uses a server and employee terminals. The server retrieves information from task management tools on the employee's terminal and analyzes this information using a generative AI model. The terminals have software installed to record task progress and communication history. This software includes, for example, task management tools, email, and chat applications, and data is collected through these.

[0077] Specifically, the server periodically retrieves task start time, end time, progress rate, and assignee information from terminals. A generative AI model is used in the process of analyzing this information and evaluating the progress and productivity of the work. This generative AI model learns from past data patterns and can perform analyses to identify the causes of abnormal delays. For example, comparing the history of similar tasks can reveal the cause of delays in the current task.

[0078] The analyzed results are visualized by the server, allowing managers to view the information in real time through a dashboard. The visualizations utilize color-coded Gantt charts and productivity bar charts, providing an easy-to-understand format. The server also has the functionality to generate reports, including improvement suggestions, based on this information and automatically send them to users.

[0079] For example, if an employee in charge of a design project experiences a delay, the server can use a generative AI model to compare past communication frequency data with delay data to identify the cause of the delay. Based on these results, the server can then suggest improvements to the manager, such as "recommend regular team meetings."

[0080] Examples of prompts to input into a generative AI model include the following:

[0081] "The design task is behind schedule. Please analyze the cause by comparing it with past data and create improvement proposals."

[0082] In this way, users can efficiently manage employee activities even in a work-from-home environment.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The server collects data from employees' terminals via a task management tool. Inputs include task start and end times, progress percentage, assigned person, and communication history (email and chat data) provided by the terminal, which the server stores in a database. Specifically, the server periodically retrieves information using an API and stores it in a unified format.

[0086] Step 2:

[0087] The server preprocesses the collected data. It receives all the raw data as input and performs data imputation, anomaly detection, and correction. This process cleans the data and then generates a reliable dataset as output. Specifically, it uses statistical methods and machine learning algorithms to process the data and prepare highly consistent data.

[0088] Step 3:

[0089] The server analyzes pre-processed data using a generative AI model to evaluate employees' work progress and productivity. The input is well-organized data, and the AI ​​model analyzes the current data by comparing it to past patterns. The output provides each employee's current task progress rate and productivity. Specifically, the AI ​​model performs the analysis using prompts and lists the results.

[0090] Step 4:

[0091] The server visualizes the analyzed results. The input is evaluation results from an AI model, and the server creates Gantt charts and bar charts of various metrics that are easy for engineers to understand. The output is visual information displayed on the user's dashboard. Specifically, it visualizes data using a graph library and enables interactive displays based on user actions.

[0092] Step 5:

[0093] The server automatically generates and sends reports to users based on the generated evaluations and analysis results. The input consists of visualizations and analysis results, and the server uses this information to output reports that include an overview of operations and improvement suggestions. Specifically, the server uses a document generation engine to create reports in PDF format and sends them to users periodically via email. This process allows managers to obtain information that supports the optimal operation of their organization.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In modern factories, there is a need to understand the progress and efficiency of a wide range of tasks in real time and for users to manage them effectively. However, in current systems, although a large amount of data is acquired, it is not often linked and properly evaluated, resulting in inefficient machine operation management. This leads to problems such as decreased overall work efficiency and delays in the early detection of abnormalities. Therefore, the present invention aims to solve these problems and improve work efficiency within factories.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for collecting business data from the user's operating device, means for preprocessing the collected data and correcting missing or abnormal information, and means for evaluating the progress rate and efficiency of the work using a generated AI model. This enables the operating status of equipment in the factory and the progress of work to be appropriately evaluated in real time, allowing the user to take immediate action.

[0099] "User-operated devices" refer to devices used by workers and managers within a factory to input and verify business data.

[0100] "Business data" refers to data that includes information on the progress and efficiency of work within the factory, as well as various operation histories.

[0101] "Means of collection" refers to a system equipped with a series of functions for efficiently acquiring business data from the user's operating device.

[0102] "Preprocessing" refers to the process of processing collected data to make it consistent by supplementing missing information and detecting and correcting anomalies.

[0103] A "generative AI model" refers to an artificial intelligence model that learns from a large amount of historical data, enabling real-time data analysis and evaluation.

[0104] "Progress rate" refers to an indicator that shows the degree to which planned work has been completed within a specific period.

[0105] "Efficiency" is an indicator that shows how effectively work is being carried out within a factory, and it is a measure that evaluates the work output in relation to the amount of resources and time used.

[0106] "Real-time" refers to a state where data collection, processing, and display occur almost simultaneously, allowing users to access the latest information immediately.

[0107] "Being able to take countermeasures" means having the ability to immediately take steps to resolve the problem based on the evaluation results obtained.

[0108] This invention provides a system consisting of specific devices and programs to improve operational efficiency at work locations within a factory. The server collects operational data through the user's operating device and centrally manages this data in real time using a cloud platform such as "Azure® IoT Hub". The data includes work progress rates and efficiency, operating information of each piece of equipment, and anomaly information.

[0109] Next, the server preprocesses the collected data using a Python program. This preprocessing involves filling in missing information and filtering out anomalous data, followed by normalization to obtain consistent data. This allows for real-time evaluation of the progress and efficiency of each task using a generative AI model.

[0110] The generative AI model is built using technologies such as "TENSORFLOW®" and is designed to perform pattern recognition and efficient root cause analysis based on past work data. For example, it refers to similar past datasets to evaluate whether the current work is proceeding according to plan and identifies the causes of any anomalies or delays.

[0111] Users can visually review evaluation results through mobile devices such as smart glasses. This system allows real-time access to visualized analysis results in Gantt charts and bar charts, enabling quick action as needed. In addition, the server automatically generates and sends detailed reports to users periodically, providing valuable information for decision-making in factory operations.

[0112] For example, if a machine's operation is behind schedule, the generative AI model will identify the cause and notify the user with improvement suggestions, such as "We recommend reviewing the maintenance frequency." An example of a prompt to the generative AI model is: "Analyze the progress rate and anomaly detection patterns from the robot process data in the factory to identify the cause of low productivity."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server collects business data from the user's operating devices. Input data includes work process progress and equipment operating information. This enables real-time data centralization. The server efficiently stores this data through a cloud platform.

[0116] Step 2:

[0117] The server preprocesses the accumulated data using Python. Here, it reads the collected raw data as input. It imputes missing data, detects and corrects anomalies, and simultaneously filters out anomalies to generate consistent, normalized data as output.

[0118] Step 3:

[0119] The server inputs pre-processed data into a generative AI model and evaluates the progress and efficiency of the work. The generative AI model, using TensorFlow, references patterns in past data and analyzes the causes of unique delays from the input data. The output provides evaluation results, including delay factors and improvement suggestions.

[0120] Step 4:

[0121] Users receive visualized evaluation results from the server using mobile devices such as smart glasses. Based on the analysis results output by the generated AI model, progress is displayed in Gantt charts and bar charts. Users review this and make decisions regarding work improvement based on the output.

[0122] Step 5:

[0123] The server automatically generates and sends detailed reports to users on a regular basis. Using the most recent operational evaluation results as input, the reports include a comprehensive analysis of operational efficiency and specific improvement suggestions. This output enables users to achieve more effective factory management.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention is a system that efficiently manages the task progress and communication activities of employees in a teleworking environment, and supports organizational management, including the emotional state of users. This system is equipped with an emotion engine that senses changes in the user's emotions and takes appropriate action.

[0126] First, the server periodically collects task management and communication data from each employee's terminal. This data includes task progress, start and end times, and communication logs between employees. Furthermore, the emotion engine uses input devices such as the terminal's camera and microphone to acquire biometric information and analyze the user's emotional state from their facial expressions and voice.

[0127] Next, the server preprocesses the collected data and evaluates the task's progress and productivity through a generative AI model. This evaluation step also integrates user sentiment data detected by the sentiment engine, taking into account psychological factors that influence task progress.

[0128] Users can view task status and emotional analysis results in real time via a dashboard. This includes a visual display of emotional trends along with progress charts. Users can identify signs of decreased productivity or emotional stress.

[0129] Furthermore, the server automatically generates reports that provide an overview of employees' task status and emotional state, along with suggestions for improvement. These reports include, for example, how emotions have affected project progress and the actions that can be taken to address these issues. For instance, employees with high stress levels might be advised to "re-evaluate their tasks" or "take a break."

[0130] For example, if an employee is working on a design task for "Project X" and the emotion engine detects an increase in their stress levels, the server will immediately incorporate this information into the evaluation and display an additional alert on the dashboard. The report will also identify the sources of stress and suggest specific action plans to address them.

[0131] In this way, users can efficiently improve organizational performance in a work-from-home environment while comprehensively considering emotional factors.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server collects task management data and communication logs from each employee's terminal. In addition, an emotion engine built into the terminal acquires the user's facial expressions and voice data through the camera and microphone, and analyzes their emotional state in real time.

[0135] Step 2:

[0136] The server sequentially preprocesses the collected data. It verifies data integrity, imputes missing values, and removes noise. Furthermore, it standardizes sentiment data and converts sentiment categories into numerical data.

[0137] Step 3:

[0138] The server inputs pre-processed data into a generated AI model to evaluate employees' task progress and productivity. During this process, emotional data is integrated to analyze the emotional impact on task progress. For example, decreased productivity in employees experiencing prolonged stress may be identified.

[0139] Step 4:

[0140] The server visualizes the evaluation results and provides them to the user. The dashboard displays a Gantt chart showing task progress, a bar chart showing productivity, and a graph showing the progression of emotions. This allows the user to intuitively understand which tasks or situations are causing stress.

[0141] Step 5:

[0142] The server automatically generates a report based on the analysis results. This report includes each employee's task status, insights into changes in their emotions, and specific improvement suggestions. For example, it might suggest re-evaluating work schedules to reduce stress.

[0143] Step 6:

[0144] Through dashboards and reports, users can continuously monitor employees' task progress and emotional state, providing feedback and adjusting tasks as needed. This enables the management and improvement of overall organizational performance, even in a remote work environment.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Effectively managing employee work progress and communication in a remote work environment is a critical challenge for many companies. In particular, understanding the impact of employees' emotional states on work efficiency in real time and taking appropriate action is difficult. In this situation, there is a need for a system that integrates and analyzes employee work data and emotional data to provide improvement suggestions.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for collecting work data from employees' information terminals, means for preprocessing the collected data and correcting missing or outlier values, and means for evaluating employees' work progress and productivity using a generated AI model. This enables real-time visualization of employees' work status and allows for comprehensive analysis and improvement suggestions based on their emotional state.

[0150] "Employee information terminals" is a general term for electronic devices such as computers, smartphones, and tablets that employees use to perform their work.

[0151] "Work data" refers to information generated when employees perform tasks, such as progress status, start and end times, and details of the work.

[0152] "Means of collection" refers to the technologies and methods used to obtain necessary data from employees' information terminals.

[0153] "Methods for preprocessing and correcting missing or outlier values" refer to processes that prepare collected data for easier analysis, including techniques for data cleaning and imputation.

[0154] A "generative AI model" is a mathematical model designed to analyze data in a specific business area using machine learning and artificial intelligence technologies.

[0155] "Biometric information" refers to information including facial expressions and voice data acquired by cameras and microphones in order to analyze the user's emotional state.

[0156] "Means of evaluation" refers to processes or technologies for analyzing collected work data and biometric information to quantify and evaluate work progress and productivity.

[0157] "Means of visualizing and providing information to users" refers to technologies that display analysis results in visual formats such as graphs and charts, making them easily understandable to users.

[0158] "Methods for automatically generating and sending reports to users" refers to technologies in which a system automatically creates a document summarizing analysis results and distributes it to users via email or other methods.

[0159] This invention is a system that efficiently manages the work progress and emotional state of employees in a teleworking environment, thereby supporting organizational management. This system is composed of various hardware and software as described below.

[0160] First, "terminals" refer to information processing devices used by employees to perform their daily tasks, and include personal computers, smartphones, and tablets. These terminals have work management applications and communication tools installed, through which work data and communication history are collected. Furthermore, biometric information (e.g., facial expressions, voice) is acquired using the terminal's camera and microphone to collect information for analyzing the user's emotional state.

[0161] Next, the server is the central device that manages and analyzes the data collected from each terminal. The server preprocesses the collected data, including imputing missing values ​​and correcting outliers. The preprocessed data is then input into a generative AI model. This generative AI model is designed to evaluate employees' work progress and productivity, and performs analysis using statistical methods, including comparison with data from similar past tasks.

[0162] Subsequently, the server provides the evaluated data to the user through a dashboard. The dashboard visually displays work progress and changes in emotions, allowing the user to intuitively understand the current situation. Furthermore, regularly generated reports send the user improvement suggestions based on evaluation results for specific tasks and their emotional state. This supports highly productive work operations in a remote work environment.

[0163] As a concrete example, by feeding a prompt such as "Analyze User A's work efficiency and stress level" to a generating AI model, it becomes possible to comprehensively evaluate the user's current situation and propose improvement measures. In this way, it is possible to improve work performance while taking into account the emotional aspects of employees.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The terminal collects various work data and communication history from employees and sends it to the server. Specifically, it periodically retrieves task progress and time information from work management applications, as well as message history from chat tools. The input consists of various data related to the employee's work, and the output is raw data sent to the server.

[0167] Step 2:

[0168] The device uses a camera and microphone to collect the user's biometric information. This includes facial expressions and voice data, which are used to capture the user's emotional state. The input for this step is biometric data acquired in real time, and the output is sent to the server as user emotion data.

[0169] Step 3:

[0170] The server preprocesses the work data and biometric information sent from the terminal. Specifically, it cleans the data, imputes missing values, removes outliers, and prepares it in a format suitable for analysis. The input is raw work data and emotion data, and the output is preprocessed, analyzable data.

[0171] Step 4:

[0172] The server analyzes pre-processed data using a generative AI model. Prompt statements are input to the AI ​​model to evaluate employee work progress and productivity. For example, a prompt such as "Analyze user A's work efficiency and stress level" can provide deeper insights. The input consists of pre-processed data and prompt statements, and the output is the evaluation result.

[0173] Step 5:

[0174] The server visualizes the evaluation results generated by the AI ​​model on a dashboard and provides them to the user. Visualized charts and graphs allow the user to intuitively understand the current situation. The input is the evaluation results, and the output is visual feedback on the dashboard.

[0175] Step 6:

[0176] The server automatically generates a report and sends it to the user, including evaluation results and improvement suggestions. Specifically, it incorporates appropriate improvement measures into the report based on the analysis results and distributes it via email or other means. The input is the evaluation results and improvement suggestions, and the output is the report sent to the user.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] In conventional work environments, workers' emotional states and stress levels affect productivity, but there is a lack of efficient means to detect and manage them. Furthermore, it is difficult to flexibly adjust the operation of machinery based on emotional states, posing challenges to improving work efficiency and reducing worker burden. Additionally, correcting outliers and missing values ​​is time-consuming, resulting in inefficient report generation.

[0180] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0181] In this invention, the server includes means for transferring information processing data from a terminal device, means for formatting the transferred data and correcting missing or abnormal values, means for measuring task progress rate and production efficiency using a generation AI computing device, and means for analyzing the emotional state of people in the work environment and adjusting the operation of mechanical devices based on the emotional state. This makes it possible to optimize work efficiency according to the emotional state and reduce the burden on workers.

[0182] A "terminal device" is a device used to input information and transfer it to a server. This includes devices equipped with cameras and microphones.

[0183] "Information processing data" refers to a series of data related to work status and communication content.

[0184] A "missing value" is a value that is incomplete or missing from a dataset.

[0185] An "outlier" refers to a value that falls outside the normal data range and may affect the analysis.

[0186] A "generative AI computing device" is a computing resource that performs analysis using generative algorithms based on collected data.

[0187] "Project progress rate" is an indicator that shows how much progress has been made on a particular project.

[0188] "Production efficiency" is the ratio that shows how much output was achieved in relation to the resources invested.

[0189] "Work environment" refers to the physical or virtual environment in which machinery and workers perform tasks together.

[0190] "Emotional state" refers to the psychological or emotional condition of a worker.

[0191] "Mechanical equipment" refers to automated facilities or robots designed to assist with specific tasks.

[0192] This invention is implemented by a system consisting of a terminal device, a server, and a user. The terminal device is equipped with a camera and microphone to collect biometric information and communication data of the worker. This collected data is automatically transferred to the server.

[0193] Upon receiving the transferred data, the server first performs data formatting. It corrects missing and outlier values ​​and arranges the dataset into a unified format. Next, it uses a generative AI computing device to measure the task progress rate and production efficiency. This measurement process also includes comparing the current data with historical data and similar work environments.

[0194] Furthermore, the server also analyzes the emotional state of workers in their work environment. For emotion analysis, it utilizes libraries such as "OpenCV" and "pyAudioAnalysis." Based on the emotional state, the server adjusts the operation of machinery in real time. As a result, it becomes possible to reduce the burden on workers and optimize work efficiency.

[0195] Users can view the visualization and analysis results of data generated by the system through a dashboard. This allows for concrete assessment of the work environment and suggestions for improvement. For example, if a worker is detected to be experiencing stress, the dashboard will display a warning in real time and suggest specific countermeasures.

[0196] As a concrete example, if a worker's concentration decreases during factory work, the server immediately analyzes the data and adjusts the operating speed of the machinery to reduce the worker's workload. This creates a work environment that maintains overall productivity while also considering the health of the workers.

[0197] An example of a prompt for a generative AI model is: "Based on the following employee's sentiment data, please assess the impact on productivity in the current task: Fatigue 50%, Stress Level 70%, Concentration 60%."

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The server receives biometric and communication data transmitted from terminal devices. Inputs include worker facial expressions, voice data, and task progress information. Outputs include preparing the received data for conversion into a formatted data format. Specifically, this involves standardizing the data format and adding timestamps.

[0201] Step 2:

[0202] The server performs preprocessing on the received data to detect and correct missing and outlier values. The input is the data formatted in step 1. The output generates a clean dataset suitable for analysis. Specifically, it corrects outliers using statistical methods and imputes missing values ​​using machine learning libraries.

[0203] Step 3:

[0204] The server analyzes clean data using a generative AI computing device to evaluate task progress and production efficiency. Preprocessed data is used as input. The output is numerical information regarding progress and efficiency. Specifically, it performs AI evaluations based on comparisons with historical data and prompt statements. Factors such as task similarity are considered in the analysis.

[0205] Step 4:

[0206] The server analyzes emotional states such as stress and fatigue, and generates instructions to adjust the operation of the machine based on the results. Input data includes facial expression analysis results and voice tone analysis results. The output is specific action commands for the machine. Specifically, the robot's operating speed is adjusted according to the emotional state to reduce the worker's workload.

[0207] Step 5:

[0208] Users can view analysis results and task progress provided by the server on a dashboard. The system receives analysis data from the server as input. Output consists of visualized analysis data and suggested actions. Specifically, it displays information using an intuitive interface with charts and graphs, and provides warnings and action suggestions when necessary.

[0209] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0216] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0221] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0222] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0223] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0224] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0225] This invention is a system that streamlines organizational management in a work-from-home environment, aiming to visualize and evaluate employees' task progress and communication activities. The system includes a server and each employee's terminal, providing information through the collection, processing, and analysis of task data.

[0226] First, the server automatically collects data periodically from each employee's terminal via a task management tool. This data includes the task's start time, end time, progress rate, assigned person, and related communication history. For example, it also includes email and chat history data used by employees during the day.

[0227] Next, the server preprocesses the collected data. Preprocessing involves imputing missing values ​​and detecting outliers to ensure data integrity. This allows the generative AI model to process the data accurately.

[0228] Subsequently, the server uses a generative AI model to evaluate the employee's task progress and productivity. This evaluation process leverages past data patterns. For example, if an employee's productivity is declining on a "design task," the cause can be analyzed by comparing it with past data for similar tasks.

[0229] The analyzed results are visualized by the server and made easily accessible to managers through a dashboard. This includes Gantt charts showing progress and bar charts showing productivity metrics.

[0230] Finally, the server automatically generates reports based on the assessments and analyses and periodically sends them to users via email. These reports include an overview of employee activity and specific improvement suggestions, providing managers with the information they need to make rational decisions. Users can also access the reports in real time from the dashboard and obtain detailed information as needed.

[0231] For example, if an employee is in charge of a design project, the server monitors its progress and, if it detects that it is behind schedule, analyzes the cause through an AI model. For instance, it might be found that infrequent communication is a contributing factor to the delay. This information is then presented to the manager in a graph, and improvement suggestions, such as "recommend regular team meetings," are automatically generated.

[0232] In this way, even in a work-from-home environment, users can accurately grasp and efficiently manage the overall picture of employee progress and communication.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server periodically collects data from each employee's terminal via the task management tool's API. This data includes task identifiers, assignee names, start and end times, progress status, and communication logs. The server stores this data in a central database.

[0236] Step 2:

[0237] The server preprocesses the collected data. This includes checking data integrity and detecting and correcting missing or outlier values. For example, if the end time is not entered, it is assumed to be in progress. The data is also standardized and converted into an input format for the AI ​​model.

[0238] Step 3:

[0239] The server inputs pre-processed data into the generated AI model and evaluates the task's progress and productivity. The AI ​​model compares this data to past data patterns and analyzes the causes of unusual delays and inefficiencies. This process utilizes delay patterns and productivity metrics frequently observed in the design phase.

[0240] Step 4:

[0241] The server visualizes the analysis results. This includes Gantt charts showing task progress and bar charts displaying individual employee productivity. Users can view this visualized data through a dashboard.

[0242] Step 5:

[0243] The server automatically generates a report based on the evaluations it receives. This report includes information on the status of employees' tasks, the causes of unusual delays, and suggestions for improvement. The server periodically sends this report to users via email.

[0244] Step 6:

[0245] Users can access reports and visualized performance data provided through the dashboard in real time, and check the status of their subordinates in detail as needed. This allows them to make immediate decisions and take measures to improve organizational performance.

[0246] (Example 1)

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

[0248] In a remote work environment, effectively managing employee work progress and productivity is a challenging task. In particular, understanding how individual employees communicate and how that impacts task progress and productivity is crucial. There is a need for efficient methods to visualize this information and use it for improvement.

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

[0250] In this invention, the server includes means for acquiring work data from an information processing device, means for preprocessing the acquired information and correcting missing or abnormal information, and means for evaluating the progress and efficiency of the work using a generated AI model. This enables accurate tracking and efficient management of employees' work progress and productivity, even in a teleworking environment.

[0251] An "information processing device" refers to a computer or terminal that can acquire, transmit, and analyze data.

[0252] "Work data" refers to information about the tasks performed by employees, including start time, end time, progress status, and information about the person in charge.

[0253] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and specifically includes imputing missing data and correcting outliers.

[0254] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to perform data analysis and pattern recognition.

[0255] "Progress status" refers to information that expresses the degree of progress of a task or project.

[0256] "Efficiency" is an indicator that shows how efficiently a task or work can be performed.

[0257] "User" refers to a person who uses the system to obtain or manage information, and in this context, it mainly refers to a manager.

[0258] "Visualization" refers to the process of representing analysis results in the form of graphs, charts, and other visual representations, making the information intuitively understandable.

[0259] A description of embodiments for carrying out this invention will be given.

[0260] The system for managing employee work progress and productivity in a remote work environment uses a server and employee terminals. The server retrieves information from task management tools on the employee's terminal and analyzes this information using a generative AI model. The terminals have software installed to record task progress and communication history. This software includes, for example, task management tools, email, and chat applications, and data is collected through these.

[0261] Specifically, the server periodically retrieves task start time, end time, progress rate, and assignee information from terminals. A generative AI model is used in the process of analyzing this information and evaluating the progress and productivity of the work. This generative AI model learns from past data patterns and can perform analyses to identify the causes of abnormal delays. For example, comparing the history of similar tasks can reveal the cause of delays in the current task.

[0262] The analyzed results are visualized by the server, allowing managers to view the information in real time through a dashboard. The visualizations utilize color-coded Gantt charts and productivity bar charts, providing an easy-to-understand format. The server also has the functionality to generate reports, including improvement suggestions, based on this information and automatically send them to users.

[0263] For example, if an employee in charge of a design project experiences a delay, the server can use a generative AI model to compare past communication frequency data with delay data to identify the cause of the delay. Based on these results, the server can then suggest improvements to the manager, such as "recommend regular team meetings."

[0264] Examples of prompts to input into a generative AI model include the following:

[0265] "The design task is behind schedule. Please analyze the cause by comparing it with past data and create improvement proposals."

[0266] In this way, users can efficiently manage employee activities even in a work-from-home environment.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The server collects data from employees' terminals via a task management tool. Inputs include task start and end times, progress percentage, assigned person, and communication history (email and chat data) provided by the terminal, which the server stores in a database. Specifically, the server periodically retrieves information using an API and stores it in a unified format.

[0270] Step 2:

[0271] The server preprocesses the collected data. It receives all the raw data as input and performs data imputation, anomaly detection, and correction. This process cleans the data and then generates a reliable dataset as output. Specifically, it uses statistical methods and machine learning algorithms to process the data and prepare highly consistent data.

[0272] Step 3:

[0273] The server analyzes pre-processed data using a generative AI model to evaluate employees' work progress and productivity. The input is well-organized data, and the AI ​​model analyzes the current data by comparing it to past patterns. The output provides each employee's current task progress rate and productivity. Specifically, the AI ​​model performs the analysis using prompts and lists the results.

[0274] Step 4:

[0275] The server visualizes the analyzed results. The input is evaluation results from an AI model, and the server creates Gantt charts and bar charts of various metrics that are easy for engineers to understand. The output is visual information displayed on the user's dashboard. Specifically, it visualizes data using a graph library and enables interactive displays based on user actions.

[0276] Step 5:

[0277] The server automatically generates and sends reports to users based on the generated evaluations and analysis results. The input consists of visualizations and analysis results, and the server uses this information to output reports that include an overview of operations and improvement suggestions. Specifically, the server uses a document generation engine to create reports in PDF format and sends them to users periodically via email. This process allows managers to obtain information that supports the optimal operation of their organization.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] In modern factories, there is a need to understand the progress and efficiency of a wide range of tasks in real time and for users to manage them effectively. However, in current systems, although a large amount of data is acquired, it is not often linked and properly evaluated, resulting in inefficient machine operation management. This leads to problems such as decreased overall work efficiency and delays in the early detection of abnormalities. Therefore, the present invention aims to solve these problems and improve work efficiency within factories.

[0281] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0282] In this invention, the server includes means for collecting business data from the user's operating device, means for preprocessing the collected data and correcting missing or abnormal information, and means for evaluating the progress rate and efficiency of the work using a generated AI model. This enables the operating status of equipment in the factory and the progress of work to be appropriately evaluated in real time, allowing the user to take immediate action.

[0283] The "user operation device" refers to a device that is operated by workers and managers in the factory for inputting and verifying business data.

[0284] "Business data" refers to data that includes information on the progress, efficiency, and various operation histories of work in the factory.

[0285] The "collecting means" refers to a mechanism equipped with a series of functions for efficiently acquiring business data from the user operation device.

[0286] "Preprocessing" refers to a process of complementing missing information, detecting and correcting abnormal information in the collected data, and processing the data into a consistent state.

[0287] The "generated AI model" refers to an artificial intelligence model that learns using a large amount of past data and enables real-time data analysis and evaluation.

[0288] "Progress rate" refers to an indicator that shows the degree of progress of planned work within a specific period.

[0289] "Efficiency" is an indicator that shows how effectively work is being done in the factory and is a measure for evaluating the work results against the usage amounts of resources and time.

[0290] "Real-time" refers to a state where data collection, processing, and display are carried out almost simultaneously, allowing users to immediately refer to the latest information.

[0291] "Being able to take countermeasures" means having the ability to immediately take means for problem-solving based on the obtained evaluation results.

[0292] This invention provides a system consisting of specific devices and programs to improve operational efficiency at work locations within a factory. The server collects operational data through user operating devices and centrally manages this data in real time using a cloud platform such as "Azure IoT Hub." The data includes work progress rates and efficiency, operating information of each piece of equipment, and anomaly information.

[0293] Next, the server preprocesses the collected data using a Python program. This preprocessing involves filling in missing information and filtering out anomalous data, followed by normalization to obtain consistent data. This allows for real-time evaluation of the progress and efficiency of each task using a generative AI model.

[0294] The generative AI model is built using tools such as TensorFlow and is designed to perform pattern recognition and efficient root cause analysis based on past work data. For example, it refers to similar past datasets to evaluate whether the current work is proceeding according to plan and identifies the causes of any anomalies or delays.

[0295] Users can visually review evaluation results through mobile devices such as smart glasses. This system allows real-time access to visualized analysis results in Gantt charts and bar charts, enabling quick action as needed. In addition, the server automatically generates and sends detailed reports to users periodically, providing valuable information for decision-making in factory operations.

[0296] For example, if a machine's operation is behind schedule, the generative AI model will identify the cause and notify the user with improvement suggestions, such as "We recommend reviewing the maintenance frequency." An example of a prompt to the generative AI model is: "Analyze the progress rate and anomaly detection patterns from the robot process data in the factory to identify the cause of low productivity."

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The server collects business data from the user's operating devices. Input data includes work process progress and equipment operating information. This enables real-time data centralization. The server efficiently stores this data through a cloud platform.

[0300] Step 2:

[0301] The server preprocesses the accumulated data using Python. Here, it reads the collected raw data as input. It imputes missing data, detects and corrects anomalies, and simultaneously filters out anomalies to generate consistent, normalized data as output.

[0302] Step 3:

[0303] The server inputs pre-processed data into a generative AI model and evaluates the progress and efficiency of the work. The generative AI model, using TensorFlow, references patterns in past data and analyzes the causes of unique delays from the input data. The output provides evaluation results, including delay factors and improvement suggestions.

[0304] Step 4:

[0305] Users receive visualized evaluation results from the server using mobile devices such as smart glasses. Based on the analysis results output by the generated AI model, progress is displayed in Gantt charts and bar charts. Users review this and make decisions regarding work improvement based on the output.

[0306] Step 5:

[0307] The server automatically generates a detailed report periodically and sends it to the user. Here, with the most recent business evaluation results as input, the report includes a comprehensive analysis of business efficiency and specific improvement proposals. Through this output, the user can achieve more effective factory management.

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

[0309] This invention is a system that efficiently manages the task progress and communication activities of employees in a telecommuting environment and supports organizational operations including the emotional state of the user. This system has an emotion engine and senses changes in the user's emotions to take appropriate actions.

[0310] First, the server periodically collects task management and communication data from the terminals of each employee. This data includes task progress, start and end times, and communication logs among employees. Furthermore, the emotion engine uses input devices such as the terminal's camera and microphone to acquire biometric information and analyzes the emotional state from the user's expressions and voices.

[0311] Next, the server preprocesses the collected data and evaluates task progress and productivity through a generated AI model. In this evaluation step, the emotion data of the user detected by the emotion engine is also integrated, taking into account the psychological factors that affect task progress.

[0312] The user can view the task status and the analysis results of emotions in real time through the dashboard. Along with a chart showing progress, the transition of emotions is visually displayed. The user can look for signs of decreased productivity or emotional stress.

[0313] Furthermore, the server automatically generates reports that provide an overview of employees' task status and emotional state, along with suggestions for improvement. These reports include, for example, how emotions have affected project progress and the actions that can be taken to address these issues. For instance, employees with high stress levels might be advised to "re-evaluate their tasks" or "take a break."

[0314] For example, if an employee is working on a design task for "Project X" and the emotion engine detects an increase in their stress levels, the server will immediately incorporate this information into the evaluation and display an additional alert on the dashboard. The report will also identify the sources of stress and suggest specific action plans to address them.

[0315] In this way, users can efficiently improve organizational performance in a work-from-home environment while comprehensively considering emotional factors.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server collects task management data and communication logs from each employee's terminal. In addition, an emotion engine built into the terminal acquires the user's facial expressions and voice data through the camera and microphone, and analyzes their emotional state in real time.

[0319] Step 2:

[0320] The server sequentially preprocesses the collected data. It verifies data integrity, imputes missing values, and removes noise. Furthermore, it standardizes sentiment data and converts sentiment categories into numerical data.

[0321] Step 3:

[0322] The server inputs pre-processed data into a generated AI model to evaluate employees' task progress and productivity. During this process, emotional data is integrated to analyze the emotional impact on task progress. For example, decreased productivity in employees experiencing prolonged stress may be identified.

[0323] Step 4:

[0324] The server visualizes the evaluation results and provides them to the user. The dashboard displays a Gantt chart showing task progress, a bar chart showing productivity, and a graph showing the progression of emotions. This allows the user to intuitively understand which tasks or situations are causing stress.

[0325] Step 5:

[0326] The server automatically generates a report based on the analysis results. This report includes each employee's task status, insights into changes in their emotions, and specific improvement suggestions. For example, it might suggest re-evaluating work schedules to reduce stress.

[0327] Step 6:

[0328] Through dashboards and reports, users can continuously monitor employees' task progress and emotional state, providing feedback and adjusting tasks as needed. This enables the management and improvement of overall organizational performance, even in a remote work environment.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0331] Effectively managing employee work progress and communication in a remote work environment is a critical challenge for many companies. In particular, understanding the impact of employees' emotional states on work efficiency in real time and taking appropriate action is difficult. In this situation, there is a need for a system that integrates and analyzes employee work data and emotional data to provide improvement suggestions.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes means for collecting work data from employees' information terminals, means for preprocessing the collected data and correcting missing or outlier values, and means for evaluating employees' work progress and productivity using a generated AI model. This enables real-time visualization of employees' work status and allows for comprehensive analysis and improvement suggestions based on their emotional state.

[0334] "Employee information terminals" is a general term for electronic devices such as computers, smartphones, and tablets that employees use to perform their work.

[0335] "Work data" refers to information generated when employees perform tasks, such as progress status, start and end times, and details of the work.

[0336] "Means of collection" refers to the technologies and methods used to obtain necessary data from employees' information terminals.

[0337] "Methods for preprocessing and correcting missing or outlier values" refer to processes that prepare collected data for easier analysis, including techniques for data cleaning and imputation.

[0338] A "generative AI model" is a mathematical model designed to analyze data in a specific business area using machine learning and artificial intelligence technologies.

[0339] "Biometric information" refers to information including facial expressions and voice data acquired by cameras and microphones in order to analyze the user's emotional state.

[0340] "Means of evaluation" refers to processes or technologies for analyzing collected work data and biometric information to quantify and evaluate work progress and productivity.

[0341] "Means of visualizing and providing information to users" refers to technologies that display analysis results in visual formats such as graphs and charts, making them easily understandable to users.

[0342] "Methods for automatically generating and sending reports to users" refers to technologies in which a system automatically creates a document summarizing analysis results and distributes it to users via email or other methods.

[0343] This invention is a system that efficiently manages the work progress and emotional state of employees in a teleworking environment, thereby supporting organizational management. This system is composed of various hardware and software as described below.

[0344] First, "terminals" refer to information processing devices used by employees to perform their daily tasks, and include personal computers, smartphones, and tablets. These terminals have work management applications and communication tools installed, through which work data and communication history are collected. Furthermore, biometric information (e.g., facial expressions, voice) is acquired using the terminal's camera and microphone to collect information for analyzing the user's emotional state.

[0345] Next, the server is the central device that manages and analyzes the data collected from each terminal. The server preprocesses the collected data, including imputing missing values ​​and correcting outliers. The preprocessed data is then input into a generative AI model. This generative AI model is designed to evaluate employees' work progress and productivity, and performs analysis using statistical methods, including comparison with data from similar past tasks.

[0346] Subsequently, the server provides the evaluated data to the user through a dashboard. The dashboard visually displays work progress and changes in emotions, allowing the user to intuitively understand the current situation. Furthermore, regularly generated reports send the user improvement suggestions based on evaluation results for specific tasks and their emotional state. This supports highly productive work operations in a remote work environment.

[0347] As a concrete example, by feeding a prompt such as "Analyze User A's work efficiency and stress level" to a generating AI model, it becomes possible to comprehensively evaluate the user's current situation and propose improvement measures. In this way, it is possible to improve work performance while taking into account the emotional aspects of employees.

[0348] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0349] Step 1:

[0350] The terminal collects various work data and communication history from employees and sends it to the server. Specifically, it periodically retrieves task progress and time information from work management applications, as well as message history from chat tools. The input consists of various data related to the employee's work, and the output is raw data sent to the server.

[0351] Step 2:

[0352] The device uses a camera and microphone to collect the user's biometric information. This includes facial expressions and voice data, which are used to capture the user's emotional state. The input for this step is biometric data acquired in real time, and the output is sent to the server as user emotion data.

[0353] Step 3:

[0354] The server preprocesses the work data and biometric information sent from the terminal. Specifically, it cleans the data, imputes missing values, removes outliers, and prepares it in a format suitable for analysis. The input is raw work data and emotion data, and the output is preprocessed, analyzable data.

[0355] Step 4:

[0356] The server analyzes pre-processed data using a generative AI model. Prompt statements are input to the AI ​​model to evaluate employee work progress and productivity. For example, a prompt such as "Analyze user A's work efficiency and stress level" can provide deeper insights. The input consists of pre-processed data and prompt statements, and the output is the evaluation result.

[0357] Step 5:

[0358] The server visualizes the evaluation results generated by the AI ​​model on a dashboard and provides them to the user. Visualized charts and graphs allow the user to intuitively understand the current situation. The input is the evaluation results, and the output is visual feedback on the dashboard.

[0359] Step 6:

[0360] The server automatically generates a report and sends it to the user, including evaluation results and improvement suggestions. Specifically, it incorporates appropriate improvement measures into the report based on the analysis results and distributes it via email or other means. The input is the evaluation results and improvement suggestions, and the output is the report sent to the user.

[0361] (Application Example 2)

[0362] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0363] In conventional work environments, workers' emotional states and stress levels affect productivity, but there is a lack of efficient means to detect and manage them. Furthermore, it is difficult to flexibly adjust the operation of machinery based on emotional states, posing challenges to improving work efficiency and reducing worker burden. Additionally, correcting outliers and missing values ​​is time-consuming, resulting in inefficient report generation.

[0364] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0365] In this invention, the server includes means for transferring information processing data from a terminal device, means for formatting the transferred data and correcting missing or abnormal values, means for measuring task progress rate and production efficiency using a generation AI computing device, and means for analyzing the emotional state of people in the work environment and adjusting the operation of mechanical devices based on the emotional state. This makes it possible to optimize work efficiency according to the emotional state and reduce the burden on workers.

[0366] A "terminal device" is a device used to input information and transfer it to a server. This includes devices equipped with cameras and microphones.

[0367] "Information processing data" refers to a series of data related to work status and communication content.

[0368] A "missing value" is a value that is incomplete or missing from a dataset.

[0369] An "outlier" refers to a value that falls outside the normal data range and may affect the analysis.

[0370] A "generative AI computing device" is a computing resource that performs analysis using generative algorithms based on collected data.

[0371] "Project progress rate" is an indicator that shows how much progress has been made on a particular project.

[0372] "Production efficiency" is the ratio that shows how much output was achieved in relation to the resources invested.

[0373] "Work environment" refers to the physical or virtual environment in which machinery and workers perform tasks together.

[0374] "Emotional state" refers to the psychological or emotional condition of a worker.

[0375] "Mechanical equipment" refers to automated facilities or robots designed to assist with specific tasks.

[0376] This invention is implemented by a system consisting of a terminal device, a server, and a user. The terminal device is equipped with a camera and microphone to collect biometric information and communication data of the worker. This collected data is automatically transferred to the server.

[0377] Upon receiving the transferred data, the server first performs data formatting. It corrects missing and outlier values ​​and arranges the dataset into a unified format. Next, it uses a generative AI computing device to measure the task progress rate and production efficiency. This measurement process also includes comparing the current data with historical data and similar work environments.

[0378] Furthermore, the server also analyzes the emotional state of workers in their work environment. For emotion analysis, it utilizes libraries such as "OpenCV" and "pyAudioAnalysis." Based on the emotional state, the server adjusts the operation of machinery in real time. As a result, it becomes possible to reduce the burden on workers and optimize work efficiency.

[0379] Users can view the visualization and analysis results of data generated by the system through a dashboard. This allows for concrete assessment of the work environment and suggestions for improvement. For example, if a worker is detected to be experiencing stress, the dashboard will display a warning in real time and suggest specific countermeasures.

[0380] As a concrete example, if a worker's concentration decreases during factory work, the server immediately analyzes the data and adjusts the operating speed of the machinery to reduce the worker's workload. This creates a work environment that maintains overall productivity while also considering the health of the workers.

[0381] An example of a prompt for a generative AI model is: "Based on the following employee's sentiment data, please assess the impact on productivity in the current task: Fatigue 50%, Stress Level 70%, Concentration 60%."

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server receives biometric and communication data transmitted from terminal devices. Inputs include worker facial expressions, voice data, and task progress information. Outputs include preparing the received data for conversion into a formatted data format. Specifically, this involves standardizing the data format and adding timestamps.

[0385] Step 2:

[0386] The server performs preprocessing on the received data to detect and correct missing and outlier values. The input is the data formatted in step 1. The output generates a clean dataset suitable for analysis. Specifically, it corrects outliers using statistical methods and imputes missing values ​​using machine learning libraries.

[0387] Step 3:

[0388] The server analyzes clean data using a generative AI computing device to evaluate task progress and production efficiency. Preprocessed data is used as input. The output is numerical information regarding progress and efficiency. Specifically, it performs AI evaluations based on comparisons with historical data and prompt statements. Factors such as task similarity are considered in the analysis.

[0389] Step 4:

[0390] The server analyzes emotional states such as stress and fatigue, and generates instructions to adjust the operation of the machine based on the results. Input data includes facial expression analysis results and voice tone analysis results. The output is specific action commands for the machine. Specifically, the robot's operating speed is adjusted according to the emotional state to reduce the worker's workload.

[0391] Step 5:

[0392] Users can view analysis results and task progress provided by the server on a dashboard. The system receives analysis data from the server as input. Output consists of visualized analysis data and suggested actions. Specifically, it displays information using an intuitive interface with charts and graphs, and provides warnings and action suggestions when necessary.

[0393] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0394] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0395] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0396] [Third Embodiment]

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

[0398] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0399] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0400] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0401] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0402] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0403] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0404] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0405] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0406] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0407] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0408] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0409] This invention is a system that streamlines organizational management in a work-from-home environment, aiming to visualize and evaluate employees' task progress and communication activities. The system includes a server and each employee's terminal, providing information through the collection, processing, and analysis of task data.

[0410] First, the server automatically collects data periodically from each employee's terminal via a task management tool. This data includes the task's start time, end time, progress rate, assigned person, and related communication history. For example, it also includes email and chat history data used by employees during the day.

[0411] Next, the server preprocesses the collected data. Preprocessing involves imputing missing values ​​and detecting outliers to ensure data integrity. This allows the generative AI model to process the data accurately.

[0412] Subsequently, the server uses a generative AI model to evaluate the employee's task progress and productivity. This evaluation process leverages past data patterns. For example, if an employee's productivity is declining on a "design task," the cause can be analyzed by comparing it with past data for similar tasks.

[0413] The analyzed results are visualized by the server and made easily accessible to managers through a dashboard. This includes Gantt charts showing progress and bar charts showing productivity metrics.

[0414] Finally, the server automatically generates reports based on the assessments and analyses and periodically sends them to users via email. These reports include an overview of employee activity and specific improvement suggestions, providing managers with the information they need to make rational decisions. Users can also access the reports in real time from the dashboard and obtain detailed information as needed.

[0415] For example, if an employee is in charge of a design project, the server monitors its progress and, if it detects that it is behind schedule, analyzes the cause through an AI model. For instance, it might be found that infrequent communication is a contributing factor to the delay. This information is then presented to the manager in a graph, and improvement suggestions, such as "recommend regular team meetings," are automatically generated.

[0416] In this way, even in a work-from-home environment, users can accurately grasp and efficiently manage the overall picture of employee progress and communication.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The server periodically collects data from each employee's terminal via the task management tool's API. This data includes task identifiers, assignee names, start and end times, progress status, and communication logs. The server stores this data in a central database.

[0420] Step 2:

[0421] The server preprocesses the collected data. This includes checking data integrity and detecting and correcting missing or outlier values. For example, if the end time is not entered, it is assumed to be in progress. The data is also standardized and converted into an input format for the AI ​​model.

[0422] Step 3:

[0423] The server inputs pre-processed data into the generated AI model and evaluates the task's progress and productivity. The AI ​​model compares this data to past data patterns and analyzes the causes of unusual delays and inefficiencies. This process utilizes delay patterns and productivity metrics frequently observed in the design phase.

[0424] Step 4:

[0425] The server visualizes the analysis results. This includes Gantt charts showing task progress and bar charts displaying individual employee productivity. Users can view this visualized data through a dashboard.

[0426] Step 5:

[0427] The server automatically generates a report based on the evaluations it receives. This report includes information on the status of employees' tasks, the causes of unusual delays, and suggestions for improvement. The server periodically sends this report to users via email.

[0428] Step 6:

[0429] Users can access reports and visualized performance data provided through the dashboard in real time, and check the status of their subordinates in detail as needed. This allows them to make immediate decisions and take measures to improve organizational performance.

[0430] (Example 1)

[0431] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0432] In a remote work environment, effectively managing employee work progress and productivity is a challenging task. In particular, understanding how individual employees communicate and how that impacts task progress and productivity is crucial. There is a need for efficient methods to visualize this information and use it for improvement.

[0433] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0434] In this invention, the server includes means for acquiring work data from an information processing device, means for preprocessing the acquired information and correcting missing or abnormal information, and means for evaluating the progress and efficiency of the work using a generated AI model. This enables accurate tracking and efficient management of employees' work progress and productivity, even in a teleworking environment.

[0435] An "information processing device" refers to a computer or terminal that can acquire, transmit, and analyze data.

[0436] "Work data" refers to information about the tasks performed by employees, including start time, end time, progress status, and information about the person in charge.

[0437] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and specifically includes imputing missing data and correcting outliers.

[0438] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to perform data analysis and pattern recognition.

[0439] "Progress status" refers to information that expresses the degree of progress of a task or project.

[0440] "Efficiency" is an indicator that shows how efficiently a task or work can be performed.

[0441] "User" refers to a person who uses the system to obtain or manage information, and in this context, it mainly refers to a manager.

[0442] "Visualization" refers to the process of representing analysis results in the form of graphs, charts, and other visual representations, making the information intuitively understandable.

[0443] A description of embodiments for carrying out this invention will be given.

[0444] The system for managing employee work progress and productivity in a remote work environment uses a server and employee terminals. The server retrieves information from task management tools on the employee's terminal and analyzes this information using a generative AI model. The terminals have software installed to record task progress and communication history. This software includes, for example, task management tools, email, and chat applications, and data is collected through these.

[0445] Specifically, the server periodically retrieves task start time, end time, progress rate, and assignee information from terminals. A generative AI model is used in the process of analyzing this information and evaluating the progress and productivity of the work. This generative AI model learns from past data patterns and can perform analyses to identify the causes of abnormal delays. For example, comparing the history of similar tasks can reveal the cause of delays in the current task.

[0446] The analyzed results are visualized by the server, allowing managers to view the information in real time through a dashboard. The visualizations utilize color-coded Gantt charts and productivity bar charts, providing an easy-to-understand format. The server also has the functionality to generate reports, including improvement suggestions, based on this information and automatically send them to users.

[0447] For example, if an employee in charge of a design project experiences a delay, the server can use a generative AI model to compare past communication frequency data with delay data to identify the cause of the delay. Based on these results, the server can then suggest improvements to the manager, such as "recommend regular team meetings."

[0448] Examples of prompts to input into a generative AI model include the following:

[0449] "The design task is behind schedule. Please analyze the cause by comparing it with past data and create improvement proposals."

[0450] In this way, users can efficiently manage employee activities even in a work-from-home environment.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The server collects data from employees' terminals via a task management tool. Inputs include task start and end times, progress percentage, assigned person, and communication history (email and chat data) provided by the terminal, which the server stores in a database. Specifically, the server periodically retrieves information using an API and stores it in a unified format.

[0454] Step 2:

[0455] The server preprocesses the collected data. It receives all the raw data as input and performs data imputation, anomaly detection, and correction. This process cleans the data and then generates a reliable dataset as output. Specifically, it uses statistical methods and machine learning algorithms to process the data and prepare highly consistent data.

[0456] Step 3:

[0457] The server analyzes pre-processed data using a generative AI model to evaluate employees' work progress and productivity. The input is well-organized data, and the AI ​​model analyzes the current data by comparing it to past patterns. The output provides each employee's current task progress rate and productivity. Specifically, the AI ​​model performs the analysis using prompts and lists the results.

[0458] Step 4:

[0459] The server visualizes the analyzed results. The input is evaluation results from an AI model, and the server creates Gantt charts and bar charts of various metrics that are easy for engineers to understand. The output is visual information displayed on the user's dashboard. Specifically, it visualizes data using a graph library and enables interactive displays based on user actions.

[0460] Step 5:

[0461] The server automatically generates and sends reports to users based on the generated evaluations and analysis results. The input consists of visualizations and analysis results, and the server uses this information to output reports that include an overview of operations and improvement suggestions. Specifically, the server uses a document generation engine to create reports in PDF format and sends them to users periodically via email. This process allows managers to obtain information that supports the optimal operation of their organization.

[0462] (Application Example 1)

[0463] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] In modern factories, there is a need to understand the progress and efficiency of a wide range of tasks in real time and for users to manage them effectively. However, in current systems, although a large amount of data is acquired, it is not often linked and properly evaluated, resulting in inefficient machine operation management. This leads to problems such as decreased overall work efficiency and delays in the early detection of abnormalities. Therefore, the present invention aims to solve these problems and improve work efficiency within factories.

[0465] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0466] In this invention, the server includes means for collecting business data from the user's operating device, means for preprocessing the collected data and correcting missing or abnormal information, and means for evaluating the progress rate and efficiency of the work using a generated AI model. This enables the operating status of equipment in the factory and the progress of work to be appropriately evaluated in real time, allowing the user to take immediate action.

[0467] "User-operated devices" refer to devices used by workers and managers within a factory to input and verify business data.

[0468] "Business data" refers to data that includes information on the progress and efficiency of work within the factory, as well as various operation histories.

[0469] "Means of collection" refers to a system equipped with a series of functions for efficiently acquiring business data from the user's operating device.

[0470] "Preprocessing" refers to the process of processing collected data to make it consistent by supplementing missing information and detecting and correcting anomalies.

[0471] A "generative AI model" refers to an artificial intelligence model that learns from a large amount of historical data, enabling real-time data analysis and evaluation.

[0472] "Progress rate" refers to an indicator that shows the degree to which planned work has been completed within a specific period.

[0473] "Efficiency" is an indicator that shows how effectively work is being carried out within a factory, and it is a measure that evaluates the work output in relation to the amount of resources and time used.

[0474] "Real-time" refers to a state where data collection, processing, and display occur almost simultaneously, allowing users to access the latest information immediately.

[0475] "Being able to take countermeasures" means having the ability to immediately take steps to resolve the problem based on the evaluation results obtained.

[0476] This invention provides a system consisting of specific devices and programs to improve operational efficiency at work locations within a factory. The server collects operational data through user operating devices and centrally manages this data in real time using a cloud platform such as "Azure IoT Hub." The data includes work progress rates and efficiency, operating information of each piece of equipment, and anomaly information.

[0477] Next, the server preprocesses the collected data using a Python program. This preprocessing involves filling in missing information and filtering out anomalous data, followed by normalization to obtain consistent data. This allows for real-time evaluation of the progress and efficiency of each task using a generative AI model.

[0478] The generative AI model is built using tools such as TensorFlow and is designed to perform pattern recognition and efficient root cause analysis based on past work data. For example, it refers to similar past datasets to evaluate whether the current work is proceeding according to plan and identifies the causes of any anomalies or delays.

[0479] Users can visually review evaluation results through mobile devices such as smart glasses. This system allows real-time access to visualized analysis results in Gantt charts and bar charts, enabling quick action as needed. In addition, the server automatically generates and sends detailed reports to users periodically, providing valuable information for decision-making in factory operations.

[0480] For example, if a machine's operation is behind schedule, the generative AI model will identify the cause and notify the user with improvement suggestions, such as "We recommend reviewing the maintenance frequency." An example of a prompt to the generative AI model is: "Analyze the progress rate and anomaly detection patterns from the robot process data in the factory to identify the cause of low productivity."

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The server collects business data from the user's operating devices. Input data includes work process progress and equipment operating information. This enables real-time data centralization. The server efficiently stores this data through a cloud platform.

[0484] Step 2:

[0485] The server preprocesses the accumulated data using Python. Here, it reads the collected raw data as input. It imputes missing data, detects and corrects anomalies, and simultaneously filters out anomalies to generate consistent, normalized data as output.

[0486] Step 3:

[0487] The server inputs pre-processed data into a generative AI model and evaluates the progress and efficiency of the work. The generative AI model, using TensorFlow, references patterns in past data and analyzes the causes of unique delays from the input data. The output provides evaluation results, including delay factors and improvement suggestions.

[0488] Step 4:

[0489] Users receive visualized evaluation results from the server using mobile devices such as smart glasses. Based on the analysis results output by the generated AI model, progress is displayed in Gantt charts and bar charts. Users review this and make decisions regarding work improvement based on the output.

[0490] Step 5:

[0491] The server automatically generates and sends detailed reports to users on a regular basis. Using the most recent operational evaluation results as input, the reports include a comprehensive analysis of operational efficiency and specific improvement suggestions. This output enables users to achieve more effective factory management.

[0492] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0493] This invention is a system that efficiently manages the task progress and communication activities of employees in a teleworking environment, and supports organizational management, including the emotional state of users. This system is equipped with an emotion engine that senses changes in the user's emotions and takes appropriate action.

[0494] First, the server periodically collects task management and communication data from each employee's terminal. This data includes task progress, start and end times, and communication logs between employees. Furthermore, the emotion engine uses input devices such as the terminal's camera and microphone to acquire biometric information and analyze the user's emotional state from their facial expressions and voice.

[0495] Next, the server preprocesses the collected data and evaluates the task's progress and productivity through a generative AI model. This evaluation step also integrates user sentiment data detected by the sentiment engine, taking into account psychological factors that influence task progress.

[0496] Users can view task status and emotional analysis results in real time via a dashboard. This includes a visual display of emotional trends along with progress charts. Users can identify signs of decreased productivity or emotional stress.

[0497] Furthermore, the server automatically generates reports that provide an overview of employees' task status and emotional state, along with suggestions for improvement. These reports include, for example, how emotions have affected project progress and the actions that can be taken to address these issues. For instance, employees with high stress levels might be advised to "re-evaluate their tasks" or "take a break."

[0498] For example, if an employee is working on a design task for "Project X" and the emotion engine detects an increase in their stress levels, the server will immediately incorporate this information into the evaluation and display an additional alert on the dashboard. The report will also identify the sources of stress and suggest specific action plans to address them.

[0499] In this way, users can efficiently improve organizational performance in a work-from-home environment while comprehensively considering emotional factors.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The server collects task management data and communication logs from each employee's terminal. In addition, an emotion engine built into the terminal acquires the user's facial expressions and voice data through the camera and microphone, and analyzes their emotional state in real time.

[0503] Step 2:

[0504] The server sequentially preprocesses the collected data. It verifies data integrity, imputes missing values, and removes noise. Furthermore, it standardizes sentiment data and converts sentiment categories into numerical data.

[0505] Step 3:

[0506] The server inputs pre-processed data into a generated AI model to evaluate employees' task progress and productivity. During this process, emotional data is integrated to analyze the emotional impact on task progress. For example, decreased productivity in employees experiencing prolonged stress may be identified.

[0507] Step 4:

[0508] The server visualizes the evaluation results and provides them to the user. The dashboard displays a Gantt chart showing task progress, a bar chart showing productivity, and a graph showing the progression of emotions. This allows the user to intuitively understand which tasks or situations are causing stress.

[0509] Step 5:

[0510] The server automatically generates a report based on the analysis results. This report includes each employee's task status, insights into changes in their emotions, and specific improvement suggestions. For example, it might suggest re-evaluating work schedules to reduce stress.

[0511] Step 6:

[0512] Through dashboards and reports, users can continuously monitor employees' task progress and emotional state, providing feedback and adjusting tasks as needed. This enables the management and improvement of overall organizational performance, even in a remote work environment.

[0513] (Example 2)

[0514] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] Effectively managing employee work progress and communication in a remote work environment is a critical challenge for many companies. In particular, understanding the impact of employees' emotional states on work efficiency in real time and taking appropriate action is difficult. In this situation, there is a need for a system that integrates and analyzes employee work data and emotional data to provide improvement suggestions.

[0516] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0517] In this invention, the server includes means for collecting work data from employees' information terminals, means for preprocessing the collected data and correcting missing or outlier values, and means for evaluating employees' work progress and productivity using a generated AI model. This enables real-time visualization of employees' work status and allows for comprehensive analysis and improvement suggestions based on their emotional state.

[0518] "Employee information terminals" is a general term for electronic devices such as computers, smartphones, and tablets that employees use to perform their work.

[0519] "Work data" refers to information generated when employees perform tasks, such as progress status, start and end times, and details of the work.

[0520] "Means of collection" refers to the technologies and methods used to obtain necessary data from employees' information terminals.

[0521] "Methods for preprocessing and correcting missing or outlier values" refer to processes that prepare collected data for easier analysis, including techniques for data cleaning and imputation.

[0522] A "generative AI model" is a mathematical model designed to analyze data in a specific business area using machine learning and artificial intelligence technologies.

[0523] "Biometric information" refers to information including facial expressions and voice data acquired by cameras and microphones in order to analyze the user's emotional state.

[0524] "Means of evaluation" refers to processes or technologies for analyzing collected work data and biometric information to quantify and evaluate work progress and productivity.

[0525] "Means of visualizing and providing information to users" refers to technologies that display analysis results in visual formats such as graphs and charts, making them easily understandable to users.

[0526] "Methods for automatically generating and sending reports to users" refers to technologies in which a system automatically creates a document summarizing analysis results and distributes it to users via email or other methods.

[0527] This invention is a system that efficiently manages the work progress and emotional state of employees in a teleworking environment, thereby supporting organizational management. This system is composed of various hardware and software as described below.

[0528] First, "terminals" refer to information processing devices used by employees to perform their daily tasks, and include personal computers, smartphones, and tablets. These terminals have work management applications and communication tools installed, through which work data and communication history are collected. Furthermore, biometric information (e.g., facial expressions, voice) is acquired using the terminal's camera and microphone to collect information for analyzing the user's emotional state.

[0529] Next, the server is the central device that manages and analyzes the data collected from each terminal. The server preprocesses the collected data, including imputing missing values ​​and correcting outliers. The preprocessed data is then input into a generative AI model. This generative AI model is designed to evaluate employees' work progress and productivity, and performs analysis using statistical methods, including comparison with data from similar past tasks.

[0530] Subsequently, the server provides the evaluated data to the user through a dashboard. The dashboard visually displays work progress and changes in emotions, allowing the user to intuitively understand the current situation. Furthermore, regularly generated reports send the user improvement suggestions based on evaluation results for specific tasks and their emotional state. This supports highly productive work operations in a remote work environment.

[0531] As a concrete example, by feeding a prompt such as "Analyze User A's work efficiency and stress level" to a generating AI model, it becomes possible to comprehensively evaluate the user's current situation and propose improvement measures. In this way, it is possible to improve work performance while taking into account the emotional aspects of employees.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The terminal collects various work data and communication history from employees and sends it to the server. Specifically, it periodically retrieves task progress and time information from work management applications, as well as message history from chat tools. The input consists of various data related to the employee's work, and the output is raw data sent to the server.

[0535] Step 2:

[0536] The device uses a camera and microphone to collect the user's biometric information. This includes facial expressions and voice data, which are used to capture the user's emotional state. The input for this step is biometric data acquired in real time, and the output is sent to the server as user emotion data.

[0537] Step 3:

[0538] The server preprocesses the work data and biometric information sent from the terminal. Specifically, it cleans the data, imputes missing values, removes outliers, and prepares it in a format suitable for analysis. The input is raw work data and emotion data, and the output is preprocessed, analyzable data.

[0539] Step 4:

[0540] The server analyzes pre-processed data using a generative AI model. Prompt statements are input to the AI ​​model to evaluate employee work progress and productivity. For example, a prompt such as "Analyze user A's work efficiency and stress level" can provide deeper insights. The input consists of pre-processed data and prompt statements, and the output is the evaluation result.

[0541] Step 5:

[0542] The server visualizes the evaluation results generated by the AI ​​model on a dashboard and provides them to the user. Visualized charts and graphs allow the user to intuitively understand the current situation. The input is the evaluation results, and the output is visual feedback on the dashboard.

[0543] Step 6:

[0544] The server automatically generates a report and sends it to the user, including evaluation results and improvement suggestions. Specifically, it incorporates appropriate improvement measures into the report based on the analysis results and distributes it via email or other means. The input is the evaluation results and improvement suggestions, and the output is the report sent to the user.

[0545] (Application Example 2)

[0546] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0547] In conventional work environments, workers' emotional states and stress levels affect productivity, but there is a lack of efficient means to detect and manage them. Furthermore, it is difficult to flexibly adjust the operation of machinery based on emotional states, posing challenges to improving work efficiency and reducing worker burden. Additionally, correcting outliers and missing values ​​is time-consuming, resulting in inefficient report generation.

[0548] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0549] In this invention, the server includes means for transferring information processing data from a terminal device, means for formatting the transferred data and correcting missing or abnormal values, means for measuring task progress rate and production efficiency using a generation AI computing device, and means for analyzing the emotional state of people in the work environment and adjusting the operation of mechanical devices based on the emotional state. This makes it possible to optimize work efficiency according to the emotional state and reduce the burden on workers.

[0550] A "terminal device" is a device used to input information and transfer it to a server. This includes devices equipped with cameras and microphones.

[0551] "Information processing data" refers to a series of data related to work status and communication content.

[0552] A "missing value" is a value that is incomplete or missing from a dataset.

[0553] An "outlier" refers to a value that falls outside the normal data range and may affect the analysis.

[0554] A "generative AI computing device" is a computing resource that performs analysis using generative algorithms based on collected data.

[0555] "Project progress rate" is an indicator that shows how much progress has been made on a particular project.

[0556] "Production efficiency" is the ratio that shows how much output was achieved in relation to the resources invested.

[0557] "Work environment" refers to the physical or virtual environment in which machinery and workers perform tasks together.

[0558] "Emotional state" refers to the psychological or emotional condition of a worker.

[0559] "Mechanical equipment" refers to automated facilities or robots designed to assist with specific tasks.

[0560] This invention is implemented by a system consisting of a terminal device, a server, and a user. The terminal device is equipped with a camera and microphone to collect biometric information and communication data of the worker. This collected data is automatically transferred to the server.

[0561] Upon receiving the transferred data, the server first performs data formatting. It corrects missing and outlier values ​​and arranges the dataset into a unified format. Next, it uses a generative AI computing device to measure the task progress rate and production efficiency. This measurement process also includes comparing the current data with historical data and similar work environments.

[0562] Furthermore, the server also analyzes the emotional state of workers in their work environment. For emotion analysis, it utilizes libraries such as "OpenCV" and "pyAudioAnalysis." Based on the emotional state, the server adjusts the operation of machinery in real time. As a result, it becomes possible to reduce the burden on workers and optimize work efficiency.

[0563] Users can view the visualization and analysis results of data generated by the system through a dashboard. This allows for concrete assessment of the work environment and suggestions for improvement. For example, if a worker is detected to be experiencing stress, the dashboard will display a warning in real time and suggest specific countermeasures.

[0564] As a concrete example, if a worker's concentration decreases during factory work, the server immediately analyzes the data and adjusts the operating speed of the machinery to reduce the worker's workload. This creates a work environment that maintains overall productivity while also considering the health of the workers.

[0565] An example of a prompt for a generative AI model is: "Based on the following employee's sentiment data, please assess the impact on productivity in the current task: Fatigue 50%, Stress Level 70%, Concentration 60%."

[0566] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0567] Step 1:

[0568] The server receives biometric and communication data transmitted from terminal devices. Inputs include worker facial expressions, voice data, and task progress information. Outputs include preparing the received data for conversion into a formatted data format. Specifically, this involves standardizing the data format and adding timestamps.

[0569] Step 2:

[0570] The server performs preprocessing on the received data to detect and correct missing and outlier values. The input is the data formatted in step 1. The output generates a clean dataset suitable for analysis. Specifically, it corrects outliers using statistical methods and imputes missing values ​​using machine learning libraries.

[0571] Step 3:

[0572] The server analyzes clean data using a generative AI computing device to evaluate task progress and production efficiency. Preprocessed data is used as input. The output is numerical information regarding progress and efficiency. Specifically, it performs AI evaluations based on comparisons with historical data and prompt statements. Factors such as task similarity are considered in the analysis.

[0573] Step 4:

[0574] The server analyzes emotional states such as stress and fatigue, and generates instructions to adjust the operation of the machine based on the results. Input data includes facial expression analysis results and voice tone analysis results. The output is specific action commands for the machine. Specifically, the robot's operating speed is adjusted according to the emotional state to reduce the worker's workload.

[0575] Step 5:

[0576] Users can view analysis results and task progress provided by the server on a dashboard. The system receives analysis data from the server as input. Output consists of visualized analysis data and suggested actions. Specifically, it displays information using an intuitive interface with charts and graphs, and provides warnings and action suggestions when necessary.

[0577] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0578] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0579] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0580] [Fourth Embodiment]

[0581] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0582] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0583] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0584] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0585] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0586] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0587] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0588] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0589] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0590] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0591] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0592] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0593] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0594] This invention is a system that streamlines organizational management in a work-from-home environment, aiming to visualize and evaluate employees' task progress and communication activities. The system includes a server and each employee's terminal, providing information through the collection, processing, and analysis of task data.

[0595] First, the server automatically collects data periodically from each employee's terminal via a task management tool. This data includes the task's start time, end time, progress rate, assigned person, and related communication history. For example, it also includes email and chat history data used by employees during the day.

[0596] Next, the server preprocesses the collected data. Preprocessing involves imputing missing values ​​and detecting outliers to ensure data integrity. This allows the generative AI model to process the data accurately.

[0597] Subsequently, the server uses a generative AI model to evaluate the employee's task progress and productivity. This evaluation process leverages past data patterns. For example, if an employee's productivity is declining on a "design task," the cause can be analyzed by comparing it with past data for similar tasks.

[0598] The analyzed results are visualized by the server and made easily accessible to managers through a dashboard. This includes Gantt charts showing progress and bar charts showing productivity metrics.

[0599] Finally, the server automatically generates reports based on the assessments and analyses and periodically sends them to users via email. These reports include an overview of employee activity and specific improvement suggestions, providing managers with the information they need to make rational decisions. Users can also access the reports in real time from the dashboard and obtain detailed information as needed.

[0600] For example, if an employee is in charge of a design project, the server monitors its progress and, if it detects that it is behind schedule, analyzes the cause through an AI model. For instance, it might be found that infrequent communication is a contributing factor to the delay. This information is then presented to the manager in a graph, and improvement suggestions, such as "recommend regular team meetings," are automatically generated.

[0601] In this way, even in a work-from-home environment, users can accurately grasp and efficiently manage the overall picture of employee progress and communication.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] The server periodically collects data from each employee's terminal via the task management tool's API. This data includes task identifiers, assignee names, start and end times, progress status, and communication logs. The server stores this data in a central database.

[0605] Step 2:

[0606] The server preprocesses the collected data. This includes checking data integrity and detecting and correcting missing or outlier values. For example, if the end time is not entered, it is assumed to be in progress. The data is also standardized and converted into an input format for the AI ​​model.

[0607] Step 3:

[0608] The server inputs pre-processed data into the generated AI model and evaluates the task's progress and productivity. The AI ​​model compares this data to past data patterns and analyzes the causes of unusual delays and inefficiencies. This process utilizes delay patterns and productivity metrics frequently observed in the design phase.

[0609] Step 4:

[0610] The server visualizes the analysis results. This includes Gantt charts showing task progress and bar charts displaying individual employee productivity. Users can view this visualized data through a dashboard.

[0611] Step 5:

[0612] The server automatically generates a report based on the evaluations it receives. This report includes information on the status of employees' tasks, the causes of unusual delays, and suggestions for improvement. The server periodically sends this report to users via email.

[0613] Step 6:

[0614] Users can access reports and visualized performance data provided through the dashboard in real time, and check the status of their subordinates in detail as needed. This allows them to make immediate decisions and take measures to improve organizational performance.

[0615] (Example 1)

[0616] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0617] In a remote work environment, effectively managing employee work progress and productivity is a challenging task. In particular, understanding how individual employees communicate and how that impacts task progress and productivity is crucial. There is a need for efficient methods to visualize this information and use it for improvement.

[0618] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0619] In this invention, the server includes means for acquiring work data from an information processing device, means for preprocessing the acquired information and correcting missing or abnormal information, and means for evaluating the progress and efficiency of the work using a generated AI model. This enables accurate tracking and efficient management of employees' work progress and productivity, even in a teleworking environment.

[0620] An "information processing device" refers to a computer or terminal that can acquire, transmit, and analyze data.

[0621] "Work data" refers to information about the tasks performed by employees, including start time, end time, progress status, and information about the person in charge.

[0622] "Preprocessing" refers to the process of preparing data to make it easier to analyze, and specifically includes imputing missing data and correcting outliers.

[0623] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to perform data analysis and pattern recognition.

[0624] "Progress status" refers to information that expresses the degree of progress of a task or project.

[0625] "Efficiency" is an indicator that shows how efficiently a task or work can be performed.

[0626] "User" refers to a person who uses the system to obtain or manage information, and in this context, it mainly refers to a manager.

[0627] "Visualization" refers to the process of representing analysis results in the form of graphs, charts, and other visual representations, making the information intuitively understandable.

[0628] A description of embodiments for carrying out this invention will be given.

[0629] The system for managing employee work progress and productivity in a remote work environment uses a server and employee terminals. The server retrieves information from task management tools on the employee's terminal and analyzes this information using a generative AI model. The terminals have software installed to record task progress and communication history. This software includes, for example, task management tools, email, and chat applications, and data is collected through these.

[0630] Specifically, the server periodically retrieves task start time, end time, progress rate, and assignee information from terminals. A generative AI model is used in the process of analyzing this information and evaluating the progress and productivity of the work. This generative AI model learns from past data patterns and can perform analyses to identify the causes of abnormal delays. For example, comparing the history of similar tasks can reveal the cause of delays in the current task.

[0631] The analyzed results are visualized by the server, allowing managers to view the information in real time through a dashboard. The visualizations utilize color-coded Gantt charts and productivity bar charts, providing an easy-to-understand format. The server also has the functionality to generate reports, including improvement suggestions, based on this information and automatically send them to users.

[0632] For example, if an employee in charge of a design project experiences a delay, the server can use a generative AI model to compare past communication frequency data with delay data to identify the cause of the delay. Based on these results, the server can then suggest improvements to the manager, such as "recommend regular team meetings."

[0633] Examples of prompts to input into a generative AI model include the following:

[0634] "The design task is behind schedule. Please analyze the cause by comparing it with past data and create improvement proposals."

[0635] In this way, users can efficiently manage employee activities even in a work-from-home environment.

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] The server collects data from employees' terminals via a task management tool. Inputs include task start and end times, progress percentage, assigned person, and communication history (email and chat data) provided by the terminal, which the server stores in a database. Specifically, the server periodically retrieves information using an API and stores it in a unified format.

[0639] Step 2:

[0640] The server preprocesses the collected data. It receives all the raw data as input and performs data imputation, anomaly detection, and correction. This process cleans the data and then generates a reliable dataset as output. Specifically, it uses statistical methods and machine learning algorithms to process the data and prepare highly consistent data.

[0641] Step 3:

[0642] The server analyzes pre-processed data using a generative AI model to evaluate employees' work progress and productivity. The input is well-organized data, and the AI ​​model analyzes the current data by comparing it to past patterns. The output provides each employee's current task progress rate and productivity. Specifically, the AI ​​model performs the analysis using prompts and lists the results.

[0643] Step 4:

[0644] The server visualizes the analyzed results. The input is evaluation results from an AI model, and the server creates Gantt charts and bar charts of various metrics that are easy for engineers to understand. The output is visual information displayed on the user's dashboard. Specifically, it visualizes data using a graph library and enables interactive displays based on user actions.

[0645] Step 5:

[0646] The server automatically generates and sends reports to users based on the generated evaluations and analysis results. The input consists of visualizations and analysis results, and the server uses this information to output reports that include an overview of operations and improvement suggestions. Specifically, the server uses a document generation engine to create reports in PDF format and sends them to users periodically via email. This process allows managers to obtain information that supports the optimal operation of their organization.

[0647] (Application Example 1)

[0648] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] In modern factories, there is a need to understand the progress and efficiency of a wide range of tasks in real time and for users to manage them effectively. However, in current systems, although a large amount of data is acquired, it is not often linked and properly evaluated, resulting in inefficient machine operation management. This leads to problems such as decreased overall work efficiency and delays in the early detection of abnormalities. Therefore, the present invention aims to solve these problems and improve work efficiency within factories.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0651] In this invention, the server includes means for collecting business data from the user's operating device, means for preprocessing the collected data and correcting missing or abnormal information, and means for evaluating the progress rate and efficiency of the work using a generated AI model. This enables the operating status of equipment in the factory and the progress of work to be appropriately evaluated in real time, allowing the user to take immediate action.

[0652] "User-operated devices" refer to devices used by workers and managers within a factory to input and verify business data.

[0653] "Business data" refers to data that includes information on the progress and efficiency of work within the factory, as well as various operation histories.

[0654] "Means of collection" refers to a system equipped with a series of functions for efficiently acquiring business data from the user's operating device.

[0655] "Preprocessing" refers to the process of processing collected data to make it consistent by supplementing missing information and detecting and correcting anomalies.

[0656] A "generative AI model" refers to an artificial intelligence model that learns from a large amount of historical data, enabling real-time data analysis and evaluation.

[0657] "Progress rate" refers to an indicator that shows the degree to which planned work has been completed within a specific period.

[0658] "Efficiency" is an indicator that shows how effectively work is being carried out within a factory, and it is a measure that evaluates the work output in relation to the amount of resources and time used.

[0659] "Real-time" refers to a state where data collection, processing, and display occur almost simultaneously, allowing users to access the latest information immediately.

[0660] "Being able to take countermeasures" means having the ability to immediately take steps to resolve the problem based on the evaluation results obtained.

[0661] This invention provides a system consisting of specific devices and programs to improve operational efficiency at work locations within a factory. The server collects operational data through user operating devices and centrally manages this data in real time using a cloud platform such as "Azure IoT Hub." The data includes work progress rates and efficiency, operating information of each piece of equipment, and anomaly information.

[0662] Next, the server preprocesses the collected data using a Python program. This preprocessing involves filling in missing information and filtering out anomalous data, followed by normalization to obtain consistent data. This allows for real-time evaluation of the progress and efficiency of each task using a generative AI model.

[0663] The generative AI model is built using tools such as TensorFlow and is designed to perform pattern recognition and efficient root cause analysis based on past work data. For example, it refers to similar past datasets to evaluate whether the current work is proceeding according to plan and identifies the causes of any anomalies or delays.

[0664] Users can visually review evaluation results through mobile devices such as smart glasses. This system allows real-time access to visualized analysis results in Gantt charts and bar charts, enabling quick action as needed. In addition, the server automatically generates and sends detailed reports to users periodically, providing valuable information for decision-making in factory operations.

[0665] For example, if a machine's operation is behind schedule, the generative AI model will identify the cause and notify the user with improvement suggestions, such as "We recommend reviewing the maintenance frequency." An example of a prompt to the generative AI model is: "Analyze the progress rate and anomaly detection patterns from the robot process data in the factory to identify the cause of low productivity."

[0666] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0667] Step 1:

[0668] The server collects business data from the user's operating devices. Input data includes work process progress and equipment operating information. This enables real-time data centralization. The server efficiently stores this data through a cloud platform.

[0669] Step 2:

[0670] The server preprocesses the accumulated data using Python. Here, it reads the collected raw data as input. It imputes missing data, detects and corrects anomalies, and simultaneously filters out anomalies to generate consistent, normalized data as output.

[0671] Step 3:

[0672] The server inputs pre-processed data into a generative AI model and evaluates the progress and efficiency of the work. The generative AI model, using TensorFlow, references patterns in past data and analyzes the causes of unique delays from the input data. The output provides evaluation results, including delay factors and improvement suggestions.

[0673] Step 4:

[0674] Users receive visualized evaluation results from the server using mobile devices such as smart glasses. Based on the analysis results output by the generated AI model, progress is displayed in Gantt charts and bar charts. Users review this and make decisions regarding work improvement based on the output.

[0675] Step 5:

[0676] The server automatically generates and sends detailed reports to users on a regular basis. Using the most recent operational evaluation results as input, the reports include a comprehensive analysis of operational efficiency and specific improvement suggestions. This output enables users to achieve more effective factory management.

[0677] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0678] This invention is a system that efficiently manages the task progress and communication activities of employees in a teleworking environment, and supports organizational management, including the emotional state of users. This system is equipped with an emotion engine that senses changes in the user's emotions and takes appropriate action.

[0679] First, the server periodically collects task management and communication data from each employee's terminal. This data includes task progress, start and end times, and communication logs between employees. Furthermore, the emotion engine uses input devices such as the terminal's camera and microphone to acquire biometric information and analyze the user's emotional state from their facial expressions and voice.

[0680] Next, the server preprocesses the collected data and evaluates the task's progress and productivity through a generative AI model. This evaluation step also integrates user sentiment data detected by the sentiment engine, taking into account psychological factors that influence task progress.

[0681] Users can view task status and emotional analysis results in real time via a dashboard. This includes a visual display of emotional trends along with progress charts. Users can identify signs of decreased productivity or emotional stress.

[0682] Furthermore, the server automatically generates reports that provide an overview of employees' task status and emotional state, along with suggestions for improvement. These reports include, for example, how emotions have affected project progress and the actions that can be taken to address these issues. For instance, employees with high stress levels might be advised to "re-evaluate their tasks" or "take a break."

[0683] For example, if an employee is working on a design task for "Project X" and the emotion engine detects an increase in their stress levels, the server will immediately incorporate this information into the evaluation and display an additional alert on the dashboard. The report will also identify the sources of stress and suggest specific action plans to address them.

[0684] In this way, users can efficiently improve organizational performance in a work-from-home environment while comprehensively considering emotional factors.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The server collects task management data and communication logs from each employee's terminal. In addition, an emotion engine built into the terminal acquires the user's facial expressions and voice data through the camera and microphone, and analyzes their emotional state in real time.

[0688] Step 2:

[0689] The server sequentially preprocesses the collected data. It verifies data integrity, imputes missing values, and removes noise. Furthermore, it standardizes sentiment data and converts sentiment categories into numerical data.

[0690] Step 3:

[0691] The server inputs pre-processed data into a generated AI model to evaluate employees' task progress and productivity. During this process, emotional data is integrated to analyze the emotional impact on task progress. For example, decreased productivity in employees experiencing prolonged stress may be identified.

[0692] Step 4:

[0693] The server visualizes the evaluation results and provides them to the user. The dashboard displays a Gantt chart showing task progress, a bar chart showing productivity, and a graph showing the progression of emotions. This allows the user to intuitively understand which tasks or situations are causing stress.

[0694] Step 5:

[0695] The server automatically generates a report based on the analysis results. This report includes each employee's task status, insights into changes in their emotions, and specific improvement suggestions. For example, it might suggest re-evaluating work schedules to reduce stress.

[0696] Step 6:

[0697] Through dashboards and reports, users can continuously monitor employees' task progress and emotional state, providing feedback and adjusting tasks as needed. This enables the management and improvement of overall organizational performance, even in a remote work environment.

[0698] (Example 2)

[0699] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] Effectively managing employee work progress and communication in a remote work environment is a critical challenge for many companies. In particular, understanding the impact of employees' emotional states on work efficiency in real time and taking appropriate action is difficult. In this situation, there is a need for a system that integrates and analyzes employee work data and emotional data to provide improvement suggestions.

[0701] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0702] In this invention, the server includes means for collecting work data from employees' information terminals, means for preprocessing the collected data and correcting missing or outlier values, and means for evaluating employees' work progress and productivity using a generated AI model. This enables real-time visualization of employees' work status and allows for comprehensive analysis and improvement suggestions based on their emotional state.

[0703] "Employee information terminals" is a general term for electronic devices such as computers, smartphones, and tablets that employees use to perform their work.

[0704] "Work data" refers to information generated when employees perform tasks, such as progress status, start and end times, and details of the work.

[0705] "Means of collection" refers to the technologies and methods used to obtain necessary data from employees' information terminals.

[0706] "Methods for preprocessing and correcting missing or outlier values" refer to processes that prepare collected data for easier analysis, including techniques for data cleaning and imputation.

[0707] A "generative AI model" is a mathematical model designed to analyze data in a specific business area using machine learning and artificial intelligence technologies.

[0708] "Biometric information" refers to information including facial expressions and voice data acquired by cameras and microphones in order to analyze the user's emotional state.

[0709] "Means of evaluation" refers to processes or technologies for analyzing collected work data and biometric information to quantify and evaluate work progress and productivity.

[0710] "Means of visualizing and providing information to users" refers to technologies that display analysis results in visual formats such as graphs and charts, making them easily understandable to users.

[0711] "Methods for automatically generating and sending reports to users" refers to technologies in which a system automatically creates a document summarizing analysis results and distributes it to users via email or other methods.

[0712] This invention is a system that efficiently manages the work progress and emotional state of employees in a teleworking environment, thereby supporting organizational management. This system is composed of various hardware and software as described below.

[0713] First, "terminals" refer to information processing devices used by employees to perform their daily tasks, and include personal computers, smartphones, and tablets. These terminals have work management applications and communication tools installed, through which work data and communication history are collected. Furthermore, biometric information (e.g., facial expressions, voice) is acquired using the terminal's camera and microphone to collect information for analyzing the user's emotional state.

[0714] Next, the server is the central device that manages and analyzes the data collected from each terminal. The server preprocesses the collected data, including imputing missing values ​​and correcting outliers. The preprocessed data is then input into a generative AI model. This generative AI model is designed to evaluate employees' work progress and productivity, and performs analysis using statistical methods, including comparison with data from similar past tasks.

[0715] Subsequently, the server provides the evaluated data to the user through a dashboard. The dashboard visually displays work progress and changes in emotions, allowing the user to intuitively understand the current situation. Furthermore, regularly generated reports send the user improvement suggestions based on evaluation results for specific tasks and their emotional state. This supports highly productive work operations in a remote work environment.

[0716] As a concrete example, by feeding a prompt such as "Analyze User A's work efficiency and stress level" to a generating AI model, it becomes possible to comprehensively evaluate the user's current situation and propose improvement measures. In this way, it is possible to improve work performance while taking into account the emotional aspects of employees.

[0717] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0718] Step 1:

[0719] The terminal collects various work data and communication history from employees and sends it to the server. Specifically, it periodically retrieves task progress and time information from work management applications, as well as message history from chat tools. The input consists of various data related to the employee's work, and the output is raw data sent to the server.

[0720] Step 2:

[0721] The device uses a camera and microphone to collect the user's biometric information. This includes facial expressions and voice data, which are used to capture the user's emotional state. The input for this step is biometric data acquired in real time, and the output is sent to the server as user emotion data.

[0722] Step 3:

[0723] The server preprocesses the work data and biometric information sent from the terminal. Specifically, it cleans the data, imputes missing values, removes outliers, and prepares it in a format suitable for analysis. The input is raw work data and emotion data, and the output is preprocessed, analyzable data.

[0724] Step 4:

[0725] The server analyzes pre-processed data using a generative AI model. Prompt statements are input to the AI ​​model to evaluate employee work progress and productivity. For example, a prompt such as "Analyze user A's work efficiency and stress level" can provide deeper insights. The input consists of pre-processed data and prompt statements, and the output is the evaluation result.

[0726] Step 5:

[0727] The server visualizes the evaluation results generated by the AI ​​model on a dashboard and provides them to the user. Visualized charts and graphs allow the user to intuitively understand the current situation. The input is the evaluation results, and the output is visual feedback on the dashboard.

[0728] Step 6:

[0729] The server automatically generates a report and sends it to the user, including evaluation results and improvement suggestions. Specifically, it incorporates appropriate improvement measures into the report based on the analysis results and distributes it via email or other means. The input is the evaluation results and improvement suggestions, and the output is the report sent to the user.

[0730] (Application Example 2)

[0731] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0732] In conventional work environments, workers' emotional states and stress levels affect productivity, but there is a lack of efficient means to detect and manage them. Furthermore, it is difficult to flexibly adjust the operation of machinery based on emotional states, posing challenges to improving work efficiency and reducing worker burden. Additionally, correcting outliers and missing values ​​is time-consuming, resulting in inefficient report generation.

[0733] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0734] In this invention, the server includes means for transferring information processing data from a terminal device, means for formatting the transferred data and correcting missing or abnormal values, means for measuring task progress rate and production efficiency using a generation AI computing device, and means for analyzing the emotional state of people in the work environment and adjusting the operation of mechanical devices based on the emotional state. This makes it possible to optimize work efficiency according to the emotional state and reduce the burden on workers.

[0735] A "terminal device" is a device used to input information and transfer it to a server. This includes devices equipped with cameras and microphones.

[0736] "Information processing data" refers to a series of data related to work status and communication content.

[0737] A "missing value" is a value that is incomplete or missing from a dataset.

[0738] An "outlier" refers to a value that falls outside the normal data range and may affect the analysis.

[0739] A "generative AI computing device" is a computing resource that performs analysis using generative algorithms based on collected data.

[0740] "Project progress rate" is an indicator that shows how much progress has been made on a particular project.

[0741] "Production efficiency" is the ratio that shows how much output was achieved in relation to the resources invested.

[0742] "Work environment" refers to the physical or virtual environment in which machinery and workers perform tasks together.

[0743] "Emotional state" refers to the psychological or emotional condition of a worker.

[0744] "Mechanical equipment" refers to automated facilities or robots designed to assist with specific tasks.

[0745] This invention is implemented by a system consisting of a terminal device, a server, and a user. The terminal device is equipped with a camera and microphone to collect biometric information and communication data of the worker. This collected data is automatically transferred to the server.

[0746] Upon receiving the transferred data, the server first performs data formatting. It corrects missing and outlier values ​​and arranges the dataset into a unified format. Next, it uses a generative AI computing device to measure the task progress rate and production efficiency. This measurement process also includes comparing the current data with historical data and similar work environments.

[0747] Furthermore, the server also analyzes the emotional state of workers in their work environment. For emotion analysis, it utilizes libraries such as "OpenCV" and "pyAudioAnalysis." Based on the emotional state, the server adjusts the operation of machinery in real time. As a result, it becomes possible to reduce the burden on workers and optimize work efficiency.

[0748] Users can view the visualization and analysis results of data generated by the system through a dashboard. This allows for concrete assessment of the work environment and suggestions for improvement. For example, if a worker is detected to be experiencing stress, the dashboard will display a warning in real time and suggest specific countermeasures.

[0749] As a concrete example, if a worker's concentration decreases during factory work, the server immediately analyzes the data and adjusts the operating speed of the machinery to reduce the worker's workload. This creates a work environment that maintains overall productivity while also considering the health of the workers.

[0750] An example of a prompt for a generative AI model is: "Based on the following employee's sentiment data, please assess the impact on productivity in the current task: Fatigue 50%, Stress Level 70%, Concentration 60%."

[0751] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0752] Step 1:

[0753] The server receives biometric and communication data transmitted from terminal devices. Inputs include worker facial expressions, voice data, and task progress information. Outputs include preparing the received data for conversion into a formatted data format. Specifically, this involves standardizing the data format and adding timestamps.

[0754] Step 2:

[0755] The server performs preprocessing on the received data to detect and correct missing and outlier values. The input is the data formatted in step 1. The output generates a clean dataset suitable for analysis. Specifically, it corrects outliers using statistical methods and imputes missing values ​​using machine learning libraries.

[0756] Step 3:

[0757] The server analyzes clean data using a generative AI computing device to evaluate task progress and production efficiency. Preprocessed data is used as input. The output is numerical information regarding progress and efficiency. Specifically, it performs AI evaluations based on comparisons with historical data and prompt statements. Factors such as task similarity are considered in the analysis.

[0758] Step 4:

[0759] The server analyzes emotional states such as stress and fatigue, and generates instructions to adjust the operation of the machine based on the results. Input data includes facial expression analysis results and voice tone analysis results. The output is specific action commands for the machine. Specifically, the robot's operating speed is adjusted according to the emotional state to reduce the worker's workload.

[0760] Step 5:

[0761] Users can view analysis results and task progress provided by the server on a dashboard. The system receives analysis data from the server as input. Output consists of visualized analysis data and suggested actions. Specifically, it displays information using an intuitive interface with charts and graphs, and provides warnings and action suggestions when necessary.

[0762] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0763] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0764] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0765] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0766] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0767] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0768] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0769] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0770] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0771] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0772] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0773] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0774] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0775] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0776] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0777] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0778] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0779] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0780] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0781] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0782] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0783] The following is further disclosed regarding the embodiments described above.

[0784] (Claim 1)

[0785] A means of collecting task data from employees' terminals,

[0786] A means of preprocessing the collected data and correcting missing values ​​and outliers,

[0787] A method for evaluating employee task progress and productivity using generative AI models,

[0788] A means of visualizing and providing evaluation results to users,

[0789] A means of automatically generating a report based on the analysis results and sending it to the user,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, comprising means for performing an analysis of the cause of abnormal delays by comparing the generated AI model with data from similar past projects.

[0793] (Claim 3)

[0794] The system according to claim 1, comprising means for analyzing the history of employees' communication activities and visualizing their impact.

[0795] "Example 1"

[0796] (Claim 1)

[0797] A means of acquiring work data from an information processing device,

[0798] A means for preprocessing acquired information and correcting missing or abnormal information,

[0799] A means of evaluating the progress and efficiency of work using a generative AI model,

[0800] A means of visualizing and providing evaluation results to the user,

[0801] A means of automatically generating a report based on the analysis results and sending it to the user,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, comprising means for performing an analysis of the cause of abnormal delays by comparing the generated AI model with information on similar past problems.

[0805] (Claim 3)

[0806] The system according to claim 1, comprising means for analyzing the activity history of information exchange and visualizing its impact.

[0807] "Application Example 1"

[0808] (Claim 1)

[0809] A means of collecting business data from the user's operating device,

[0810] A means of preprocessing the collected data and correcting missing or anomalous information,

[0811] A method for evaluating the progress rate and efficiency of work using a generative AI model,

[0812] A means of visualizing and providing evaluation results to users,

[0813] A means of automatically generating a report based on the analysis results and communicating it to the user,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising means for the generating AI model to perform an analysis of the cause of abnormal delays by comparing it with data from similar past operations.

[0817] (Claim 3)

[0818] The system according to claim 1, comprising means for analyzing the history of communication activities of a task and visualizing its impact.

[0819] "Example 2 of combining an emotion engine"

[0820] (Claim 1)

[0821] A means of collecting work data from employees' information terminals,

[0822] A means of preprocessing the collected data and correcting missing values ​​and outliers,

[0823] A method for evaluating employees' work progress and productivity using generative AI models,

[0824] A means of acquiring biometric information during the evaluation process and analyzing the user's emotional state,

[0825] A means of integrating evaluation results and sentiment data, visualizing them, and providing them to the user,

[0826] A means of automatically generating a report based on the analysis results and sending it to the user,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, comprising means for performing an analysis of the cause of abnormal delays by comparing the generated AI model with data from similar past operations.

[0830] (Claim 3)

[0831] The system according to claim 1, comprising means for analyzing the history of an employee's communication activities and visualizing their impact.

[0832] "Application example 2 when combining with an emotional engine"

[0833] (Claim 1)

[0834] A means for transferring information processing data from a terminal device,

[0835] A means of formatting the transferred data and correcting missing or outlier values,

[0836] A means of measuring the progress rate of tasks and production efficiency using a generation AI computing device,

[0837] A means of visually representing and providing measurement results to users,

[0838] A means of automatically generating a report based on the analysis results and sending it to the user,

[0839] A means for analyzing the emotional state of a person in the work environment and adjusting the operation of a machine based on that emotional state,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, further comprising means for the generating AI computing device to analyze the cause of abnormal delays by comparing them with information from similar past operations.

[0843] (Claim 3)

[0844] The system according to claim 1, comprising means for analyzing a person's communication activity records and visualizing the impact thereof. [Explanation of Symbols]

[0845] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting task data from employees' terminals, A means of preprocessing the collected data and correcting missing values ​​and outliers, A method for evaluating employee task progress and productivity using generative AI models, A means of visualizing and providing evaluation results to users, A means of automatically generating a report based on the analysis results and sending it to the user, A system that includes this.

2. The system according to claim 1, further comprising means for performing an analysis of the cause of abnormal delays by comparing the generated AI model with data from similar past projects.

3. The system according to claim 1, comprising means for analyzing the history of employees' communication activities and visualizing their impact.

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